Investigation of Cognitive and Emotional Mechanisms Associated With Anxiety Using Structural Equation Modeling and Eye-tracking Analysis of Attentional Bias

Investigation of Cognitive and Emotional Mechanisms Associated With Anxiety Using Structural Equation Modeling and Eye-tracking Analysis of Attentional Bias | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigation of Cognitive and Emotional Mechanisms Associated With Anxiety Using Structural Equation Modeling and Eye-tracking Analysis of Attentional Bias Gizem Baki Kaşıkçı, Aynur Feyzioğlu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8564618/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background This study investigated cognitive–emotional mechanisms underlying anxiety within the Self-Regulatory Executive Function (S-REF) framework. It tested whether sensory processing sensitivity (SPS) is associated with trait anxiety through metacognitive beliefs and emotion regulation difficulties, and assessed threat-related attentional bias using eye-tracking. Methods The study was conducted in two phases. In Phase 1, self-report data were collected from 395 university students, and structural equation modeling (SEM) tested the associations among SPS, metacognitions, emotion regulation difficulties, and trait anxiety. In Phase 2, participants were selected using an extreme-groups approach based on trait anxiety scores (high trait anxiety: n = 30; control: n = 37) and completed a free-viewing task presenting fearful, sad, happy, and neutral faces while eye movements were recorded with an EyeLink 1000 Plus system. Results SEM supported a full mediation model: SPS did not directly predict trait anxiety, but showed significant indirect effects via metacognitive beliefs, emotion regulation difficulties, and a serial pathway. Eye-tracking findings indicated that high trait anxiety was primarily characterized by sustained attention to threat and difficulty disengaging, reflected in longer dwell time, higher fixation counts, and increased re-visits to fearful faces, rather than group differences in initial orienting (first fixation location). Furthermore, negative metacognitive beliefs about uncontrollability and danger and limited access to emotion regulation strategies were positively associated with late-stage threat maintenance indices (e.g., dwell time and dwell-time percentage). Conclusion Integrating self-report and physiological measures, findings suggest that SPS confers vulnerability to anxiety primarily through threat-focused metacognitions and inflexible emotion regulation processes. These vulnerabilities are reflected behaviorally in sustained threat-monitoring and disengagement difficulty patterns. Anxiety attentional bias emotion regulation eye-tracking metacognition sensory processing sensitivity Figures Figure 1 BACKGROUND Emotions are important psychological structures that shape how an individual thinks, feels, and behaves in daily life [ 1 ]. Understanding the impact of emotions on individuals requires an explanation of emotion regulation processes. Emotion regulation refers to consciously or unconsciously influencing the onset, intensity, or duration of emotions [ 2 ]. Difficulties in emotion regulation are consistently linked to psychological distress and are widely conceptualized as a transdiagnostic vulnerability factor across disorders [ 3 – 7 ]. In anxiety—particularly generalized anxiety—maladaptive regulation patterns and heightened emotional reactivity have been associated with greater symptom persistence and functional impairment [ 8 – 11 ]. Accordingly, individual differences in the capacity to monitor, accept, and flexibly manage emotional experiences may be relevant for understanding vulnerability to anxiety-related outcomes [ 12 , 13 ]. Anxiety is also characterized by biases in information processing. Converging evidence suggests that anxiety is associated with selective processing of threat-related cues. Sensitivity-based theories highlight stable individual differences in how people perceive and process internal and external stimuli, even when exposed to similar environments [ 14 , 15 ]. Sensory Processing Sensitivity (SPS) is an inherited temperament trait characterized by heightened sensitivity to environmental and internal cues, alongside deeper cognitive processing and stronger emotional/physiological reactivity [ 15 – 18 ]. Individuals high in SPS are often described as “highly sensitive individuals” [ 17 – 20 ]. Although SPS shows associations with constructs such as introversion, behavioral inhibition, shyness, and neuroticism, it is conceptualized as a distinct temperament trait [ 21 – 25 ]. Four core characteristics are emphasized in defining SPS: sensory sensitivity, behavioral inhibition, depth of processing, and emotional/physiological reactivity [ 16 , 26 ]. Sensory sensitivity refers to heightened responsiveness to internal or external stimuli [ 27 , 28 ]. Behavioral inhibition involves the tendency to pause and evaluate in potentially threatening situations and is associated with the behavioral inhibition system [ 29 , 30 ]. Depth of processing is defined by more detailed processing of sensory information [ 26 ]. Emotional and physiological reactivity is related to more intense emotional experiences due to lower sensory thresholds [ 16 ]. SPS has been associated with internalizing symptoms, including anxiety and depression, as well as emotion regulation difficulties [ 31 – 34 ]. However, SPS as a sensitivity trait does not, by itself, specify why some individuals report elevated anxiety. One possibility is that the depth-of-processing feature of SPS is accompanied by more detailed monitoring of internal states during periods of heightened arousal. In some individuals, such monitoring may be linked to more maladaptive interpretations of internal experience and greater endorsement of negative metacognitive beliefs (e.g., beliefs about uncontrollability or danger). Accordingly, SPS-related salience of internal cues and emotional arousal may be more strongly associated with anxiety when higher-order appraisals and metacognitive beliefs bias monitoring and coping responses. Understanding how predispositions such as SPS translate into symptoms requires higher-order cognitive processes. Sensory sensitivity alone does not necessitate psychopathology; rather, cognitive appraisals of this sensitivity may shape whether it is associated with anxiety-related outcomes. When such experiences are interpreted through a threat-focused lens, individuals may be more likely to endorse negative metacognitive beliefs. In this context, metacognition—defined as beliefs about and regulation of one’s own thinking processes—provides a useful framework [ 35 – 38 ]. The systematic examination of metacognitions in adult psychopathology gained momentum with Wells and Matthews’ Self-Regulatory Executive Function Model (S-REF) [ 39 ]. The S-REF model emphasizes metacognitive structures and self-regulatory processes in the persistence of emotional disorders, focusing on how internal experiences are interpreted and managed [ 39 – 41 ]. Within this framework, negative metacognitive beliefs about control and threat are linked to higher anxiety and a weakened sense of cognitive control [ 42 ], and such beliefs show associations with psychological symptoms in both clinical and community samples [ 43 – 46 ]. The model further highlights repetitive negative thinking (e.g., worry and rumination) and threat-focused attentional processes as key mechanisms through which distress is maintained over time [ 41 ]. According to the S-REF model, if an automatic thought is perceived as important due to metacognitive beliefs and labeled as a threat, dysfunctional coping patterns are activated [ 47 ]. These patterns are called Cognitive Attentional Syndrome (CAS) [ 40 , 48 ]. CAS includes repetitive worry and rumination. Threat monitoring is also a main component of CAS [ 40 , 48 ]. These processes increase the repetitive focus on negative thoughts and emotions [ 49 ]. Threat monitoring can strengthen the subjective feeling of danger and contribute to the continuation of emotional arousal [ 48 ]. As a result, this cycle can perpetuate and intensify emotional distress over time [ 49 ]. In the S-REF model, threat monitoring closely aligns with the clinical concept of attentional bias. Within the S-REF framework, threat monitoring is conceptualized not merely as automatic orienting to danger, but as a coping-related attentional process. Accordingly, in an eye-tracking paradigm, this process may be reflected in 'difficulty in disengagement' or 'sustained attention' (longer dwell time) rather than just early vigilance. This theoretical distinction motivates H4 and H5, which emphasize late-stage attentional maintenance over initial orienting. Attentional bias may involve rapid orienting, prolonged attention to certain stimuli, or difficulty disengaging from them. With the impact of cognitive approaches on the clinical field, attentional bias has gained an important place in the explanations of mood disorders [ 50 – 53 ]. A biased attention pattern toward emotionally meaningful stimuli can increase cognitive vulnerability [ 50 ]. In anxiety, attentional bias is most consistently examined in relation to threat, with evidence indicating preferential processing of threat-related cues and potential relevance for symptom maintenance [ 54 – 62 ]. Importantly, attentional bias may vary across processing stages: some individuals show early orienting, whereas others show later avoidance, contributing to ongoing debate regarding the timing and nature of the bias [ 61 , 62 ]. Attentional bias is considered a multi-component construct [ 63 , 64 ]. Three components are commonly emphasized: facilitated orientation toward threat, difficulty disengaging from threat or sustained attention, and attentional avoidance [ 63 , 65 ]. Threat orientation refers to faster allocation of attention to threatening stimuli [ 65 ]. Difficulty disengaging reflects problems shifting attention away after focusing on threat [ 64 ]. Attentional avoidance involves the conscious and strategic redirection of attention away from threat [ 66 – 68 ]. The underlying cognitive mechanism of attentional bias in anxiety is controversial. Williams et al. [ 53 ] emphasized the early and automatic orientation to threat in anxious individuals. This view is related to findings showing that emotional value can be evaluated at a very early stage [ 69 , 70 ]. Opposing approaches argued that the main problem appears in later stages and that avoidance and the suppression of detailed processing of the threat are decisive [ 71 , 72 ]. Integrative approaches suggest that early orientation and late avoidance can be seen together [ 53 , 73 ]. Another view accepts the difficulty in disengaging from the threat, rather than the initial perception, as the basic mechanism [ 74 , 75 ]. The general consensus is that anxiety is connected to attentional biases related to threat. However, empirical findings are not always consistent. This situation makes it necessary to use measurements where components can be separated. Methodologically, early research relied heavily on reaction time-based tasks such as the Emotional Stroop [ 76 ], modified dot-probe [ 77 ] and emotional spatial cueing paradigms [ 74 ]. However, these tasks provide limited temporal specificity and can be influenced by motor response demands, raising concerns about reliability and consistency [ 62 ]. Eye-tracking offers a more direct and continuous assessment of attention by recording where individuals look, for how long, and in what sequence [ 78 , 79 ]. Based on the “eye-mind” assumption, eye-movement metrics are used to infer ongoing information processing [ 79 , 80 ] enabling finer-grained tests of early orienting versus later maintenance/avoidance patterns [ 81 , 82 ]. When these theoretical and methodological frameworks are considered together, it becomes important to address all components simultaneously in explaining anxiety. SPS can be viewed as a vulnerability reflecting heightened sensitivity to environmental and internal stimuli [ 14 – 20 ]. Associations between this vulnerability and psychopathology are often discussed in relation to higher-order cognitive and emotional mechanisms. The S-REF model emphasizes the role of dysfunctional metacognitive beliefs that lead to threat interpretations of internal experiences and the self-regulatory weaknesses that maintain these beliefs in psychopathology [ 39 – 41 , 49 ]. The CAS component of the model includes automatic coping processes such as worry, rumination, and threat monitoring [ 48 , 83 ]. These processes closely overlap with threat-focused attentional patterns in the attentional bias literature [ 51 , 54 – 58 , 84 , 85 ]. The Present Study The present study aims to examine, within the S-REF framework, the relationship between SPS as a sensory-level vulnerability and anxiety through metacognitive beliefs and emotion regulation difficulties [ 39 , 49 ]. In addition, the study aims to objectively assess attentional bias patterns reflecting the threat monitoring component of CAS using eye-tracking methodology [ 48 , 79 , 83 , 86 ]. In the literature, metacognition and emotion regulation are typically examined using scale-based correlational designs, while attentional bias is studied separately using laboratory-based experimental designs. This separation makes it difficult to discuss perceptual and automatic components of the model within the same theoretical framework. Accordingly, this study adopts a dual-phase design conducted within the same sample. In the first phase, the relationships extending from SPS to metacognition and emotion regulation difficulties, and subsequently to anxiety symptoms, are examined using structural equation modeling. In the second phase, threat-related attentional bias patterns of groups with different anxiety levels are examined using eye-tracking. Furthermore, the study aims to investigate whether the cognitive and emotional vulnerabilities identified in the structural model (i.e., metacognitions and emotion regulation difficulties) are related to the physiological attentional patterns observed in the eye-tracking task. Overall, the study integrates self-report and eye-tracking indicators to link higher-order processes with perceptual components of the S-REF model [ 39 , 41 , 49 , 83 ]. The following hypotheses (H) were proposed: H1 Metacognitive beliefs mediate the relationship between Sensory Processing Sensitivity (SPS) and trait anxiety. H2 Difficulties in emotion regulation mediate the relationship between SPS and trait anxiety. H3 Metacognitive beliefs and difficulties in emotion regulation act as serial mediators in the relationship between SPS and trait anxiety. H4 High trait anxiety will be characterized by sustained attentional engagement and difficulty disengaging from threat-related stimuli, reflected in longer dwell time, higher fixation count, and increased re-visits (run count) to threat-related faces during free viewing. H5 Dysfunctional metacognitive beliefs (particularly negative beliefs about uncontrollability and danger) and limited access to emotion regulation strategies will be positively associated with late-stage attentional maintenance indices (e.g., dwell time, dwell time percentage) on threat-related stimuli. METHODS Participants and Procedure Utilizing a convenience sampling method, the study recruited 395 university students, 240 female (60.8%) and 155 male (39.2%). Participants were aged 18–44 years ( M = 22.58, SD = 4.80). A priori power analysis (G*Power 3.1) was used to estimate the minimum number of participants needed for each group in the eye-tracking study [ 87 ]. The values for the power analysis were determined based on related literature and the meta-analysis study of Armstrong and colleagues [ 88 ]. In the analysis, the effect size was entered as f = .24, α = .05, and power = .90 for a 2 (group: anxiety vs. control) × 4 (facial expression: happy, fearful, sad, neutral) mixed design ANOVA. Power analysis indicated that a minimum of 17 participants per group was sufficient. To account for potential data loss and enhance statistical power, the target sample size was set at 30 participants per group. For the eye-tracking phase, participants were selected based on The State and Trait Anxiety Inventory-Trait Form (STAI-II) scores from the screening sample. Accordingly, those with a score above the mean + 1 standard deviation (44.59 + 8.20) were defined as the "High Trait Anxiety Group)" (≥ 52.79; min–max: 53–68, avg: 57.17). Those with a score below the mean − 1 standard deviation (44.59 − 8.20) were defined as the "Control Group" (≤ 36.39; min–max: 28–36, avg: 33.84). Although participants did not receive a formal clinical diagnosis, the mean STAI-II score of the High Trait Anxiety group (57.17) exceeds clinical cutoff values reported in the literature, suggesting that the findings may be informative for understanding mechanisms relevant to clinical populations. Among participants in the screening study, 45 control-group and 45 anxiety-group participants who met these conditions were invited to the second stage. Following data collection, data quality checks were performed. Participants with a calibration error greater than 1.0° visual angle or track loss exceeding 20% were excluded from the analysis (n = 13). Excluded and retained participants did not differ in STAI-II scores (p > .05). Consequently, the final analyses were conducted with 67 participants. Exclusion criteria included neurological or psychiatric diagnoses, regular psychotropic/neurological medication use, and uncorrected vision problems. Prior to participation, informed consent was obtained from all individual participants included in the study. Ethical approval was obtained from the Hamidiye Non-Interventional Scientific Research Ethics Committee of the University of Health Sciences (Approval No: 533146). All stages of the research were planned in accordance with the Declaration of Helsinki ensuring the protection of participants’ welfare and rights. Measures State-Trait Anxiety Inventory The State and Trait Anxiety Inventory (STAI) was developed by Spielberger, Gorsuch, and Lushene [ 89 ] to determine individuals' state and trait anxiety levels. The scale evaluates state anxiety (STAI-I) and trait anxiety (STAI-II). The Turkish adaptation and the study of its psychometric properties were conducted by Öner and Le Compte [ 90 ]. In this study, only the STAI-II was used to determine the general anxiety level of the participants. The STAI-II consists of a total of 20 items. The scale demonstrated good internal consistency in the present study (Cronbach’s α = .81). Difficulties in Emotion Regulation Scale The Difficulties in Emotion Regulation Scale (DERS) was developed by Gratz and Roemer [ 12 ] to evaluate the difficulties individuals experience in their emotion regulation processes. The Turkish adaptation of the scale was conducted by Rugancı and Gençöz [ 91 ]. In the adapted form, the scale consists of the following sub-dimensions: awareness, clarity, acceptance, strategies, impulse, and goals. In the reliability analysis performed within the scope of this study, the internal consistency Cronbach α value of the whole scale was found to be .93. Metacognitions Questionnaire-30 The Metacognitions Questionnaire-30 (MCQ-30) was developed by Wells and Cartwright-Hatton [ 92 ] to evaluate various metacognitive beliefs and processes within the framework of the metacognitive model of psychological disorders. The scale consists of a total of 30 items. The scale includes five dimensions that are related to each other but conceptually different: (1) “positive beliefs” about worry, which measures how much the person believes worry is functional; (2) “negative beliefs” about worry, which evaluates beliefs about how uncontrollable and dangerous worry is; (3) “cognitive confidence,” which measures the person's trust in their memory; (4) “need to control thoughts,” which evaluates beliefs regarding the necessity of controlling thoughts and the consequences of not doing so; and (5) “cognitive self-consciousness,” which evaluates the tendency to monitor one's own thoughts and the inward focus of attention. The Turkish adaptation and psychometric evaluation of the scale were done by Yılmaz, Gençöz, and Wells [ 93 ]. In the reliability analysis performed within the scope of this study, the internal consistency Cronbach α value of the whole scale was found to be .88. Sensory Processing Sensitivity Scale (Highly Sensitive Person Scale) The Sensory Processing Sensitivity Scale (Highly Sensitive Person Scale) is a 27-item scale developed by Aron and Aron [ 17 ] to measure the sensory processing sensitivity levels of individuals. The scale items cover features that determine sensory sensitivity, such as being disturbed by intense stimuli like loud noise and bright light, startling easily, getting over-aroused during multi-tasking, and sensitivity to aesthetic values. While the original form of the scale shows a one-dimensional feature, Turkish adaptation studies revealed a four-dimensional structure. In the psychometric evaluation by Şengül-İnal and Sümer [ 94 ], it is stated that the scale consists of four sub-dimensions: (1) sensitivity to external stimuli, (2) aesthetic sensitivity, (3) harm avoidance, and (4) sensitivity to overstimulation. The internal consistency Cronbach α value of the 27-item scale was found to be .90. Eye-Tracking Study The EyeLink 1000 Plus (SR Research Ltd., Ottawa, Ontario) desktop eye-tracking system was used to record eye movements in this study. This system uses the infrared corneal reflection technique and collects data at a 500 Hz sampling rate. The procedure was non-invasive. A chin and forehead rest was used during the data collection process to minimize head movements and keep the distance between the eyes and the screen constant. The distance between the participants' eyes and the screen was set to 80 cm. The presentation of stimuli and data recording were done on a 21-inch monitor (LG Full HD, 1920x1080 pixels, 60 Hz) using the Experiment Builder (SR Research Ltd., Ottawa, Ontario) software. While designing the eye-tracking study, the facial expressions used in the experimental task were selected from the FACES database [ 95 ], developed at the Max Planck Institute for Human Development, Center for Lifespan Psychology. Sad, fearful, happy, and neutral face photos of 36 models, balanced in terms of age and gender, were included in this study. Adobe Photoshop was used to make the physical features of the stimuli similar. The face photos were cropped to keep only the face area, the backgrounds were removed, and they were converted to grayscale. A free-viewing task paradigm was used in the research to evaluate attentional bias. A 9-point calibration task and validation process were completed to check if the measurements were accurate. First, a preliminary trial (practice) was applied to the participants, and then the experiment stage started. In the experiment flow, a gray screen appears first and stays on the screen for 1000 ms. At the beginning of each trial, a fixation point (black plus sign) was presented on a gray background for 1000 ms to focus the participant's attention on the center. After the fixation screen, a set of 4 pictures (sad, fearful, happy, and neutral facial expressions) placed in four different corners of the screen remained on the screen simultaneously for 10,000 ms. A blank gray screen was shown for 1000 ms between each trial. The study consists of a total of 36 trials, including 1 practice trial and 35 experimental trials. In each trial, 4 different emotional expressions of the same model were presented together. To prevent location bias, the position of the emotional expressions on the screen (top-right, bottom-left, etc.) was changed and balanced across trials. The 10,000 ms stimulus presentation duration was intentionally selected to capture sustained attentional engagement and difficulty in disengagement rather than early reflexive orienting. Within the Self-Regulatory Executive Function (S-REF) framework, threat monitoring is conceptualized as a coping-related, perseverative attentional process rather than a rapid vigilance response. Accordingly, the free-viewing paradigm and extended presentation duration were optimized to assess late-stage attentional maintenance processes that are theoretically central to the Cognitive Attentional Syndrome (CAS). The raw data obtained were prepared for analysis using the EyeLink Data Viewer (SR Research Ltd., Ottawa, Ontario) software. In the analyses, each facial expression presented on the screen (happy, sad, fearful, neutral) was defined as a separate Area of Interest (AOI). Data Analysis Data analysis proceeded in two distinct phases. Before the analyses, the suitability of the data set for the relevant analyses was examined. First, missing values and outliers were removed from the data set. After calculating descriptive statistics and zero-order correlations for the questionnaire measures, the reliability of the scales was tested with Cronbach’s alpha coefficients. At the stage where the relational model of the study was tested, preliminary analyses and statistical assumption tests were performed first. In line with Kline’s [ 96 ] suggestions, a two-stage SEM approach was used. In the first stage, the measurement model was verified. Then, the structural model, which examines the serial mediation role of metacognitive beliefs and emotion regulation difficulties in the effect of sensory processing sensitivity on anxiety, was tested. The significance of the mediation effects was evaluated using the bootstrapping resampling method with 5000 samples and 95% bias-corrected confidence intervals [ 97 , 98 ]. In the experimental stage of the study, a 2 (group: anxiety, control) × 4 (stimulus type: fearful, happy, neutral, sad) mixed design ANOVA was conducted to examine if there was a significant difference in attentional bias parameters for different stimulus types/valences (fearful, happy, neutral, sad) between the anxiety and control groups. Given the aggregated AOI-level indices across trials per emotion category, mixed ANOVA was considered appropriate for the present design. The group variable was included in the analysis as a between-subjects factor, and the stimulus type variable was included as a within-subjects factor. After performing descriptive analyses regarding attentional bias parameters for different stimulus types, it was examined whether the variables included in the analysis met the normal distribution assumption using Kolmogorov–Smirnov and Shapiro–Wilk tests; additionally, skewness and kurtosis values were checked. The assumption of sphericity was examined with Mauchly’s Test of Sphericity. While evaluating the group (anxiety, control) × stimulus type interaction with ANOVA analyses, post hoc analyses were performed to reveal the source of the significant interaction effect. The Bonferroni correction was used to minimize possible Type 1 errors that might arise in multiple comparisons. To examine links between self-report measures and eye-tracking indices, bivariate Pearson correlations were computed and complemented with 95% BCa bootstrap confidence intervals (5,000 resamples). In the experimental stage of the study, the data were prepared for analysis using EyeLink Data Viewer software (SR Research Ltd., Ottawa, Ontario). IBM SPSS Statistics 27 and AMOS Graphics 24 programs were used for all statistical analyses of the study. RESULTS Preliminary Analyses Descriptive statistics (means, standard deviations, skewness, and kurtosis), correlations, and reliabilities for the study variables are presented in Table 1. Table 1 Descriptive statistics, reliabilities and correlations for the study variables Descriptive statistics and reliabilities Correlations Variables Mean SD Skewness Kurtosis α 1 2 3 4 1.Anxiety 44.59 8.20 0.50 -0.03 .81 — 2.Metacognition 64.39 13.47 0.42 -0.11 .88 .534** — 3.Difficulties in emotion regulation 85.87 22.60 0.35 -0.22 .93 .641** .494** — 4.Sensory processing sensitivity 17.61 3.78 -0.08 -0.07 .90 .295** .262** .323** — Note. *p < .05, **p < .01 Structural Equation Modeling A serial multiple mediation model was tested using structural equation modeling (SEM) in AMOS to examine whether metacognitive beliefs and difficulties in emotion regulation transmit the association between sensory processing sensitivity (SPS) and trait anxiety. Following preliminary analyses, a two-step SEM approach was adopted. First, the measurement model was evaluated; second, the hypothesized structural model was tested, and indirect effects were examined using bootstrapping (5,000 resamples; bias-corrected 95% confidence intervals). The measurement model comprised four latent constructs (SPS, metacognition, emotion regulation difficulties, and anxiety) indicated by 19 observed variables. Prior to parceling, the unidimensionality of the anxiety scale was verified via Exploratory Factor Analysis (EFA), as parceling is strongly recommended only when the underlying structure is structurally valid. Item parceling was employed not only to reduce measurement error and improve indicator reliability but also to optimize the ratio of sample size to estimated parameters [ 99 ]. This technique also contributes to meeting the normality assumptions of the data [ 100 ]. Four parcels were created using the item-to-construct balance approach, distributing items with high, medium, and low factor loadings across parcels based on exploratory factor analysis. The measurement model demonstrated acceptable fit: χ²(143, N = 395) = 389.98, p < .001, χ²/df = 2.73, CFI = .92, GFI = .90, AGFI = .87, IFI = .92, SRMR = .067, RMSEA = .066, 90% CI [.058, .074]. After confirming the measurement model, the hypothesized serial mediation model was tested. First, a partial mediation model including a direct path from SPS to anxiety was estimated and exhibited acceptable fit: χ²(144, N = 395) = 409.34, p .05). Accordingly, a full mediation model was estimated by constraining this direct effect to zero. The full mediation model also provided acceptable fit: χ²(145, N = 395) = 410.34, p < .001, χ²/df = 2.83, CFI = .92, GFI = .90, AGFI = .86, IFI = .92, SRMR = .070, RMSEA = .070, 90% CI [.060, .076]. The two nested models were compared using a chi-square difference test, indicating that removing the direct path did not significantly worsen model fit, Δχ²(1) = 1.00, p = .317. In addition, information criteria slightly favored the full mediation model (AIC = 500.340; BIC = 679.389) over the partial mediation model (AIC = 501.338; BIC = 684.367). Taken together—non-significant direct effect, non-significant Δχ², and marginally lower AIC/BIC—the more parsimonious full mediation model was retained as the final model (Fig. 1 ). Note SPS-ES sensitivity to external stimuli; SPS-AS aesthetic sensitivity, SPS-HA harm avoidance; SPS-OE sensitivity to overstimulation; MCQ-SC cognitive self-consciousness; MCQ-CT need to control thoughts; MCQ-PB positive beliefs; MCQ-NB negative beliefs; MCQ-CC cognitive confidence; DERS-AW awareness; DERS-IMP impulse; DERS-STR strategies; DERS-AC acceptance; DERS-CL clarity; DERS-GO goals. *** p < .001, ** p < .01, * p < .05 Bootstrapping Procedure The significance of the indirect effects in the model was tested using the bootstrap method (5000 samples, bias-corrected 95% confidence intervals). Results indicated that all three hypothesized mediation paths were statistically significant. When the structural paths were examined, the effect of sensory processing sensitivity on metacognition was significant (β = .401, p < .001). The effect of sensory processing sensitivity on emotion regulation difficulties was also found to be significant (β = .185, p < .01). The effect of metacognition on emotion regulation difficulties was also significant (β = .636, p < .001). The effect of metacognition on anxiety was also found to be significant (β = .540, p < .001). The effect of emotion regulation difficulties on anxiety was significant (β = .352, p < .001). When the results regarding indirect effects were examined, it was observed that the indirect effect of sensory processing sensitivity on anxiety through metacognition and emotion regulation difficulties was significant (β = .255, p < .001). Additionally, the indirect effect of sensory processing sensitivity on anxiety was significant solely through metacognition (β = .217, p < .001). Similarly, the indirect effect of sensory processing sensitivity on anxiety was also found to be significant through emotion regulation difficulties (β = .065, p < .01). These findings indicate that the association between SPS and anxiety is accounted for by indirect paths through metacognitive beliefs and emotion regulation difficulties in this cross-sectional sample. Therefore, the association between sensory processing sensitivity and anxiety was predominantly indirect in this sample, with metacognitive beliefs and emotion regulation difficulties accounting for a substantial proportion of this relationship. The results are presented in Table 2. Table 2 Direct and indirect effects of serial mediation model Model pathways Coefficient 95% CI B β Lower Upper Direct effect Sensory processing sensitivity ◊ Metacognition .379*** .401*** .188 .626 Sensory processing sensitivity ◊ Difficulties in emotion regulation .643*** .185** .278 1.032 Metacognition ◊ Difficulties in emotion regulation 2.347*** .636*** 1.464 4.795 Metacognition ◊ Anxiety 1.095*** .540*** .575 2.556 Difficulties in emotion regulation ◊ Anxiety .194*** .352*** .104 .279 Indirect effect Sensory processing sensitivity ◊ (Metacognition – Difficulties in emotion regulation) ◊ Anxiety .172*** .255*** .105 .262 Sensory processing sensitivity ◊ (Metacognition) ◊ Anxiety .415*** .217*** .282 .600 Sensory processing sensitivity ◊ (Difficulties in emotion regulation) ◊ Anxiety .124*** .065** .055 .210 Note. *** p < .001, ** p < .01, * p < .05 Eye-Tracking Results In this study, eye-tracking measurements of anxiety (N = 30) and control (N = 37) groups regarding different facial expressions (fearful, happy, neutral, sad) were compared. In all analyses, the Greenhouse–Geisser correction was applied when the sphericity assumption was not met. Descriptive statistics are presented in Table 3 , and ANOVA results are in Table 4 . Table 3 Descriptive statistics of eye-tracking indices for different facial expressions in anxiety and control groups Indices Group Fearful (Mean ± SD) Happy (Mean ± SD) Neutral (Mean ± SD) Sad (Mean ± SD) First fixation location Control .252 (.045) .260 (.056) .230 (.064) .257 (.056) Anxiety .254 (.062) .256 (.061) .234 (.051) .253 (.059) First fixation duration (ms) Control 352.92 (86.45) 375.94 (71.83) 349.00 (93.77) 359.76 (109.44) Anxiety 405.96 (92.29) 407.61 (100.65) 415.85 (110.34) 399.32 (77.18) Dwell time (ms) Control 1209.61 (462.28) ᵃᵇ 1652.52 (779.36) ᶜ 1349.67 (508.80) ᵇ 1185.20 (421.50) ᵃ Anxiety 1833.96 (335.31) ᵇ 1727.76 (363.94) ᵃᵇ 1691.79 (309.19) ᵃᵇ 1647.71 (247.65) ᵃ Dwell time percentage Control .212 (.054) ᵃᵇ .292 (.126) ᶜ .231 (.064) ᵇ .203 (.049) ᵃ Anxiety .255 (.031) ᵇ .238 (.028) ᵃᵇ .234 (.025) ᵃᵇ .231 (.030) ᵃ Fixation count Control 3.25 (1.45) ᵃᵇ 3.95 (1.67) ᶜ 3.40 (1.37) ᵇ 3.08 (1.32) ᵃ Anxiety 4.45 (1.00) ᵇ 4.22 (0.91) ᵃᵇ 4.02 (1.02) ᵃ 3.99 (1.00) ᵃ Run count Control 2.04 (0.89) ᵃ 2.25 (0.79) ᵇ 2.07 (0.78) ᵃ 2.00 (0.83) ᵃ Anxiety 2.47 (0.51) ᵇ 2.46 (0.51) ᵃᵇ 2.38 (0.50) ᵃᵇ 2.33 (0.45) ᵃ Note. Means within the same row that are marked with different superscript letters (a, b, c) differ significantly from each other ( p < .05). Values sharing at least one letter (e.g., ab vs. a or ab vs. b ) do not differ significantly. No superscript letters were assigned for the first fixation location and first fixation duration parameters, as no significant main effect or interaction involving Stimulus Type was observed for these measures. Table 4 ANOVA results for eye-tracking study Indices Effect F p η²ₚ First fixation location Group 1.53 .220 .023 Stimulus Type 2.16 .094 .032 Group x Stimulus Type 0.08 .972 .001 First fixation duration Group 5.70 .020* .081 Stimulus Type 0.81 .492 .012 Group x Stimulus Type 1.42 .237 .021 Dwell time (ms) Group 22.44 < .001* .257 Stimulus Type 5.57 .007* .079 Group x Stimulus Type 5.81 .005* .082 Dwell time percentage Group 3.34 .072 .049 Stimulus Type 5.37 .011* .076 Group x Stimulus Type 6.20 .006* .087 Fixation count Group 7.64 .007* .105 Stimulus Type 6.78 .002* .094 Group x Stimulus Type 4.97 .011* .071 Run count Group 3.84 .054 .056 Stimulus Type 9.32 < .001* .125 Group x Stimulus Type 2.95 .048* .043 Note. Because the assumption of sphericity was violated, the Greenhouse–Geisser correction was applied. p < .05. The ANOVA results for first fixation location rates showed that the main effect of stimulus type was not significant, F (3, 195) = 2.16, p = .094, η²ₚ = .032. The main effect of the group was also not significant, F (1, 65) = 1.53, p = .220, η²ₚ = .023. Additionally, the Group × Stimulus Type interaction was not found to be significant, F (3, 195) = 0.08, p = .972, η²ₚ = .001. These results demonstrate that the anxiety and control groups did not differ significantly in terms of first fixation location rates for different emotional face types. In other words, the anxiety level did not have a significant effect on which stimulus type the participants directed their first gaze to. According to the ANOVA results regarding first fixation duration, the main effect of stimulus type is not significant, F (3, 195) = 0.81, p = .492, η²ₚ = .012. The Group × Stimulus Type interaction was also not found to be significant, F (3, 195) = 1.42, p = .237, η²ₚ = .021. However, the main effect of group was significant, F (1, 65) = 5.70, p = .020, η²ₚ = .081. The average first fixation duration of the anxiety group (Mean = 407.18 ms, SE = 14.88) is longer compared to the control group (Mean = 359.41 ms, SE = 13.39) (Mean diff. = 47.78 ms). This suggests that the anxiety and control groups differed significantly in terms of first fixation durations. The analysis results exhibited that the stimulus type had a significant effect on dwell time, F (1.79, 116.45) = 5.57, p = .007, η²ₚ = .079. Additionally, the group (anxiety, control) × stimulus type interaction was found to be significant, F (1.79, 116.45) = 5.81, p = .005, η²ₚ = .082. Furthermore, the main effect of the group was also found to be significant, F (1,65) = 22.44, p < .001, η²ₚ = .257, indicating a large effect size. The average dwell time of the anxiety group (Mean = 1725.31 ms, SE = 58.99) is significantly longer compared to the control group’s dwell time (Mean = 1349.25 ms, SE = 53.13) (Mean diff. = 376.06 ms), and this difference varied by stimulus type. Bonferroni-corrected pairwise comparisons, performed to examine the source of the interaction, exhibited that the Control group looked at Happy facial expressions (Mean = 1652.52) for a significantly longer time compared to Fearful (Mean = 1209.61) and Sad (Mean = 1185.20) expressions. This pattern suggests a positive attentional bias in the control group. In contrast, this clear preference for Happy faces was not observed in the Anxiety group. This group directed the longest dwell time to Fearful faces (Mean = 1833.96), and this duration was found to be significantly higher compared to Sad faces (Mean = 1647.71). The analysis results exhibited that the stimulus type had a significant effect on dwell time percentage, F (1.53, 99.68) = 5.37, p = .011, η²ₚ = .076. Also, the group × stimulus type interaction was found to be significant, F (1.53, 99.68) = 6.20, p = .006, η²ₚ = .087. Conversely, the main effect of the group was not found to be significant, F (1,65) = 3.34, p = .072, η²ₚ = .049. This result indicates that there is no significant difference between the anxiety and control groups in terms of general dwell time percentage. Bonferroni-corrected comparisons exhibited that the dwell time percentage of the control group was highest for happy faces (29.2%), and this rate was found to be higher than other expressions. In the anxiety group, the rate for fearful faces (25.5%) was found to be higher compared to sad faces (23.1%) (Table 5 ). The analysis results revealed that the stimulus type had a significant effect on the fixation count, F (1.77, 115.14) = 6.78, p = .002, η²ₚ = .094. Also, the group × stimulus type interaction was found to be significant, F (1.77, 115.14) = 4.97, p = .011, η²ₚ = .071. Additionally, the main effect of the group is also statistically significant, F (1,65) = 7.64, p = .007, η²ₚ = .105. The average fixation count of the anxiety group (Mean = 4.17, SE = .20) is significantly higher compared to the control group (Mean = 3.42, SE = .18) (Mean diff. = .75). This finding indicates that the anxiety level has a medium effect on the fixation count. Bonferroni-corrected comparisons exhibited that the number of fixations on happy faces in the control group (Mean = 3.95) was higher compared to fearful (Mean = 3.25) and sad (Mean = 3.08) faces. In the anxiety group, the highest fixation count was observed on fearful faces (Mean = 4.45), and this value was found to be higher compared to sad faces (Mean = 3.99) (Table 5 ). The analysis results revealed that the stimulus type had a significant effect on the run count, F (2.32, 150.55) = 9.32, p < .001, η²ₚ = .125. Also, the group × stimulus type interaction was found to be significant, F (2.32, 150.55) = 2.95, p = .048, η²ₚ = .043. Conversely, the main effect of the group was not found to be significant, F (1,65) = 3.84, p = .054, η²ₚ = .056. This result shows that there is no significant difference generally between the anxiety and control groups in terms of run count. Bonferroni-corrected comparisons exhibited that the control group's number of runs (looking again) for happy faces (Mean = 2.25) was higher than for fearful (Mean = 2.04) and sad (Mean = 2.00) faces. In the anxiety group, the number of runs for fearful faces (Mean = 2.47) was found to be higher compared to sad faces (Mean = 2.33) (Table 5 ). Relationships Between Eye-Tracking Indices and Cognitive-Emotional Vulnerabilities To examine the link between cognitive-emotional vulnerabilities and threat-related attentional maintenance, bivariate Pearson correlations were computed and 95% BCa bootstrap confidence intervals (5,000 resamples) were reported (Table 5 ). Table 5 Correlation Between Eye-Tracking Indices and Cognitive-Emotional Vulnerabilities Variable 1 2 3 4 5 1. Difficulties in emotion regulation — 2. Difficulties in emotion regulation-Strategies .90** — 3. Metacognition-Negative Beliefs .68** .67** — 4. Dwell time for fearful faces .49** .47** .29* — 5. Dwell time percentage for fearful faces .41** .35** .35** .78** — Note. *p < .05, **p < .01 Difficulties in emotion regulation were positively associated with attentional maintenance toward fearful faces, as indexed by longer dwell time (r = .49, 95% BCa CI [.313, .631], p < .001) and higher dwell time percentage (r = .42, 95% BCa CI [.217, .571], p = .001). In addition, limited access to emotion regulation strategies was associated with both dwell time (r = .47, 95% BCa CI [.292, .615], p < .001) and dwell time percentage (r = .35, 95% BCa CI [.145, .518], p = .004), suggesting that greater regulatory inflexibility is linked to sustained attentional engagement with threat cues. Regarding metacognition, negative beliefs about uncontrollability and danger were also associated with attentional maintenance, as reflected in dwell time (r = .294, 95% BCa CI [.077, .490], p = .016) and dwell time percentage (r = .348, 95% BCa CI [.134, .534], p = .004), although these associations were comparatively modest in magnitude. DISCUSSION Correlation analyses exhibited statistically significant relationships in the expected direction between the main structures in the study. However, the most critical finding of the study in terms of theory appeared in the model tested with SEM: It was found that sensory processing sensitivity (SPS) did not have a direct effect on anxiety (p = .317), and the relationship between SPS and anxiety was explained by indirect paths working through metacognitions and emotion regulation difficulties. This finding is consistent with approaches that treat SPS not as a direct indicator of psychopathology, but as a predisposition trait that can increase the risk level depending on the context and accompanying processes [ 101 , 102 ]. The view of Differential Susceptibility and Vantage Sensitivity approaches [ 103 , 104 ], stating that highly sensitive individuals may experience more risk in negative conditions, is supported by the presence of mediator variables in the current model. These findings suggest that SPS is not directly associated with anxiety after accounting for metacognitions and emotion regulation difficulties; instead, SPS is indirectly associated with anxiety through these processes. The fact that the findings are consistent with emotion regulation-based and transdiagnostic models also strengthens this interpretation [ 105 – 107 ]. In the model, one of the important indirect paths from SPS to anxiety occurred through metacognitions (SPS → MCQ → STAI; β = .22). The fact that SPS was positively associated with dysfunctional metacognitions (β = .40) indicates that the increased awareness and deep processing tendencies of highly sensitive individuals toward not only environmental but also internal stimuli [ 26 ] may cause them to focus on their own mental processes and develop negative beliefs (metacognitions) about these processes. This finding aligns with the S-REF model [ 39 , 49 ] and its current applications [ 108 ]. When deep processing tendency is combined with negative metacognitive beliefs, the weakening of attentional control [ 6 , 109 ] and the inability to use attentional resources flexibly may strengthen more rigid and avoidant cognitive patterns related to anxiety [ 110 ]. Therefore, metacognitions may represent a key correlate linking SPS-related reactivity with anxiety symptoms in the tested cross-sectional model. The second critical path in the model works through difficulties in emotion regulation (SPS → DERS → STAI; β = .07). SPS may be associated with more intense emotional reactivity, which could coincide with greater reported difficulties in emotion regulation [ 16 , 17 ]. Processing and regulating this intense emotional data may require more cognitive/emotional resources [ 33 ]. In conditions of intense arousal, components of emotion regulation such as awareness, acceptance, and impulse control may be strained [ 12 ]. Consistent with transdiagnostic approaches [ 111 , 112 ], this finding suggests that the emotional intensity brought by high sensitivity exceeds the individual's acceptance capacity (DERS-Acceptance) and increases the tendency towards dysfunctional strategies (suppression, avoidance, etc.) (DERS-Strategies). As the ERT approach emphasizes [ 113 , 114 ] trying to manage high reactivity with inflexible strategies may lead to the continuation of anxiety symptoms. Indeed, a comprehensive meta-analysis by Aldao, Nolen-Hoeksema, and Schweizer [ 4 ] also shows that dysfunctional emotion regulation strategies are strongly related to psychopathology. When findings supporting the relationship between SPS and emotion regulation difficulties [ 33 ] and studies pointing to the mediation of emotion regulation in the transformation of temperamental traits into anxiety symptoms [ 105 ] are evaluated together, results indicate that emotion regulation processes can be a critical intermediate mechanism in this transformation. A novel contribution of this study is the identification of the significant serial mediation path SPS → MCQ → DERS → STAI (β = .26), where two processes operate together. This finding indicates that not a single mechanism, but sequential processes that trigger each other, play a role in the development of anxiety. Although the proposed model is theoretically grounded in the S-REF framework, the present findings should be interpreted as evidence of patterned associations rather than causal effects. Given the cross-sectional nature of the data, the directionality of the paths represents a theory-driven assumption rather than an empirical demonstration of temporal precedence. The strong relationship between metacognition and emotion regulation difficulties in the model (β = .64) is consistent with approaches suggesting that not only the intensity of the emotional experience but also the evaluations and beliefs regarding this experience can determine the regulation capacity [ 11 , 107 , 115 ]. Accordingly, when a highly sensitive individual interprets their already intense internal experience through a threat-focused metacognitive filter, their emotion regulation capacity may be disrupted more easily, and anxiety symptoms may become stronger. In conclusion, the model demonstrates a sequential and holistic cognitive-emotional chain: high sensitivity (SPS) → threat-based metacognitive beliefs → dysfunctional emotion regulation → anxiety symptoms. This pattern points out that handling metacognitive and emotional processes together may be important in understanding anxiety symptoms and determining intervention goals. Crucially, the depletion of cognitive resources caused by this dysfunctional metacognitive activity and emotion regulation effort is theoretically expected to impair top-down attentional control. Consistent with this interpretation, this pattern may be reflected behaviorally as a reduced capacity to disengage attention from threatening stimuli, a hypothesis directly addressed by the eye-tracking phase of this study. In this study, attentional bias was handled as a multi-component structure and evaluated through vigilance (orienting toward threat), difficulty in disengagement/attention maintenance, and avoidance components [ 63 , 65 ]. This approach is consistent with the literature suggesting that attention processes toward threat stimuli exhibit a multi-stage structure covering both early, automatic orienting mechanisms and later, controlled attention maintenance or avoidance processes [ 61 , 66 , 74 , 116 , 117 ]. This triple structure also overlaps with theoretical frameworks that handle attention processes on the orienting–disengagement–focusing axis [ 118 ]. Current studies also focus on distinguishing not only the existence of attentional bias but also through which cognitive mechanisms it emerges [ 65 ]; the eye-tracking method allows for capturing this distinction more directly [ 88 , 119 ]. In the current study, indicators were matched with components based on relevant theoretical models and eye-tracking literature [ 62 , 65 , 74 , 88 , 120 ]: first fixation location was used for vigilance; first fixation duration, total dwell time, percentage of total dwell time, fixation count, and run count were used for difficulty in disengagement/maintenance [ 62 , 121 ]. Analyses were based on gaze and fixation-based indicators calculated over the entire trial duration [ 122 , 123 ]. The findings generally indicate that, within the present free-viewing paradigm, attentional bias in high anxiety was primarily reflected in sustained attention and difficulty disengaging from threat-related stimuli, whereas robust evidence for early reflexive vigilance was not observed. This result is consistent with approaches arguing that attentional bias cannot be reduced to a single mechanism but is shaped by the dynamic interaction of different cognitive sub-processes [ 62 , 64 ]. Although early vigilance effects were not a primary focus of the present study, this was a deliberate methodological choice rather than a limitation. The extended stimulus duration and free-viewing paradigm were designed to maximize sensitivity to attentional maintenance and disengagement difficulty, which are central to metacognitive accounts of anxiety (e.g., the S-REF model). Within the scope of the Vigilance component, the first fixation location, which reflects how quickly and preferentially attention is spatially directed to the threat, was examined. Analyses exhibited that the main effect of the group and the group × stimulus type interaction were not significant in terms of the first fixation location; therefore, we did not observe a reliable group difference in initial orienting (first fixation location) toward threat stimuli under the present free-viewing conditions. Although early models [ 53 , 67 ] and classic dot-probe studies [ 77 ] suggest that individuals experiencing anxiety will demonstrate automatic orienting to threat stimuli, current eye-tracking studies demonstrate that this effect is highly sensitive to context (e.g., perceptual load; [ 124 ] and eccentricity; [ 125 ]) [ 124 , 126 ]. The features of the free-viewing paradigm used in the current study (for example, low perceptual load or stimulus eccentricity) may explain why the early orienting effect remained weak. Meta-analysis findings also demonstrate that the early orienting effect does not appear consistently in all studies and the effect size is generally small-to-medium [ 120 ]. In conclusion, the lack of group difference in terms of the first fixation location suggests that processes of maintaining attention and/or difficulty in disengagement following threat perception may be more decisive in high anxiety than early orienting. Delayed Disengagement / Maintenance: In the high trait anxiety group, a pattern of longer dwell time, more fixations, and more run counts (looking again) on threat stimuli was observed; this situation indicated that the bias is related to processes of inability to disengage from the threat (disengagement difficulty) rather than noticing the threat quickly [ 64 , 74 , 116 ]. The findings parallel the Attentional Control Theory [ 65 , 68 ] which proposes that anxiety increases maintaining attention on threat by weakening top-down attentional control, and meta-analytical findings regarding free-viewing tasks [ 119 , 127 ]. As reported by Georgiou et al. [ 128 ], in this study too, the difficulty in disengagement was found to be specific to "threat content" (fearful faces) rather than a general negativity bias; a similar effect was not observed for sad faces. In this respect, the findings are consistent with the threat-specific attention maintenance model predicted by Weierich, Treat, and Hollingworth [ 62 ]. More frequent returns to threat stimuli (run count values) may reflect re-engagement with threat-related information, a pattern commonly discussed as threat monitoring in the attentional bias literature. In conclusion, when duration, rate, and frequency-based indicators are handled together, results indicate that individuals with high anxiety levels display a pattern of prolonged attention maintenance and difficulty in disengagement on threat stimuli. This pattern parallels theoretical approaches emphasizing the maintenance/difficulty in disengagement component in anxiety [ 64 , 65 , 68 ]; and current meta-analytical findings supporting this [ 127 ]. Avoidance, which is the third component of attentional bias, refers to the individual's tendency to strategically shift their attention to another direction or move away from the threat after perceiving the threat stimulus [ 129 ]. Theoretical models emphasize that avoidance is generally a delayed mechanism and reflects the high-level control processes of the attention system [ 130 , 131 ]. In eye-tracking studies, avoidance is generally defined by indirect indicators such as shorter total dwell times toward the threat region, rapid disengagement after the first fixation, or directing fewer or later fixations to the threat region. These patterns are interpreted as a strategic attention-shifting tendency aimed at moving away from threat information or reducing the processing of the threat. In the current study, indicators typically interpreted as attentional avoidance (e.g., shortened dwell time or reduced fixation count toward threat stimuli) were not evident within the limits of the present paradigm. In contrast, more frequent returns to the threat stimulus appear more consistent with threat-monitoring accounts than with avoidance, as typically operationalized in eye-tracking studies. Although meta-analytical findings [ 127 ] state that the vigilance–avoidance model may emerge under certain conditions, the picture observed in the current study points to the dominance of the vigilance–maintenance cycle rather than avoidance. One of the remarkable findings of the study is the attention profile displayed by the control group. The control group not only displayed shorter dwell times and lower fixation counts toward faces containing threats; they also consistently directed their attention to happy faces. Statistical analyses exhibited that the control group's dwell times for happy faces were significantly longer than for all other faces (fearful, sad, neutral). This suggests that the attention system of healthy individuals displays an adaptive positive bias, where they actively orient toward positive and safe stimuli to maintain emotional balance, beyond just ignoring the threat. The fact that this protective mechanism did not activate in the high trait anxiety group, along with the high number of returns to the threat, shows that these individuals have difficulty using safe cues in the environment and continue to focus on threat information. The analyses examining the relationship between self-report measures and eye-tracking indices provided critical insights into the cognitive mechanisms of attentional bias. Although total metacognition scores were not directly associated with eye-tracking metrics, the specific sub-dimension of 'Negative beliefs about uncontrollability and danger' was significantly correlated with increased dwell time on fearful faces. This finding is theoretically consistent with the S-REF model, which posits that it is not the mere presence of metacognitions, but specifically the belief that 'worry is uncontrollable and dangerous' that drives threat monitoring [ 39 , 40 ]. Furthermore, the strong correlation observed between DERS-Strategies and threat dwell time suggests that the inability to disengage from threat is closely linked to a deficit in accessing effective emotion regulation strategies. Together, these findings imply that the 'vigilance-maintenance' pattern observed in high anxiety is fueled by a specific cognitive-emotional combination: the belief that internal experiences are dangerous (Metacognition) and the lack of tools to manage the resulting arousal (Regulation). Limitations and Future Directions Because group classification was based on trait anxiety scores in a non-clinical student sample, caution is warranted when generalizing the present findings to clinically diagnosed anxiety disorders (e.g., social anxiety disorder or panic disorder). Meta-analytic evidence indicates that threat-related attentional bias effects are more consistent in clinically anxious populations [ 120 ]. Future studies should therefore examine whether the robust threat-maintenance and disengagement difficulty pattern observed here replicates with comparable magnitude in clinical samples. Because sensory processing sensitivity, metacognitive beliefs, emotion regulation difficulties, and anxiety were assessed using self-report measures, shared method variance may have inflated the observed associations in the structural model. Although eye-tracking indices provided objective indicators of attentional processes, common method bias cannot be fully ruled out. Furthermore, the eye-tracking sample was selected using an extreme-groups approach to maximize phenotypic variance; therefore, the observed correlations between self-report and physiological measures reflect associations within these distinct phenotypes rather than a continuous distribution across the general population. In addition, the absence of strong early vigilance effects alongside pronounced sustained-attention differences may be partly attributable to the long stimulus presentation duration (10,000 ms) and the free-viewing paradigm, which may favor later-stage attentional processes. Future research using shorter exposure durations, higher perceptual load, or temporally constrained paradigms may provide a more sensitive test of early orienting mechanisms. Age and gender were not included as covariates in the present analyses. Future studies should examine whether the observed pattern of results remains robust after adjusting for demographic variables. CONCLUSION When the SEM and eye-tracking findings in this study are evaluated holistically, findings are consistent with the view that SPS alone may be insufficient to account for anxiety symptom severity; instead, anxiety symptoms co-vary with metacognitive beliefs and emotion regulation difficulties that reflect threat-oriented interpretations of internal experiences. The structural model revealed that sensory processing sensitivity poses an indirect risk through negative metacognitive beliefs and emotion regulation difficulties, rather than predicting anxiety directly. This pattern aligns with the basic assumption of the S-REF model: Labeling internal experiences as "dangerous" or "uncontrollable" weakens the individual's perception of cognitive control and triggers the Cognitive-Attentional Syndrome (CAS). Accordingly, SPS-related reactivity may be associated with higher anxiety symptom severity particularly when accompanied by more threat-oriented metacognitive beliefs and greater emotion regulation difficulties. Eye-tracking findings point to the behavioral equivalent of this cognitive-emotional cycle. In the high trait anxiety group, the fact that attentional bias is characterized by difficulty in disengaging attention and a maintenance/monitoring pattern after orienting to the threat content, rather than a reflexive first orientation, presents a profile that overlaps with the "threat monitoring" component of CAS. In this non-clinical high-trait-anxiety sample, group differences were more evident in sustained attention/maintenance indices than in initial orienting indices. The consistent positive orientation observed in the control group shows that in healthy functioning, emotion regulation is related not only to reducing the threat but also to actively processing safe/positive cues. This holistic picture highlights two points in terms of clinical intervention goals: (i) instead of treating sensitivity itself as a "problem," focusing on changing metacognitive beliefs that interpret the internal experience triggered by sensitivity as threat-based, and (ii) strengthening emotion regulation skills that support flexibility under intense arousal. Interventions targeting metacognitive beliefs and emotion regulation skills may help reduce threat-focused attention patterns; intervention studies are needed to evaluate whether such changes translate into reduced threat monitoring. Abbreviations AGFI Adjusted Goodness-of-Fit Index AIC Akaike Information Criterion ANOVA Analysis of Variance AOI Area of Interest BCa Bias-Corrected and Accelerated BIC Bayesian Information Criterion CAS Cognitive Attentional Syndrome CFI Comparative Fit Index DERS Difficulties in Emotion Regulation Scale EFA Exploratory Factor Analysis FACES FACES Database of Facial Expressions GFI Goodness-of-Fit Index IFI Incremental Fit Index MCQ-30 Metacognitions Questionnaire-30 MCQ-CC Cognitive Confidence MCQ-CT Need to Control Thoughts MCQ-NB Negative Beliefs about Uncontrollability and Danger MCQ-PB Positive Beliefs about Worry MCQ-SC Cognitive Self-Consciousness RMSEA Root Mean Square Error of Approximation SEM Structural Equation Modeling SPS Sensory Processing Sensitivity SPS-AS Aesthetic Sensitivity SPS-ES Sensitivity to External Stimuli SPS-HA Harm Avoidance SPS-OE Sensitivity to Overstimulation S-REF Self-Regulatory Executive Function Model SRMR Standardized Root Mean Square Residual STAI State–Trait Anxiety Inventory STAI-I State–Trait Anxiety Inventory-State Anxiety Scale STAI-II State–Trait Anxiety Inventory-Trait Anxiety Scale Declarations Ethics approval and consent to participate: • Prior to participation, informed consent was obtained from all individual participants included in the study. Ethical approval was obtained from the Hamidiye Non-Interventional Scientific Research Ethics Committee of the University of Health Sciences (Approval No: 533146). Consent for publication: • Not applicable. Competing interests: • The authors declare that there is no conflict of interest, given that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding: The authors did not receive support from any organization for the submitted work. Author Contribution G.B.K and A.F. contributed to the study conception and design. G.B.K. performed the data collection and statistical analysis. G.B.K. wrote the first draft of the manuscript. A.F. supervised the study and critically reviewed the manuscript. All authors read and approved the final manuscript. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8564618","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581170368,"identity":"b9bedd3c-2548-4365-9afb-23afd79edc70","order_by":0,"name":"Gizem Baki Kaşıkçı","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACAwY2hgMJDBJAJvMBICEhQ4oWtgSQFh6itEABjwGYJKjFnP1Y4oEHvyzs+cXOfH51o8aCh4H98NEN+LRY9qQdOJDYJ8EsOTt3m3XOMaDDeNLSbuB12IH0hgOJPRJsBrdztxnnsAG1SPCY4ddy/jlYC4/97Zxnxjn/iNFyA+iwhB8SEgbSOcyPc9uI0GI541nCgcQGCQOJ22lmzLl9EjxshPxizp9m/PHHnzp7/tnJjz/nfKuT42c/fAyvFjBgbANTbBJgkqByMPgDJpk/EKd6FIyCUTAKRhoAABJrSVvd4qNtAAAAAElFTkSuQmCC","orcid":"","institution":"University of Health Sciences","correspondingAuthor":true,"prefix":"","firstName":"Gizem","middleName":"Baki","lastName":"Kaşıkçı","suffix":""},{"id":581170370,"identity":"69da6849-47b8-4d89-aea9-2de891e899fe","order_by":1,"name":"Aynur Feyzioğlu","email":"","orcid":"","institution":"University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Aynur","middleName":"","lastName":"Feyzioğlu","suffix":""}],"badges":[],"createdAt":"2026-01-09 23:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8564618/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8564618/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101362496,"identity":"36f034f3-0949-46d3-8170-05a91a196827","added_by":"auto","created_at":"2026-01-29 00:29:31","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":295456,"visible":true,"origin":"","legend":"\u003cp\u003eStandardized factor loadings for the Model. Arrows indicate statistically significant paths\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote.\u003c/strong\u003e \u003cem\u003eSPS-ES\u003c/em\u003esensitivity to external stimuli; \u003cem\u003eSPS-AS\u003c/em\u003e aesthetic sensitivity, \u003cem\u003eSPS-HA\u003c/em\u003eharm avoidance; \u003cem\u003eSPS-OE\u003c/em\u003e sensitivity to overstimulation; \u003cem\u003eMCQ-SC\u003c/em\u003ecognitive self-consciousness; \u003cem\u003eMCQ-CT\u003c/em\u003e need to control thoughts; \u003cem\u003eMCQ-PB\u003c/em\u003epositive beliefs; \u003cem\u003eMCQ-NB\u003c/em\u003e negative beliefs; \u003cem\u003eMCQ-CC\u003c/em\u003e cognitive confidence; \u003cem\u003eDERS-AW\u003c/em\u003e awareness; \u003cem\u003eDERS-IMP\u003c/em\u003e impulse; \u003cem\u003eDERS-STR\u003c/em\u003estrategies; \u003cem\u003eDERS-AC\u003c/em\u003e acceptance; \u003cem\u003eDERS-CL\u003c/em\u003e clarity; \u003cem\u003eDERS-GO\u003c/em\u003egoals. \u003cem\u003e*** p \u0026lt; .001, ** p \u0026lt; .01, * p \u0026lt; .05\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8564618/v1/1dda34bb9a9709ba4d864292.jpeg"},{"id":101398052,"identity":"8e5f619b-0e3c-4936-bc3b-baa8241504f8","added_by":"auto","created_at":"2026-01-29 09:39:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1610200,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8564618/v1/ca149b4f-7416-473b-a143-e8d75941a963.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eInvestigation of Cognitive and Emotional Mechanisms Associated With Anxiety Using Structural Equation Modeling and Eye-tracking Analysis of Attentional Bias\u003c/p\u003e","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eEmotions are important psychological structures that shape how an individual thinks, feels, and behaves in daily life [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Understanding the impact of emotions on individuals requires an explanation of emotion regulation processes. Emotion regulation refers to consciously or unconsciously influencing the onset, intensity, or duration of emotions [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Difficulties in emotion regulation are consistently linked to psychological distress and are widely conceptualized as a transdiagnostic vulnerability factor across disorders [\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In anxiety\u0026mdash;particularly generalized anxiety\u0026mdash;maladaptive regulation patterns and heightened emotional reactivity have been associated with greater symptom persistence and functional impairment [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Accordingly, individual differences in the capacity to monitor, accept, and flexibly manage emotional experiences may be relevant for understanding vulnerability to anxiety-related outcomes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Anxiety is also characterized by biases in information processing. Converging evidence suggests that anxiety is associated with selective processing of threat-related cues.\u003c/p\u003e \u003cp\u003eSensitivity-based theories highlight stable individual differences in how people perceive and process internal and external stimuli, even when exposed to similar environments [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Sensory Processing Sensitivity (SPS) is an inherited temperament trait characterized by heightened sensitivity to environmental and internal cues, alongside deeper cognitive processing and stronger emotional/physiological reactivity [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Individuals high in SPS are often described as \u0026ldquo;highly sensitive individuals\u0026rdquo; [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Although SPS shows associations with constructs such as introversion, behavioral inhibition, shyness, and neuroticism, it is conceptualized as a distinct temperament trait [\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFour core characteristics are emphasized in defining SPS: sensory sensitivity, behavioral inhibition, depth of processing, and emotional/physiological reactivity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Sensory sensitivity refers to heightened responsiveness to internal or external stimuli [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Behavioral inhibition involves the tendency to pause and evaluate in potentially threatening situations and is associated with the behavioral inhibition system [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Depth of processing is defined by more detailed processing of sensory information [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Emotional and physiological reactivity is related to more intense emotional experiences due to lower sensory thresholds [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSPS has been associated with internalizing symptoms, including anxiety and depression, as well as emotion regulation difficulties [\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, SPS as a sensitivity trait does not, by itself, specify why some individuals report elevated anxiety. One possibility is that the depth-of-processing feature of SPS is accompanied by more detailed monitoring of internal states during periods of heightened arousal. In some individuals, such monitoring may be linked to more maladaptive interpretations of internal experience and greater endorsement of negative metacognitive beliefs (e.g., beliefs about uncontrollability or danger). Accordingly, SPS-related salience of internal cues and emotional arousal may be more strongly associated with anxiety when higher-order appraisals and metacognitive beliefs bias monitoring and coping responses.\u003c/p\u003e \u003cp\u003eUnderstanding how predispositions such as SPS translate into symptoms requires higher-order cognitive processes. Sensory sensitivity alone does not necessitate psychopathology; rather, cognitive appraisals of this sensitivity may shape whether it is associated with anxiety-related outcomes. When such experiences are interpreted through a threat-focused lens, individuals may be more likely to endorse negative metacognitive beliefs. In this context, metacognition\u0026mdash;defined as beliefs about and regulation of one\u0026rsquo;s own thinking processes\u0026mdash;provides a useful framework [\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe systematic examination of metacognitions in adult psychopathology gained momentum with Wells and Matthews\u0026rsquo; Self-Regulatory Executive Function Model (S-REF) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The S-REF model emphasizes metacognitive structures and self-regulatory processes in the persistence of emotional disorders, focusing on how internal experiences are interpreted and managed [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Within this framework, negative metacognitive beliefs about control and threat are linked to higher anxiety and a weakened sense of cognitive control [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], and such beliefs show associations with psychological symptoms in both clinical and community samples [\u003cspan additionalcitationids=\"CR44 CR45\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The model further highlights repetitive negative thinking (e.g., worry and rumination) and threat-focused attentional processes as key mechanisms through which distress is maintained over time [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to the S-REF model, if an automatic thought is perceived as important due to metacognitive beliefs and labeled as a threat, dysfunctional coping patterns are activated [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. These patterns are called Cognitive Attentional Syndrome (CAS) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. CAS includes repetitive worry and rumination. Threat monitoring is also a main component of CAS [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. These processes increase the repetitive focus on negative thoughts and emotions [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Threat monitoring can strengthen the subjective feeling of danger and contribute to the continuation of emotional arousal [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. As a result, this cycle can perpetuate and intensify emotional distress over time [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the S-REF model, threat monitoring closely aligns with the clinical concept of attentional bias. Within the S-REF framework, threat monitoring is conceptualized not merely as automatic orienting to danger, but as a coping-related attentional process. Accordingly, in an eye-tracking paradigm, this process may be reflected in 'difficulty in disengagement' or 'sustained attention' (longer dwell time) rather than just early vigilance. This theoretical distinction motivates H4 and H5, which emphasize late-stage attentional maintenance over initial orienting.\u003c/p\u003e \u003cp\u003eAttentional bias may involve rapid orienting, prolonged attention to certain stimuli, or difficulty disengaging from them. With the impact of cognitive approaches on the clinical field, attentional bias has gained an important place in the explanations of mood disorders [\u003cspan additionalcitationids=\"CR51 CR52\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. A biased attention pattern toward emotionally meaningful stimuli can increase cognitive vulnerability [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In anxiety, attentional bias is most consistently examined in relation to threat, with evidence indicating preferential processing of threat-related cues and potential relevance for symptom maintenance [\u003cspan additionalcitationids=\"CR55 CR56 CR57 CR58 CR59 CR60 CR61\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Importantly, attentional bias may vary across processing stages: some individuals show early orienting, whereas others show later avoidance, contributing to ongoing debate regarding the timing and nature of the bias [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAttentional bias is considered a multi-component construct [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Three components are commonly emphasized: facilitated orientation toward threat, difficulty disengaging from threat or sustained attention, and attentional avoidance [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Threat orientation refers to faster allocation of attention to threatening stimuli [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Difficulty disengaging reflects problems shifting attention away after focusing on threat [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Attentional avoidance involves the conscious and strategic redirection of attention away from threat [\u003cspan additionalcitationids=\"CR67\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe underlying cognitive mechanism of attentional bias in anxiety is controversial. Williams et al. [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] emphasized the early and automatic orientation to threat in anxious individuals. This view is related to findings showing that emotional value can be evaluated at a very early stage [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Opposing approaches argued that the main problem appears in later stages and that avoidance and the suppression of detailed processing of the threat are decisive [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Integrative approaches suggest that early orientation and late avoidance can be seen together [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Another view accepts the difficulty in disengaging from the threat, rather than the initial perception, as the basic mechanism [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. The general consensus is that anxiety is connected to attentional biases related to threat. However, empirical findings are not always consistent. This situation makes it necessary to use measurements where components can be separated.\u003c/p\u003e \u003cp\u003eMethodologically, early research relied heavily on reaction time-based tasks such as the Emotional Stroop [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e], modified dot-probe [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e] and emotional spatial cueing paradigms [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. However, these tasks provide limited temporal specificity and can be influenced by motor response demands, raising concerns about reliability and consistency [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Eye-tracking offers a more direct and continuous assessment of attention by recording where individuals look, for how long, and in what sequence [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Based on the \u0026ldquo;eye-mind\u0026rdquo; assumption, eye-movement metrics are used to infer ongoing information processing [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e] enabling finer-grained tests of early orienting versus later maintenance/avoidance patterns [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhen these theoretical and methodological frameworks are considered together, it becomes important to address all components simultaneously in explaining anxiety. SPS can be viewed as a vulnerability reflecting heightened sensitivity to environmental and internal stimuli [\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18 CR19\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Associations between this vulnerability and psychopathology are often discussed in relation to higher-order cognitive and emotional mechanisms. The S-REF model emphasizes the role of dysfunctional metacognitive beliefs that lead to threat interpretations of internal experiences and the self-regulatory weaknesses that maintain these beliefs in psychopathology [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The CAS component of the model includes automatic coping processes such as worry, rumination, and threat monitoring [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. These processes closely overlap with threat-focused attentional patterns in the attentional bias literature [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan additionalcitationids=\"CR55 CR56 CR57\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eThe Present Study\u003c/h3\u003e\n\u003cp\u003eThe present study aims to examine, within the S-REF framework, the relationship between SPS as a sensory-level vulnerability and anxiety through metacognitive beliefs and emotion regulation difficulties [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In addition, the study aims to objectively assess attentional bias patterns reflecting the threat monitoring component of CAS using eye-tracking methodology [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. In the literature, metacognition and emotion regulation are typically examined using scale-based correlational designs, while attentional bias is studied separately using laboratory-based experimental designs. This separation makes it difficult to discuss perceptual and automatic components of the model within the same theoretical framework. Accordingly, this study adopts a dual-phase design conducted within the same sample. In the first phase, the relationships extending from SPS to metacognition and emotion regulation difficulties, and subsequently to anxiety symptoms, are examined using structural equation modeling. In the second phase, threat-related attentional bias patterns of groups with different anxiety levels are examined using eye-tracking. Furthermore, the study aims to investigate whether the cognitive and emotional vulnerabilities identified in the structural model (i.e., metacognitions and emotion regulation difficulties) are related to the physiological attentional patterns observed in the eye-tracking task. Overall, the study integrates self-report and eye-tracking indicators to link higher-order processes with perceptual components of the S-REF model [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe following hypotheses (H) were proposed:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003cp\u003eMetacognitive beliefs mediate the relationship between Sensory Processing Sensitivity (SPS) and trait anxiety.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e \u003cp\u003eDifficulties in emotion regulation mediate the relationship between SPS and trait anxiety.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003eMetacognitive beliefs and difficulties in emotion regulation act as serial mediators in the relationship between SPS and trait anxiety.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH4\u003c/strong\u003e \u003cp\u003eHigh trait anxiety will be characterized by sustained attentional engagement and difficulty disengaging from threat-related stimuli, reflected in longer dwell time, higher fixation count, and increased re-visits (run count) to threat-related faces during free viewing.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH5\u003c/strong\u003e \u003cp\u003eDysfunctional metacognitive beliefs (particularly negative beliefs about uncontrollability and danger) and limited access to emotion regulation strategies will be positively associated with late-stage attentional maintenance indices (e.g., dwell time, dwell time percentage) on threat-related stimuli.\u003c/p\u003e \u003c/p\u003e "},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eParticipants and Procedure\u003c/h2\u003e \u003cp\u003eUtilizing a convenience sampling method, the study recruited 395 university students, 240 female (60.8%) and 155 male (39.2%). Participants were aged 18\u0026ndash;44 years (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22.58, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.80).\u003c/p\u003e \u003cp\u003eA priori power analysis (G*Power 3.1) was used to estimate the minimum number of participants needed for each group in the eye-tracking study [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. The values for the power analysis were determined based on related literature and the meta-analysis study of Armstrong and colleagues [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. In the analysis, the effect size was entered as f\u0026thinsp;=\u0026thinsp;.24, α\u0026thinsp;=\u0026thinsp;.05, and power\u0026thinsp;=\u0026thinsp;.90 for a 2 (group: anxiety vs. control) \u0026times; 4 (facial expression: happy, fearful, sad, neutral) mixed design ANOVA. Power analysis indicated that a minimum of 17 participants per group was sufficient. To account for potential data loss and enhance statistical power, the target sample size was set at 30 participants per group.\u003c/p\u003e \u003cp\u003eFor the eye-tracking phase, participants were selected based on The State and Trait Anxiety Inventory-Trait Form (STAI-II) scores from the screening sample. Accordingly, those with a score above the mean\u0026thinsp;+\u0026thinsp;1 standard deviation (44.59\u0026thinsp;+\u0026thinsp;8.20) were defined as the \"High Trait Anxiety Group)\" (\u0026ge;\u0026thinsp;52.79; min\u0026ndash;max: 53\u0026ndash;68, avg: 57.17). Those with a score below the mean \u0026minus;\u0026thinsp;1 standard deviation (44.59\u0026thinsp;\u0026minus;\u0026thinsp;8.20) were defined as the \"Control Group\" (\u0026le;\u0026thinsp;36.39; min\u0026ndash;max: 28\u0026ndash;36, avg: 33.84). Although participants did not receive a formal clinical diagnosis, the mean STAI-II score of the High Trait Anxiety group (57.17) exceeds clinical cutoff values reported in the literature, suggesting that the findings may be informative for understanding mechanisms relevant to clinical populations. Among participants in the screening study, 45 control-group and 45 anxiety-group participants who met these conditions were invited to the second stage. Following data collection, data quality checks were performed. Participants with a calibration error greater than 1.0\u0026deg; visual angle or track loss exceeding 20% were excluded from the analysis (n\u0026thinsp;=\u0026thinsp;13). Excluded and retained participants did not differ in STAI-II scores (p\u0026thinsp;\u0026gt;\u0026thinsp;.05). Consequently, the final analyses were conducted with 67 participants. Exclusion criteria included neurological or psychiatric diagnoses, regular psychotropic/neurological medication use, and uncorrected vision problems.\u003c/p\u003e \u003cp\u003e Prior to participation, informed consent was obtained from all individual participants included in the study. Ethical approval was obtained from the Hamidiye Non-Interventional Scientific Research Ethics Committee of the University of Health Sciences (Approval No: 533146). All stages of the research were planned in accordance with the Declaration of Helsinki ensuring the protection of participants\u0026rsquo; welfare and rights.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eState-Trait Anxiety Inventory\u003c/h2\u003e \u003cp\u003eThe State and Trait Anxiety Inventory (STAI) was developed by Spielberger, Gorsuch, and Lushene [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e] to determine individuals' state and trait anxiety levels. The scale evaluates state anxiety (STAI-I) and trait anxiety (STAI-II). The Turkish adaptation and the study of its psychometric properties were conducted by \u0026Ouml;ner and Le Compte [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. In this study, only the STAI-II was used to determine the general anxiety level of the participants. The STAI-II consists of a total of 20 items. The scale demonstrated good internal consistency in the present study (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.81).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDifficulties in Emotion Regulation Scale\u003c/h3\u003e\n\u003cp\u003eThe Difficulties in Emotion Regulation Scale (DERS) was developed by Gratz and Roemer [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] to evaluate the difficulties individuals experience in their emotion regulation processes. The Turkish adaptation of the scale was conducted by Rugancı and Gen\u0026ccedil;\u0026ouml;z [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. In the adapted form, the scale consists of the following sub-dimensions: awareness, clarity, acceptance, strategies, impulse, and goals. In the reliability analysis performed within the scope of this study, the internal consistency Cronbach α value of the whole scale was found to be .93.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMetacognitions Questionnaire-30\u003c/h2\u003e \u003cp\u003eThe Metacognitions Questionnaire-30 (MCQ-30) was developed by Wells and Cartwright-Hatton [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e] to evaluate various metacognitive beliefs and processes within the framework of the metacognitive model of psychological disorders. The scale consists of a total of 30 items. The scale includes five dimensions that are related to each other but conceptually different: (1) \u0026ldquo;positive beliefs\u0026rdquo; about worry, which measures how much the person believes worry is functional; (2) \u0026ldquo;negative beliefs\u0026rdquo; about worry, which evaluates beliefs about how uncontrollable and dangerous worry is; (3) \u0026ldquo;cognitive confidence,\u0026rdquo; which measures the person's trust in their memory; (4) \u0026ldquo;need to control thoughts,\u0026rdquo; which evaluates beliefs regarding the necessity of controlling thoughts and the consequences of not doing so; and (5) \u0026ldquo;cognitive self-consciousness,\u0026rdquo; which evaluates the tendency to monitor one's own thoughts and the inward focus of attention. The Turkish adaptation and psychometric evaluation of the scale were done by Yılmaz, Gen\u0026ccedil;\u0026ouml;z, and Wells [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. In the reliability analysis performed within the scope of this study, the internal consistency Cronbach α value of the whole scale was found to be .88.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSensory Processing Sensitivity Scale (Highly Sensitive Person Scale)\u003c/h3\u003e\n\u003cp\u003eThe Sensory Processing Sensitivity Scale (Highly Sensitive Person Scale) is a 27-item scale developed by Aron and Aron [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] to measure the sensory processing sensitivity levels of individuals. The scale items cover features that determine sensory sensitivity, such as being disturbed by intense stimuli like loud noise and bright light, startling easily, getting over-aroused during multi-tasking, and sensitivity to aesthetic values. While the original form of the scale shows a one-dimensional feature, Turkish adaptation studies revealed a four-dimensional structure. In the psychometric evaluation by Şeng\u0026uuml;l-İnal and S\u0026uuml;mer [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e], it is stated that the scale consists of four sub-dimensions: (1) sensitivity to external stimuli, (2) aesthetic sensitivity, (3) harm avoidance, and (4) sensitivity to overstimulation. The internal consistency Cronbach α value of the 27-item scale was found to be .90.\u003c/p\u003e\n\u003ch3\u003eEye-Tracking Study\u003c/h3\u003e\n\u003cp\u003eThe EyeLink 1000 Plus (SR Research Ltd., Ottawa, Ontario) desktop eye-tracking system was used to record eye movements in this study. This system uses the infrared corneal reflection technique and collects data at a 500 Hz sampling rate. The procedure was non-invasive. A chin and forehead rest was used during the data collection process to minimize head movements and keep the distance between the eyes and the screen constant. The distance between the participants' eyes and the screen was set to 80 cm. The presentation of stimuli and data recording were done on a 21-inch monitor (LG Full HD, 1920x1080 pixels, 60 Hz) using the Experiment Builder (SR Research Ltd., Ottawa, Ontario) software.\u003c/p\u003e \u003cp\u003eWhile designing the eye-tracking study, the facial expressions used in the experimental task were selected from the FACES database [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e], developed at the Max Planck Institute for Human Development, Center for Lifespan Psychology. Sad, fearful, happy, and neutral face photos of 36 models, balanced in terms of age and gender, were included in this study. Adobe Photoshop was used to make the physical features of the stimuli similar. The face photos were cropped to keep only the face area, the backgrounds were removed, and they were converted to grayscale.\u003c/p\u003e \u003cp\u003eA free-viewing task paradigm was used in the research to evaluate attentional bias. A 9-point calibration task and validation process were completed to check if the measurements were accurate. First, a preliminary trial (practice) was applied to the participants, and then the experiment stage started. In the experiment flow, a gray screen appears first and stays on the screen for 1000 ms. At the beginning of each trial, a fixation point (black plus sign) was presented on a gray background for 1000 ms to focus the participant's attention on the center. After the fixation screen, a set of 4 pictures (sad, fearful, happy, and neutral facial expressions) placed in four different corners of the screen remained on the screen simultaneously for 10,000 ms. A blank gray screen was shown for 1000 ms between each trial. The study consists of a total of 36 trials, including 1 practice trial and 35 experimental trials. In each trial, 4 different emotional expressions of the same model were presented together. To prevent location bias, the position of the emotional expressions on the screen (top-right, bottom-left, etc.) was changed and balanced across trials.\u003c/p\u003e \u003cp\u003eThe 10,000 ms stimulus presentation duration was intentionally selected to capture sustained attentional engagement and difficulty in disengagement rather than early reflexive orienting. Within the Self-Regulatory Executive Function (S-REF) framework, threat monitoring is conceptualized as a coping-related, perseverative attentional process rather than a rapid vigilance response. Accordingly, the free-viewing paradigm and extended presentation duration were optimized to assess late-stage attentional maintenance processes that are theoretically central to the Cognitive Attentional Syndrome (CAS).\u003c/p\u003e \u003cp\u003eThe raw data obtained were prepared for analysis using the EyeLink Data Viewer (SR Research Ltd., Ottawa, Ontario) software. In the analyses, each facial expression presented on the screen (happy, sad, fearful, neutral) was defined as a separate Area of Interest (AOI).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eData analysis proceeded in two distinct phases. Before the analyses, the suitability of the data set for the relevant analyses was examined. First, missing values and outliers were removed from the data set. After calculating descriptive statistics and zero-order correlations for the questionnaire measures, the reliability of the scales was tested with Cronbach\u0026rsquo;s alpha coefficients.\u003c/p\u003e \u003cp\u003eAt the stage where the relational model of the study was tested, preliminary analyses and statistical assumption tests were performed first. In line with Kline\u0026rsquo;s [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e] suggestions, a two-stage SEM approach was used. In the first stage, the measurement model was verified. Then, the structural model, which examines the serial mediation role of metacognitive beliefs and emotion regulation difficulties in the effect of sensory processing sensitivity on anxiety, was tested. The significance of the mediation effects was evaluated using the bootstrapping resampling method with 5000 samples and 95% bias-corrected confidence intervals [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the experimental stage of the study, a 2 (group: anxiety, control) \u0026times; 4 (stimulus type: fearful, happy, neutral, sad) mixed design ANOVA was conducted to examine if there was a significant difference in attentional bias parameters for different stimulus types/valences (fearful, happy, neutral, sad) between the anxiety and control groups. Given the aggregated AOI-level indices across trials per emotion category, mixed ANOVA was considered appropriate for the present design. The group variable was included in the analysis as a between-subjects factor, and the stimulus type variable was included as a within-subjects factor. After performing descriptive analyses regarding attentional bias parameters for different stimulus types, it was examined whether the variables included in the analysis met the normal distribution assumption using Kolmogorov\u0026ndash;Smirnov and Shapiro\u0026ndash;Wilk tests; additionally, skewness and kurtosis values were checked. The assumption of sphericity was examined with Mauchly\u0026rsquo;s Test of Sphericity. While evaluating the group (anxiety, control) \u0026times; stimulus type interaction with ANOVA analyses, post hoc analyses were performed to reveal the source of the significant interaction effect. The Bonferroni correction was used to minimize possible Type 1 errors that might arise in\u003c/p\u003e \u003cp\u003emultiple comparisons. To examine links between self-report measures and eye-tracking indices, bivariate Pearson correlations were computed and complemented with 95% BCa bootstrap confidence intervals (5,000 resamples).\u003c/p\u003e \u003cp\u003eIn the experimental stage of the study, the data were prepared for analysis using EyeLink Data Viewer software (SR Research Ltd., Ottawa, Ontario). IBM SPSS Statistics 27 and AMOS Graphics 24 programs were used for all statistical analyses of the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePreliminary Analyses\u003c/h2\u003e \u003cp\u003eDescriptive statistics (means, standard deviations, skewness, and kurtosis), correlations, and reliabilities for the study variables are presented in Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;1 Descriptive statistics, reliabilities and correlations for the study variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eDescriptive statistics and reliabilities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c12\" namest=\"c7\"\u003e \u003cp\u003eCorrelations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.Metacognition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.534**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.Difficulties in emotion regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.641**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.494**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.Sensory processing sensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.295**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.262**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.323**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003cb\u003eNote.\u003c/b\u003e \u003cem\u003e*p\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.05, \u003cem\u003e**p\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStructural Equation Modeling\u003c/h2\u003e \u003cp\u003eA serial multiple mediation model was tested using structural equation modeling (SEM) in AMOS to examine whether metacognitive beliefs and difficulties in emotion regulation transmit the association between sensory processing sensitivity (SPS) and trait anxiety. Following preliminary analyses, a two-step SEM approach was adopted. First, the measurement model was evaluated; second, the hypothesized structural model was tested, and indirect effects were examined using bootstrapping (5,000 resamples; bias-corrected 95% confidence intervals).\u003c/p\u003e \u003cp\u003eThe measurement model comprised four latent constructs (SPS, metacognition, emotion regulation difficulties, and anxiety) indicated by 19 observed variables. Prior to parceling, the unidimensionality of the anxiety scale was verified via Exploratory Factor Analysis (EFA), as parceling is strongly recommended only when the underlying structure is structurally valid. Item parceling was employed not only to reduce measurement error and improve indicator reliability but also to optimize the ratio of sample size to estimated parameters [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]. This technique also contributes to meeting the normality assumptions of the data [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. Four parcels were created using the item-to-construct balance approach, distributing items with high, medium, and low factor loadings across parcels based on exploratory factor analysis. The measurement model demonstrated acceptable fit: χ\u0026sup2;(143, N\u0026thinsp;=\u0026thinsp;395)\u0026thinsp;=\u0026thinsp;389.98, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;2.73, CFI\u0026thinsp;=\u0026thinsp;.92, GFI\u0026thinsp;=\u0026thinsp;.90, AGFI\u0026thinsp;=\u0026thinsp;.87, IFI\u0026thinsp;=\u0026thinsp;.92, SRMR\u0026thinsp;=\u0026thinsp;.067, RMSEA\u0026thinsp;=\u0026thinsp;.066, 90% CI [.058, .074].\u003c/p\u003e \u003cp\u003eAfter confirming the measurement model, the hypothesized serial mediation model was tested. First, a partial mediation model including a direct path from SPS to anxiety was estimated and exhibited acceptable fit: χ\u0026sup2;(144, N\u0026thinsp;=\u0026thinsp;395)\u0026thinsp;=\u0026thinsp;409.34, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;2.84, CFI\u0026thinsp;=\u0026thinsp;.92, GFI\u0026thinsp;=\u0026thinsp;.89, AGFI\u0026thinsp;=\u0026thinsp;.86, IFI\u0026thinsp;=\u0026thinsp;.92, SRMR\u0026thinsp;=\u0026thinsp;.069, RMSEA\u0026thinsp;=\u0026thinsp;.068, 90% CI [.061, .076]. However, the direct SPS\u0026rarr;anxiety path was not statistically significant (β\u0026thinsp;=\u0026thinsp;.05, p\u0026thinsp;\u0026gt;\u0026thinsp;.05). Accordingly, a full mediation model was estimated by constraining this direct effect to zero. The full mediation model also provided acceptable fit: χ\u0026sup2;(145, N\u0026thinsp;=\u0026thinsp;395)\u0026thinsp;=\u0026thinsp;410.34, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;2.83, CFI\u0026thinsp;=\u0026thinsp;.92, GFI\u0026thinsp;=\u0026thinsp;.90, AGFI\u0026thinsp;=\u0026thinsp;.86, IFI\u0026thinsp;=\u0026thinsp;.92, SRMR\u0026thinsp;=\u0026thinsp;.070, RMSEA\u0026thinsp;=\u0026thinsp;.070, 90% CI [.060, .076].\u003c/p\u003e \u003cp\u003eThe two nested models were compared using a chi-square difference test, indicating that removing the direct path did not significantly worsen model fit, Δχ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;1.00, p\u0026thinsp;=\u0026thinsp;.317. In addition, information criteria slightly favored the full mediation model (AIC\u0026thinsp;=\u0026thinsp;500.340; BIC\u0026thinsp;=\u0026thinsp;679.389) over the partial mediation model (AIC\u0026thinsp;=\u0026thinsp;501.338; BIC\u0026thinsp;=\u0026thinsp;684.367). Taken together\u0026mdash;non-significant direct effect, non-significant Δχ\u0026sup2;, and marginally lower AIC/BIC\u0026mdash;the more parsimonious full mediation model was retained as the final model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003e \u003cem\u003eSPS-ES\u003c/em\u003e sensitivity to external stimuli; \u003cem\u003eSPS-AS\u003c/em\u003e aesthetic sensitivity, \u003cem\u003eSPS-HA\u003c/em\u003e harm avoidance; \u003cem\u003eSPS-OE\u003c/em\u003e sensitivity to overstimulation; \u003cem\u003eMCQ-SC\u003c/em\u003e cognitive self-consciousness; \u003cem\u003eMCQ-CT\u003c/em\u003e need to control thoughts; \u003cem\u003eMCQ-PB\u003c/em\u003e positive beliefs; \u003cem\u003eMCQ-NB\u003c/em\u003e negative beliefs; \u003cem\u003eMCQ-CC\u003c/em\u003e cognitive confidence; \u003cem\u003eDERS-AW\u003c/em\u003e awareness; \u003cem\u003eDERS-IMP\u003c/em\u003e impulse; \u003cem\u003eDERS-STR\u003c/em\u003e strategies; \u003cem\u003eDERS-AC\u003c/em\u003e acceptance; \u003cem\u003eDERS-CL\u003c/em\u003e clarity; \u003cem\u003eDERS-GO\u003c/em\u003e goals. \u003cem\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;.001, ** p\u0026thinsp;\u0026lt;\u0026thinsp;.01, * p\u0026thinsp;\u0026lt;\u0026thinsp;.05\u003c/em\u003e\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBootstrapping Procedure\u003c/h2\u003e \u003cp\u003eThe significance of the indirect effects in the model was tested using the bootstrap method (5000 samples, bias-corrected 95% confidence intervals). Results indicated that all three hypothesized mediation paths were statistically significant. When the structural paths were examined, the effect of sensory processing sensitivity on metacognition was significant (β\u0026thinsp;=\u0026thinsp;.401, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). The effect of sensory processing sensitivity on emotion regulation difficulties was also found to be significant (β\u0026thinsp;=\u0026thinsp;.185, p\u0026thinsp;\u0026lt;\u0026thinsp;.01). The effect of metacognition on emotion regulation difficulties was also significant (β\u0026thinsp;=\u0026thinsp;.636, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). The effect of metacognition on anxiety was also found to be significant (β\u0026thinsp;=\u0026thinsp;.540, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). The effect of emotion regulation difficulties on anxiety was significant (β\u0026thinsp;=\u0026thinsp;.352, p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003eWhen the results regarding indirect effects were examined, it was observed that the indirect effect of sensory processing sensitivity on anxiety through metacognition and emotion regulation difficulties was significant (β\u0026thinsp;=\u0026thinsp;.255, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Additionally, the indirect effect of sensory processing sensitivity on anxiety was significant solely through metacognition (β\u0026thinsp;=\u0026thinsp;.217, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Similarly, the indirect effect of sensory processing sensitivity on anxiety was also found to be significant through emotion regulation difficulties (β\u0026thinsp;=\u0026thinsp;.065, p\u0026thinsp;\u0026lt;\u0026thinsp;.01).\u003c/p\u003e \u003cp\u003eThese findings indicate that the association between SPS and anxiety is accounted for by indirect paths through metacognitive beliefs and emotion regulation difficulties in this cross-sectional sample. Therefore, the association between sensory processing sensitivity and anxiety was predominantly indirect in this sample, with metacognitive beliefs and emotion regulation difficulties accounting for a substantial proportion of this relationship. The results are presented in Table\u0026nbsp;2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;2 Direct and indirect effects of serial mediation model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel pathways\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLower\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eUpper\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDirect effect\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensory processing sensitivity \u0026loz; Metacognition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.379***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.401***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e.626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensory processing sensitivity \u0026loz; Difficulties in emotion regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.643***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.185**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetacognition \u0026loz; Difficulties in emotion regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.347***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.636***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e4.795\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetacognition \u0026loz; Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.095***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.540***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2.556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifficulties in emotion regulation \u0026loz; Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.194***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.352***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndirect effect\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensory processing sensitivity \u0026loz; (Metacognition \u0026ndash; Difficulties in emotion regulation) \u0026loz; Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.172***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.255***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensory processing sensitivity \u0026loz; (Metacognition) \u0026loz; Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.415***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.217***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e.600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensory processing sensitivity \u0026loz; (Difficulties in emotion regulation) \u0026loz; Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.124***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.065**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote.\u003c/b\u003e \u003cem\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;.001, ** p\u0026thinsp;\u0026lt;\u0026thinsp;.01, * p\u0026thinsp;\u0026lt;\u0026thinsp;.05\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEye-Tracking Results\u003c/h2\u003e \u003cp\u003eIn this study, eye-tracking measurements of anxiety (N\u0026thinsp;=\u0026thinsp;30) and control (N\u0026thinsp;=\u0026thinsp;37) groups regarding different facial expressions (fearful, happy, neutral, sad) were compared. In all analyses, the Greenhouse\u0026ndash;Geisser correction was applied when the sphericity assumption was not met. Descriptive statistics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and ANOVA results are in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of eye-tracking indices for different facial expressions in anxiety and control groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFearful \u003c/p\u003e \u003cp\u003e\u003cem\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHappy \u003c/p\u003e \u003cp\u003e\u003cem\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003cp\u003e\u003cem\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSad \u003c/p\u003e \u003cp\u003e\u003cem\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFirst fixation location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.252 (.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.260 (.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.230 (.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.257 (.056)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.254 (.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.256 (.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.234 (.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.253 (.059)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFirst fixation duration (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e352.92 (86.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e375.94 (71.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e349.00 (93.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e359.76 (109.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e405.96 (92.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e407.61 (100.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e415.85 (110.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e399.32 (77.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDwell time (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1209.61 (462.28) ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1652.52 (779.36) ᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1349.67 (508.80) ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1185.20 (421.50) ᵃ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1833.96 (335.31) ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1727.76 (363.94) ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1691.79 (309.19) ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1647.71 (247.65) ᵃ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDwell time percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.212 (.054) ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.292 (.126) ᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.231 (.064) ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.203 (.049) ᵃ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.255 (.031) ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.238 (.028) ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.234 (.025) ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.231 (.030) ᵃ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFixation count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.25 (1.45) ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.95 (1.67) ᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.40 (1.37) ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.08 (1.32) ᵃ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.45 (1.00) ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.22 (0.91) ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.02 (1.02) ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.99 (1.00) ᵃ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRun count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04 (0.89) ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.25 (0.79) ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.07 (0.78) ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00 (0.83) ᵃ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.47 (0.51) ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.46 (0.51) ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.38 (0.50) ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.33 (0.45) ᵃ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote.\u003c/b\u003e Means within the same row that are marked with different superscript letters (a, b, c) differ significantly from each other (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05). Values sharing at least one letter (e.g., \u003cem\u003eab\u003c/em\u003e vs. \u003cem\u003ea\u003c/em\u003e or \u003cem\u003eab\u003c/em\u003e vs. \u003cem\u003eb\u003c/em\u003e) do not differ significantly. No superscript letters were assigned for the first fixation location and first fixation duration parameters, as no significant main effect or interaction involving Stimulus Type was observed for these measures.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA results for eye-tracking study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eη\u0026sup2;ₚ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFirst fixation location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStimulus Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup x Stimulus Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFirst fixation duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.020*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStimulus Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup x Stimulus Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDwell time (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStimulus Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.007*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup x Stimulus Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.005*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDwell time percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStimulus Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.011*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup x Stimulus Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.006*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFixation count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.007*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStimulus Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup x Stimulus Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.011*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRun count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStimulus Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup x Stimulus Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.048*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eNote.\u003c/b\u003e Because the assumption of sphericity was violated, the Greenhouse\u0026ndash;Geisser correction was applied. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e \u003cp\u003eThe ANOVA results for first fixation location rates showed that the main effect of stimulus type was not significant, \u003cem\u003eF\u003c/em\u003e(3, 195)\u0026thinsp;=\u0026thinsp;2.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.094, η\u0026sup2;ₚ = .032. The main effect of the group was also not significant, \u003cem\u003eF\u003c/em\u003e(1, 65)\u0026thinsp;=\u0026thinsp;1.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.220, η\u0026sup2;ₚ = .023. Additionally, the Group \u0026times; Stimulus Type interaction was not found to be significant, \u003cem\u003eF\u003c/em\u003e(3, 195)\u0026thinsp;=\u0026thinsp;0.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.972, η\u0026sup2;ₚ = .001. These results demonstrate that the anxiety and control groups did not differ significantly in terms of first fixation location rates for different emotional face types. In other words, the anxiety level did not have a significant effect on which stimulus type the participants directed their first gaze to.\u003c/p\u003e \u003cp\u003eAccording to the ANOVA results regarding first fixation duration, the main effect of stimulus type is not significant, \u003cem\u003eF\u003c/em\u003e(3, 195)\u0026thinsp;=\u0026thinsp;0.81, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.492, η\u0026sup2;ₚ = .012. The Group \u0026times; Stimulus Type interaction was also not found to be significant, \u003cem\u003eF\u003c/em\u003e(3, 195)\u0026thinsp;=\u0026thinsp;1.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.237, η\u0026sup2;ₚ = .021. However, the main effect of group was significant, \u003cem\u003eF\u003c/em\u003e(1, 65)\u0026thinsp;=\u0026thinsp;5.70, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.020, η\u0026sup2;ₚ = .081. The average first fixation duration of the anxiety group (Mean\u0026thinsp;=\u0026thinsp;407.18 ms, SE\u0026thinsp;=\u0026thinsp;14.88) is longer compared to the control group (Mean\u0026thinsp;=\u0026thinsp;359.41 ms, SE\u0026thinsp;=\u0026thinsp;13.39) (Mean diff. = 47.78 ms). This suggests that the anxiety and control groups differed significantly in terms of first fixation durations.\u003c/p\u003e \u003cp\u003eThe analysis results exhibited that the stimulus type had a significant effect on dwell time, \u003cem\u003eF\u003c/em\u003e(1.79, 116.45)\u0026thinsp;=\u0026thinsp;5.57, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.007, η\u0026sup2;ₚ = .079. Additionally, the group (anxiety, control) \u0026times; stimulus type interaction was found to be significant, \u003cem\u003eF\u003c/em\u003e(1.79, 116.45)\u0026thinsp;=\u0026thinsp;5.81, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.005, η\u0026sup2;ₚ = .082. Furthermore, the main effect of the group was also found to be significant, \u003cem\u003eF\u003c/em\u003e(1,65)\u0026thinsp;=\u0026thinsp;22.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, η\u0026sup2;ₚ = .257, indicating a large effect size. The average dwell time of the anxiety group (Mean\u0026thinsp;=\u0026thinsp;1725.31 ms, SE\u0026thinsp;=\u0026thinsp;58.99) is significantly longer compared to the control group\u0026rsquo;s dwell time (Mean\u0026thinsp;=\u0026thinsp;1349.25 ms, SE\u0026thinsp;=\u0026thinsp;53.13) (Mean diff. = 376.06 ms), and this difference varied by stimulus type.\u003c/p\u003e \u003cp\u003eBonferroni-corrected pairwise comparisons, performed to examine the source of the interaction, exhibited that the Control group looked at Happy facial expressions (Mean\u0026thinsp;=\u0026thinsp;1652.52) for a significantly longer time compared to Fearful (Mean\u0026thinsp;=\u0026thinsp;1209.61) and Sad (Mean\u0026thinsp;=\u0026thinsp;1185.20) expressions. This pattern suggests a positive attentional bias in the control group. In contrast, this clear preference for Happy faces was not observed in the Anxiety group. This group directed the longest dwell time to Fearful faces (Mean\u0026thinsp;=\u0026thinsp;1833.96), and this duration was found to be significantly higher compared to Sad faces (Mean\u0026thinsp;=\u0026thinsp;1647.71).\u003c/p\u003e \u003cp\u003eThe analysis results exhibited that the stimulus type had a significant effect on dwell time percentage, \u003cem\u003eF\u003c/em\u003e (1.53, 99.68)\u0026thinsp;=\u0026thinsp;5.37, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.011, η\u0026sup2;ₚ = .076. Also, the group \u0026times; stimulus type interaction was found to be significant, \u003cem\u003eF\u003c/em\u003e (1.53, 99.68)\u0026thinsp;=\u0026thinsp;6.20, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.006, η\u0026sup2;ₚ = .087. Conversely, the main effect of the group was not found to be significant, \u003cem\u003eF\u003c/em\u003e (1,65)\u0026thinsp;=\u0026thinsp;3.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.072, η\u0026sup2;ₚ = .049. This result indicates that there is no significant difference between the anxiety and control groups in terms of general dwell time percentage.\u003c/p\u003e \u003cp\u003eBonferroni-corrected comparisons exhibited that the dwell time percentage of the control group was highest for happy faces (29.2%), and this rate was found to be higher than other expressions. In the anxiety group, the rate for fearful faces (25.5%) was found to be higher compared to sad faces (23.1%) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe analysis results revealed that the stimulus type had a significant effect on the fixation count, \u003cem\u003eF\u003c/em\u003e(1.77, 115.14)\u0026thinsp;=\u0026thinsp;6.78, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002, η\u0026sup2;ₚ = .094. Also, the group \u0026times; stimulus type interaction was found to be significant, \u003cem\u003eF\u003c/em\u003e(1.77, 115.14)\u0026thinsp;=\u0026thinsp;4.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.011, η\u0026sup2;ₚ = .071. Additionally, the main effect of the group is also statistically significant, \u003cem\u003eF\u003c/em\u003e(1,65)\u0026thinsp;=\u0026thinsp;7.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.007, η\u0026sup2;ₚ = .105. The average fixation count of the anxiety group (Mean\u0026thinsp;=\u0026thinsp;4.17, SE\u0026thinsp;=\u0026thinsp;.20) is significantly higher compared to the control group (Mean\u0026thinsp;=\u0026thinsp;3.42, SE\u0026thinsp;=\u0026thinsp;.18) (Mean diff. = .75). This finding indicates that the anxiety level has a medium effect on the fixation count.\u003c/p\u003e \u003cp\u003eBonferroni-corrected comparisons exhibited that the number of fixations on happy faces in the control group (Mean\u0026thinsp;=\u0026thinsp;3.95) was higher compared to fearful (Mean\u0026thinsp;=\u0026thinsp;3.25) and sad (Mean\u0026thinsp;=\u0026thinsp;3.08) faces. In the anxiety group, the highest fixation count was observed on fearful faces (Mean\u0026thinsp;=\u0026thinsp;4.45), and this value was found to be higher compared to sad faces (Mean\u0026thinsp;=\u0026thinsp;3.99) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe analysis results revealed that the stimulus type had a significant effect on the run count, \u003cem\u003eF\u003c/em\u003e (2.32, 150.55)\u0026thinsp;=\u0026thinsp;9.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, η\u0026sup2;ₚ = .125. Also, the group \u0026times; stimulus type interaction was found to be significant, \u003cem\u003eF\u003c/em\u003e (2.32, 150.55)\u0026thinsp;=\u0026thinsp;2.95, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.048, η\u0026sup2;ₚ = .043. Conversely, the main effect of the group was not found to be significant, \u003cem\u003eF\u003c/em\u003e (1,65)\u0026thinsp;=\u0026thinsp;3.84, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.054, η\u0026sup2;ₚ = .056. This result shows that there is no significant difference generally between the anxiety and control groups in terms of run count.\u003c/p\u003e \u003cp\u003eBonferroni-corrected comparisons exhibited that the control group's number of runs (looking again) for happy faces (Mean\u0026thinsp;=\u0026thinsp;2.25) was higher than for fearful (Mean\u0026thinsp;=\u0026thinsp;2.04) and sad (Mean\u0026thinsp;=\u0026thinsp;2.00) faces. In the anxiety group, the number of runs for fearful faces (Mean\u0026thinsp;=\u0026thinsp;2.47) was found to be higher compared to sad faces (Mean\u0026thinsp;=\u0026thinsp;2.33) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRelationships Between Eye-Tracking Indices and Cognitive-Emotional Vulnerabilities\u003c/h2\u003e \u003cp\u003eTo examine the link between cognitive-emotional vulnerabilities and threat-related attentional maintenance, bivariate Pearson correlations were computed and 95% BCa bootstrap confidence intervals (5,000 resamples) were reported (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation Between Eye-Tracking Indices and Cognitive-Emotional Vulnerabilities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Difficulties in emotion regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Difficulties in emotion regulation-Strategies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.90**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Metacognition-Negative Beliefs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.68**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.67**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Dwell time for fearful faces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.49**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.47**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.29*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Dwell time percentage for fearful faces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.41**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.35**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.35**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.78**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote.\u003c/b\u003e \u003cem\u003e*p\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.05, \u003cem\u003e**p\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDifficulties in emotion regulation were positively associated with attentional maintenance toward fearful faces, as indexed by longer dwell time (r\u0026thinsp;=\u0026thinsp;.49, 95% BCa CI [.313, .631], p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and higher dwell time percentage (r\u0026thinsp;=\u0026thinsp;.42, 95% BCa CI [.217, .571], p\u0026thinsp;=\u0026thinsp;.001). In addition, limited access to emotion regulation strategies was associated with both dwell time (r\u0026thinsp;=\u0026thinsp;.47, 95% BCa CI [.292, .615], p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and dwell time percentage (r\u0026thinsp;=\u0026thinsp;.35, 95% BCa CI [.145, .518], p\u0026thinsp;=\u0026thinsp;.004), suggesting that greater regulatory inflexibility is linked to sustained attentional engagement with threat cues. Regarding metacognition, negative beliefs about uncontrollability and danger were also associated with attentional maintenance, as reflected in dwell time (r\u0026thinsp;=\u0026thinsp;.294, 95% BCa CI [.077, .490], p\u0026thinsp;=\u0026thinsp;.016) and dwell time percentage (r\u0026thinsp;=\u0026thinsp;.348, 95% BCa CI [.134, .534], p\u0026thinsp;=\u0026thinsp;.004), although these associations were comparatively modest in magnitude.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eCorrelation analyses exhibited statistically significant relationships in the expected direction between the main structures in the study. However, the most critical finding of the study in terms of theory appeared in the model tested with SEM: It was found that sensory processing sensitivity (SPS) did not have a direct effect on anxiety (p\u0026thinsp;=\u0026thinsp;.317), and the relationship between SPS and anxiety was explained by indirect paths working through metacognitions and emotion regulation difficulties.\u003c/p\u003e \u003cp\u003eThis finding is consistent with approaches that treat SPS not as a direct indicator of psychopathology, but as a predisposition trait that can increase the risk level depending on the context and accompanying processes [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]. The view of Differential Susceptibility and Vantage Sensitivity approaches [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e], stating that highly sensitive individuals may experience more risk in negative conditions, is supported by the presence of mediator variables in the current model. These findings suggest that SPS is not directly associated with anxiety after accounting for metacognitions and emotion regulation difficulties; instead, SPS is indirectly associated with anxiety through these processes. The fact that the findings are consistent with emotion regulation-based and transdiagnostic models also strengthens this interpretation [\u003cspan additionalcitationids=\"CR106\" citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the model, one of the important indirect paths from SPS to anxiety occurred through metacognitions (SPS \u0026rarr; MCQ \u0026rarr; STAI; β\u0026thinsp;=\u0026thinsp;.22). The fact that SPS was positively associated with dysfunctional metacognitions (β\u0026thinsp;=\u0026thinsp;.40) indicates that the increased awareness and deep processing tendencies of highly sensitive individuals toward not only environmental but also internal stimuli [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] may cause them to focus on their own mental processes and develop negative beliefs (metacognitions) about these processes. This finding aligns with the S-REF model [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and its current applications [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e]. When deep processing tendency is combined with negative metacognitive beliefs, the weakening of attentional control [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e] and the inability to use attentional resources flexibly may strengthen more rigid and avoidant cognitive patterns related to anxiety [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. Therefore, metacognitions may represent a key correlate linking SPS-related reactivity with anxiety symptoms in the tested cross-sectional model.\u003c/p\u003e \u003cp\u003eThe second critical path in the model works through difficulties in emotion regulation (SPS \u0026rarr; DERS \u0026rarr; STAI; β\u0026thinsp;=\u0026thinsp;.07). SPS may be associated with more intense emotional reactivity, which could coincide with greater reported difficulties in emotion regulation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Processing and regulating this intense emotional data may require more cognitive/emotional resources [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In conditions of intense arousal, components of emotion regulation such as awareness, acceptance, and impulse control may be strained [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsistent with transdiagnostic approaches [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e], this finding suggests that the emotional intensity brought by high sensitivity exceeds the individual's acceptance capacity (DERS-Acceptance) and increases the tendency towards dysfunctional strategies (suppression, avoidance, etc.) (DERS-Strategies). As the ERT approach emphasizes [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e, \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e] trying to manage high reactivity with inflexible strategies may lead to the continuation of anxiety symptoms. Indeed, a comprehensive meta-analysis by Aldao, Nolen-Hoeksema, and Schweizer [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] also shows that dysfunctional emotion regulation strategies are strongly related to psychopathology. When findings supporting the relationship between SPS and emotion regulation difficulties [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and studies pointing to the mediation of emotion regulation in the transformation of temperamental traits into anxiety symptoms [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e] are evaluated together, results indicate that emotion regulation processes can be a critical intermediate mechanism in this transformation.\u003c/p\u003e \u003cp\u003eA novel contribution of this study is the identification of the significant serial mediation path SPS \u0026rarr; MCQ \u0026rarr; DERS \u0026rarr; STAI (β\u0026thinsp;=\u0026thinsp;.26), where two processes operate together. This finding indicates that not a single mechanism, but sequential processes that trigger each other, play a role in the development of anxiety. Although the proposed model is theoretically grounded in the S-REF framework, the present findings should be interpreted as evidence of patterned associations rather than causal effects. Given the cross-sectional nature of the data, the directionality of the paths represents a theory-driven assumption rather than an empirical demonstration of temporal precedence.\u003c/p\u003e \u003cp\u003eThe strong relationship between metacognition and emotion regulation difficulties in the model (β\u0026thinsp;=\u0026thinsp;.64) is consistent with approaches suggesting that not only the intensity of the emotional experience but also the evaluations and beliefs regarding this experience can determine the regulation capacity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e]. Accordingly, when a highly sensitive individual interprets their already intense internal experience through a threat-focused metacognitive filter, their emotion regulation capacity may be disrupted more easily, and anxiety symptoms may become stronger.\u003c/p\u003e \u003cp\u003eIn conclusion, the model demonstrates a sequential and holistic cognitive-emotional chain: high sensitivity (SPS) \u0026rarr; threat-based metacognitive beliefs \u0026rarr; dysfunctional emotion regulation \u0026rarr; anxiety symptoms. This pattern points out that handling metacognitive and emotional processes together may be important in understanding anxiety symptoms and determining intervention goals.\u003c/p\u003e \u003cp\u003eCrucially, the depletion of cognitive resources caused by this dysfunctional metacognitive activity and emotion regulation effort is theoretically expected to impair top-down attentional control. Consistent with this interpretation, this pattern may be reflected behaviorally as a reduced capacity to disengage attention from threatening stimuli, a hypothesis directly addressed by the eye-tracking phase of this study.\u003c/p\u003e \u003cp\u003eIn this study, attentional bias was handled as a multi-component structure and evaluated through vigilance (orienting toward threat), difficulty in disengagement/attention maintenance, and avoidance components [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. This approach is consistent with the literature suggesting that attention processes toward threat stimuli exhibit a multi-stage structure covering both early, automatic orienting mechanisms and later, controlled attention maintenance or avoidance processes [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e]. This triple structure also overlaps with theoretical frameworks that handle attention processes on the orienting\u0026ndash;disengagement\u0026ndash;focusing axis [\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e]. Current studies also focus on distinguishing not only the existence of attentional bias but also through which cognitive mechanisms it emerges [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]; the eye-tracking method allows for capturing this distinction more directly [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the current study, indicators were matched with components based on relevant theoretical models and eye-tracking literature [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e]: first fixation location was used for vigilance; first fixation duration, total dwell time, percentage of total dwell time, fixation count, and run count were used for difficulty in disengagement/maintenance [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e]. Analyses were based on gaze and fixation-based indicators calculated over the entire trial duration [\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e, \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe findings generally indicate that, within the present free-viewing paradigm, attentional bias in high anxiety was primarily reflected in sustained attention and difficulty disengaging from threat-related stimuli, whereas robust evidence for early reflexive vigilance was not observed. This result is consistent with approaches arguing that attentional bias cannot be reduced to a single mechanism but is shaped by the dynamic interaction of different cognitive sub-processes [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Although early vigilance effects were not a primary focus of the present study, this was a deliberate methodological choice rather than a limitation. The extended stimulus duration and free-viewing paradigm were designed to maximize sensitivity to attentional maintenance and disengagement difficulty, which are central to metacognitive accounts of anxiety (e.g., the S-REF model).\u003c/p\u003e \u003cp\u003eWithin the scope of the Vigilance component, the first fixation location, which reflects how quickly and preferentially attention is spatially directed to the threat, was examined. Analyses exhibited that the main effect of the group and the group \u0026times; stimulus type interaction were not significant in terms of the first fixation location; therefore, we did not observe a reliable group difference in initial orienting (first fixation location) toward threat stimuli under the present free-viewing conditions. Although early models [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] and classic dot-probe studies [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e] suggest that individuals experiencing anxiety will demonstrate automatic orienting to threat stimuli, current eye-tracking studies demonstrate that this effect is highly sensitive to context (e.g., perceptual load; [\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e] and eccentricity; [\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e]) [\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e, \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e]. The features of the free-viewing paradigm used in the current study (for example, low perceptual load or stimulus eccentricity) may explain why the early orienting effect remained weak. Meta-analysis findings also demonstrate that the early orienting effect does not appear consistently in all studies and the effect size is generally small-to-medium [\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e]. In conclusion, the lack of group difference in terms of the first fixation location suggests that processes of maintaining attention and/or difficulty in disengagement following threat perception may be more decisive in high anxiety than early orienting.\u003c/p\u003e \u003cp\u003eDelayed Disengagement / Maintenance: In the high trait anxiety group, a pattern of longer dwell time, more fixations, and more run counts (looking again) on threat stimuli was observed; this situation indicated that the bias is related to processes of inability to disengage from the threat (disengagement difficulty) rather than noticing the threat quickly [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e]. The findings parallel the Attentional Control Theory [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] which proposes that anxiety increases maintaining attention on threat by weakening top-down attentional control, and meta-analytical findings regarding free-viewing tasks [\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e, \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs reported by Georgiou et al. [\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e], in this study too, the difficulty in disengagement was found to be specific to \"threat content\" (fearful faces) rather than a general negativity bias; a similar effect was not observed for sad faces. In this respect, the findings are consistent with the threat-specific attention maintenance model predicted by Weierich, Treat, and Hollingworth [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. More frequent returns to threat stimuli (run count values) may reflect re-engagement with threat-related information, a pattern commonly discussed as threat monitoring in the attentional bias literature.\u003c/p\u003e \u003cp\u003eIn conclusion, when duration, rate, and frequency-based indicators are handled together, results indicate that individuals with high anxiety levels display a pattern of prolonged attention maintenance and difficulty in disengagement on threat stimuli. This pattern parallels theoretical approaches emphasizing the maintenance/difficulty in disengagement component in anxiety [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]; and current meta-analytical findings supporting this [\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAvoidance, which is the third component of attentional bias, refers to the individual's tendency to strategically shift their attention to another direction or move away from the threat after perceiving the threat stimulus [\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e]. Theoretical models emphasize that avoidance is generally a delayed mechanism and reflects the high-level control processes of the attention system [\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e, \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e]. In eye-tracking studies, avoidance is generally defined by indirect indicators such as shorter total dwell times toward the threat region, rapid disengagement after the first fixation, or directing fewer or later fixations to the threat region. These patterns are interpreted as a strategic attention-shifting tendency aimed at moving away from threat information or reducing the processing of the threat.\u003c/p\u003e \u003cp\u003eIn the current study, indicators typically interpreted as attentional avoidance (e.g., shortened dwell time or reduced fixation count toward threat stimuli) were not evident within the limits of the present paradigm. In contrast, more frequent returns to the threat stimulus appear more consistent with threat-monitoring accounts than with avoidance, as typically operationalized in eye-tracking studies. Although meta-analytical findings [\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e] state that the vigilance\u0026ndash;avoidance model may emerge under certain conditions, the picture observed in the current study points to the dominance of the vigilance\u0026ndash;maintenance cycle rather than avoidance.\u003c/p\u003e \u003cp\u003eOne of the remarkable findings of the study is the attention profile displayed by the control group. The control group not only displayed shorter dwell times and lower fixation counts toward faces containing threats; they also consistently directed their attention to happy faces. Statistical analyses exhibited that the control group's dwell times for happy faces were significantly longer than for all other faces (fearful, sad, neutral). This suggests that the attention system of healthy individuals displays an adaptive positive bias, where they actively orient toward positive and safe stimuli to maintain emotional balance, beyond just ignoring the threat. The fact that this protective mechanism did not activate in the high trait anxiety group, along with the high number of returns to the threat, shows that these individuals have difficulty using safe cues in the environment and continue to focus on threat information.\u003c/p\u003e \u003cp\u003eThe analyses examining the relationship between self-report measures and eye-tracking indices provided critical insights into the cognitive mechanisms of attentional bias. Although total metacognition scores were not directly associated with eye-tracking metrics, the specific sub-dimension of 'Negative beliefs about uncontrollability and danger' was significantly correlated with increased dwell time on fearful faces. This finding is theoretically consistent with the S-REF model, which posits that it is not the mere presence of metacognitions, but specifically the belief that 'worry is uncontrollable and dangerous' that drives threat monitoring [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Furthermore, the strong correlation observed between DERS-Strategies and threat dwell time suggests that the inability to disengage from threat is closely linked to a deficit in accessing effective emotion regulation strategies. Together, these findings imply that the 'vigilance-maintenance' pattern observed in high anxiety is fueled by a specific cognitive-emotional combination: the belief that internal experiences are dangerous (Metacognition) and the lack of tools to manage the resulting arousal (Regulation).\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e \u003cp\u003eBecause group classification was based on trait anxiety scores in a non-clinical student sample, caution is warranted when generalizing the present findings to clinically diagnosed anxiety disorders (e.g., social anxiety disorder or panic disorder). Meta-analytic evidence indicates that threat-related attentional bias effects are more consistent in clinically anxious populations [\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e]. Future studies should therefore examine whether the robust threat-maintenance and disengagement difficulty pattern observed here replicates with comparable magnitude in clinical samples. Because sensory processing sensitivity, metacognitive beliefs, emotion regulation difficulties, and anxiety were assessed using self-report measures, shared method variance may have inflated the observed associations in the structural model. Although eye-tracking indices provided objective indicators of attentional processes, common method bias cannot be fully ruled out. Furthermore, the eye-tracking sample was selected using an extreme-groups approach to maximize phenotypic variance; therefore, the observed correlations between self-report and physiological measures reflect associations within these distinct phenotypes rather than a continuous distribution across the general population. In addition, the absence of strong early vigilance effects alongside pronounced sustained-attention differences may be partly attributable to the long stimulus presentation duration (10,000 ms) and the free-viewing paradigm, which may favor later-stage attentional processes. Future research using shorter exposure durations, higher perceptual load, or temporally constrained paradigms may provide a more sensitive test of early orienting mechanisms. Age and gender were not included as covariates in the present analyses. Future studies should examine whether the observed pattern of results remains robust after adjusting for demographic variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eWhen the SEM and eye-tracking findings in this study are evaluated holistically, findings are consistent with the view that SPS alone may be insufficient to account for anxiety symptom severity; instead, anxiety symptoms co-vary with metacognitive beliefs and emotion regulation difficulties that reflect threat-oriented interpretations of internal experiences. The structural model revealed that sensory processing sensitivity poses an indirect risk through negative metacognitive beliefs and emotion regulation difficulties, rather than predicting anxiety directly. This pattern aligns with the basic assumption of the S-REF model: Labeling internal experiences as \"dangerous\" or \"uncontrollable\" weakens the individual's perception of cognitive control and triggers the Cognitive-Attentional Syndrome (CAS). Accordingly, SPS-related reactivity may be associated with higher anxiety symptom severity particularly when accompanied by more threat-oriented metacognitive beliefs and greater emotion regulation difficulties.\u003c/p\u003e \u003cp\u003eEye-tracking findings point to the behavioral equivalent of this cognitive-emotional cycle. In the high trait anxiety group, the fact that attentional bias is characterized by difficulty in disengaging attention and a maintenance/monitoring pattern after orienting to the threat content, rather than a reflexive first orientation, presents a profile that overlaps with the \"threat monitoring\" component of CAS. In this non-clinical high-trait-anxiety sample, group differences were more evident in sustained attention/maintenance indices than in initial orienting indices. The consistent positive orientation observed in the control group shows that in healthy functioning, emotion regulation is related not only to reducing the threat but also to actively processing safe/positive cues.\u003c/p\u003e \u003cp\u003eThis holistic picture highlights two points in terms of clinical intervention goals: (i) instead of treating sensitivity itself as a \"problem,\" focusing on changing metacognitive beliefs that interpret the internal experience triggered by sensitivity as threat-based, and (ii) strengthening emotion regulation skills that support flexibility under intense arousal. Interventions targeting metacognitive beliefs and emotion regulation skills may help reduce threat-focused attention patterns; intervention studies are needed to evaluate whether such changes translate into reduced threat monitoring.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAGFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdjusted Goodness-of-Fit Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAkaike Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of Variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAOI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea of Interest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBCa\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBias-Corrected and Accelerated\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBayesian Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCognitive Attentional Syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComparative Fit Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDERS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifficulties in Emotion Regulation Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExploratory Factor Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFACES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFACES Database of Facial Expressions\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGoodness-of-Fit Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIncremental Fit Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCQ-30\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMetacognitions Questionnaire-30\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCQ-CC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCognitive Confidence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCQ-CT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeed to Control Thoughts\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCQ-NB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNegative Beliefs about Uncontrollability and Danger\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCQ-PB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePositive Beliefs about Worry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCQ-SC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCognitive Self-Consciousness\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRMSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRoot Mean Square Error of Approximation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStructural Equation Modeling\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSensory Processing Sensitivity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPS-AS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAesthetic Sensitivity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPS-ES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSensitivity to External Stimuli\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPS-HA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHarm Avoidance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPS-OE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSensitivity to Overstimulation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eS-REF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSelf-Regulatory Executive Function Model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSRMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandardized Root Mean Square Residual\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTAI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eState\u0026ndash;Trait Anxiety Inventory\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTAI-I\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eState\u0026ndash;Trait Anxiety Inventory-State Anxiety Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTAI-II\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eState\u0026ndash;Trait Anxiety Inventory-Trait Anxiety Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e \u003cp\u003e \u0026bull; Prior to participation, informed consent was obtained from all individual participants included in the study. Ethical approval was obtained from the Hamidiye Non-Interventional Scientific Research Ethics Committee of the University of Health Sciences (Approval No: 533146).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003e\u0026bull; Not applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003e\u0026bull; The authors declare that there is no conflict of interest, given that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThe authors did not receive support from any organization for the submitted work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eG.B.K and A.F. contributed to the study conception and design. G.B.K. performed the data collection and statistical analysis. G.B.K. wrote the first draft of the manuscript. A.F. supervised the study and critically reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis article is derived from the doctoral dissertation submitted to University of Health Sciences by Gizem Baki Kaşık\u0026ccedil;ı in fulfillment of the requirements for the PhD degree in the Clinical Psychology Department.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGross JJ. Conceptual foundations of emotion regulation. In: Gross JJ, Ford BQ, editors. Handbook of emotion regulation. 3rd ed. New York: Guilford Press; 2024. pp. 3\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGross JJ. Emotion regulation: current status and future prospects. Psychol Inq. 2015;26:1\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCole PM, Michel MK, Teti LOD. The development of emotion regulation and dysregulation: a clinical perspective. Monogr Soc Res Child Dev. 1994;59:73\u0026ndash;100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1540-5834.1994.tb01278.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1540-5834.1994.tb01278.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAldao A, Nolen-Hoeksema S, Schweizer S. Emotion-regulation strategies across psychopathology: a meta-analytic review. 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Hypervigilance\u0026ndash;avoidance pattern in spider phobia. J Anxiety Disord. 2005;19:105\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anxiety, attentional bias, emotion regulation, eye-tracking, metacognition, sensory processing sensitivity","lastPublishedDoi":"10.21203/rs.3.rs-8564618/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8564618/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study investigated cognitive\u0026ndash;emotional mechanisms underlying anxiety within the Self-Regulatory Executive Function (S-REF) framework. It tested whether sensory processing sensitivity (SPS) is associated with trait anxiety through metacognitive beliefs and emotion regulation difficulties, and assessed threat-related attentional bias using eye-tracking.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study was conducted in two phases. In Phase 1, self-report data were collected from 395 university students, and structural equation modeling (SEM) tested the associations among SPS, metacognitions, emotion regulation difficulties, and trait anxiety. In Phase 2, participants were selected using an extreme-groups approach based on trait anxiety scores (high trait anxiety: n\u0026thinsp;=\u0026thinsp;30; control: n\u0026thinsp;=\u0026thinsp;37) and completed a free-viewing task presenting fearful, sad, happy, and neutral faces while eye movements were recorded with an EyeLink 1000 Plus system.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSEM supported a full mediation model: SPS did not directly predict trait anxiety, but showed significant indirect effects via metacognitive beliefs, emotion regulation difficulties, and a serial pathway. Eye-tracking findings indicated that high trait anxiety was primarily characterized by sustained attention to threat and difficulty disengaging, reflected in longer dwell time, higher fixation counts, and increased re-visits to fearful faces, rather than group differences in initial orienting (first fixation location). Furthermore, negative metacognitive beliefs about uncontrollability and danger and limited access to emotion regulation strategies were positively associated with late-stage threat maintenance indices (e.g., dwell time and dwell-time percentage).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIntegrating self-report and physiological measures, findings suggest that SPS confers vulnerability to anxiety primarily through threat-focused metacognitions and inflexible emotion regulation processes. These vulnerabilities are reflected behaviorally in sustained threat-monitoring and disengagement difficulty patterns.\u003c/p\u003e","manuscriptTitle":"Investigation of Cognitive and Emotional Mechanisms Associated With Anxiety Using Structural Equation Modeling and Eye-tracking Analysis of Attentional Bias","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 00:29:26","doi":"10.21203/rs.3.rs-8564618/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"193949551841679016238923054723186795423","date":"2026-05-11T09:04:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191773904175119261513924802149116335698","date":"2026-04-09T18:04:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-25T12:17:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9233930567486567806526960970091557352","date":"2026-01-22T16:17:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-22T01:19:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-13T19:14:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-12T12:51:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-12T12:48:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2026-01-09T23:14:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"53e95291-496d-4d53-9802-264ea3e1f83d","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"193949551841679016238923054723186795423","date":"2026-05-11T09:04:30+00:00","index":103,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-29T00:29:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 00:29:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8564618","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8564618","identity":"rs-8564618","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}