Exploring HRV and EEG Correlates of Intuition: Insights into Analytical and Non-Analytical Perception

This study investigates the physiological correlates of intuitive processes by examining the relationship between heart rate variability (HRV), electroencephalogram (EEG) activity, and two types of perception: analytical (AP) and non-analytical (NAP). A sample of 110 healthy university students completed tasks designed to measure AP and NAP under both resting and testing conditions, while HRV and EEG data were simultaneously recorded. Correlational analyses revealed that higher AP scores were positively associated with HRV indices such as RMSSD, PNN50, HF power, and HRV mean during rest, indicating a connection between analytical intuition and parasympathetic regulation. In contrast, NAP scores were negatively correlated with most HRV indices during testing, while positively associated with mean heart rate, suggesting heightened arousal and reduced autonomic flexibility. EEG analyses showed no significant brain wave correlates for AP, but trend-level differences in gamma variability were noted. NAP performance was associated with increased high beta activity and alpha peak frequency variability, indicating greater cortical arousal and modulation. Group comparisons revealed significant physiological differences between high and low performers, especially in HRV during testing and in EEG patterns at rest. Stepwise regression identified HRV mean, LF peak, broad beta mean, and SMR variability as significant predictors of NAP scores, explaining 17.3% of the variance. No significant predictors emerged for AP. These findings suggest that AP and NAP are supported by distinct physiological states, with AP linked to baseline autonomic stability and NAP to dynamic arousal and neural flexibility. Exploring HRV and EEG Correlates of Intuition: Insights into Analytical and Non-Analytical Perception

This study investigates the physiological correlates of intuitive processes by examining the relationship between heart rate variability (HRV), electroencephalogram (EEG) activity, and two types of perception: analytical (AP) and non-analytical (NAP). A sample of 110 healthy university students completed tasks designed to measure AP and NAP under both resting and testing conditions, while HRV and EEG data were simultaneously recorded. Correlational analyses revealed that higher AP scores were positively associated with HRV indices such as RMSSD, PNN50, HF power, and HRV mean during rest, indicating a connection between analytical intuition and parasympathetic regulation. In contrast, NAP scores were negatively correlated with most HRV indices during testing, while positively associated with mean heart rate, suggesting heightened arousal and reduced autonomic flexibility. EEG analyses showed no significant brain wave correlates for AP, but trend-level differences in gamma variability were noted. NAP performance was associated with increased high beta activity and alpha peak frequency variability, indicating greater cortical arousal and modulation. Group comparisons revealed significant physiological differences between high and low performers, especially in HRV during testing and in EEG patterns at rest. Stepwise regression identified HRV mean, LF peak, broad beta mean, and SMR variability as significant predictors of NAP scores, explaining 17.3% of the variance. No significant predictors emerged for AP. These findings suggest that AP and NAP are supported by distinct physiological states, with AP linked to baseline autonomic stability and NAP to dynamic arousal and neural flexibility. The study offers empirical support for a dual-process view of intuition grounded in heart-brain interaction.

Analytical Intuition, Non-Analytical Intuition, Heart Rate Indices and Intuition, Brain Wave Indices and Intuition, Correlation between HRV and EEG. Predictive Variables for Intuition. Exploring HRV and EEG Correlates of Intuition: Analytical and Non-Analytical Perception 1. Introduction Intuition is a multifaceted construct situated at the intersection of philosophy and psychology. Philosophically, it has been described as a direct, non-inferential grasp of truth, consciousness, or inner reality (Gündoğan, 2024; Bibika, 2024; Soyaslan, 2024). Psychologically, intuition is understood as a rapid, unconscious, emotionally charged process grounded in holistic associations and distinct from analytical reasoning (Hammond, 1996; Pretz et al., 2014). Dual-process theories classify it as implicit, automatic, and evolutionarily adaptive (Gore & Saddler-Smith, 2011; Cai Shi & Lucietto, 2022). Recent literature emphasizes that intuition is not a unitary phenomenon but varies according to context, function, and underlying cognitive processes (Cai Shi & Lucietto, 2021; Pretz et al., 2014). While traditional philosophical accounts frame intuition as a singular mental faculty (Nado, 2014; Patton, 2003), empirical classifications differentiate forms such as problem-solving, moral, emotional, creative, relational, inferential, and somatic intuitions (Glöckner & Witteman, 2010; McCraty, 2015). These distinctions reflect the increasing recognition that intuitive processes may shift depending on familiarity, complexity, and time pressure in decision-making environments. Based on these typologies, intuition can be conceptualized in three interrelated forms. The first is an explicit, experience-based comprehension operating in familiar contexts and relying on accumulated schemas and reasoning (Evans & Stanovich, 2013; Pretz et al., 2014). The second reflects an implicit, unconscious sensitivity to environmental cues in unfamiliar situations, shaped by past learning and heuristics (Gigerenzer, 2007; Gore & Saddler-Smith, 2011). The third describes an affective, immediate form of knowing arising in emotionally salient or uncertain conditions, marked by visceral signals and somatic markers (Bechara & Damasio, 2005; McCraty, 2015; Holzer, 2022). Accurate responses derived through schema-based reasoning or implicit perception are widely regarded as essential functions of intelligence (Simon, 1990; Ericsson & Charness, 1994; Kahneman & Klein, 2009). However, non-analytical perception involving bodily signals and emotional resonance may offer an alternative pathway to insight, one that enhances self-awareness and instinctive decision-making. Recent research suggests that the heart plays an active role in intuitive processing, lending physiological support to the metaphor “follow your heart” (Holzer, 2022; Damasio, 1994). Once seen primarily as a symbolic center of wisdom and emotion (Salem, 2009), the heart is now recognized as a neurophysiologically complex organ with its own intrinsic nervous system, or “heart brain,” capable of bidirectional communication with the central nervous system (Armour & Ardell, 1994; Cantin & Genest, 1986; Tiller et al., 1996; McCraty et al., 2004a). These pathways—including vagal input, electromagnetic fields, and neurochemical signals—contribute to emotion regulation, stress response, and cognitive coherence (Lacey & Lacey, 1978; Rein et al., 1995; McCraty, 2000). McCraty et al. (2004a) found that heart rate activity decreased several seconds prior to participants viewing emotional stimuli, suggesting a non-analytical anticipatory mechanism. This idea is expanded in later studies showing that the heart may act not only as a responder but also as a transducer of intuitive signals, potentially preceding cortical activity (McCraty et al., 2004b; McCraty, 2015; Dunn et al., 2010). Palser et al. (2021), Soosalu et al. (2019), and Mulukom (2024) support this embodied view, reporting that individuals more attuned to bodily states—particularly cardiac signals—demonstrate greater intuitive accuracy. Supporting this, McCraty and Zayas (2014) observed that coherent heart rhythms enhance emotional self-regulation and facilitate deeper intuitive access. Hodgkinson et al. (2008) highlight the dynamic interplay between affect and cognition, suggesting that cardiac signals shape conscious evaluations. Sands (2022) further proposes that the heart’s electromagnetic field may influence both intra- and interpersonal intuitive processing. Electroencephalography (EEG) has also proven valuable in identifying neural correlates of intuition. Beyond its clinical uses, EEG studies show that intuitive individuals often exhibit increased theta and alpha activity—brainwave patterns associated with emotional integration, creativity, and holistic thinking (Azhari & Hernandez, 2016; Uyulan et al., 2022). In contrast, analytical cognition is typically marked by elevated beta wave activity, linked to focused reasoning and linear thought. This study investigates the dynamic interplay between heart rate variability (HRV) and electroencephalographic (EEG) activity under both resting and task-oriented conditions. Participants engage in two tasks: one requiring analytical perception (AP), involving interpretation of explicit or inferable cues; and the other requiring non-analytical perception (NAP), based on intuitive recognition of implicit or ambiguous signals. While the distinction between analytical and non-analytical forms of intuition is well documented in cognitive and philosophical literature, few studies have examined whether these processes correspond to distinct physiological states. Drawing from dual-process theories and embodied cognition frameworks, analytical intuition (AP) is assumed to operate in structured, familiar environments and may be supported by parasympathetic regulation and cortical coherence. This is consistent with findings that parasympathetic dominance facilitates executive functioning and attentional stability, both of which are essential for analytical discrimination (Thayer & Lane, 2000; Kemp et al., 2012). In contrast, non-analytical intuition (NAP), often triggered by emotionally salient or uncertain conditions, is expected to engage sympathetic arousal, heightened interoceptive awareness, and more variable brainwave dynamics. Accordingly, the study analyzes HRV markers and EEG wave patterns during AP and NAP tasks to identify their distinct physiological signatures. Through correlational analysis, group comparisons, and stepwise regression, the study aims to determine whether distinct physiological indicators are associated with each form of intuitive performance. In line with this framework, the study is guided by a set of research questions that explore associations, group differences, and predictive relations between heart-brain activity and intuitive processing. These findings are expected to contribute to the empirical grounding of intuition as a measurable, neurophysiologically anchored construct with implications for education, clinical practice, and decision-making. Although theoretical models suggest that intuitive modes of cognition may rely on distinct physiological mechanisms, empirical studies integrating both HRV and EEG in the same experimental design remain limited. This study addresses this gap by simultaneously examining autonomic and neural correlates of analytical and non-analytical intuitive performance. 2. Methods and Materials The research was carried out with the endorsement of the ethics committee of Kocaeli University’s Social and Human Sciences department, as well as the consent of the dean of the faculty where the research took place (Ethics Committee Decision no 20, made during the meetings 2024/06. For writing the report of the research, Chatgpt.com artificial intelligence applications was utilized to a limited extent, specifically for tasks such as literature review, paraphrasing, and translation. 2. 1. Research Design The research is descriptive and employs a relational methodology. The study entailed examining the correlation between heart rate variability (HRV) indicators and concurrently recorded brain waves over a duration of five minutes, during both a resting state and a testing state in which participants addressed two separate categories of enquiries: analytical perception (AP) and utilizing non-analytical perception (ANP) to derive answers purportedly linked to intuitive success. The disparities in heart rate and brain wave indices between samples with higher and lower AP and NAP scores were also examined. The correlations between heart rate and brain wave indices were analyzed in both resting and testing settings. Ultimately, the heart rate and brain wave variables that may serve as predictors of AP and NAP success were assessed using the stepwise regression method. 2.1.1. Research Questions Given the descriptive and correlational nature of the present study, the research was guided by the following questions rather than specific hypotheses: • What are the associations between analytical (AP) and non-analytical (NAP) intuitive performance and heart rate variability (HRV) indices during resting and task conditions? • What are the associations between AP and NAP scores and electroencephalographic (EEG) wave patterns recorded during rest and task phases? • Do individuals with high and low AP/NAP performance levels differ significantly in terms of HRV and EEG indicators? • What are the interrelations between HRV and EEG parameters under resting and task-oriented conditions? • To what extent do HRV and EEG variables serve as predictors of analytical and non-analytical intuitive performance? These questions structured the analysis plan and provided a framework for interpreting the observed physiological and neurocognitive patterns. 2. 2. Population and samples of the research The study examines the student population enrolled in the Faculty of Education at Kocaeli University. The research sample comprises 147 male and female students, predominantly in the 1st and 2nd grades, aged 19-23, from diverse disciplines. The students were informed about the research, and their agreement was obtained, indicating their voluntary involvement in the study. The shortened general health scale, developed by Demiral et al. (2006), was administered to the samples with appropriate authorization. The scale has 8 subdimensions on 3 main dimensions as physical, social and psychological. Participants whose psychological health scores fell below the reference threshold or who were previously diagnosed with psychiatric conditions were excluded from the analysis. Analyses were conducted both with all samples 147 and with those remaining 110 healthy subjects. 2. 3. Heart Rate and Brain waves Indices The recorded indices of heart rate and brainwaves and summaries of how they are interpreted psychologically and biologically can be found in the study by Shaffer and Ginsberg (2017) and Campbell et al. (2021) The estimated brain wave indices include delta, theta, alpha, sensorimotor rhythm (SMR), wide beta, high beta, gamma, low inhibit, reward, and high inhibit wave forms, along with their respective means and standard deviations. In addition to theta/alpha, theta/wide beta, and theta/SMR ratios were computed. Furthermore, the mean, standard deviation, and mode of the alpha peak frequency were computed. A detailed summary of the HRV and EEG indices used in this study is available in Supplementary Tables 1 and 2. 2. 4. Data Collection Tools 2. 4. 1. Kyto2935 HRV Sensors The sensor used to measure heart rate variability is Kyto2935, the operation frequency is 2402–2480 MHz, the modulation type is GFSK, Bluetooth version 4.0, the bitrate of the transmitter is 1 Mbps, and it has 40 channels. Shenzhen Asia Test Technology Co., Ltd. tested the device for validity and reliability. It is found to accomplish FCC standards, part 15.247 (FCC ID: 2ALC3KYT02935). The report reveals that the level of confidence for the sensors was found to be 95%. In the literature review, many research papers on HRV were found using the Kyto2935 device, like Cheng et al. (2019) and Laurman (2023). 2. 4. 2. Elite HRV Bluetooth APP The Elite HRV Bluetooth App calculates various HRV metrics by directly obtaining the R-R intervals, the time intervals between successive heartbeats, from compatible devices. Scholarly investigations assess the accuracy and consistency of the application. Moreover, it has been noted that it has been utilized in various research studies. Chhetri et al. (2022) and Ramon et al. (2022) determined that the Elite HRV Bluetooth application is dependable for assessing heart rate variability at rest, consistent with data from the Polar V-800 monitor. Perrotta et al. (2017) identified a robust correlation between Elite HRV and Kubios HRV 2.2, whereas Himariotis et al. (2022) reported no significant differences in lnRMSSD data between the software in seated or supine positions. 2. 4. 3. EEG Equipment; Procomp Infiniti and Biograph Infiniti The following components to record EEG were utilized in the study: Three electrodes: Active electrode for the head, and grand electrodes for the earlobe. The Biograph Infiniti Software system (version 5.0) operates with a filtering frequency of 60 Hz, utilizing the ProComp differential amplifier (Thought Technology Ltd, Montreal, Quebec) for EEG sessions. The ProComp Infiniti and Biograph Infiniti systems have been widely utilized in various research contexts, demonstrating their validity and effectiveness in capturing physiological data across multiple domains. Some samples are Lier et al. (2019), Groeneveld et al. (2019), Nazari et al. (2012), Schnabel (2019), Stępnik et al. (2023), Richesin et al. (2020), Morel and Hautier (2016). Warmbrodt et al. (2021), Ziaee et al. (2016) and Kingsnorth et al. (2011) are some other papers that implemented Procomp and Biograph Infiniti equipment in their research. 2. 4. 4. Test/Activity for Intuitive Performance 2.4.4.1. Item Development and Expert Review Drawing on dual-process theories (e.g., Kahneman, 2011; Evans & Stanovich, 2013), the literature distinguishes two forms of intuition: analytical perception (AP), involving conscious reasoning and cue-based interpretation, and non-analytical perception (NAP), which reflects intuitive insight beyond conscious awareness. Based on this conceptual distinction, a custom item set was developed to measure intuitive performance through image-based tasks. Initially, approximately 40 items were created, each designed to engage either analytical or intuitive cognitive processes. The AP items required the observation and interpretation of explicit or inferable visual clues. In contrast, NAP items were constructed to elicit intuitive responses in the absence of visible indicators, demanding spontaneous decision-making without rational deduction. Three faculty members from the field of educational sciences reviewed the item pool for content relevance and construct alignment. Items deemed ambiguous or misaligned with their intended cognitive domain were excluded. 2.4.4.2. Cognitive Process Validation (Think-Aloud Study) To assess whether the items reliably triggered the targeted cognitive processes, a think-aloud protocol was implemented with 15 volunteer students. Participants were asked to verbalize their thought processes while responding to each item. Their verbal data were examined to determine whether the response strategy relied on visible clues and reasoning (indicating AP) or on intuitive guesses without observable rationale (indicating NAP). Items that yielded mixed or contradictory strategies were removed from the pool. 2.4.4.3. Pilot Study and Item Selection Criteria The refined set of items was administered to a pilot group of 84 students to evaluate difficulty levels and response patterns. For the analytical perception section, 12 items were retained, with difficulty indices ranging from 30% to 74.1% (M = 57%). For the non-analytical perception section, 18 items were selected, each linked to a predetermined ”correct” answer based on intuitive recognition, with difficulty rates between 15% and 66.7% (M = 47%). Item clarity and task administration procedures were optimized based on feedback from the pilot. 2.4.4.4. Item Statistics and Internal Consistency Item-total correlations (point-biserial) were calculated to assess the contribution of each item to the total performance score. In the AP section, all 12 items demonstrated significant positive correlations ranging from r = .168 to .376. In the NAP section, 13 out of 18 items showed significant item-total correlations (r = .180 to .378), while the remaining 5 displayed non-significant but positive correlations. Traditional internal consistency measures such as Cronbach’s alpha may underestimate the reliability of tests composed of dichotomous and abstract items (Downing, 2004). In such cases, item-total correlations—especially point-biserial coefficients—are considered more suitable indicators of item quality and internal consistency. Accordingly, the significant and positive item-total correlations observed in both the Analytical Perception (AP) and Non-Analytical Perception (NAP) sections indicate that the items reliably reflect the underlying intuitive processes they are intended to measure, despite the limitations of classical reliability coefficients. The analysis yielded r = .127, p > .05 (R² = .016), suggesting low overlap and supporting the cognitive distinctiveness of AP and NAP dimensions. 2. 5. Data Collection Prior to the data collecting procedure, all samples were administered the abbreviated version of the General Health Inventory, as established by Demiral et al. (2006). All participants underwent heart rhythm and brainwave measures during both rest and a task in which participants respond to two distinct categories of enquiries: analytical (interpreting implicit signals) and employing non-analytical (insight beyond conscious reasoning) perception to uncover answers believed to correlate with intuitive success. The heart rate recordings were collected by measuring the right ear using KYTO2935 finger and ear sensors for 5 minutes in a specially prepared unoccupied room. At the same time, the brain wave recordings were also measured for 5 minutes from Cz point as to 10-20 system using Procomp Infiniti amplifier with related sensors and cables. Participants were instructed to assume a relaxed posture, breathe effortlessly, and maintain their typical body position and breathing pattern. The measurements were primarily taken during the daytime, specifically between the hours of 11 and 17. Throughout the procedure, the measurements of each person were documented following a brief period of acclimation lasting 10-15 seconds. Throughout the procedure, no practices that could divert or draw the attention of persons were permitted or deliberated. The entire data collection process took approximately 30 minutes for each individual. The values obtained as a result of heart rate measurements were instantly recorded on the forms. The EEG data obtained were calculated for different wave forms and transferred to excel database using Biograph Infiniti software. All data collected was cross-verified by two individuals to ensure accurate data entry. 2.6. Data Analysis Data collected using the specified tools was analyzed within the SPSS version 27 database. The analyses were conducted on healthy sample data, based on the general health inventory and its psychology related subdimensions established by Demiral et al. (2006). The initial analysis focused on the bivariate correlations (Spearman’s rho, due to non-normality) between HRV and EEG indices and AP/NAP scores under rest and test conditions. Additionally, to extract further insights, the samples were classified based on their intuitive success in the test about the presumed talents into two categories: high and low score groups as to -/+ 1 standard deviation above and below the means. Subsequently, heart rate and brainwave indices were examined between high and low score groups to determine any significant disparities in these metrics. Mann Whitney U test was implemented to analyze the differences. Furthermore, in order to see the connection between heart and brain, the Spearman correlations between heart rate and brain wave indices were analyzed in both resting and testing settings. Lastly, the heart rate and brainwave variables that may serve as predictors of AP and NAP success were assessed using the stepwise regression method. While the .05 significance level was taken into account when examining the analysis results, relationships and differences approaching significance up to .10 were also examined assuming that they would show a general trend. 3. Results The results are presented under four main sections: (1) correlation analyses between heart rate variability (HRV) and electroencephalogram (EEG) indices with participants’ performance scores, (2) comparisons of physiological responses between high and low achieving groups, (3) interrelationships between HRV and EEG indicators under resting and task conditions, and (4) multiple regression analyses to identify predictors of success in analytical (AP- interpreting implicit and explicit signs) and non-analytical (NAP - intuitive insight beyond conscious reasoning or observable cues) tasks. All analyses were conducted on data obtained from physiologically healthy participants. Due to non-normal distributions observed in several variables, non-parametric statistical methods—Spearman’s rho for correlations, Mann–Whitney U for group comparisons, and stepwise multiple regression—were employed. 3.1. Correlation Analyses The results regarding heart rate and brainwave indices during rest and test are presented in the following sections. 3.1.1. Correlations between Heart Rate Indices, AP and NAP scores during Rest and Test, [t]Table 1 near here[/t] Statistics obtained during resting position for RMSSD, LnRMSSD, PNN50, HF Power and HRV mean indices have significant positive correlations with AP scores that can be seen in Table 1. Since increase in these indices imply calm and relax mood in heart rate indices, it means that being successful in AP is positively correlated with having more regular heart rate or being in calm and peaceful mood. Upon examining the relationships between heart rate indexes and the number of correct answers in NAP type items, it is observed that MRRINT and HR mean have significant correlations in a resting state. MRRINT has negative but HR mean has positive correlation. Success in NAP scores is associated with decrease in MRRINT and increase in HR mean. A high resting heart rate average seems to be associated with success in NAP scoring. In test mode, notable correlations are found between RMSSD, SDNN, LnRMSSD, PNN50, MRRINT, total power, LF power, HF power and HRV mean and the number of correct answers in NAP type items. The only positive correlation is between HR mean, all the others are negative. Explanation for this positive correlation is that a higher heart rate may indicate increased arousal or alertness, which could help improve cognitive/intuitive performance, especially in tasks requiring concentration or quick response. The negative correlations between HR indices and performance could indicate that tasks requiring high levels of focus or concentration may benefit from a lower state of physiological variability, more sympathetic activation and less parasympathetic influence. While AP success exhibits a positive correlation with heart rhythm indices, especially at rest, NAP success mostly exhibits a negative correlation except for HR mean in the test situation. AP scores (both resting and testing) are positively associated with HR indices except for MRRINT in testing mode, indicating higher parasympathetic activity and better autonomic flexibility. This suggests that individuals with higher AP scores may have better stress regulation and emotional resilience. Higher NAP scores are consistently associated with lower HR indices (e.g., reduced RMSSD, PNN50, HF, and LF power), reflecting decreased autonomic flexibility. This could indicate increased stress, cognitive load, or arousal associated with higher NAP during testing. The LF/HF ratio, LF peak, and HF peak indices were excluded from the table due to their lack of substantial connection in all states. 3.1.2. Correlations between Brain Wave Indices, AP and NAP Scores during Rest and Test. No significant correlation exists between AP, NAP scores and brain waves during rest and test mode. AP scores at resting manner has nearing significance negative correlation with SMR standard deviation and NAP scores has nearing significance negative correlation with high beta (high inhibit) mean and positive correlation with alpha peak frequency standard deviation. 3.2. Comparing Means of Low and High Achieving Samples The samples were categorized into high and low score groups based on their intuitive performance (AP and NAP scores), and heart rate and brain wave indices were compared between these groups to identify significant differences. The sample was categorized into low and high groups based on scores that fell outside the range of +/-1 standard deviation. The classifications indicate that there are 35 individuals in the low group (5 points and below) and 39 individuals in the high group (8 points and above) based on the AP scores, resulting in a total of 74 individuals. The NAP scores indicate that there are 46 individuals categorized in the low group (scores of 7 and below) and 40 individuals in the high group (scores of 10 and above), resulting in a cumulative total of 86 individuals. The differences between these groups were assessed using Mann Whitney U test to determine their significance. 3.2.1. Analysis of the differences in Heart Rate indices during rest and test between high and low achieving groups according to AP and NAP scores. No significant differences were observed in AP scores for heart rate indices at rest between the high and low successful groups, with the exception of HF power. The analysis of disparities in HF power indices (x̄l= 575.5 - x̄h= 831.2) yielded MWU=223 with p<.05. The effect size computed for the Mann-Whitney U test was r = .291, indicating a medium effect size as per Cohen’s (1988) standards. Furthermore, RMSSD, LnRMSSD, and PNN50 values also suggest a difference nearing significance. The MWU values are 232, 231, and 235.5, with corresponding significance values of .051, .051, and .06. Table 2 presents analyses of Heart Rate Indexes in both rest and test states concerning NAP scores. [t]Table 2 near here[/t] In the resting and testing state, while nearly all indices, except for the mean heart rate, are lower in the successful group, no differences are statistically significant in the resting state. The disparities in RMSSD, LnRMSSD, total power, LF power, HF power, and HRV mean values under the test condition are significant. The effect sizes are moderate in magnitude. Nonetheless, the values of SDNN, PNN50, and mean HR exhibit differences that approach statistical significance. All indices, with the exception of the HR mean, are significantly lower in the highly successful group. In the test mode, RMSSD, SDNN, and HRV mean values exhibited a slight increase in the low successful group, whereas a decrease was observed in the high successful group compared to rest condition. A notable alteration in the rest and test conditions is the variation detected in LF and HF power. In the test condition, these values dramatically rose in the low successful group, whereas they either remained constant or exhibited a slight decrease in the high successful group. 3.2.2. Analysis of the differences in brainwave indices during rest and test between high and low achieving groups according to AP and NAP scores in the healthy samples data. No significant differences were observed between the EEG indices of the high and low successful groups during the resting state concerning the AP scores. During the testing phase, a notable disparity was observed solely in the standard deviations of the Gamma waves. In the low-score group, the mean of the gamma standard deviations is x̄= 1.572, whereas in the high-score group, this value is x̄= 1.014. The MWU value is 218.5, with p < 0.05. The effect size computed for the Mann-Whitney U test is r = 0.166. Significant differences were noted in the high beta (high inhibit) mean and alpha peak frequency standard deviation values of brain wave indices recorded during the resting state between high and low successful groups in NAP scores. The effect sizes (r) were moderate. The high beta (high inhibit) values of the highly successful group were found to be lower than those of the lowly successful group. Despite the alpha peak frequency values appearing similar, the mean ranks differ. The average rank of the low successful group is 25.57, whereas that of the high successful group is 36.94. No significant differences were detected between the NAP scores of the low and high successful groups regarding brain wave indices in the healthy sample data during the test state. 3.3. Correlations between Heart Rate and Brain Wave Indices during Rest and Test 3.3.1. Relationships between heart rate and brain wave indices in rest state Analysis of Table 3 reveals bidirectional clusters of correlations between heart rate variability (HRV) and brain wave indices during the resting state. From the HRV perspective, LF peak (a marker of sympathetic activity) shows significant negative correlations with theta mean (rho = –.263, p < .01), low inhibit mean (rho = –.263, p < .01), theta/wide beta ratio (rho = –.223, p < .05), theta/SMR ratio (rho = –.215, p < .05), delta standard deviation (rho = –.204, p < .05), and theta standard deviation (rho = –.244, p .05), theta/alpha ratio (rho = –.173, p > .05), and wide beta standard deviation (rho = .162, p > .05). These findings suggest that increased sympathetic activity is associated with suppression of slower brain rhythms linked to emotional processing and memory. HF peak, reflecting parasympathetic activation, positively correlates with wide beta mean (rho = .202, p < .05), alpha peak frequency mode (rho = .193, p < .05), and SMR standard deviation (rho = –.188, p < .05), while negatively correlating with theta/wide beta ratio (rho = –.188, p < .05) and theta/SMR ratio (rho = –.196, p .05), reward mean (rho = .163, p > .05), and wide beta standard deviation (rho = .181, p < .05). These results imply that parasympathetic activation may enhance cognitive readiness and attention, although excessive levels might suppress inhibitory control under low arousal conditions. From the EEG perspective, theta/wide beta ratio and theta/SMR ratio exhibit extensive connections to HRV indices. The theta/wide beta ratio is significantly correlated with SDNN (rho = .252, p < .01), total power (rho = .193, p < .05), LF power (rho = .235, p < .05), LF peak (rho = –.223, p < .05), and HF peak (rho = –.188, p .05), PNN50 (rho = .173, p > .05), HF power (rho = .177, p > .05), and HRV mean (rho = .179, p > .05). The theta/SMR ratio is significantly correlated with SDNN (rho = .222, p < .05), LF power (rho = .207, p < .05), LF peak (rho = .207, p < .05), and negatively with HF peak (rho = –.189, p < .05). These relationships underscore the regulatory function of autonomic flexibility in balancing attentional control and emotional regulation. Finally, wide beta standard deviation, an indicator of cognitive stress, negatively correlates with nearly all HRV indices: RMSSD (rho = –.218, p < .05), SDNN (rho = –.252, p < .01), LnRMSSD (rho = –.220, p < .05), PNN50 (rho = –.193, p < .05), total power (rho = –.196, p < .05), LF power (rho = –.214, p < .05), HF power (rho = –.192, p < .05), and HRV mean (rho = –.215, p .05) and HF peak (rho = .181, p < .05). These correlations indicate that increased variability in beta activity may be linked to reduced autonomic adaptability and higher stress sensitivity. In summary, the resting-state data suggest that LF and HF peaks are central HRV indices associated with shifts in cortical activity, particularly in theta and SMR-related waveforms. Conversely, from the EEG perspective, theta-based ratios and wide beta variability appear to be the most sensitive to changes in autonomic regulation. These findings highlight a reciprocal, tightly coupled relationship between cardiac and neural systems in supporting emotional balance, attentional control, and cognitive resilience. [t]Table 3 near here[/t] 3.3.2. Relationships between heart rate and brain wave indices in testing state Testing-state data reveals significant and near-significant correlations between heart rate variability (HRV) metrics and brain wave activity, particularly involving the theta/alpha ratio and the alpha peak frequency mode. These findings reflect the dynamic interplay between autonomic regulation and cortical processes related to attention, emotional control, and cognitive resilience. From the HRV perspective, the LF/HF ratio is significantly correlated with alpha mean (rho = .223, p < .05), alpha standard deviation (rho = .219, p < .05), and the theta/alpha ratio (rho = –.220, p < .05). Additionally, HR mean negatively correlates with the theta/alpha ratio (rho = –.204, p < .05), suggesting that increased sympathetic activity or physiological arousal is linked to reduced attentional regulation. Near-significant correlations are also found between the theta/SMR ratio (rho = –.160, p > .05) and SMR standard deviation (rho = .168, p > .05), both of which are associated with motor inhibition and cognitive control. These patterns imply that stress-induced autonomic shifts may lead to reduced cognitive inhibition and greater distractibility during task performance. The theta/alpha ratio demonstrates significant correlations with MRRINT (rho = .209, p < .05), LF/HF ratio (rho = –.220, p < .05), HF peak (rho = .193, p < .05), and HR mean (rho = –.204, p .05) is also observed. The inverse relationship between theta/alpha ratio and sympathetic markers suggests that parasympathetic shifts may promote attentional focus and reduce stress-related interference, while sympathetic activation disrupts this balance. Significant negative correlations are also observed between the alpha peak frequency mode and several HRV indicators: SDNN (rho = –.205, p < .05), total power (rho = –.218, p < .05), and LF power (rho = –.208, p .05), LnRMSSD (rho = –.170, p > .05), PNN50 (rho = –.175, p > .05), and HF power (rho = –.159, p > .05). These relationships suggest that autonomic rigidity, reflected in lower HRV, may impair cognitive flexibility and emotional regulation. The alpha peak frequency mode, often associated with cognitive readiness and processing efficiency, emerges as a sensitive marker of stress-related autonomic shifts. 3.4. Multiple Regression Analyses for AP and NAP Success Taking Heart Rate and Brainwave Indices as Predictors Not any variable of heart rate indices and brainwaves significantly predict AP scores either in rest or test mode except for gamma standard deviation in test mode. But some variables both of heart rate and brain waves significantly predict NAP success. When NAP score variable set dependent, normal P-P plot of regression standardized residuals shows highly positive correlation supporting the normality of the residuals. As for homoscedasticity, scatterplot of standardized residual with standardized predicted values demonstrates almost zero correlation with a shape of a cone with no outfit [t]Table 4 near here[/t] Model summary shows adjusted R square and change statistics when each independent variable entered. The first independent variable entered is HRV which represents .065 of dependent NAP scores. The next independent entered is LF peak and the model with both variables represents .124 of change in NAP scores, the third is wide beta mean and with it, the model represents .149 of change and last entered independent is SMR standard deviation and the model turns out to represent .173 of the change in the dependent variable that is NAP scores. All Anova values of change are statistically significant. Durbin Watson value estimated is 2.503 indicating there is no autocorrelation. [t]Table 5 near here[/t] Anova statistics in the table above displays all independent variables entered stepwise has statistically significant effect on dependent variable. Also the table below displays the standardized beta coefficients when each independent variable entered. All VIF statistics are below 10, tolerance values are above .100 and condition indexes for collinearity diagnostics below 30 proving that no perfect multicollinearity. [t]Table 6 near here[/t] These four variables represent a .173 change in NAP, whereby a decrease in HRV and wide beta mean corresponds with an increase in NAP. As the LF peak and SMR standard deviation rise, the NAP escalates. 4. Discussion This study investigated whether two forms of intuitive cognition—analytical perception (AP) and non-analytical perception (NAP)—engage different neurophysiological mechanisms, particularly across heart rate variability (HRV) and electroencephalographic (EEG) markers. The findings offer substantial evidence that AP and NAP reflect distinct cognitive-affective dynamics, which manifest as measurable cardiac and cortical signatures. 4.1. Analytical Perception and Parasympathetic Regulation AP performance was positively correlated with parasympathetic HRV indices—such as RMSSD, PNN50, HF Power, and HRV Mean—during resting conditions (see Table 7). These results support the assumption that analytical intuition operates under a calm and coherent physiological baseline, consistent with theories of cognitive stability and attentional control (Thayer & Lane, 2000; McCraty & Zayas, 2014). The absence of task-phase correlations for AP further emphasizes that its success may stem from internal preparedness and cognitive economy rather than in-the-moment physiological reactivity. Interestingly, when comparing high and low AP performers, group differences showed lower RMSSD, PNN50, and LnRMSSD values in the high-scoring group. This apparent discrepancy between correlation and mean comparisons can be interpreted as an indication of physiological efficiency—where optimal performance is not necessarily accompanied by exaggerated physiological activity but rather by economized regulation (Grossman, 1992). 4.2. Non-Analytical Perception and Sympathetic Activation NAP performance, by contrast, revealed sympathetic arousal patterns both during rest (e.g., higher heart rate) and especially under task conditions, where nearly all HRV indices showed negative associations. This profile aligns with theories of somatic intuition (Bechara & Damasio, 2005) and intuitive arousal (Holzer, 2022), suggesting that successful NAP may depend on heightened physiological vigilance and interoceptive tuning. EEG markers such as increased high beta mean, high inhibit mean, and alpha peak frequency variability in high-NAP performers indicate cortical excitability and adaptive flexibility, supporting dual-process frameworks of fast, emotion-laden cognition (Evans & Stanovich, 2013; Azhari & Hernandez, 2016). This dynamic profile of NAP highlights that emotional salience, uncertainty, and bodily responsiveness may constitute essential elements of spontaneous intuitive functioning—challenging the traditional framing of intuition as purely unconscious and effortless. 4.3. Integration of HRV–EEG Coupling and Predictive Dynamics Analyses of HRV–EEG interactions revealed nuanced mechanisms of heart–brain coordination. For instance, LF peak was negatively associated with slow-wave EEG activity (theta, delta), whereas HF peak showed positive correlations with beta and alpha activity, supporting the bidirectional modulation of attentional and affective states (Lacey & Lacey, 1978; McCraty, 2000). These patterns suggest that autonomic flexibility, rather than any singular state of arousal, supports adaptive intuitive functioning. Regression analysis further refined these insights: Four predictors—HRV Mean (–), LF Peak (+), Wide Beta Mean (–), and SMR SD (+)—jointly explained 17.3% of the variance in NAP scores. No significant predictors emerged for AP, reinforcing its more stable and non-reactive physiological profile. These findings indicate that NAP performance is driven by a dynamic interplay of heightened arousal and moment-to-moment cortical adaptation, while AP thrives in structured and regulated internal conditions. 5. Conclusion This study offers empirical support for the view that intuition is not a monolithic construct, but rather a neurophysiologically differentiated cognitive function, comprised of at least two distinct modalities—Analytical Perception (AP) and Non-Analytical Perception (NAP). By simultaneously examining HRV and EEG markers across resting and task conditions, we provide one of the first integrative accounts of how cardiac and cortical dynamics jointly shape intuitive performance. The findings contribute to the ongoing epistemological debate in cognitive science by showing that: AP appears to operate within a parasympathetically regulated, stable internal milieu, enabling reflective yet intuitive reasoning in structured contexts. NAP, on the other hand, is characterized by sympathetic arousal and cortical variability, indicating a fast, embodied, and emotionally responsive mode of intuitive cognition. These distinctions reinforce dual-process theories of reasoning (Evans & Stanovich, 2013), but go further by mapping them onto measurable physiological correlates, offering a biopsychological basis for what had traditionally been treated as a philosophical or phenomenological domain. Beyond theoretical advancement, the study’s findings hold practical implications for training, selection, and development in fields requiring rapid, high-stakes decision-making—such as medicine, aviation, or leadership. Enhancing intuitive skill may not only be a matter of experience but also of physiological tuning and coherence, a possibility that invites future work on biofeedback and neuroadaptive interventions. Nevertheless, the study is not without limitations. The moderate sample size and cross-sectional design limit generalizability. Furthermore, the overlap between EEG and HRV indicators under certain conditions warrants more refined, possibly temporally dissociated measurement designs. Despite these constraints, this research advances a compelling framework for studying intuition as a measurable, embodied cognitive capacity. It invites future work to explore how bodily states, brain rhythms, and conscious awareness dynamically interact in shaping human judgment—particularly in uncertain, high-complexity environments where intuition is most vital. 6. Recommendations and Future Directions Future research should aim to deepen our understanding of intuition by integrating it with broader cognitive ability metrics. Incorporating measures such as intelligence quotient (IQ), working memory capacity, or fluid reasoning could reveal how general cognitive resources modulate intuitive performance. Additionally, refining the design of AP/NAP item sets—through cross-validation across different populations, cultural contexts, and task formats—will improve measurement reliability and facilitate generalizability, especially in the context of binary, abstract tasks often used to elicit non-analytical responses. Physiological modulation techniques such as biofeedback, HRV training, or mindfulness interventions present promising avenues for enhancing intuitive function by stabilizing autonomic regulation. Future experimental studies could explore how such interventions affect performance on intuitive tasks and alter underlying HRV or EEG patterns. Methodologically, the separation of EEG and HRV data collection across tasks may help eliminate signal interference and allow for more precise causal inferences. To strengthen the external validity of the findings, larger and more diverse samples—encompassing a range of ages, cultural backgrounds, and professional experiences—should be included in follow-up studies. Such designs may uncover developmental trajectories or domain-specific patterns in intuitive physiology. Finally, the practical application of these findings holds potential in high-stakes decision-making domains such as emergency medicine, aviation, and clinical diagnostics, where intuitive accuracy and speed are often critical. Tailored training programs based on physiological feedback could be developed to support individuals in such fields, further bridging the gap between theoretical insight and real-world utility. Laurman, J. (2023). 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Correlations between Heart Rate Indices, AP and NAP Scores during Rest and Test | Spearman Correlation Coefficient | AP, Resting | NAP, Resting | AP, Testing | NAP, Testing | | | RMSSD | rho= | .203 * | -.143 | .114 | -.254 ** | | Sig. | .033 | .138 | .236 | .008 | | | N | 110 | 109 | 110 | 109 | | | SDNN | rho= | .126 | -.176 | .028 | -.241 * | | Sig. | .191 | .068 | .771 | .011 | | | N | 110 | 109 | 110 | 109 | | | LnRMSSD | rho= | .204 * | -.139 | .115 | -.251 ** | | Sig. | .032 | .149 | .232 | .008 | | | N | 110 | 109 | 110 | 109 | | | PNN50 | rho= | .196 * | -.128 | .133 | -.224 * | | Sig. | .041 | .186 | .165 | .019 | | | N | 110 | 109 | 110 | 109 | | | Mean RR Interval | rho= | .060 | -.192 * | .124 | -.191 * | | Sig. | .536 | .046 | .197 | .047 | | | N | 110 | 109 | 110 | 109 | | | Total Power | rho= | .146 | -.134 | .029 | -.224 * | | Sig. | .128 | .166 | .762 | .019 | | | N | 110 | 109 | 110 | 109 | | | Low Frequency Power | rho= | .090 | -.115 | .005 | -.302 ** | | Sig. | .352 | .236 | .957 | .001 | | | N | 110 | 109 | 110 | 109 | | | High Frequency Power | rho= | .216 * | -.104 | .079 | -.222 * | | Sig. | .024 | .280 | .414 | .020 | | | N | 110 | 109 | 110 | 109 | | | Heart Rate Mean | rho= | -.051 | .195 * | -.104 | .202 * | | Sig. | .593 | .042 | .279 | .036 | | | N | 110 | 109 | 110 | 109 | | | Heart Rate Variability Mean | rho= | .222 * | -.144 | .122 | -.254 ** | | Sig. | .020 | .136 | .205 | .008 | | | N | 110 | 109 | 110 | 109 | Table 2. Mann Whitney U Statistics for Disparities between Low and High Achieving Groups in NAP Scores during Rest and Test | Disparities between NAP scores of Low and High achieving groups | Resting | Testing | |||||||| | N L/H | Means L/H | MWU | Sig. | Effect size for MWU r | N L/H | Means L/H | MWU | Sig. | Effect size for MWU r | | | RMSSD | 36/28 | 35,01/29,05 | 422 | 0,27 | -0,14 | 36/28 | 36,52/26,71 | 326,5 | 0,02 | -0,30 | | SDNN | 36/28 | 54,14/49,28 | 386 | 0,11 | -0,20 | 36/28 | 59,72/45,72 | 360 | 0,05 | -0,24 | | LnRMSSD | 36/28 | 3,41/3,28 | 424 | 0,28 | -0,14 | 36/28 | 3,49/3,20 | 331 | 0,02 | -0,29 | | PNN50 | 36/28 | 12,75/9,54 | 436 | 0,36 | -0,12 | 36/28 | 14,36/36,86 | 370 | 0,07 | -0,23 | | Mean RR Interval | 36/28 | 740,56/711,63 | 390 | 0,12 | -0,19 | 36/28 | 722,62/772,55 | 390 | 0,12 | -0,19 | | Total Power | 36/28 | 1922,02/1409,39 | 402 | 0,17 | -0,17 | 36/28 | 2338,17/1514,94 | 350 | 0,04 | -0,26 | | LF/HF Ratio | 36/28 | 2,09/2,03 | 489,5 | 0,84 | -0,02 | 36/28 | 2,19/2,37 | 476,5 | 0,71 | -0,05 | | LF Power | 36/28 | 1211,29/842,59 | 406 | 0,19 | -0,17 | 36/28 | 12051,53/789,16 | 279 | 0,00 | -0,38 | | HF Power | 36/28 | 711,05/560,36 | 433 | 0,34 | -0,12 | 36/28 | 6026,23/464,53 | 339 | 0,03 | -0,28 | | LF Peak | 36/28 | 0,09/0,09 | 492,5 | 0,88 | -0,02 | 36/28 | 0,10/0,11 | 409,5 | 0,20 | -0,16 | | HF Peak | 36/28 | 5,01/0,23 | 439,5 | 0,38 | -0,11 | 36/28 | 0,20/0,21 | 467 | 0,62 | -0,06 | | HR Mean | 36/28 | 83,19/86,50 | 388,5 | 0,12 | -0,20 | 36/28 | 85,56/89,54 | 370,5 | 0,07 | -0,23 | | HRV Mean | 36/28 | 52,44/50,46 | 425 | 0,28 | -0,13 | 36/28 | 53,83/49,39 | 337 | 0,02 | -0,28 | Table 3. All Correlations between Heart Rate and Brain Wave Indices during Rest and Test. | RMSSD | SDNN | LnRMSSD | PNN50 | MRRINT | Total Power | LF/HF ratio | LF Power | HF Power | LF Peak | HF Peak | HR Mean | HRV Mean | | | Delta Mean | -R◐ | |||||||||||| | Theta mean | -R◉ ** | |||||||||||| | Alpha mean | T◉ * | -T◐ | ||||||||||| | SMR mean | -R◉ * | R◐ | R ● * | |||||||||| | Wide Beta mean | -R◐ | -R◐ | R◉ * | |||||||||| | High Beta mean | ||||||||||||| | Gamma mean | ||||||||||||| | Lowinhibit Mean | -R◉ ** | |||||||||||| | Reward mean | -R◉ * | .R◐ | .R◉ * | |||||||||| | High Inhibit Mean | ||||||||||||| | Theta/Alpha means ratio | R◐ | T◐ | T◉ * /R◐ | -T◉ * | -R◐ | T◉ * | -T◉ * /R◐ | |||||| | Theta/Wide Beta means ratio | R◐ | R◉ ** | R◐ | R◐ | R◉ * | R◉ * | R◐ | -R◉ * | -R◉ * | R◐ | ||| | Theta/SMR means ratio | R◉ * | R◉ * | -R◉ * | T◐/-R◉ * | -T◐ | |||||||| | Alpha Peak Frequency mean | ||||||||||||| | Alpha Peak Freq Std. Dev. | ||||||||||||| | Alpha Peak Frequency mode | -T◐ | -T◉ ** | -T◐ | -T◐ | -T◉ * | -T◉ * | -T◐ | R◉ * | ||||| | Delta Standard Deviation | -R◉ * | |||||||||||| | Theta Standard Deviation | -R◉ * | |||||||||||| | Alpha Standard Deviation | -.R◐ | .T◉ * | -T◐ | .R◐ | ||||||||| | SMR Standard Deviation | .R◐ | -.T◐/-R◐ | R◉ * | .T◐/.R◐ | ||||||||| | Wide Beta Standard Deviation | -R◉ * | -R◉ * | -R◉ * | -R◉ * | -R◉ * | -R◉ * | -R◉ * | R◐ | R◐ | -R◉ * | ||| | High Beta Standard Deviation | ||||||||||||| | Gamma Standard Deviation | T: Testing State, R: Resting, -:Negative corr., ◉: Significant (p<.05), ◐: Near Significant (p=.05-.10), *: P<.05, **:P<.01, No star: Not significant. (Highlighted bands denote regions of increased correlational density across physiological domains). Table 4. Stepwise Regression Model Summary | Model Summary e | |||||||||| | Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | Change Statistics | Durbin-Watson | |||| | R Square Change | F Change | df1 | df2 | Sig. F Change | |||||| | 1 | .272 a | .074 | .065 | 1.74 | .074 | 8.39 | 1 | 105 | .005 | | | 2 | .375 b | .141 | .124 | 1.69 | .067 | 8.07 | 1 | 104 | .005 | | | 3 | .416 c | .173 | .149 | 1.66 | .032 | 4.03 | 1 | 103 | .047 | | | 4 | .452 d | .204 | .173 | 1.64 | .031 | 4.04 | 1 | 102 | .047 | 2.503 | | a. Predictors: (Constant), Heart Rate Variability Mean | |||||||||| | b. Predictors: (Constant), Heart Rate Variability Mean, Frequency Domain Low Frequency Peak | |||||||||| | c. Predictors: (Constant), Heart Rate Variability Mean, Frequency Domain Low Frequency Peak, Wide beta mean | |||||||||| | d. Predictors: (Constant), Heart Rate Variability Mean, Frequency Domain Low Frequency Peak, Wide beta mean, SMR Ss. | |||||||||| | e. Dependent Variable: NAP score. (Intuition as extrasensory perception) | Table 5. Stepwise Regression Anova Table | ANOVA a | |||||| | Model | Sum of Squares | df | Mean Square | F | Sig. | | | 1 | Regression | 25.504 | 1 | 25.504 | 8.395 | .005 b | | Residual | 319.000 | 105 | 3.038 | ||| | Total | 344.505 | 106 | |||| | 2 | Regression | 48.469 | 2 | 24.234 | 8.514 | .000 c | | Residual | 296.036 | 104 | 2.846 | ||| | Total | 344.505 | 106 | |||| | 3 | Regression | 59.610 | 3 | 19.870 | 7.184 | .000 d | | Residual | 284.894 | 103 | 2.766 | ||| | Total | 344.505 | 106 | |||| | 4 | Regression | 70.450 | 4 | 17.613 | 6.555 | .000 e | | Residual | 274.054 | 102 | 2.687 | ||| | Total | 344.505 | 106 | |||| | a. Dependent Variable: NAP score (Intuition as extrasensory perception) | |||||| | b. Predictors: (Constant), Heart Rate Variability Mean | |||||| | c. Predictors: (Constant), Heart Rate Variability Mean, Frequency Domain Low Frequency Peak | |||||| | d. Predictors: (Constant), Heart Rate Variability Mean, Frequency Domain Low Frequency Peak, Wide beta mean | |||||| | e. Predictors: (Constant), Heart Rate Variability Mean, Frequency Domain Low Frequency Peak, Wide beta mean, SMR Standard Deviation | Table 6. Stepwise Regression Coefficients | Coefficients a | |||||||| | Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | Collinearity Statistics | ||| | B | Std. Error | Beta | Tolerance | VIF | |||| | 1 | (Constant) | 11.839 | 1.214 | 9.751 | .000 | ||| | Heart Rate Variability Mean | -.067 | .023 | -.272 | -2.897 | .005 | 1.000 | 1.000 | | | 2 | (Constant) | 10.619 | 1.251 | 8.488 | .000 | ||| | Heart Rate Variability Mean | -.080 | .023 | -.324 | -3.494 | .001 | .961 | 1.040 | | | Frequency Domain Low Frequency Peak | 17.986 | 6.332 | .263 | 2.840 | .005 | .961 | 1.040 | | | 3 | (Constant) | 11.301 | 1.279 | 8.835 | .000 | ||| | Heart Rate Variability Mean | -.082 | .023 | -.333 | -3.639 | .000 | .959 | 1.043 | | | Frequency Domain Low Frequency Peak | 16.079 | 6.314 | .235 | 2.546 | .012 | .940 | 1.064 | | | Wide Beta Mean | -.116 | .058 | -.182 | -2.007 | .047 | .971 | 1.030 | | | 4 | (Constant) | 11.348 | 1.261 | 8.999 | .000 | ||| | Heart Rate Variability Mean | -.082 | .022 | -.334 | -3.705 | .000 | .959 | 1.043 | | | Frequency Domain Low Frequency Peak | 14.103 | 6.300 | .206 | 2.238 | .027 | .917 | 1.091 | | | Wide Beta Mean | -.248 | .087 | -.390 | -2.852 | .005 | .418 | 2.394 | | | SMR Standard Deviation | .372 | .185 | .271 | 2.009 | .047 | .429 | 2.329 | | | a. Dependent Variable: NAP scores | Table 7. Summary of significant and near-significant correlations between AP and NAP task scores and physiological indices (HRV and EEG) during rest and task conditions. | Heart Rate | Brain Waves | |||| | Resting | Testing | Resting | Testing | || | Correlations (Rho) | Analytical Perception (AP) Scores | +◉ RMSSD +◉ LnRMSSD +◉ PNN50 +◉ HF Power +◉ HRV Mean | NO | +◐ SMR Std.Dev. | NO | | Non-analytical perception (NAP) Scores | -◉ MRRInt +◉ HR mean | -◉ RMSSD -◉ SDNN -◉ LnRMSSD -◉ PNN50 -◉ MRRINT -◉ Total Power -◉ LF Power -◉ HF Power +◉ HR mean -◉ HRV Mean | -◐ High Beta Mean -◐ High Inhibit Mean +◐Alpha Peak Frequency Std.Dev. | NO | | | Compare Means (MWU) | Analytical Perception (AP) Scores | ◐RMSSD x̄l>x̄h ◐LnRMSSD x̄l>x̄h ◐PNN50 x̄l>x̄h ◉HF Power x̄lx̄h | | Non-analytical perception (NAP) Scores | NO | ◉ RMSSD x̄l>x̄h ◐ SDNN x̄l>x̄h ◉ LnRMSSD x̄l>x̄h ◐ PNN50 x̄l>x̄h ◉ Total Power x̄l>x̄h ◉ LF Power x̄l>x̄h ◉ HF Power x̄l>x̄h ◐ HR Mean x̄lx̄h | ◐ High Beta Mean x̄l>x̄h ◐ High Inhibit Mean x̄l>x̄h ◐ Alpha Peak Frequency Ss x̄l>x̄h | NO. | ◉ Significant (p<.05), ◐ Nearly Significant (p= .05 - .10), “+” positive correlation, “-“ negative correlation, x̄ Figure 1. Some samples for AP type items Figure 2. Some samples for NAP type items

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