Ambiguity tolerance and resting-state functional connectivity: A preregistered conceptual replication in a Japanese sample

Ambiguity tolerance and resting-state functional connectivity: A preregistered conceptual replication in a Japanese sample | 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 Ambiguity tolerance and resting-state functional connectivity: A preregistered conceptual replication in a Japanese sample Jimpei Hitsuwari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8351701/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Ambiguity tolerance has recently attracted renewed interest; however, its neural substrates remain underspecified, particularly considering their multidimensional structure. This preregistered study conceptually replicated and extended Liu et al. ( 2023 ) by examining how three dimensions of the Multidimensional Attitude toward Ambiguity Scale (MAAS)—Discomfort with Ambiguity (DA), Absolutism (AB), and Need for Complexity (NC)—relate to resting-state functional connectivity in a Japanese sample. Thirty-nine healthy young adults completed MRI scans and self-report measures. Based on prior work, associations were hypothesized between DA and amygdala–insula connectivity, AB and orbitofrontal–anterior cingulate connectivity, and NC and inferior parietal–middle frontal/middle cingulate connectivity. Region of interest (ROI)-to-ROI analyses controlling for age, sex, and head motion revealed no significant associations between any MAAS dimension and the corresponding connectivity pairs. Effect sizes were negligible, although NC showed a small zero-order correlation with inferior parietal lobule–middle cingulate cortex connectivity. These findings contrast with earlier reports using unidimensional ambiguity tolerance measures, suggesting that previously observed neural correlates may not map directly onto specific MAAS dimensions. The results highlight the need for larger, well-powered studies integrating multidimensional constructs and cross-cultural samples to clarify the neural architecture of ambiguity tolerance. Cognitive Neuroscience Psychology Ambiguity tolerance Resting-state functional connectivity Multidimensional Attitude toward Ambiguity Scale (MAAS) Neural correlate Conceptual replication Figures Figure 1 1. Introduction Ambiguity tolerance, the tendency to perceive ambiguous situations as desirable or threatening, has been a topic of psychological inquiry since Frenkel-Brunswik ( 1949 ) introduced the concept. A recent scientometric analysis showed substantial growth in this research area, with nearly 70% of publications emerging in the past decade, and expanding into medical education, career development, entrepreneurship, and political psychology (Rubiales-Núñez et al., 2024 ). Despite this growth, neuroimaging studies examining the neural correlates of ambiguity tolerance remain scarce. Liu et al. ( 2023 ) investigated resting-state functional connectivity in a Chinese sample (N = 315) and reported that higher ambiguity tolerance was associated with stronger connectivity within integration and control networks (inferior parietal lobule [IPL]–middle frontal gyrus [MFG]/middle cingulate cortex [MCC]), whereas lower tolerance was linked to stronger connectivity in threat and error-monitoring circuits (orbitofrontal cortex [OFC]–anterior cingulate cortex [ACC]). These findings were interpreted as reflecting a balance between the neural systems supporting flexible information integration and those treating ambiguity as a threat. Other studies have linked ambiguity tolerance with gray matter density in the right inferior frontal gyrus, supporting flexible reappraisal and inhibition (Tong et al., 2023 ), and to differential activation patterns in visual association areas during ambiguous stimulus processing (Mazhirina et al., 2020 ). However, Tanaka et al. ( 2015 ) found no association between self-reported ambiguity intolerance and gray matter volume, highlighting inconsistencies in the literature. Dimensionality is critical issue in ambiguity tolerance research. Historically, the construct suffered from conceptual fragmentation, grouping emotional discomfort, black-and-white thinking, and novelty-seeking under a single label (Lauriola et al., 2016 ). To address this, Lauriola et al. ( 2016 ) developed the Multidimensional Attitudes toward Ambiguity Scale (MAAS), identifying three robust factors: Discomfort with Ambiguity (DA), reflecting anxiety in ambiguous situations; Absolutism (AB), the tendency toward dichotomous terms; and Need for Complexity (NC), a preference for complexity, multiplicity, and novelty 1 . MAAS-based research demonstrates distinct associations for each dimension. For instance, DA and NC differentially predict attitudes toward artificial intelligence (Hitsuwari & Takano, 2025 ), AB predicts reduced analytical thinking (van Zyl, 2021 ), and a haiku intervention selectively reduces AB, leaving DA and NC unchanged (Hitsuwari & Nomura, 2023a ). Given the multidimensional nature of ambiguity tolerance, earlier neuroimaging findings based on unidimensional measures may obscure dimension-specific neural correlates. This study conceptually replicated and extended Liu et al. ( 2023 ) by examining how the three MAAS dimensions relate to resting-state functional connectivity in a Japanese sample. Based on Liu et al.'s findings and the theoretical distinctiveness among MAAS subscales, three preregistered hypotheses were formulated: H1 (Discomfort with Ambiguity): Higher DA scores will be associated with stronger functional connectivity between the amygdala and anterior insula, reflecting emotional threat responses to uncertainty. H2 (Absolutism): Higher AB scores will be associated with stronger functional connectivity between the left OFC and ACC, corresponding to a circuit implicated in ambiguity-related warning signals. H3 (Need for Complexity): Higher NC scores will be positively associated with connectivity between the left IPL and left MFG/MCC, reflecting flexible integration of ambiguous information. 2. Method Ethical approval was obtained from the Graduate School of Education, Kyoto University (CPE-510), and the Institute for the Future of Human Society MRI Research Facility, Kyoto University (22 − 003). All procedures were conducted in accordance with the Declaration of Helsinki and the approved institutional guidelines. Written informed consent was obtained prior to participation. This study was preregistered on the Open Science Framework ( https://osf.io/vhbq2 ). The analysis scripts and behavioral data are publicly available at https://osf.io/84ev5 . 2.1 Participants Forty-two participants without neurological or psychiatric disorders were recruited through an electronic bulletin board at Kyoto University. After excluding incomplete responses, 39 participants remained (M age = 21.69, SD = 1.64; 24 men, 15 women). All had normal or corrected-to-normal vision and no MRI contra-indications. 2.2 Questionnaires 2.2.1 Multidimensional Attitude toward Ambiguity Scale (MAAS) Ambiguity tolerance was assessed using the Japanese version of the MAAS (Hitsuwari & Nomura, 2021 ). The 21-item scale comprises three seven item subscales: DA (negative emotional reactions to ambiguity), AB (rigid, dichotomous thinking), and NC (desire for novel and complex information). Participants rated each item on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). The mean subscale scores were calculated. 2.2.2 Other Measures Additional questionnaires were administered as part of a larger study protocol: the shortened Japanese versions of the Plymouth Sensory Imagery Questionnaire (Hitsuwari & Nomura, 2023b ), the Interpersonal Reactivity Index (Himichi et al., 2017 ), the Awe subscale of the Dispositional Positive Emotion Scales (Nomura et al., 2022 ), the Southampton Nostalgia Scale (Nagamine & Toyama, 2019 ), and the Big Five Scale (Namikawa et al., 2012 ), though not analyzed in the present study. 2.3 MRI Acquisition MRI images were acquired using a 3-Tesla Siemens Verio scanner using a 32-channel head coil at the Institute for the Future of Human Society, Kyoto University. Resting-state functional images were acquired using a multiband echo-planar imaging sequence: multiband factor = 4; number of slices = 76; slice thickness = 2.0 mm; voxel size = 2 × 2 × 2 mm; flip angle = 80°; repetition time (TR) = 2000 ms; echo time (TE) = 43 ms; field of view = 192 mm; and acquisition matrix = 96 × 96. Each resting-state scan lasted 6 minutes and yielded 180 volumes. Participants kept their eyes open and fixated on a central crosshair. High-resolution T1-weighted structural images were acquired using a magnetization-prepared rapid acquisition gradient echo (MPRAGE) sequence: number of slices = 208; slice thickness = 1 mm; voxel size = 1.0 × 1.0 × 1.0 mm³; flip angle = 9°; TR = 2250 ms; TE = 3.51 ms; field of view = 256 mm; acquisition matrix = 256 × 256 mm). 2.4 Data Analysis 2.4.1 Preprocessing Functional image pre-processing was performed using SPM25 in MATLAB R2024b. The first four volumes were discarded for T1 equilibration, leaving 176 volumes per participant for analysis. Preprocessing steps included: (1) slice timing correction with the middle slice as the reference, (2) motion realignment, (3) co-registration of functional images to the T1-weighted structural image, (4) normalization to Montreal Neurological Institute (MNI) space using deformation fields derived from structural image segmentation, and (5) spatial smoothing with an 8-mm full-width at half-maximum (FWHM) Gaussian kernel. 2.4.2. Functional Connectivity Analysis Resting-state functional connectivity analysis was conducted using the CONN toolbox version 22 (Whitfield-Gabrieli & Nieto-Castanon, 2012 ). Denoising was performed using the anatomical component-based noise correction method (aCompCor; Behzadi et al., 2007 ), regressing 16 principal components from white matter and cerebrospinal fluid signals, along with 12 motion parameters (six realignment parameters and their first-order derivatives). Data were bandpass- filtered (0.01–0.1 Hz) and applied to remove low-frequency drift and high-frequency physiological noise, and linear detrending was performed. 2.4.3 Regions of Interest Based on Liu et al. ( 2023 ), spherical regions of interest (ROIs; 6-mm radius) were defined following MNI coordinates: bilateral amygdala (left: −22, − 6, −14; right: 26, − 2, −16), bilateral anterior insula (left: −32, 23, 4 and − 26, 26, 4; right: 35, 24, 0 and 39, 18, 2), left OFC (− 30, 54, − 4; −38, 50, − 12; −28, 52, − 14), bilateral ACC (6, 22, 31 and 6, 30, 36), left IPL (− 48, − 39, 30), left MFG (− 12, 33, 60), and MCC (3, − 30, 36). For ROIs with multiple coordinate peaks, the signals were averaged across all spheres to create a single bilateral or composite ROI. 2.4.4 Statistical Analysis Fisher's z-transformed correlation coefficients were computed for three ROI pairs corresponding to hypotheses: (H1) bilateral amygdala–bilateral anterior insula, (H2) left OFC–bilateral ACC, and (H3) left IPL–left–MFG MFG/MCC. Multiple regression tested associations between MAAS subscale scores and functional connectivity, controlling for age, sex, and mean framewise displacement (FD) as a measure of head motion. A Bonferroni-corrected threshold of p < .017 (.05/3) was applied. 3. Results Descriptive statistics are presented in Table 1 . The sample consisted of 39 healthy young adults (24 male and 15 female) with a mean age of 21.7 years (SD = 1.6, range = 18–25 years). The mean frame-wise displacement was 0.153 mm (SD = 0.050), indicating low head motion throughout the scanning session. MAAS subscales showed low intercorrelations, except for a moderate positive correlation between DA and NC ( r = .42, p = .008). Table 1 Descriptive statistics for demographic variables, MAAS subscales, and functional connectivity measures Variable M SD Range Age (years) 21.69 1.64 18–25 Discomfort with Ambiguity 4.88 0.83 3.00–6.57 Absolutism 2.93 0.85 1.29–4.71 Need for Complexity 4.37 1.1 1.86–6.71 Mean FD (mm) 0.15 0.05 0.08–0.27 FC Amygdala–AI -0.02 0.18 — FC OFC–ACC 0.21 0.21 — FC IPL–MFG/MCC -0.1 0.13 — I examined the associations between the three MAAS subscales and their hypothesized functional connectivity pairs, controlling for age, sex, and mean framewise displacement. Contrary to the preregistered hypotheses, none of the predicted associations reached statistical significance. For Hypothesis 1, Discomfort with Ambiguity was not significantly associated with connectivity between the bilateral amygdala and anterior insula ( β = 0.003, t (34) = 0.07, p = .942). For Hypothesis 2, Absolutism showed no significant association with connectivity between the left OFC and ACC ( β = −0.027, t (34) = − 0.67, p = .509). For Hypothesis 3, Need for Complexity was not significantly associated with connectivity between the left IPL and the MFG/MCC ( β = 0.019, t (34) = 0.93, p = .359). All p-values were substantially above the Bonferroni-corrected threshold ( α = .017), and the standardized effect sizes were negligible (all |β| < 0.03). Figure 1 presents scatter plots for each MAAS subscale and its corresponding connectivity measure. As a supplementary analysis, I examined Need for Complexity in relation to each target region separately. Neither IPL–MFG connectivity ( β = 0.007, t (34) = 0.24, p = .810) nor the IPL–MCC connectivity ( β = 0.032, t (34) = 1.05, p = .301) was significant, confirming that averaging across regions did not mask potential effects. Notably, the zero-order correlation between NC and IPL–MCC connectivity showed a small-to-medium effect size ( r = .202, p = .217), although not statistically significant. Note Shaded areas represent 95% confidence intervals. 4. Discussion This study aimed to conceptually replicate and extend Liu et al. ( 2023 ) by examining associations between the three dimensions of ambiguity tolerance and resting-state functional connectivity in a Japanese sample. Contrary to preregistered hypotheses, none of the hypothesized associations reached statistical significance. Discomfort with Ambiguity was unrelated to amygdala–anterior insula connectivity, Absolutism showed no relationship with OFC–ACC connectivity, and Need for Complexity was not associated with IPL–MFG/MCC connectivity. These null findings are consistent with those of Tanaka et al. ( 2015 ), who also reported no structural correlates of ambiguity intolerance and brain structure. Several methodological differences from Liu et al. ( 2023 ) may explain the divergent findings. First, Liu et al. employed seed-to-voxel whole-brain analyses, whereas the present study employed ROI-to-ROI analyses due to sample size constraints. Although the ROIs were defined using Liu et al.'s coordinates, subtle differences in ROI construction may have influenced connectivity estimates. Second, and more critically, ambiguity tolerance was operationalized differently: Liu et al. used a unidimensional construct, while I examined three MAAS dimensions. This constitutes a conceptual replication, and the neural correlates identified by Liu et al. may apply to composite measures rather than distinct MAAS subscales. Third, cultural differences between samples may be relevant. Liu et al. studied Chinese participants, whereas the present sample comprised young Japanese adults. Although MAAS demonstrates cross-cultural validity (Lauriola et al., 2016 ), neural correlates of ambiguity-tolerance dimensions may vary across cultural contexts (Hitsuwari & Nomura, 2021 ; Spector et al., 2001 ). Despite the null findings, this study contributes meaningfully to the literature. Preregistered replication, including null results, are essential to establish the robustness of scientific findings and counteract publication bias. The neuroimaging literature on ambiguity tolerance remains limited and inconsistent, some studies report significant associations (Liu et al., 2023 ; Tong et al., 2023 ), whereas others do not (Tanaka et al., 2015 ). The present findings underscore the need for well-powered, preregistered investigations to clarify the neural substrates of ambiguity tolerance. Notably, the zero-order correlation between Need for Complexity and IPL–MCC connectivity ( r = .202) resembles small-to-medium effect sizes reported by Liu et al., suggesting that larger samples may be required to detect such associations reliably. 4.1 Limitations This study has several limitations. First, the sample size reduced statistical power. Second, the exclusively young adult sample limits generalizability. Third, only resting-state connectivity was examined, and task-based paradigms involving ambiguous stimuli may reveal associations that are undetectable at rest. Fourth, the conceptual differences between this study's multidimensional approach and Liu et al.'s unidimensional measure complicate direct comparison. 4.2 Future Directions Future research should address these limitations by conducting well-powered samples and examining both unidimensional and multidimensional measures of ambiguity tolerance. Multi-site collaborations would facilitate larger datasets and enable cross-cultural comparisons. In addition, combining resting-state and task-based fMRI approaches may provide a more comprehensive account of how ambiguity tolerance is instantiated in the brain. 5. Conclusion In conclusion, this preregistered study did not replicate associations between ambiguity tolerance and resting-state functional connectivity reported by Liu et al. ( 2023 ). Although the null findings may reflect a genuine absence of effects, methodological differences prevent definitive conclusions. The observed small-to-medium effect size for the NC–IPL–MCC association suggests that the neural correlates of ambiguity tolerance and brain connectivity warrant further investigation in larger samples. Declarations Ethical statement Ethical approval was obtained from the Graduate School of Education, Kyoto University (CPE-510), and the Institute for the Future of Human Society MRI Research Facility, Kyoto University (22-003). All procedures were conducted in accordance with the Declaration of Helsinki and the approved institutional guidelines. Written informed consent was obtained prior to participation. This study was preregistered on the Open Science Framework (https://osf.io/vhbq2). The analysis scripts and behavioral data are publicly available (https://osf.io/84ev5). Conflicts of Interest There are no competing interests to declare. Acknowledgement I thank Editage (https://www.editage.jp/) for English language editing. Declaration of generative AI in scientific writing During the preparation of this work the author used Claude Sonnet 4.5 (Anthropic, 2025) in order to assist with writing MATLAB scripts for neuroimaging data analysis and drafting the manuscript in English. After using this tool, the author reviewed and edited the content as needed and takes full responsibility for the content of the published article. Funding This work was supported by the Japan Society for the Promotion of Science Overseas Research Fellowship and crowdfunding through academist (https://academist-cf.com/fanclubs/358). References Behzadi Y, Restom K, Liau J, Liu TT (2007) A component-based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 37(1):90–101. https://doi.org/10.1016/j.neuroimage.2007.04.042 Frenkel-Brunswik E (1949) Intolerance of ambiguity as an emotional and perceptual personality variable. J Pers 18(1):108–143. https://doi.org/10.1111/j.1467-6494.1949.tb01236.x Himichi T, Osanai H, Goto T, Fujita H, Kawamura Y, Davis MH, Nomura M (2017) Development of a Japanese version of the Interpersonal Reactivity Index. 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South Afr J Psychol 51:107–120. https://doi.org/10.1177/0081246320953715 Whitfield-Gabrieli S, Nieto-Castanon A (2012) Conn: A functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect 2(3):125–141. https://doi.org/10.1089/brain.2012.0073 Footnotes In the original MAAS, Absolutism is labeled “Moral Absolutism/Splitting” and Need for Complexity is labeled “Need for Complexity and Novelty.” We use the abbreviated terms throughout this paper for brevity. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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14:13:16","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":69796,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8351701/v1/654507e0eecfb6d301a69908.html"},{"id":98435737,"identity":"fd4ba29f-37ba-4c22-87fb-ebb5da0a70a7","added_by":"auto","created_at":"2025-12-17 16:54:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":235937,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAssociations between MAAS subscales and resting-state functional connectivity. (a) Discomfort with Ambiguity – amygdala–anterior insula. (b) Absolutism – OFC–ACC. (c) Need for Complexity – IPL–MFG/MCC.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1scatter.png","url":"https://assets-eu.researchsquare.com/files/rs-8351701/v1/3b2407adabda18d2ff76e605.png"},{"id":98623614,"identity":"2ecb722c-6461-43b6-983f-41cc2d8de881","added_by":"auto","created_at":"2025-12-19 17:07:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":732036,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8351701/v1/22ba5834-a376-40c0-a39b-66c636ddd4dd.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAmbiguity tolerance and resting-state functional connectivity: A preregistered conceptual replication in a Japanese sample\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAmbiguity tolerance, the tendency to perceive ambiguous situations as desirable or threatening, has been a topic of psychological inquiry since Frenkel-Brunswik (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1949\u003c/span\u003e) introduced the concept. A recent scientometric analysis showed substantial growth in this research area, with nearly 70% of publications emerging in the past decade, and expanding into medical education, career development, entrepreneurship, and political psychology (Rubiales-N\u0026uacute;\u0026ntilde;ez et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite this growth, neuroimaging studies examining the neural correlates of ambiguity tolerance remain scarce. Liu et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) investigated resting-state functional connectivity in a Chinese sample (N\u0026thinsp;=\u0026thinsp;315) and reported that higher ambiguity tolerance was associated with stronger connectivity within integration and control networks (inferior parietal lobule [IPL]\u0026ndash;middle frontal gyrus [MFG]/middle cingulate cortex [MCC]), whereas lower tolerance was linked to stronger connectivity in threat and error-monitoring circuits (orbitofrontal cortex [OFC]\u0026ndash;anterior cingulate cortex [ACC]). These findings were interpreted as reflecting a balance between the neural systems supporting flexible information integration and those treating ambiguity as a threat. Other studies have linked ambiguity tolerance with gray matter density in the right inferior frontal gyrus, supporting flexible reappraisal and inhibition (Tong et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and to differential activation patterns in visual association areas during ambiguous stimulus processing (Mazhirina et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, Tanaka et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found no association between self-reported ambiguity intolerance and gray matter volume, highlighting inconsistencies in the literature.\u003c/p\u003e \u003cp\u003eDimensionality is critical issue in ambiguity tolerance research. Historically, the construct suffered from conceptual fragmentation, grouping emotional discomfort, black-and-white thinking, and novelty-seeking under a single label (Lauriola et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). To address this, Lauriola et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) developed the Multidimensional Attitudes toward Ambiguity Scale (MAAS), identifying three robust factors: Discomfort with Ambiguity (DA), reflecting anxiety in ambiguous situations; Absolutism (AB), the tendency toward dichotomous terms; and Need for Complexity (NC), a preference for complexity, multiplicity, and novelty\u003csup\u003e1\u003c/sup\u003e. MAAS-based research demonstrates distinct associations for each dimension. For instance, DA and NC differentially predict attitudes toward artificial intelligence (Hitsuwari \u0026amp; Takano, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), AB predicts reduced analytical thinking (van Zyl, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and a haiku intervention selectively reduces AB, leaving DA and NC unchanged (Hitsuwari \u0026amp; Nomura, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the multidimensional nature of ambiguity tolerance, earlier neuroimaging findings based on unidimensional measures may obscure dimension-specific neural correlates. This study conceptually replicated and extended Liu et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) by examining how the three MAAS dimensions relate to resting-state functional connectivity in a Japanese sample. Based on Liu et al.'s findings and the theoretical distinctiveness among MAAS subscales, three preregistered hypotheses were formulated:\u003c/p\u003e \u003cp\u003eH1 (Discomfort with Ambiguity): Higher DA scores will be associated with stronger functional connectivity between the amygdala and anterior insula, reflecting emotional threat responses to uncertainty.\u003c/p\u003e \u003cp\u003eH2 (Absolutism): Higher AB scores will be associated with stronger functional connectivity between the left OFC and ACC, corresponding to a circuit implicated in ambiguity-related warning signals.\u003c/p\u003e \u003cp\u003eH3 (Need for Complexity): Higher NC scores will be positively associated with connectivity between the left IPL and left MFG/MCC, reflecting flexible integration of ambiguous information.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e was obtained from the Graduate School of Education, Kyoto University (CPE-510), and the Institute for the Future of Human Society MRI Research Facility, Kyoto University (22\u0026thinsp;\u0026minus;\u0026thinsp;003). All procedures were conducted in accordance with the Declaration of Helsinki and the approved institutional guidelines. Written informed consent was obtained prior to participation. This study was preregistered on the Open Science Framework (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/vhbq2\u003c/span\u003e\u003cspan address=\"https://osf.io/vhbq2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The analysis scripts and behavioral data are publicly available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/84ev5\u003c/span\u003e\u003cspan address=\"https://osf.io/84ev5\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eForty-two participants without neurological or psychiatric disorders were recruited through an electronic bulletin board at Kyoto University. After excluding incomplete responses, 39 participants remained (M\u003csub\u003eage\u003c/sub\u003e = 21.69, SD\u0026thinsp;=\u0026thinsp;1.64; 24 men, 15 women). All had normal or corrected-to-normal vision and no MRI contra-indications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Questionnaires\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Multidimensional Attitude toward Ambiguity Scale (MAAS)\u003c/h2\u003e \u003cp\u003eAmbiguity tolerance was assessed using the Japanese version of the MAAS (Hitsuwari \u0026amp; Nomura, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The 21-item scale comprises three seven item subscales: DA (negative emotional reactions to ambiguity), AB (rigid, dichotomous thinking), and NC (desire for novel and complex information). Participants rated each item on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). The mean subscale scores were calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Other Measures\u003c/h2\u003e \u003cp\u003eAdditional questionnaires were administered as part of a larger study protocol: the shortened Japanese versions of the Plymouth Sensory Imagery Questionnaire (Hitsuwari \u0026amp; Nomura, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e), the Interpersonal Reactivity Index (Himichi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), the Awe subscale of the Dispositional Positive Emotion Scales (Nomura et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the Southampton Nostalgia Scale (Nagamine \u0026amp; Toyama, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and the Big Five Scale (Namikawa et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), though not analyzed in the present study.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 MRI Acquisition\u003c/h2\u003e \u003cp\u003eMRI images were acquired using a 3-Tesla Siemens Verio scanner using a 32-channel head coil at the Institute for the Future of Human Society, Kyoto University. Resting-state functional images were acquired using a multiband echo-planar imaging sequence: multiband factor\u0026thinsp;=\u0026thinsp;4; number of slices\u0026thinsp;=\u0026thinsp;76; slice thickness\u0026thinsp;=\u0026thinsp;2.0 mm; voxel size\u0026thinsp;=\u0026thinsp;2 \u0026times; 2 \u0026times; 2 mm; flip angle\u0026thinsp;=\u0026thinsp;80\u0026deg;; repetition time (TR)\u0026thinsp;=\u0026thinsp;2000 ms; echo time (TE)\u0026thinsp;=\u0026thinsp;43 ms; field of view\u0026thinsp;=\u0026thinsp;192 mm; and acquisition matrix\u0026thinsp;=\u0026thinsp;96 \u0026times; 96. Each resting-state scan lasted 6 minutes and yielded 180 volumes. Participants kept their eyes open and fixated on a central crosshair.\u003c/p\u003e \u003cp\u003eHigh-resolution T1-weighted structural images were acquired using a magnetization-prepared rapid acquisition gradient echo (MPRAGE) sequence: number of slices\u0026thinsp;=\u0026thinsp;208; slice thickness\u0026thinsp;=\u0026thinsp;1 mm; voxel size\u0026thinsp;=\u0026thinsp;1.0 \u0026times; 1.0 \u0026times; 1.0 mm\u0026sup3;; flip angle\u0026thinsp;=\u0026thinsp;9\u0026deg;; TR\u0026thinsp;=\u0026thinsp;2250 ms; TE\u0026thinsp;=\u0026thinsp;3.51 ms; field of view\u0026thinsp;=\u0026thinsp;256 mm; acquisition matrix\u0026thinsp;=\u0026thinsp;256 \u0026times; 256 mm).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data Analysis\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Preprocessing\u003c/h2\u003e \u003cp\u003eFunctional image pre-processing was performed using SPM25 in MATLAB R2024b. The first four volumes were discarded for T1 equilibration, leaving 176 volumes per participant for analysis. Preprocessing steps included: (1) slice timing correction with the middle slice as the reference, (2) motion realignment, (3) co-registration of functional images to the T1-weighted structural image, (4) normalization to Montreal Neurological Institute (MNI) space using deformation fields derived from structural image segmentation, and (5) spatial smoothing with an 8-mm full-width at half-maximum (FWHM) Gaussian kernel.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Functional Connectivity Analysis\u003c/h2\u003e \u003cp\u003eResting-state functional connectivity analysis was conducted using the CONN toolbox version 22 (Whitfield-Gabrieli \u0026amp; Nieto-Castanon, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Denoising was performed using the anatomical component-based noise correction method (aCompCor; Behzadi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), regressing 16 principal components from white matter and cerebrospinal fluid signals, along with 12 motion parameters (six realignment parameters and their first-order derivatives). Data were bandpass- filtered (0.01\u0026ndash;0.1 Hz) and applied to remove low-frequency drift and high-frequency physiological noise, and linear detrending was performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Regions of Interest\u003c/h2\u003e \u003cp\u003eBased on Liu et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), spherical regions of interest (ROIs; 6-mm radius) were defined following MNI coordinates: bilateral amygdala (left: \u0026minus;22, \u0026minus;\u0026thinsp;6, \u0026minus;14; right: 26, \u0026minus;\u0026thinsp;2, \u0026minus;16), bilateral anterior insula (left: \u0026minus;32, 23, 4 and \u0026minus;\u0026thinsp;26, 26, 4; right: 35, 24, 0 and 39, 18, 2), left OFC (\u0026minus;\u0026thinsp;30, 54, \u0026minus;\u0026thinsp;4; \u0026minus;38, 50, \u0026minus;\u0026thinsp;12; \u0026minus;28, 52, \u0026minus;\u0026thinsp;14), bilateral ACC (6, 22, 31 and 6, 30, 36), left IPL (\u0026minus;\u0026thinsp;48, \u0026minus;\u0026thinsp;39, 30), left MFG (\u0026minus;\u0026thinsp;12, 33, 60), and MCC (3, \u0026minus;\u0026thinsp;30, 36). For ROIs with multiple coordinate peaks, the signals were averaged across all spheres to create a single bilateral or composite ROI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eFisher's z-transformed correlation coefficients were computed for three ROI pairs corresponding to hypotheses: (H1) bilateral amygdala\u0026ndash;bilateral anterior insula, (H2) left OFC\u0026ndash;bilateral ACC, and (H3) left IPL\u0026ndash;left\u0026ndash;MFG MFG/MCC. Multiple regression tested associations between MAAS subscale scores and functional connectivity, controlling for age, sex, and mean framewise displacement (FD) as a measure of head motion. A Bonferroni-corrected threshold of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.017 (.05/3) was applied.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eDescriptive statistics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The sample consisted of 39 healthy young adults (24 male and 15 female) with a mean age of 21.7 years (SD\u0026thinsp;=\u0026thinsp;1.6, range\u0026thinsp;=\u0026thinsp;18\u0026ndash;25 years). The mean frame-wise displacement was 0.153 mm (SD\u0026thinsp;=\u0026thinsp;0.050), indicating low head motion throughout the scanning session. MAAS subscales showed low intercorrelations, except for a moderate positive correlation between DA and NC (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.008).\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics for demographic variables, MAAS subscales, and functional connectivity measures\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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\u003eM\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\u003eRange\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscomfort with Ambiguity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00\u0026ndash;6.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolutism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.29\u0026ndash;4.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeed for Complexity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.86\u0026ndash;6.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean FD (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u0026ndash;0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC Amygdala\u0026ndash;AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC OFC\u0026ndash;ACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC IPL\u0026ndash;MFG/MCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\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\u003eI examined the associations between the three MAAS subscales and their hypothesized functional connectivity pairs, controlling for age, sex, and mean framewise displacement. Contrary to the preregistered hypotheses, none of the predicted associations reached statistical significance.\u003c/p\u003e \u003cp\u003eFor Hypothesis 1, Discomfort with Ambiguity was not significantly associated with connectivity between the bilateral amygdala and anterior insula (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, \u003cem\u003et\u003c/em\u003e(34)\u0026thinsp;=\u0026thinsp;0.07, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.942). For Hypothesis 2, Absolutism showed no significant association with connectivity between the left OFC and ACC (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;0.027, \u003cem\u003et\u003c/em\u003e(34)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.67, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.509). For Hypothesis 3, Need for Complexity was not significantly associated with connectivity between the left IPL and the MFG/MCC (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019, \u003cem\u003et\u003c/em\u003e(34)\u0026thinsp;=\u0026thinsp;0.93, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.359).\u003c/p\u003e \u003cp\u003eAll p-values were substantially above the Bonferroni-corrected threshold (\u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.017), and the standardized effect sizes were negligible (all \u003cem\u003e|β|\u003c/em\u003e \u0026lt; 0.03). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents scatter plots for each MAAS subscale and its corresponding connectivity measure.\u003c/p\u003e \u003cp\u003eAs a supplementary analysis, I examined Need for Complexity in relation to each target region separately. Neither IPL\u0026ndash;MFG connectivity (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007, \u003cem\u003et\u003c/em\u003e(34)\u0026thinsp;=\u0026thinsp;0.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.810) nor the IPL\u0026ndash;MCC connectivity (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032, \u003cem\u003et\u003c/em\u003e(34)\u0026thinsp;=\u0026thinsp;1.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.301) was significant, confirming that averaging across regions did not mask potential effects. Notably, the zero-order correlation between NC and IPL\u0026ndash;MCC connectivity showed a small-to-medium effect size (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.202, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.217), although not statistically significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eShaded areas represent 95% confidence intervals.\u003c/p\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study aimed to conceptually replicate and extend Liu et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) by examining associations between the three dimensions of ambiguity tolerance and resting-state functional connectivity in a Japanese sample. Contrary to preregistered hypotheses, none of the hypothesized associations reached statistical significance. Discomfort with Ambiguity was unrelated to amygdala\u0026ndash;anterior insula connectivity, Absolutism showed no relationship with OFC\u0026ndash;ACC connectivity, and Need for Complexity was not associated with IPL\u0026ndash;MFG/MCC connectivity. These null findings are consistent with those of Tanaka et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), who also reported no structural correlates of ambiguity intolerance and brain structure.\u003c/p\u003e \u003cp\u003eSeveral methodological differences from Liu et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) may explain the divergent findings. First, Liu et al. employed seed-to-voxel whole-brain analyses, whereas the present study employed ROI-to-ROI analyses due to sample size constraints. Although the ROIs were defined using Liu et al.'s coordinates, subtle differences in ROI construction may have influenced connectivity estimates. Second, and more critically, ambiguity tolerance was operationalized differently: Liu et al. used a unidimensional construct, while I examined three MAAS dimensions. This constitutes a conceptual replication, and the neural correlates identified by Liu et al. may apply to composite measures rather than distinct MAAS subscales. Third, cultural differences between samples may be relevant. Liu et al. studied Chinese participants, whereas the present sample comprised young Japanese adults. Although MAAS demonstrates cross-cultural validity (Lauriola et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), neural correlates of ambiguity-tolerance dimensions may vary across cultural contexts (Hitsuwari \u0026amp; Nomura, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Spector et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the null findings, this study contributes meaningfully to the literature. Preregistered replication, including null results, are essential to establish the robustness of scientific findings and counteract publication bias. The neuroimaging literature on ambiguity tolerance remains limited and inconsistent, some studies report significant associations (Liu et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tong et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), whereas others do not (Tanaka et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The present findings underscore the need for well-powered, preregistered investigations to clarify the neural substrates of ambiguity tolerance. Notably, the zero-order correlation between Need for Complexity and IPL\u0026ndash;MCC connectivity (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.202) resembles small-to-medium effect sizes reported by Liu et al., suggesting that larger samples may be required to detect such associations reliably.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Limitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, the sample size reduced statistical power. Second, the exclusively young adult sample limits generalizability. Third, only resting-state connectivity was examined, and task-based paradigms involving ambiguous stimuli may reveal associations that are undetectable at rest. Fourth, the conceptual differences between this study's multidimensional approach and Liu et al.'s unidimensional measure complicate direct comparison.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Future Directions\u003c/h2\u003e \u003cp\u003eFuture research should address these limitations by conducting well-powered samples and examining both unidimensional and multidimensional measures of ambiguity tolerance. Multi-site collaborations would facilitate larger datasets and enable cross-cultural comparisons. In addition, combining resting-state and task-based fMRI approaches may provide a more comprehensive account of how ambiguity tolerance is instantiated in the brain.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this preregistered study did not replicate associations between ambiguity tolerance and resting-state functional connectivity reported by Liu et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although the null findings may reflect a genuine absence of effects, methodological differences prevent definitive conclusions. The observed small-to-medium effect size for the NC\u0026ndash;IPL\u0026ndash;MCC association suggests that the neural correlates of ambiguity tolerance and brain connectivity warrant further investigation in larger samples.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Graduate School of Education, Kyoto University (CPE-510), and the Institute for the Future of Human Society MRI Research Facility, Kyoto University (22-003). All procedures were conducted in accordance with the Declaration of Helsinki and the approved institutional guidelines. Written informed consent was obtained prior to participation. This study was preregistered on the Open Science Framework (https://osf.io/vhbq2). The analysis scripts and behavioral data are publicly available (https://osf.io/84ev5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI thank Editage (https://www.editage.jp/) for English language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI in scientific writing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the author used Claude Sonnet 4.5 (Anthropic, 2025) in order to assist with writing MATLAB scripts for neuroimaging data analysis and drafting the manuscript in English. After using this tool, the author reviewed and edited the content as needed and takes full responsibility for the content of the published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Japan Society for the Promotion of Science Overseas Research Fellowship and crowdfunding through academist (https://academist-cf.com/fanclubs/358).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBehzadi Y, Restom K, Liau J, Liu TT (2007) A component-based noise correction method (CompCor) for BOLD and perfusion based fMRI. 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Brain Connect 2(3):125\u0026ndash;141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/brain.2012.0073\u003c/span\u003e\u003cspan address=\"10.1089/brain.2012.0073\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e In the original MAAS, Absolutism is labeled \u0026ldquo;Moral Absolutism/Splitting\u0026rdquo; and Need for Complexity is labeled \u0026ldquo;Need for Complexity and Novelty.\u0026rdquo; We use the abbreviated terms throughout this paper for brevity.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"bf6bcc5f-dd12-4770-acea-a7922640cae9","identifier":"10.13039/501100001691","name":"Japan Society for the Promotion of Science","awardNumber":"Overseas Research Fellowship","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Helmut Schmidt University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ambiguity tolerance, Resting-state functional connectivity, Multidimensional Attitude toward Ambiguity Scale (MAAS), Neural correlate, Conceptual replication","lastPublishedDoi":"10.21203/rs.3.rs-8351701/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8351701/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAmbiguity tolerance has recently attracted renewed interest; however, its neural substrates remain underspecified, particularly considering their multidimensional structure. This preregistered study conceptually replicated and extended Liu et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) by examining how three dimensions of the Multidimensional Attitude toward Ambiguity Scale (MAAS)\u0026mdash;Discomfort with Ambiguity (DA), Absolutism (AB), and Need for Complexity (NC)\u0026mdash;relate to resting-state functional connectivity in a Japanese sample. Thirty-nine healthy young adults completed MRI scans and self-report measures. Based on prior work, associations were hypothesized between DA and amygdala\u0026ndash;insula connectivity, AB and orbitofrontal\u0026ndash;anterior cingulate connectivity, and NC and inferior parietal\u0026ndash;middle frontal/middle cingulate connectivity. Region of interest (ROI)-to-ROI analyses controlling for age, sex, and head motion revealed no significant associations between any MAAS dimension and the corresponding connectivity pairs. Effect sizes were negligible, although NC showed a small zero-order correlation with inferior parietal lobule\u0026ndash;middle cingulate cortex connectivity. These findings contrast with earlier reports using unidimensional ambiguity tolerance measures, suggesting that previously observed neural correlates may not map directly onto specific MAAS dimensions. The results highlight the need for larger, well-powered studies integrating multidimensional constructs and cross-cultural samples to clarify the neural architecture of ambiguity tolerance.\u003c/p\u003e","manuscriptTitle":"Ambiguity tolerance and resting-state functional connectivity: A preregistered conceptual replication in a Japanese sample","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-16 14:13:11","doi":"10.21203/rs.3.rs-8351701/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"849de03f-b31b-452c-8df1-d9575fb16dbd","owner":[],"postedDate":"December 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59596072,"name":"Cognitive Neuroscience"},{"id":59596073,"name":"Psychology"}],"tags":[],"updatedAt":"2025-12-16T14:13:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-16 14:13:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8351701","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8351701","identity":"rs-8351701","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}