Intolerance of Uncertainty Predicts Physiological and Subjective Responses to Experiential Uncertainty

Intolerance of Uncertainty Predicts Physiological and Subjective Responses to Experiential Uncertainty | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 29 October 2025 V1 Latest version Share on Intolerance of Uncertainty Predicts Physiological and Subjective Responses to Experiential Uncertainty Authors : Tess Reid 0009-0004-3106-1268 and jolie wormwood [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176174849.95165912/v1 212 views 108 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Individual differences in people’s tolerance for uncertainty are associated with mental health and well-being. However, much of the literature on responses to uncertainty has relied on retrospective self-report measures of how one believes they tend to act in uncertain contexts or has inferred how people feel about uncertainty based on their behavior in decision-making tasks. Here, we piloted a novel experiential uncertainty task in which participants were asked to insert their hands into an opaque box and feel an unknown object, and we assessed subjective affective reactions and physiological reactivity in anticipation of completing the task. Across both studies, we found participants higher in intolerance of uncertainty (IU) demonstrated less parasympathetic nervous system reactivity during uncertainty compared to those lower in IU. In Study 2, we found that this association was disrupted by presentation of biofeedback during the task, particularly false biofeedback indicating a rapid heart rate. Additionally, while we failed to observe associations between IU and self-reported affective valence and arousal during uncertainty (Study 1), we found participants high in IU reported heightened experiences of several negative emotions (i.e., nervousness, worry, and stress) during experiential uncertainty (Study 2). Taken together, these findings suggest that individuals high in IU not only maintain unfavorable attitudes toward uncertainty in broad retrospective self-report measures, they also experience instances of uncertainty differently than individuals lower in IU in the moment, both subjectively and physiologically. Intolerance of Uncertainty Predicts Physiological and Subjective Responses to Experiential Uncertainty Tess Reid 1 & Jolie B. Wormwood 1 1 Department of Psychology, University of New Hampshire Abstract Individual differences in people’s tolerance for uncertainty are associated with mental health and well-being. However, much of the literature on responses to uncertainty has relied on retrospective self-report measures of how one believes they tend to act in uncertain contexts or has inferred how people feel about uncertainty based on their behavior in decision-making tasks. Here, we piloted a novel experiential uncertainty task in which participants were asked to insert their hands into an opaque box and feel an unknown object, and we assessed subjective affective reactions and physiological reactivity in anticipation of completing the task. Across both studies, we found participants higher in intolerance of uncertainty (IU) demonstrated less parasympathetic nervous system reactivity during uncertainty compared to those lower in IU. In Study 2, we found that this association was disrupted by presentation of biofeedback during the task, particularly false biofeedback indicating a rapid heart rate. Additionally, while we failed to observe associations between IU and self-reported affective valence and arousal during uncertainty (Study 1), we found participants high in IU reported heightened experiences of several negative emotions (i.e., nervousness, worry, and stress) during experiential uncertainty (Study 2). Taken together, these findings suggest that individuals high in IU not only maintain unfavorable attitudes toward uncertainty in broad retrospective self-report measures, they also experience instances of uncertainty differently than individuals lower in IU in the moment, both subjectively and physiologically. Intolerance of Uncertainty Predicts Physiological and Subjective Responses to Experiential Uncertainty Understanding how people process and respond to uncertainty is a central facet of psychological research across a variety of domains. For example, behavioral economists and decision-making researchers examine how people make financial and behavioral decisions under conditions of risk, where outcome probabilities are known, and uncertainty, where outcome probabilities are unknown or unknowable (Lipshitz & Strauss, 1997; Volz & Gigerenzer, 2012). Additionally, there is a large body of literature on curiosity in social and cognitive science, finding that curiosity often increases under uncertain conditions and functions to motivate individuals to seek out information to reduce uncertainty (Van Lieshout et al., 2021). These cross-disciplinary efforts have revealed stable individual differences when it comes to how people approach and respond to uncertainty (e.g., risk seeking and risk aversion; Beauchamp et al., 2015). Here, we examine one such individual difference, intolerance of uncertainty—the degree to which individuals tend to be distressed and/or incapacitated by a perceived lack of salient information within a given context (Carleton et al., 2007)—with a focus on its relationship to affective experience during uncertainty. Individual differences in intolerance of uncertainty (IU) have been studied most extensively in the clinical literature, revealing it as a transdiagnostic symptom associated with many different psychopathologies, including generalized anxiety disorder (GAD), obsessive-compulsive disorder (OCD), and social anxiety (Boswell et al., 2013; Norr et al., 2013). Indeed, it has been argued that IU plays a critical role in the development and maintenance of anxiety disorders such that it should be considered a predispositional characteristic for the development of these disorders (Carelton, 2012). Among substantial support for this claim, reductions in both OCD and GAD symptomology resulting from clinical intervention have been consistently accompanied by reductions in IU (Bomyea et al., 2015; Knowles & Olatunji, 2023), though further research is required to support directional claims. Additionally, research investigating the relationship between depression and IU has identified a consistent association (Dugas et al., 2004; McEvoy & Mahoney, 2012), with several studies finding that the IU is related to depression by way of rumination (Huang et al., 2019; Jensen et al., 2016; Yook et al., 2010). Yook et al. (2010) suggest it may be that rumination about uncertainty can give rise to pessimistic certainty—a common feature of depression—as a means to mitigate negative feelings about uncertainty. Similarly, Jenson et al. (2016) posit that depression may, in part, be a product of an inability or refusal to manage uncertainty-related distress and could be critical in understanding the progression of anxiety to depression. Individual differences in IU have also garnered interest outside of the clinical domain, particularly among learning and decision-making researchers. For example, work on classical threat conditioning has identified IU as an important factor in explaining differences in conditioning in non-clinical populations. Consistently, individuals with high IU (compared to low IU) have been found to require more explicit information that a stimulus is no longer threatening for successful threat extinction to occur, exhibit worse extinction retention, and appear to overgeneralize threat, especially when unexpected uncertainty is high (Morriss et al., 2021). In decision-making work, IU also appears to explain differences in decision-making behavior. Specifically, high IU has been associated with increased information sampling prior to making decisions (Wake et al., 2022), prioritizing uncertainty avoidance over other decision-making factors (i.e., choosing smaller, riskier rewards to reduce the amount of time spent in uncertainty; Luhmann et al., 2011), indecisiveness in everyday decision-making (Appel et al., 2024), and increased engagement in decisional safety behaviors to reduce uncertainty (e.g., asking for advice to mitigate feelings of uncertainty; Appel et al., 2024). Taken together, IU research within and beyond the clinical domain suggests that understanding individual differences in IU is crucial to understanding a breadth of maladaptive human experiences and behaviors. Uncertainty as an Affective Experience Despite interdisciplinary interest in responses to uncertainty, and its critical role in mental health, there is a lack of empirical data concerning the actual experience of uncertainty in terms of its phenomenology and how this may relate to the development and maintenance of individual differences in IU. To date, much of the literature on responses to uncertainty has relied either on retrospective self-report measures of how one believes they tend to act in uncertain environments or has inferred how people feel about uncertainty based on their behavioral outcomes in decision-making tasks (e.g., propensity to take risks in economic gambling tasks). That is, there has been little attention paid to what it means to experience uncertainty in the moment in terms of both subjective affective experience and physiological reactivity. Examining these experiential features of uncertainty (i.e., affective experience and physiological activity) among those higher and lower in IU may help us understand how individual differences in IU manifest or develop and ultimately result in downstream differences in cognition and behavior. Indeed, decision-making researchers have argued that, under conditions of uncertainty, affective experiences may be the primary drivers of decision-making and behavior, serving as a summary that is more efficient than rational deliberation when probabilistic information about outcomes is incomplete (Slovic et al., 2005). Similarly, other researchers have emphasized the role of anticipatory emotions under both risky and uncertain conditions, arguing that they are not merely epiphenomenal, but rather crucial sources of information that guide decision-making and behavior (Loewenstein et al., 2001). Here, we propose that, at their core, experiences of uncertainty involve affective or emotional components—one finds uncertainty pleasant or unpleasant, activating or deactivating. For some, encountering uncertainty may be an experience of fear, anxiety, or worry (Carelton, 2007), and for others, it may be an experience of excitement, curiosity, or anticipation (Zaleskiewicz, 2001). To this end, the application of emotion theory may provide insight. The Theory of Constructed Emotion (TCE; Barrett, 2017), adopts an active inference perspective to explain subjective experience and posits a central role for physiological information from the body in affective and emotional experience. According to this perspective, the brain uses information from the internal bodily systems (e.g., the stomach, lungs, heart) regarding their ongoing and predicted activity (Barrett, 2017; Craig, 2002) to monitor current physiological needs and make adjustments for predicted needs amidst changing environmental and physiological demands, a regulatory process referred to as allostasis (Barrett & Simmons, 2015). Little of this process occurs at the level of conscious awareness, however, a low-dimensional summary of physiological ongoings can be made available to consciousness in the form of affect, or general feelings of pleasantness/unpleasantness (valence) and activation/deactivation (arousal) (Barett, 2017; Russell, 1980). From this perspective, basic affective feelings are a direct reflection of physiological information about the ongoing and predicted needs of the body. To the extent that experiences of uncertainty have affective features at their core, this theoretical perspective suggests that experiences of uncertainty are themselves grounded in physiological activity. Thus, studying both subjective and physiological responses to uncertainty may provide insight into individual differences in IU. The Present Studies Guided by this framework, we conducted two studies to examine the affective components of experiences of uncertainty and the bodily activity from which they are thought to arise (e.g., respiratory, cardiovascular, and electrodermal activity) in real time, and to examine their relationship to individual differences in IU. To do this, we designed a novel task to evoke feelings of uncertainty in which participants were asked to insert their hand into an opaque box and feel an unknown object while their physiological, behavioral, and self-reported experiences were recorded. This task allowed for the direct examination of experiential uncertainty as, during the time before they put their hand into the box, participants had a near complete lack of contextual information relevant to the anticipated experience. In both studies, we measured several indices of autonomic nervous system (ANS) activity that past research has suggested are relevant to affective and emotional experiences (Mauss & Robinson, 2009; Siegel et al., 2018), including electrodermal activity, interbeat interval, and heartrate variability. Electrodermal activity (EDA) is a measure of skin conductance that reflects activity in the eccrine sweat glands, which are innervated exclusively by the sympathetic nervous system (Stern et al., 2000), and heightened EDA is commonly associated with affective arousal (Critchley, 2002). Interbeat interval (IBI) is a measure of the time (in ms) between consecutive heartbeats, an inverse measure of heart rate. IBI is often used as an estimate of physiological or affective arousal with lower IBI indicating a faster heartrate and heightened arousal (Berntson et al., 2007). Lastly, respiratory sinus arrythmia (RSA) is a measure of high-frequency heart rate variability, capturing variation in heart rate associated with the respiratory cycle. RSA is a common measure of parasympathetic nervous system activity, and momentary reductions in RSA are associated with heightened affective arousal (Berntson et al., 1993; Frazier et al., 2004). Employing a multimodal measurement approach allows for a more detailed assessment of peripheral physiological activity during experiential uncertainty, including estimates of end-organ function (i.e., IBI), sympathetic nervous system activity (i.e., EDA), and parasympathetic nervous system activity (i.e., RSA). Study 1 The primary aim of Study 1 is to examine how individual differences in IU relate to affective reactions to experiential uncertainty, including self-reported feelings of pleasantness/unpleasantness and activation/deactivation, as well as the bodily activity from which these affective feelings are thought to arise (e.g., respiratory, cardiovascular, and electrodermal activity). We predicted that individuals higher in IU would have more negative and higher arousal reactions to an uncertainty task in which they were asked to put their hand into an opaque box and feel an unknown object. Physiologically, this would present as greater parasympathetic withdrawal and greater sympathetic activation in anticipation of completing the task. Subjectively, this would present as higher arousal ratings and lower valence ratings on self-report scales completed immediately prior to the task. Methods Methods and analyses for both studies were not pre-registered. Data, materials, and analytic code for both studies are publicly available via the open science framework (https://osf.io/32zpu). Participants Participants were recruited from the University of New Hampshire Psychology Department’s undergraduate student participant pool. Potential participants were ineligible to participate if they were below the age of 18, did not speak English fluently, had a history of skin allergies or sensitivity to adhesives, had a history of cardiovascular disease, and/or had a history of fainting/faintness in medical settings. Participants (N=149) were predominantly white (89.31%), female (67.33%), and between the ages of 18-25 (93.33%). Participants received one credit toward completion of a psychology class of their choosing in exchange for their participation. Procedure Experimental sessions were conducted in person and lasted approximately one hour. Experimenters began by orienting participants to the lab space and providing an overview of the study. Participants were then guided through an informed consent protocol and given the opportunity to ask any questions. If informed consent was given, participants were then fitted with physiological recording equipment (described below) and seated in front of a computer. Participants first completed a 5-minute resting baseline during which they reported basic demographic information on the computer, followed by two tasks that are not relevant to the present investigation (a heartbeat tracking task and semantic priming task; for details, see supplemental online materials). They then completed the experiential uncertainty task and several self-report questionnaires. After these were completed, participants were given a second consent form related to public use of audio and video recordings and were debriefed by the experimenter. Tasks and Measures Physiological Recording Participants’ respiratory, electrodermal (EDA), and electrocardiogram (ECG) data were continuously recorded throughout the duration of the experimental session via wired connections to an 8-slot BioNex Chassis (Model 50-3711-08) at a 1000 Hz sampling rate. For ECG recording, pre-gelled Ag/AgCl electrodes were placed on the distal right collarbone and left and right lower ribs, and a 0.5 Hz – 45 Hz bandpass filter was applied to the raw signal. Before electrode placement, placement sites were cleaned with an alcohol prep pad and lightly abraded with gauze. For EDA recording, two Ag/AgCl electrodes covered in isotonic paste (0.5% chloride salt) were placed on the thenar and hypothenar eminences of the left palm. Before placement, participants were asked to wash their hands thoroughly with warm water and hand soap (provided by the experimenter). For the duration of the experiment, participants were asked to keep their palms face-up on the desk to avoid excess signal artifacts caused by movement. Respiration was collected via a Sleepmate piezoelectric respiration belt (Model 50-4504-00) placed around the participants’ lower chest and a low cutoff filter of 5 Hz was applied to the raw signal. Participants were asked to refrain from wearing bulky sweatshirts or sweaters, as the respiration belt was placed over their clothing. All physiological data was processed using physiological scoring software from MindWare Technologies LTD (v3.2.13) and subjected to visual inspection by a trained scorer to identify excessive noise, artifact, or arrhythmia. We were not able to process physiological data from four participants due to technical issues during data collection. An additional three participants had physiological data with excessive noise or artifact, and their physiological data was excluded from analyses (one participant was excluded from IBI and RSA analyses, two were excluded from SCL analyses). In addition to these exclusions, out of a total of 730 60-second baseline segments inspected, two (0.27%) were excluded from calculations due to excessive noise or artifact. Additionally, one participant did not complete the IU survey and was excluded from analyses with this variable ECG data was used to derive measures of interbeat interval (IBI; the time between consecutive heartbeats in milliseconds) and respiratory sinus arrhythmia (RSA; variation in IBI across the respiratory cycle). RSA, a measure of high frequency heartrate variability, was derived from the spectral power for the resampled IBI time series for the respiratory frequency band (0.12-0.40 Hz). RSA was used to index parasympathetic nervous system activity. EDA data was used to derive mean skin conductance level (SCL) which we used to index sympathetic nervous system activity. Resting Baseline Participants were asked to sit quietly for five minutes to establish a measure of their physiological activity at rest. During this task, the experimenter left the room and returned once the five minutes had passed. Participants were asked to sit still in an upright, yet relaxed position during this time, and complete a brief online survey that asked about basic demographic information including their gender identity, race/ethnicity, and age. They were also asked to answer relevant health questions including their approximate height and weight, handedness, history related to cardiovascular health, whether or not they smoke, and if they were taking any medications that might influence cardiovascular activity. Experiential Uncertainty Task Participants were told they would be completing a task in which they would put their hand in an opaque box, feel an unknown object, and provide guesses as to what the object was. For half of the sample, the object was a rubber snake, for the other half it was a pile of plastic bugs; though the object itself is of little importance as we were primarily interested in participants’ response to anticipating putting their hand into the box. Audio and video were recorded for the duration of the experiential uncertainty task for data processing purposes. Self-Reported Affect. After the basic task was described, participants were asked to complete a short paper questionnaire about how they were feeling in anticipation of completing the task. The questionnaire included ratings of arousal (feelings of activation/deactivation) and valence (feelings of pleasantness/unpleasantness) on 5-point scales ranging from 1 “very deactivated” to 5 “very activated” and 1 “very unpleasant” to 5 “very pleasant,” respectively. In addition, participants were provided with an open-ended text box and asked to describe how they were feeling in the moment. Anticipatory Period. Participants were then asked to turn their chair 90 degrees while keeping their left palm face-up on the desk as the box was placed to their right at shoulder level (see Figure 1). Before placing their hands in the box, participants were asked to sit quietly and look straight ahead for 30 seconds to get a measure of their cardiac, respiratory, and electrodermal activity in anticipation of placing their hand in the box. Figure 1 . Experimental setup for the experiential uncertainty task. Guessing Period. At the end of the anticipatory period, the experimenter alerted the participant that they could now put their hand into the box and verbalize their guesses as to what the object was. Once a correct guess was given, the experimenter would inform the participant that they were correct and may remove their hand from the box. In our sample, all participants guessed correctly within an allotted timeframe of 2 minutes. Response time ranged from 3.04 to 72.69 seconds ( M = 13.17 sec, Median = 10.22 sec, SD = 9.68 sec). At the end of the guessing period, the physiological recording equipment was removed from the participant. Individual Difference Measures Intolerance of Uncertainty. Participants’ tendency to feel negatively toward uncertainty was assessed using the short version of the Intolerance of Uncertainty Scale (IUS-12) (Carleton et al., 2007). This scale asks participants to rate how characteristic 12 statements are of them on 5-point scales ranging from 1 “not at all characteristic of me” to 5 “entirely characteristic of me”. The scale assesses distress related to uncertainty about future events, using statements like “Unforeseen events upset me greatly,” as well as behavioral reactions to uncertainty using statements like “When I am uncertain I can’t function well.” Scores were calculated by summing responses to all items with higher scores indicating greater IU ( M = 34.38, SD = 8.48, Cronbach’s α = .87, N = 149). Supplemental Individual Difference Measures . To explore how intolerance of uncertainty may relate to other individual differences, participants also completed several additional measures related to risk-taking and awareness of bodily activity, including the Need for Arousal Scale (Figner et al., 2009), the stimulating risk-taking subscale of the Stimulating – Instrumental Risk Taking Inventory (Zaleskiewiez, 2001), the Body Awareness Questionnaire (Shields et al., 1989), and the Heartbeat Tracking Task to measure interoceptive accuracy (Schandry, 1981). Results exploring associations between these variables and intolerance of uncertainty are reported in the supplemental materials (see Tables S1 and S2). Results Individuals’ affective reactions to experiential uncertainty were assessed with five measures: self-reported valence before the task, self-reported arousal before the task, and reactivity scores for IBI, RSA, and SCL. For the physiological measures, we derived reactivity scores (i.e., change scores) by calculating the difference between each measure during the 30-second anticipatory period directly preceding placing their hand in the box and the 5-minute baseline period. Consistent with best practices, all analyses involving RSA controlled for concurrent respiratory reactivity (Quigley et al., 2024). Descriptive information for these reactivity variables is provided in Table 1. To assess associations between IU and affective reactions to experiential uncertainty, we conducted a series of separate linear regressions predicting each affective response variable from IU (see Table 1). Consistent with predictions, results revealed that IU was associated with significantly reduced parasympathetic reactivity in anticipation of completing the experiential uncertainty task, as evidenced by larger decreases in RSA reactivity ( b = -.02, SE b = .01, β = -.20, t (141)= -2.66, p = .009). Contrary to predictions, however, IU was not significantly associated with self-reported valence or arousal in anticipation of completing the task, though associations were in the predicted direction (more negative valence and higher arousal). IU was also not associated with significant changes in sympathetic activation (SCL reactivity) nor end-organ function (IBI reactivity) in anticipation of completing the task. See Table 1. Table 1. Associations between IU and Affective Reactions to Experiential Uncertainty IBI Reactivity 32.67 (59.87) -.17 .59 -.02 -.289 .773 .001 RSA Reactivity + .66 (1.01) -.02 .01 -.20 -2.66 .009* .039 SCL Reactivity 1.10 (3.09) -.014 .03 -.04 -.47 .642 .002 Valence 3.64 (.84) -.01 .01 -.05 -.56 .578 .002 Arousal 3.37 (.94) .02 .01 .13 1.60 .111 .017 Note: * p <.05; + all models predicting RSA reactivity included respiratory reactivity as a control variable. sr 2 is the squared semi-partial correlation, an effect size estimate indicating the proportion of variance in each outcome measure uniquely explained by IU. Despite affect ratings being relatively neutral (Arousal: M = 3.37, SD = .94; Valence: M = 3.64, SD = .84), participants frequently used emotionally relevant words in their open-ended descriptions. Across all descriptions, emotionally relevant words were used 277 times (per-person M = 1.88, SD = .87), 79 of which were unique instances. The most frequently used emotion words were nervous (used by 32/149 participants, 21.48%) and excited (used by 20/149 participants, 13.42%). This suggests that, despite fairly neutral affect ratings, people’s subjective experiences of uncertainty involve a variety of discrete emotional experiences. Discussion Results from Study 1 revealed that IU was significantly associated with less parasympathetic reactivity in anticipation of completing the experiential uncertainty task, suggesting that individuals higher in IU have unique physiological responses to experiential uncertainty—specifically physiological responses associated with increased arousal. It should be noted, however, that we did not observe a relationship between IU and IBI or SCL reactivity, suggesting that this pattern of responding is specific to parasympathetic reactivity in this task. Interestingly, IU was not related to self-reported valence or arousal in anticipation of completing the task, despite significant differences in physiological arousal. This suggests those high in IU are producing bodily responses indicative of greater arousal in response to uncertainty, but this isn’t necessarily being experienced subjectively as an affective experience of higher arousal or more negative valence. This lack of relationship between IU and self-reported affect was unexpected. Although ratings of valence and arousal in anticipation of completing the task were fairly neutral on average, participants frequently used emotion words in their open-ended descriptions. Thus, one possibility is that participants are conceptualizing their experiences as instances of specific emotions that may be difficult to describe in terms of more basic underlying affective dimensions (e.g., despite experiencing some nervousness, one might report feeling fairly good overall) or missed due to reducing affective valence to a single bi-polar dimension (e.g., one might feel both worrisome and excited). Overall, findings from Study 1 suggest that self-reported subjective experience and objective physiological activity cannot be used interchangeably when assessing how affective reactions to experiential uncertainty relate to individual differences in IU. Higher IU was associated with reduced parasympathetic reactivity in the absence of self-reported differences in subjective arousal. Thus, results suggest that activity in the ANS in response to experiences of uncertainty may be particularly important for understanding individual differences in IU. Study 2 Findings from Study 1 suggest that responses to experiential uncertainty associated with individual differences in IU may manifest predominantly at the physiological level as decreased parasympathetic withdrawal. In Study 2, we sought to replicate and build upon these findings. We again examined how IU relates to both subjective and physiological responding to experiential uncertainty, but we also attempted to manipulate information about ongoing physiological activity using an adapted version of a biofeedback paradigm from a recent study (Hill et al., 2024). Grounded in a similar theoretical perspective on the role of the body in affective and emotional experience, this recent study demonstrated how manipulating biofeedback concerning ongoing physiological activity can influence subjective experience. In the study, participants completed a postural threat task during which they were asked to balance either on the ground or on a platform while a heartbeat tone was played. Participants were told this was their own heartbeats when, in reality, it was a recording that was either faster or slower than the average heartrate. Researchers found that participants who were played heartbeats that were faster than average found the task more challenging and reported feeling more fearful and unstable when balancing on the platform, despite no observed differences in actual heartrate across feedback conditions. This suggests one’s emotional experience can be directly manipulated by providing false interoceptive information, even in the absence of objective physiological changes. In Study 2, we used a modified version of this biofeedback paradigm to attempt to manipulate individuals’ affective responses to experiential uncertainty. Specifically, participants were randomly assigned to receive no biofeedback, accurate biofeedback (i.e., participants were played tones aligned with their ongoing heartbeats), or false biofeedback (i.e., participants were tones at a rate faster than their current heartbeats). This design allows us to assess the effect of providing false feedback indicative of heightened arousal while ruling out possible alternative explanations for any observed differences between the no feedback and false feedback conditions. That is, the inclusion of the accurate feedback control enabled us to examine whether any observed differences between the no feedback and false feedback conditions could be due to exposure to auditory stimuli or to directing attention to the body. Additionally, we sought to better understand the relationship between IU and subjective emotional experience in Study 2. Although we did not find any significant associations between IU and self-reported affective arousal and valence in Study 1, we saw frequent use of emotion terms in participants’ open-ended responses concerning how they were feeling. Constructionist theories of emotion hold that identical physiological responses can be conceptualized in vastly different ways across contexts and individuals (Barrett, 2017), suggesting that exploring individuals’ subjective experiences of more context-specific emotions may be fruitful. To explore this possibility, in Study 2 we replaced subjective valence and arousal ratings with rating scales of six discrete emotion terms frequently reported in participants’ open-ended descriptions from Study 1. Sample Size Given that a past study using similar false biofeedback manipulations found large effect sizes for comparisons of self-reported emotion across conditions (Cohen’s d = 0.79 – 1.78; Hill et al., 2024), we conservatively planned power for detecting medium effect sizes when comparing across conditions. An a priori power analysis conducted in G*Power (v3.1.9.7) suggested we would be appropriately powered (80%, two-tailed α=.05) to detect a medium effect size ( Cohen’s f = 0.25) in one-way ANOVAs comparing outcomes across our three study conditions with a sample of N =160. With a sample of 160 participants, we should also be appropriately powered to detect small-to-medium effect size interactions between condition and IU in multiple regression analyses (Cohen’s f 2 = 0.06). We recruited slightly more participants than this to account for potential missing data. Participants Participants consisted of 173 undergraduate students, recruited from the University of New Hampshire Psychology Department’s undergraduate student participant pool. Our sample was predominantly young adults (96.53% between the ages of 18-21), self-identifying as white (81.44%) and female (68.21%). Participants received course credit in exchange for their participation. Participants were excluded from participating if they were below the age of 18, did not speak English fluently, had a history of skin allergies or sensitivity to adhesives, had a history of cardiovascular disease, and/or had a history of fainting/faintness in medical settings. Procedure The procedure is nearly identical to that of Study 1 with a few modifications. As in Study 1, following informed consent, participants were connected to physiological recording equipment and completed a 5-minute resting baseline, followed by the uncertainty task. Half of the participants completed the questionnaires after the uncertainty task and half completed them immediately after informed consent. At the end of the session, all participants completed a short survey with items asking about their perception of the uncertainty task, were given a second consent form related to public use of audio and video recordings from the session, and were debriefed by the experimenter. Modifications to tasks and measures relative to Study 1 are outlined below. Tasks and Measures Resting Baseline To mitigate potential movement artifacts during baseline, participants did not complete the demographics questionnaire during the baseline period as they did in Study 1. Instead, the demographic questions were combined with the other self-report surveys. Uncertainty Task As in Study 1, participants were asked to complete a task in which they put their hand in an opaque box, felt an unknown object, and provided guesses as to what the object was. The procedure for this task is identical to that of Study 1, except for the modifications noted below. Biofeedback Conditions. Throughout the task, some participants heard biofeedback (i.e., beeps they were told were in sync with their heartbeats) played aloud. Participants were randomly assigned to receive accurate feedback (in-sync with their heartbeats), false feedback indicating a fast heart rate (125 bpm), or no feedback. The false feedback speed was selected to be faster than three standard deviations above the mean heart rate in Study 1 and was pilot tested amongst members of the lab for believability. For both the accurate and fast feedback conditions, the experimenter informed the participant that we were interested in how access to bodily information influences the experience of uncertainty so beeps that were in-sync with their heartbeats would be played aloud for the duration of the task. Self-Reported Emotion. After the basic task was described and biofeedback (i.e., beeps) began playing for the relevant conditions, participants were again asked to complete a short paper questionnaire about how they were feeling in anticipation of completing the task. In Study 1, participants rated their subjective affect in terms of valence and arousal. In Study 2, these questions were replaced with ratings of specific emotions. The questionnaire included ratings of six different emotions (i.e., worry, nervousness, excitement, curiosity, stress, and neutrality) on 5-point scales ranging from 1 “not at all” to 5 “very much.” In addition, participants were again provided with an open-ended text box and asked to describe how they were feeling in the moment. Certainty Trial. As a sensitivity check, to attempt to isolate the effect of uncertainty itself, participants were asked to complete a second trial of the task immediately following completion of the first. This trial was identical to the first trial with the exception that participants knew what the object in the box was. For Study 2, we changed the object to a ceramic mug for all participants due to concerns that some participants may find the objects from Study 1 (plastic snakes and bugs) to be aversive in and of themselves. This trial included the same self-reported emotion questionnaire, anticipatory period, and guessing period as the uncertainty trial. For the certainty trial, participants were instructed to put their hand into the box and feel around for a few seconds, but they were told they do not need to verbalize guesses as they already know what the object is. Sensitivity analyses related to the certainty trial are reported in the supplemental materials but generally demonstrated that the uncertainty trial was more evocative than the certainty trial (Table S3) and that observed associations between IU and subjective and physiological activity were specific to only the uncertainty trial (Tables S4 – S6). Self-Report Questionnaires Participants also completed several self-report questionnaires via Qualtrics on the computer to assess relevant individual differences and collect demographic information. Participants completed the same demographic questions as in Study 1 as well as the same measure of IU. Task-Related Questions. Participants were also asked to use 5-point scales to retrospectively rate how enjoyable they found the uncertainty task on a scale from 1 “not at all” to 5 “very much,” and how certain they were that the biofeedback they heard was actually their heartbeat on a scale from 1 “do not believe” to 5 “completely believe,” or “I did not hear any audio.” Supplemental Individual Difference Measures In addition, to explore how IU may relate to other individual differences, participants also completed several additional measures related to mental health and awareness of bodily activity, including the Multidimensional Assessment of Interoceptive Awareness, version 2 (MAIA-2; Mehling et al., 2018), the Heartrate Tracking Task (HRT; Schandry, 1981), the Penn State Worry Questionnaire (PSWQ; Meyer et al., 1990), a depression symptom severity scale (PHQ-8; Kroenke et al., 2009), a somatic symptom severity scale (PHQ-15; Kroenke et al., 2002), and an anxiety symptom severity scale (GAD-7; Spitzer et al., 2006). Results exploring associations between these variables and intolerance of uncertainty are reported in the supplemental materials (see Tables S7 – S8). Results Outcome Variables As in Study 1, individuals’ affective reactions to experiential uncertainty were assessed with several measures, including self-report ratings of emotions before the task and reactivity scores for IBI, RSA, and SCL during the anticipatory period. Emotion ratings were combined into two index variables for analyses: positive emotions (curiosity and excitement; α = .81) and negative emotions (nervousness, stress, worry; α = .83). An exploratory factor analysis using principal axis factoring as the extraction method confirmed that emotion ratings formed these two factors with neutral ratings loading on its own factor. As we did not have any specific predictions regarding ratings of neutrality, this item was not included in analyses. As in Study 1, we derived reactivity scores for physiological measures by calculating the difference between each measure during the 30-second anticipatory period directly preceding the box task and the 5-minute baseline period. Physiological data were visually inspected by a trained scorer to identify excessive noise, artifact, or arrhythmia. We were not able to process physiological data from five participants due to technical issues during data collection. An additional seven participants had physiological data with excessive noise or artifact and their physiological data was excluded from analyses (two participants were excluded from IBI and RSA analyses, five were excluded from SCL analyses). Additionally, three participants did not complete the IU survey and are excluded from analyses with this variable. Descriptive information for outcome variables is provided in Table 2, both overall and by feedback condition. Differences Across Feedback Conditions A series of one-way analysis of variance tests were conducted to determine if any of the outcome variables differed between feedback conditions. Contrary to predictions, these analyses failed to reveal significant differences in affective reactions to uncertainty in the false feedback condition relative to either of the other two conditions. IBI reactivity differed significantly by condition, F (2, 162) = 3.44, p = .034, but post-hoc paired comparisons using Tukey’s HSD test revealed that participants in the accurate feedback condition ( M = 33.23, SD = 62.71) had higher IBI reactivity compared to those in the no feedback condition ( M = 5.52, SD = 59.18), p = .044, but neither differed from participants in the false feedback condition ( M = 29.40, SD = 59.04), p s > .05. Table 2. Affective reactions to experiential uncertainty overall and by feedback condition Positive Emotions 3.23 (.84) 3.19 (.88) 3.30 (.82) 3.20 (.81) .296 .744 Negative Emotions 1.81 (.74) 1.71 (.73) 1.91 (.77) 1.81 (.72) 1.11 .332 RSA Reactivity .58 (.97) .44 (.90) .53 (.98) .78 (1.0) 1.91 .151 IBI Reactivity 22.57 (61.22) 5.52 (59.18) 29.40 (59.04) 33.23 (62.71) 3.44 .034* SCL Reactivity 3.98 (2.39) 3.91 (2.40) 3.84 (1.90) 4.18 (2.79) .298 .743 Note: Cells report means with standard deviations in parentheses * p < .05 Relationships Between Physiological Measures and IU We next assessed the relationship between each of the outcome measures and IU. To do this, we conducted a series of hierarchical linear regressions where we entered IU and condition as simultaneous predictors in the first step to examine the main effect of IU controlling for condition and then added an interaction between IU and condition in the second step to examine whether the relationship between IU and each outcome variable differed as a function of condition. Full model details for all analyses can be found in the supplemental materials (Table S9). RSA. First, we predicted RSA reactivity from IU, controlling for change in respiratory rate and feedback condition. The overall model at step one was significant, accounting for approximately 15.6% of the variance in RSA reactivity, F (4, 157) = 7.27, p < .001, R 2 = .156. IU did not significantly predict RSA reactivity when controlling for condition, b = -.02, SE b = .01, β = -.14, t (157) = -1.91, p = .058, sr 2 = .020. However, this null main effect was qualified by a significant interaction, F inc (2, 155) = 3.10, p = .048, R 2 inc = .033, such that the inclusion of interaction terms in the second step of the hierarchical regression analyses significantly improved model fit, explaining an additional 3.3% of variance in RSA reactivity (see Figure 2). To explore the nature of this significant interaction, we conducted separate regression analyses predicting RSA reactivity from IU (controlling for respiratory rate reactivity) within each biofeedback condition separately. Consistent with Study 1, IU was a significant negative predictor of RSA reactivity in the no feedback condition, b = -.04, SE b = .01, β = -.36, t (52) = -2.83, p = .007, sr 2 = .124. This relationship was not significant in either the false feedback condition, b = .01, SE b = .02, β = .11, t (50) = .81, p = .422, sr 2 = .012, or the accurate feedback condition, b = -.02, SE b = .01, β = -.18, t (51) = -1.47, p = .148, sr 2 = .034. Figure 2. IU predicting RSA change by feedback condition IBI. A multiple regression predicting IBI reactivity from IU and feedback condition was not significant, F (3, 158) = 2.20, p = .090, R 2 = .040. IU was not a significant predictor of IBI reactivity when controlling for biofeedback condition, b = -.31, SE b = .55, β = -.04, t (158) = -.56, p = .579, sr 2 = .002. The interaction between IU and biofeedback condition was also not significant, F inc (2, 156) = .87, p = .421, R 2 inc = .011. SCL. A multiple regression predicting SCL reactivity from IU and feedback condition was not significant, F (3, 154) = .32, p = .807, R 2 = .006. IU was not a significant predictor of SCL reactivity when controlling for biofeedback condition, b = .02, SE b = .02, β = .06, t (154) = .75, p = .454, sr 2 = .004. The interaction between IU and biofeedback condition was also not significant, F inc (2, 152) = .05, p = .949, R 2 inc = .001. Negative Emotions. A multiple regression predicting negative emotions during experiential uncertainty from IU and feedback condition was significant, F (3, 166) = 9.28, p < .001, R 2 = .144, accounting for 14.4% of the variance in self-reported negative emotions. IU was a significant positive predictor of negative emotions when controlling for feedback condition, b = .03, SE b = .01, β = .36, t (166) = 5.01, p < .001, sr 2 = .130. The interaction between IU and feedback condition was not significant, F inc (2, 164) = 1.73, p = .181, R 2 inc = .017, suggesting this association did not differ by condition. Positive Emotions. A multiple regression predicting positive emotions during experiential uncertainty from IU and biofeedback condition was not significant, F (3, 166) = .21, p = .891, R 2 = .004. IU was not a significant predictor of self-reported positive emotions when controlling for condition, b = -.01, SE b = .01, β = -.01, t (166) = -.14, p = .890, sr 2 = .001. The interaction between IU and biofeedback condition was also not significant, F inc (2, 164) = 2.04, p = .134, R 2 inc = .024. Open-Ended Emotion Responses. As in Study 1, participants used a variety of emotion words to describe their anticipatory emotions in their open-ended emotion responses. In Study 2, emotionally relevant words were used 275 times (per-person M = 1.71, SD = .81) 71 of which were unique instances. The most frequently used emotion words were curious (used by 52/173 participants, 30.06%) and excited (used by 39/173 participants, 22.54%). Believability. Additionally, we examined participants’ retrospective belief ratings of the biofeedback stimuli. Overall, belief that biofeedback genuinely reflected one’s heartbeat was moderate, with a mean rating of 3.44 ( SD = 1.32) on a 5-point scale. As expected, belief ratings were higher for those in the accurate feedback condition ( M = 4.00, SD = 1.07) compared to those in the false feedback condition ( M = 2.85, SD = 1.32) (See Figure S1 in the supplemental materials). However, belief ratings did not appear to influence the relationships observed in the primary analyses, as separate sensitivity analyses excluding those with belief ratings less than two and less than three revealed the same pattern of results and significance as reported above (see Table S10 in the supplemental materials). Discussion In Study 2, we found that IU predicted decreases in RSA reactivity for individuals in the no feedback condition, replicating our findings from Study 1. This provides further evidence that, compared to those lower in IU, individuals higher in IU may experience physiological responses associated with heightened arousal, specifically increased parasympathetic withdrawal, during experiential uncertainty. Interestingly, this was not true for individuals in the false and accurate feedback conditions, suggesting that providing heartrate feedback disrupts the observed relationship between IU and RSA reactivity, regardless of the accuracy of that feedback. This said, in post-hoc paired comparisons, slopes from the accurate feedback and no feedback conditions did not differ significantly from one another, suggesting that providing false feedback is particularly disruptive. One potential explanation for this may be that auditory feedback introduces distraction, diverting participants’ attention from the anticipated task and attenuating feelings of uncertainty. Previous research has found that people high in IU tend to engage in more rumination, which can be defined as a maladaptive coping strategy involving repetitive thinking about the sources and consequences of one’s distress (Huang et al., 2019). Speculatively, providing auditory feedback may disrupt ruminative thinking, consequently attenuating negative responding to the task for those high in IU. In Study 2, those higher in IU also reported significantly higher ratings of negative emotions in anticipation of completing the task, regardless of condition. This suggests that individuals higher in IU have stronger negative subjective responses to experiential uncertainty, in addition to heightened physiological responding. This differs from our Study 1 finding that IU was unrelated to self-reported affect ratings. One possible explanation for this difference may be that the task is not evocative enough to induce changes in more general affect, though is sufficiently evocative to induce changes in more granular emotional experiences. Consistent with this speculation, participants generally reported more positive than negative emotions in anticipation of the task, and post-task enjoyment ratings were also fairly high overall (M = 3.55, SD = 1.00). If only self-reporting affective valence, participants may have rated their experience as mostly pleasant overall, and we would have missed these more minimal elevations in the subjective experience of negative emotions. Moreover, we did not find any significant associations between IU and self-reported positive emotions in anticipation of the task, suggesting these more subtle negative emotions may be particularly critical to capture for understanding affective experiences during uncertainty among those higher in IU. Overall, Study 2 provides evidence that high IU individuals’ subjective experience of uncertainty is more negative than those lower in IU, though differences may be limited to experiences of specific negative emotions (e.g., worry, stress, nervousness). Although the introduction of biofeedback had a significant impact on the relationship between IU and RSA, providing biofeedback appeared to have minimal effect on participants’ affective experiences of uncertainty more generally. Participants in the accurate feedback condition had significantly larger increases in IBI in anticipation of completing the task compared to those in the no feedback condition, suggesting that providing accurate interoceptive feedback may have a regulatory effect, reducing physiological arousal during experiential uncertainty. However, this interpretation should be made with caution. Speculatively, this difference may also be attributable to differences across conditions in terms of movement and posture (Berntson et al., 1993). For instance, the accurate feedback is sensitive to movement and disturbance of the ECG leads can disrupt the feedback momentarily; thus, participants in this condition may have been more still during the anticipatory period than those in other conditions. Contrary to previous work that found providing false feedback indicative of a faster heart rate increased perceived task difficulty and decreased self-reported task enjoyment (Hill et al., 2024), providing false feedback indicative of a faster heartrate did not appear to influence participants’ subjective experiences of uncertainty in our study, as no differences in self-reported emotion ratings across feedback conditions were observed. One reason for this may be due to differences in task demands. Hill et al. (2024) used a balancing task to induce uncertainty, requiring participants to maintain physical stability. It may be that false heartbeat feedback is effective at altering one’s perception of task difficulty and enjoyability only when the task is physically demanding. Overall, findings from Study 2 provide additional evidence that individuals high in IU have stronger physiological arousal responses during experiential uncertainty and that providing auditory biofeedback during uncertainty may disrupt this relationship. Additionally, results indicate that high IU individuals’ subjective experience of uncertainty is more negative than those lower in IU, such that they endorse greater feelings of nervousness, stress, and worry during experiential uncertainty. Lastly, findings from Study 2 suggest that, although providing false interoceptive feedback may not effectively manipulate affective experiences of uncertainty during seated tasks, providing accurate feedback may have a slight regulatory effect on IBI during uncertainty, though further investigation is required. General Discussion The present set of studies applies constructionist theories of emotion (e.g., the TCE; Barrett, 2017) to examine experiential uncertainty as an affective experience, including both subjective affective responses and the bodily activity from which they are thought to arise, in pursuit of a better understanding of individual differences in IU. Across studies, results show that individuals high in IU have physiological responses indicative of greater arousal under conditions of uncertainty, namely decreased parasympathetic reactivity. Additionally, findings from Study 2 provide evidence that high IU individuals’ subjective experiences of uncertainty are more negative compared to those lower in IU, such that they report experiencing greater nervousness, worry, and stress during experiential uncertainty. Taken together, these findings suggest that individuals high in IU not only maintain unfavorable attitudes toward uncertainty in broad retrospective self-report measures, they also experience instances of uncertainty differently than individuals lower in IU in the moment, both subjectively and physiologically. Theoretical frameworks across disciplines have emphasized the critical role that affect and emotion play in experiences of and responses to uncertainty (Kahneman & Tversky, 1979; Loewenstein et al., 2001; Slovic et al., 2005). Our findings are consistent with this. For instance, Lowenstein and colleagues (2001) emphasize the role of anticipatory feelings in decision-making under uncertain conditions, positing that they serve as cursory impressions that guide decision-making, especially when time is limited. In our studies, these anticipatory affective inputs were found to be more negative and indicative of heightened physiological arousal in individuals high in IU when faced with uncertainty. Considering IU is consistently associated with dysfunctional decision-making behavior (Appel et al., 2024; Luhmann et al., 2011; Morriss et al., 2021; Wake et al., 2022) and anticipatory affect is thought to guide such behavior, our findings suggest one way in which affective reactions to uncertainty may contribute to the development and maintenance of heightened IU. For instance, according to affect-as-information perspectives, emotional impressions are thought to serve as informative summaries, enabling faster and more efficient action (Loewenstein et al., 2001; Slovic et al., 2005), and our findings suggest that these summaries may be distorted for individuals high in IU. If so, it is possible that, over time, as high IU individuals repeatedly experience affective responses that are disproportionate for a given context, they may begin to regard affective information as unreliable, instead relying on deliberative strategies like increased information sampling (Wake et al., 2022) and engagement in decisional safety behaviors (Appel et al., 2024), both of which have been found to occur more frequently among high IU individuals. Similarly, they may attempt to avoid experiences of uncertainty for which deliberative reasoning is frequently ineffective (e.g., when probabilistic information about outcomes is unknown or ambiguous; Volz & Gigerenzer, 2012)—another behavior common among high IU individuals (Luhmann et al., 2011). Though results provide compelling evidence that subjective and physiological components of affective experience during experiential uncertainty are associated with individual differences in IU, results should be regarded in light of several notable limitations. First, though the experimental task appears to have successfully evoked uncertainty, it did so in a highly controlled environment. For instance, participants were assured that the object in the box could not hurt them in any way and that they could end their participation at any point. These procedures, though important for minimizing participant distress, may provide a degree of certainty and control that may not reflect everyday experiences of uncertainty. Additionally, participants are assured that their performance on the task is inconsequential. While this is intended to reduce the chances of performance anxiety being a confounding variable, it may not be representative of real-life instances of uncertainty, which often involve impactful, personally relevant consequences (e.g., deciding to change career paths, navigating social relationships). More research is needed to confirm whether our findings persist for instances of uncertainty in everyday life where contextual factors are highly variable (e.g., there may be far more possible outcomes and their probabilities may be more or less ambiguous, stakes may be higher and/or more personally relevant). Additionally, the nature of our experimental design prevents us from making directional claims about the role of affective experience in the development and maintenance of IU. While it is certainly possible that individuals’ affective responses contribute to the development of (in)tolerant dispositions, it is also possible that dispositional differences in (in)tolerance lead to differences in affective responding. This relationship may also be reciprocal, such that unpleasant affective responding and unfavorable beliefs about uncertainty reinforce one another. Here, the claim that affective experience may play a developmental role in individual differences in IU is merely speculative. Lastly, both of our samples were relatively homogenous, consisting largely of white, female, young adults. More research with demographically representative samples is needed to assess the generalizability of our findings to the broader population. References Appel, H., Krasko, J., Luhmann, M., & Gerlach, A. L. (2024). Intolerance of uncertainty predicts indecisiveness and safety behavior in real-life decision making: Results from an experience sampling study. Journal of Research in Personality , 110 , 104490. https://doi.org/10.1016/j.jrp.2024.104490 Aylward, J., Valton, V., Ahn, W.-Y., Bond, R. L., Dayan, P., Roiser, J. P., & Robinson, O. J. (2019). Altered learning under uncertainty in unmedicated mood and anxiety disorders. Nature Human Behaviour , 3 (10), 1116–1123. https://doi.org/10.1038/s41562-019-0628-0 Barrett, L. F. (2017). The theory of constructed emotion: an active inference account of interoception and categorization. Social Cognitive and Affective Neuroscience , 12 (1), 1–23. https://doi.org/10.1093/scan/nsw154 Barrett, L. F., & Simmons, W. K. (2015). Interoceptive predictions in the brain. Nature Reviews Neuroscience , 16 (7), 419–429. https://doi.org/10.1038/nrn3950 Beauchamp, J., Cesarini, D., & Johannesson, M. (2015). The psychometric and empirical properties of measures of risk preferences. Journal of Risk and Uncertainty, 54 (3), 203–237. https://doi.org/10.1007/s11166-017-9261-3 Berntson, G. G., Cacioppo, J. T., & Quigley, K. S. (1991). Autonomic determinism: The modes of autonomic control, the doctrine of autonomic space, and the laws of autonomic constraint. Psychological Review , 98 (4), 459–487. https://doi.org/10.1037/0033-295X.98.4.459 Berntson, G. G., Cacioppo, J. T., & Quigley, K. S. (1993). Respiratory sinus arrhythmia: Autonomic origins, physiological mechanisms, and psychophysiological implications. Psychophysiology , 30 (2), 183–196. https://doi.org/10.1111/j.1469-8986.1993.tb01731.x Berntson, G. G., Quigley, K. S., & Lozano, D. (2007). Cardiovascular psychophysiology. In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson (Eds.), Handbook of psychophysiology (3rd ed., pp. 182–210). Cambridge University Press. https://doi.org/10.1017/CBO9780511546396.008 Bomyea, J., Ramsawh, H., Ball, T. M., Taylor, C. T., Paulus, M. P., Lang, A. J., & Stein, M. B. (2015). Intolerance of uncertainty as a mediator of reductions in worry in a cognitive behavioral treatment program for generalized anxiety disorder. Journal of Anxiety Disorders , 33 , 90–94. https://doi.org/10.1016/j.janxdis.2015.05.004 Bosch-Domènech, A., & Silvestre, J. (2010). Averting risk in the face of large losses: Bernoulli vs. Tversky and Kahneman. Economics Letters , 107 (2), 180–182. https://doi.org/10.1016/j.econlet.2010.01.018 Boswell, J. F., Thompson-Hollands, J., Farchione, T. J., & Barlow, D. H. (2013). Intolerance of uncertainty: a common factor in the treatment of emotional disorders. Journal of Clinical Psychology, 69 (6), 630–645. https://doi.org/10.1002/jclp.21965 Bromiley, P. (2010). Looking at prospect theory. Strategic Management Journal , 31 (12), 1357–1370. https://doi.org/10.1002/smj.885 Carleton, R. N. (2012). The intolerance of uncertainty construct in the context of anxiety disorders: Theoretical and practical perspectives. Expert Review of Neurotherapeutics , 12 (8), 937–947. https://doi.org/10.1586/ern.12.82 Carleton, R. N., Duranceau, S., Shulman, E. P., Zerff, M., Gonzales, J., & Mishra, S. (2016). Self-reported intolerance of uncertainty and behavioural decisions. Journal of Behavior Therapy and Experimental Psychiatry , 51 , 58–65. https://doi.org/10.1016/j.jbtep.2015.12.004 Carleton, R. N., Norton, M. A. P. J., & Asmundson, G. J. G. (2007). Fearing the unknown: A short version of the Intolerance of Uncertainty Scale. Journal of Anxiety Disorders , 21 (1), 105–117. https://doi.org/10.1016/j.janxdis.2006.03.014 Craig, A. D. (2002). How do you feel? Interoception: the sense of the physiological condition of the body. Nature Reviews Neuroscience , 3 (8), 655–666. https://doi.org/10.1038/nrn894 Critchley, H. D. (2002). Electrodermal Responses: What Happens in the Brain. The Neuroscientist , 8 (2), 132–142. https://doi.org/10.1177/107385840200800209 Dekkers, L. M. S., Jansen, B. R. J., Salemink, E., & Huizenga, H. M. (2017). Intolerance of Uncertainty Scale: Measurement invariance among adolescent boys and girls and relationships with anxiety and risk taking. Journal of Behavior Therapy and Experimental Psychiatry , 55 , 57–65. https://doi.org/10.1016/j.jbtep.2016.11.009 Dugas, M. J., Schwartz, A., & Francis, K. (2004). Intolerance of uncertainty, worry, and depression. Cognitive Therapy and Research , 28 (6), 835–842. https://doi.org/10.1007/s10608-004-0669-0 Dunn, B. D., Galton, H. C., Morgan, R., Evans, D., Oliver, C., Meyer, M., Cusack, R., Lawrence, A. D., & Dalgleish, T. (2010). Listening to your heart: How interoception shapes emotion experience and intuitive decision making. Psychological Science , 21 (12), 1835–1844. https://doi.org/10.1177/0956797610389191 Feldman, M. J., Siegel, E., Barrett, L. F., Quigley, K. S., & Wormwood, J. B. (2022). Affect and social judgment: The roles of physiological reactivity and interoceptive sensitivity. Affective Science, 3 (2), 464–479. https://doi.org/10.1007/s42761-022-00114-9 Figner, B., Mackinlay, R. J., Wilkening, F., & Weber, E. U. (2009). Affective and deliberative processes in risky choice: Age differences in risk taking in the Columbia Card Task. Journal of Experimental Psychology: Learning, Memory, and Cognition , 35 (3), 709–730. https://doi.org/10.1037/a0014983 Frazier, T. W., Strauss, M. E., & Steinhauer, S. R. (2004). Respiratory sinus arrhythmia as an index of emotional response in young adults. Psychophysiology , 41 (1), 75–83. https://doi.org/10.1046/j.1469-8986.2003.00131.x Gendron, M., & Barrett, L. F. (2018). Emotion perception as conceptual synchrony. Emotion Review , 10 (2), 101–110. https://doi.org/10.1177/1754073917705717 Glasgow, S., Imbriano, G., Jin, J., Zhang, X., & Mohanty, A. (2022). Threat and uncertainty in the face of perceptual decision-making in anxiety. Journal of Psychopathology and Clinical Science , 131 (3), 265–277. https://doi.org/10.1037/abn0000729 Grassi, G., Turri, C., Vailati, S., Dell’Oro, R., & Mancia, G. (1999). Muscle and skin sympathetic nerve traffic during the “white-coat” effect. Circulation , 100 (3), 222–225. https://doi.org/10.1161/01.CIR.100.3.222 Harless, D. W., & Camerer, C. F. (1994). The predictive utility of generalized expected utility theories. Econometrica , 62 (6), 1251. https://doi.org/10.2307/2951749 Hill, M. W., Johnson, E., & Ellmers, T. J. (2024). The influence of false interoceptive feedback on emotional state and balance responses to height-induced postural threat. Biological Psychology , 189 , 108803. https://doi.org/10.1016/j.biopsycho.2024.108803 Huang, V., Yu, M., Carleton, R. N., & Beshai, S. (2019). Intolerance of uncertainty fuels depressive symptoms through rumination: Cross-sectional and longitudinal studies. PLOS ONE , 14 (11), e0224865. https://doi.org/10.1371/journal.pone.0224865 James, W. (1884). What is an emotion? Mind , os-IX (34), 188–205. https://doi.org/10.1093/mind/os-IX.34.188 Jensen, D., Cohen, J. N., Mennin, D. S., Fresco, D. M., & Heimberg, R. G. (2016). Clarifying the unique associations among intolerance of uncertainty, anxiety, and depression. Cognitive Behaviour Therapy , 45 (6), 431–444. https://doi.org/10.1080/16506073.2016.1197308 Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica , 47 (2). Knowles, K. A., & Olatunji, B. O. (2023). Intolerance of uncertainty as a cognitive vulnerability for obsessive-compulsive disorder: A qualitative review. Clinical Psychology: Science and Practice , 30 (3), 317–330. https://doi.org/10.1037/cps0000150 Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2002). The PHQ-15: Validity of a new measure for evaluating the severity of somatic symptoms. Psychosomatic Medicine , 64 (2), 258–266. https://doi.org/10.1097/00006842-200203000-00008 Kroenke, K., Strine, T. W., Spitzer, R. L., Williams, J. B. W., Berry, J. T., & Mokdad, A. H. (2009). The PHQ-8 as a measure of current depression in the general population. Journal of Affective Disorders , 114 (1–3), 163–173. https://doi.org/10.1016/j.jad.2008.06.026 Lindquist, K. A. (2013). Emotions emerge from more basic psychological ingredients: A modern psychological constructionist model. Emotion Review , 5 (4), 356–368. https://doi.org/10.1177/1754073913489750 Lindquist, K. A. (2017). The role of language in emotion: Existing evidence and future directions. Current Opinion in Psychology , 17 , 135–139. https://doi.org/10.1016/j.copsyc.2017.07.006 Lindquist, K. A., Gendron, M., Barrett, L. F., & Dickerson, B. C. (2014). Emotion perception, but not affect perception, is impaired with semantic memory loss. Emotion , 14 (2), 375–387. https://doi.org/10.1037/a0035293 Lindquist, K. A., MacCormack, J. K., & Shablack, H. (2015). The role of language in emotion: Predictions from psychological constructionism. Frontiers in Psychology , 6 . https://doi.org/10.3389/fpsyg.2015.00444 Lipshitz, R., & Strauss, O. (1997). Coping with uncertainty: A naturalistic decision-making analysis. Organizational Behavior and Human Decision Processes , 69 (2), 149–163. https://doi.org/10.1006/obhd.1997.2679 Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological Bulletin , 127 (2), 267–286. https://doi.org/10.1037/0033-2909.127.2.267 Luhmann, C. C., Ishida, K., & Hajcak, G. (2011). Intolerance of uncertainty and decisions about delayed, probabilistic rewards. Behavior Therapy , 42 (3), 378–386. https://doi.org/10.1016/j.beth.2010.09.002 Mauss, I. B., & Robinson, M. D. (2009). Measures of emotion: A review. Cognition & Emotion , 23 (2), 209–237. https://doi.org/10.1080/02699930802204677 McEvoy, P. M., & Mahoney, A. E. J. (2012). To be sure, to be sure: Intolerance of uncertainty mediates symptoms of various anxiety disorders and depression. Behavior Therapy , 43 (3), 533–545. https://doi.org/10.1016/j.beth.2011.02.007 Mehling, W. E., Acree, M., Stewart, A., Silas, J., & Jones, A. (2018). The Multidimensional Assessment of Interoceptive Awareness, Version 2 (MAIA-2). PLOS ONE , 13 (12), e0208034. https://doi.org/10.1371/journal.pone.0208034 Meyer, T. J., Miller, M. L., Metzger, R. L., & Borkovec, T. D. (1990). Development and validation of the Penn State Worry Questionnaire. Behaviour Research and Therapy , 28 (6), 487–495. https://doi.org/10.1016/0005-7967(90)90135-6 Morriss, J., Zuj, D. V., & Mertens, G. (2021). The role of intolerance of uncertainty in classical threat conditioning: Recent developments and directions for future research. International Journal of Psychophysiology , 166 , 116–126. https://doi.org/10.1016/j.ijpsycho.2021.05.011 Norr, A. M., Oglesby, M. E., Capron, D. W., Raines, A. M., Korte, K. J., & Schmidt, N. B. (2013). Evaluating the unique contribution of intolerance of uncertainty relative to other cognitive vulnerability factors in anxiety psychopathology. Journal of Affective Disorders , 151 (1), 136–142. https://doi.org/10.1016/j.jad.2013.05.063 Nwogugu, M. (2006). A further critique of cumulative prospect theory and related approaches. Applied Mathematics and Computation , 179 (2), 451–465. https://doi.org/10.1016/j.amc.2005.11.102 Pollatos, O., Kirsch, W., & Schandry, R. (2005). On the relationship between interoceptive awareness, emotional experience, and brain processes. Cognitive Brain Research , 25 (3), 948–962. https://doi.org/10.1016/j.cogbrainres.2005.09.019 Quigley, K. S., Gianaros, P. J., Norman, G. J., Jennings, J. R., Berntson, G. G., & De Geus, E. J. C. (2024). Publication guidelines for human heart rate and heart rate variability studies in psychophysiology—Part 1: Physiological underpinnings and foundations of measurement. Psychophysiology , e14604. https://doi.org/10.1111/psyp.14604 Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology , 39 (6), 1161–1178. https://doi.org/10.1037/h0077714 Sadeh, N., & Bredemeier, K. (2021). Engaging in risky and impulsive behaviors to alleviate distress mediates associations between intolerance of uncertainty and externalizing psychopathology. Journal of Personality Disorders , 35 (3), 393–408. https://doi.org/10.1521/pedi_2019_33_456 Satpute, A. B., Nook, E. C., Narayanan, S., Shu, J., Weber, J., & Ochsner, K. N. (2016). Emotions in “black and white” or shades of gray? How we think about emotion shapes our perception and neural representation of emotion. Psychological Science , 27 (11), 1428–1442. https://doi.org/10.1177/0956797616661555 Schandry, R. (1981). Heart beat perception and emotional experience. Psychophysiology , 18 (4), 483–488. https://doi.org/10.1111/j.1469-8986.1981.tb02486.x Schachter, S., & Singer, J. E. (1962). Cognitive, social, and physiological determinants of emotional state. Psychological Review , 69 (5), 379–399. Shields, S. A., Mallory, M. E., & Simon, A. (1989). The Body Awareness Questionnaire: Reliability and validity. Journal of Personality Assessment , 53 (4), 802–815. https://doi.org/10.1207/s15327752jpa5304_16 Siegel, E. H., Sands, M. K., Van Den Noortgate, W., Condon, P., Chang, Y., Dy, J., Quigley, K. S., & Barrett, L. F. (2018). Emotion fingerprints or emotion populations? A meta-analytic investigation of autonomic features of emotion categories. Psychological Bulletin , 144 (4), 343–393. https://doi.org/10.1037/bul0000128 Slovic, P., Peters, E., Finucane, M. L., & MacGregor, D. G. (2005). Affect, risk, and decision making. Health Psychology , 24 (4, Suppl), S35–S40. https://doi.org/10.1037/0278-6133.24.4.S35 Smith, A. R., Ebert, E. E., & Broman-Fulks, J. J. (2016). The relationship between anxiety and risk taking is moderated by ambiguity. Personality and Individual Differences , 95 , 40–44. https://doi.org/10.1016/j.paid.2016.02.018 Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006). A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine , 166 (10), 1092. https://doi.org/10.1001/archinte.166.10.1092 Stern, R. M., Ray, W. J., & Quigley, K. S. (2000). Psychophysiological recording . Oxford University Press, Incorporated. Van Lieshout, L. L. F., De Lange, F. P., & Cools, R. (2021). Uncertainty increases curiosity, but decreases happiness. Scientific Reports , 11 (1), 14014. https://doi.org/10.1038/s41598-021-93464-6 Volz, K. G., & Gigerenzer, G. (2012). Cognitive processes in decisions under risk are not the same as in decisions under uncertainty. Frontiers in Neuroscience , 6 . https://doi.org/10.3389/fnins.2012.00105 Wake, S., Verde, A. D., Biagi, N., Van Reekum, C. M., & Morriss, J. (2022). Just let me check: The role of individual differences in self-reported anxiety and obsessive-compulsive features on subjective, behavioural, and physiological indices during a checking task. International Journal of Psychophysiology , 179 , 43–55. https://doi.org/10.1016/j.ijpsycho.2022.06.011 Wehrwein, E. A., Orer, H. S., & Barman, S. M. (2016). Overview of the anatomy, physiology, and pharmacology of the autonomic nervous system. In R. Terjung (Ed.), Comprehensive Physiology (pp. 1239–1278). Wiley. https://doi.org/10.1002/cphy.c150037 Wormwood, J. B., Quigley, K. S., & Barrett, L. F. (2022). Emotion and threat detection: The roles of affect and conceptual knowledge. Emotion , 22 (8), 1929–1941. http://dx.doi.org/10.1037/emo0000884 Wundt, W. (1980). Outlines of Psychology: V. The Principles and Laws of Psychical Causality. In R. W. Rieber (Ed.), Wilhelm Wundt and the Making of a Scientific Psychology (pp. 179–195). Springer US. https://doi.org/10.1007/978-1-4684-8340-6_7 Yook, K., Kim, K.-H., Suh, S. Y., & Lee, K. S. (2010). Intolerance of uncertainty, worry, and rumination in major depressive disorder and generalized anxiety disorder. Journal of Anxiety Disorders , 24 (6), 623–628. https://doi.org/10.1016/j.janxdis.2010.04.003 Zaleskiewicz, T. (2001). Beyond risk seeking and risk aversion: Personality and the dual nature of economic risk taking. European Journal of Personality , 15 (1), S105–S122. https://doi.org/10.1002/per.426 Zemack-Rugar, Y., Bettman, J. R., & Fitzsimons, G. J. (2007). The effects of nonconsciously priming emotion concepts on behavior. Journal of Personality and Social Psychology , 93 (6), 927–939. https://doi.org/10.1037/0022-3514.93.6.927 Information & Authors Information Version history V1 Version 1 29 October 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Tess Reid 0009-0004-3106-1268 University of New Hampshire View all articles by this author jolie wormwood [email protected] University of New Hampshire View all articles by this author Metrics & Citations Metrics Article Usage 212 views 108 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Tess Reid, jolie wormwood. Intolerance of Uncertainty Predicts Physiological and Subjective Responses to Experiential Uncertainty. Authorea . 29 October 2025. DOI: https://doi.org/10.22541/au.176174849.95165912/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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