Emotional Coherence under Hypoxia and Aging: A Registered Report on Subjective–Physiological Coupling

Emotional Coherence under Hypoxia and Aging: A Registered Report on Subjective–Physiological Coupling | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 5 January 2026 V1 Latest version Share on Emotional Coherence under Hypoxia and Aging: A Registered Report on Subjective–Physiological Coupling Authors : Pierrick Laulan 0000-0003-3033-7782 [email protected] , Grégoire Millet , and Ulrike Rimmele Authors Info & Affiliations https://doi.org/10.22541/au.176759330.09620042/v1 200 views 102 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Emotional episodes involve partially coordinated changes in experience and autonomic activity, and subjective–physiological coherence appears to vary with aging and physiological stress. Physiological accounts of emotional aging propose that age-related changes in autonomic and interoceptive function reduce the informativeness of bodily signals, weakening the coupling between feelings and bodily responses. We will test this proposal in three groups ( N = 90): younger adults in normoxia, younger adults under acute hypoxia (FiO 2 < 12%), and older adults in normoxia. Participants will view emotional images and provide trial-wise ratings of affect and approach motivation while heart rate and skin conductance are recorded. Emotional coherence will be indexed by within-person correlations between bodily intensity ratings and autonomic reactivity. We will test hypotheses that aging and hypoxia reduce coherence and that hypoxic younger adults resemble older adults. Secondary analyses will examine whether heart-rate variability and interoceptive accuracy account for group differences in coherence. Emotional Coherence under Hypoxia and Aging: A Registered Report on Subjective–Physiological Coupling Pierrick Laulan a,b,c,d , Grégoire Millet e & Ulrike Rimmele f a Emotion and Memory Laboratory, Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland b Center for Interdisciplinary Study of Gerontology and Vulnerability (CIGEV), University of Geneva, Geneva, Switzerland c Swiss Center of Affective Sciences (CISA), University of Geneva, Geneva, Switzerland d Neurocenter, University of Geneva e Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland f Institute of Psychology, University of Lausanne, Lausanne, Switzerland Corresponding Author : Pierrick Laulan, Emotion and Memory Laboratory, Faculty of Psychology and Educational Sciences, Center for Interdisciplinary Study of Gerontology and Vulnerability (CIGEV), University of Geneva, 22 chemin de Pinchat, CH- 1227 Carouge Switzerland. E-mail address: [email protected] Abstract Emotional episodes involve partially coordinated changes in experience and autonomic activity, and subjective–physiological coherence appears to vary with aging and physiological stress. Physiological accounts of emotional aging propose that age-related changes in autonomic and interoceptive function reduce the informativeness of bodily signals, weakening the coupling between feelings and bodily responses. We will test this proposal in three groups ( N = 90): younger adults in normoxia, younger adults under acute hypoxia (FiO₂ < 12%), and older adults in normoxia. Participants will view emotional images and provide trial-wise ratings of affect and approach motivation while heart rate and skin conductance are recorded. Emotional coherence will be indexed by within-person correlations between bodily intensity ratings and autonomic reactivity. We will test hypotheses that aging and hypoxia reduce coherence and that hypoxic younger adults resemble older adults. Secondary analyses will examine whether heart-rate variability and interoceptive accuracy account for group differences in coherence. Keywords: emotional coherence; aging; hypoxia; interoception; heart-rate variability; skin conductance Research Transparency Statement General Disclosures Conflict of interest. The authors declare that they have no conflicts of interest relevant to the content of this article. Funding. This research is supported by a Spark grant from the Swiss National Science Foundation (SNSF; grant number blinded for review). The funder has no role in the study design, data collection, analysis, interpretation, or decision to submit the manuscript for publication. Ethics. The study protocol has been submitted to the Cantonal Research Ethics Committee, Vaud (CER-VD) and will be conducted in accordance with the Declaration of Helsinki. Any modifications requested by the committee will be implemented via formal amendments before data collection begins. At the time of this Stage 1 submission, no data have been collected. Use of AI tools. ChatGPT was used to assist with language editing and formatting of the manuscript. All scientific content (study design, hypotheses, methods, and interpretation) was developed and verified by the authors, who take full responsibility for the final text. Study-Specific Disclosures Preregistration. This article is submitted as a Stage 1 Registered Report to Psychophysiology. The introduction, methods, and analysis plan described here constitute the preregistered protocol. Upon in-principle acceptance, the approved protocol will be archived on the Open Science Framework (OSF), and any deviations from the registered plan will be transparently documented in the Stage 2 report. Data. Upon acceptance of the Stage 2 manuscript, de-identified data sufficient to reproduce all reported analyses will be made publicly available on OSF. Data will be shared in a tidy, analysis-ready format, accompanied by a detailed codebook. Materials. Because of copyright restrictions, the original IAPS images cannot be redistributed. However, we will share the full list of picture identifiers, all trial-wise stimulus properties (e.g., normative valence and arousal ratings, low-level visual features), and all experimental scripts (e.g., task code for the emotional image and interoception tasks) on OSF. Code. All analysis scripts (R code for data cleaning, computation of coherence indices, and statistical models) will be uploaded to OSF at Stage 2, allowing full reproducibility of the reported results. Emotional episodes involve coordinated changes in subjective experience, physiology, and behavior (e.g., Scherer, 2005). Constructionist and interoceptive models propose that conscious feelings emerge when the brain integrates interoceptive signals with situational cues, goals, and conceptual knowledge (Barrett, 2017; Lindquist, 2013; MacCormack & Lindquist, 2017). From this perspective, coherence between subjective feelings and autonomic responses should vary systematically across individuals and contexts, depending on how bodily signals are weighted in affective inferences (Mauss et al., 2005, 2024). Within this framework, we use emotional coherence to refer specifically to subjective–physiological coherence: the within-person covariation between self-reported affect and autonomic reactivity across repeated emotional events. Laboratory studies in young adults report small-to-moderate correlations between subjective affect and indices such as heart rate and skin conductance (Mauss et al., 2005; Sze et al., 2010). Although effect sizes are modest, greater coherence has been associated with higher well-being and more adaptive emotion regulation (Brown et al., 2020; Sommerfeldt et al., 2019), and coherence is sensitive to emotion-regulation strategies, with suppression reducing and acceptance preserving coupling (Dan-Glauser & Gross, 2013). These findings suggest that understanding when and why subjective–physiological coherence is preserved or disrupted is theoretically and clinically important. Aging provides a natural context in which emotional coherence may change. Older adults often report higher positive affect and lower negative affect than younger adults despite accumulating health challenges, a pattern sometimes described as an aging paradox (Mather & Carstensen, 2005; Mather, 2024). At the same time, aging is accompanied by alterations in cardiovascular and autonomic function, including reduced heart-rate variability (HRV) and changes in baroreflex sensitivity (Maxwell et al., 2022). Vagal components of HRV are commonly interpreted as markers of autonomic capacity and flexibility that support emotion generation, regulation, and interoceptive integration (Lischke et al., 2021; Pinna et al., 2020; Tuck et al., 2016; Williams et al., 2015). The Physiological Hypothesis of Emotional Aging (PHEA) proposes that age-related changes in peripheral physiology and interoception make bodily signals noisier and less precise, reducing their impact on emotional inferences (MacCormack et al., 2023; Pfeifer & Cawkwell, 2025). Empirical work shows a heterogeneous pattern, with some interoceptive abilities declining, others remaining stable or increasing, and age-related shifts in how interoceptive sensitivity relates to affective outcomes (Haustein et al., 2024; Khalsa et al., 2009; Mikkelsen et al., 2019; Murphy et al., 2017; Ulus et al., 2022). Empirical evidence for age-related changes in emotional coherence is beginning to accumulate but remains limited. Film-based studies suggest that older adults can report equal or greater subjective arousal than younger adults while showing attenuated physiological responses, consistent with a partial decoupling of feelings from autonomic activation (Smith et al., 2005; Fernández-Aguilar et al., 2020). However, few studies have used standardized, multi-trial paradigms optimized for estimating individual differences in coherence, and existing findings vary across response systems and tasks (Brown et al., 2020; Sato, 2024). Importantly, age effects on coherence may be emotion-specific: Lohani et al. (2018) found that older adults showed greater coherence than younger adults during sadness, an emotion particularly relevant to aging concerns. Taken together, these findings make PHEA a compelling, but still incompletely tested, framework for hypothesizing reduced subjective–physiological coherence in older adults. Beyond aging, acute hypoxia offers a complementary, experimentally controllable way to perturb bodily states. Reducing inspired oxygen to FiO₂ ≈ 11–13% (≈4,000 m altitude equivalent) reliably lowers arterial oxygen saturation (SpO₂) and alters autonomic activity in young adults (Bärtsch & Gibbs, 2007; Tarumi & Zhang, 2018). Meta-analytic evidence indicates that acute hypoxia in this range can impair reaction time, accuracy, and memory (McMorris et al., 2017; Ramírez-delaCruz et al., 2024), and hypoxia has been associated with increased fatigue and negative affect, especially when combined with other stressors (Stavrou et al., 2018; Kious et al., 2019). From an interoceptive standpoint, hypoxia simultaneously perturbs peripheral physiology and vagal and chemosensory afferents that inform the brain about bodily needs (Burtscher et al., 2022; Porges, 1992). If emotional coherence depends on the reliability and weight of these signals (Sze et al., 2010; Brown et al., 2020; Mauss et al., 2024), then acute hypoxia may transiently weaken the coupling between subjective affect and autonomic responses. Supporting this possibility, recent evidence indicates that hypoxia-related conditions impair interoceptive accuracy, with oxygen saturation positively predicting heartbeat detection performance (Çaman et al., 2024). At the same time, associative memory for emotional material appears preserved under moderate normobaric hypoxia but becomes selectively disrupted at higher, hypobaric altitudes, with neutral scenes sometimes recognized more accurately than negative ones (Gatti et al., 2024), underscoring that hypoxia can reshape emotional–cognitive processing in nuanced ways. Conceptually, hypoxia and aging may converge on reduced emotional coherence via partially overlapping mechanisms. Aging involves long-term, multifactorial physiological and neurocognitive changes (MacCormack et al., 2023; Mather, 2024), whereas acute normobaric hypoxia induces a transient physiological challenge with concomitant effects on attention, executive functioning, and fatigue (McMorris et al., 2017; Ramírez-delaCruz et al., 2024; Stavrou et al., 2018). From a socioemotional selectivity (SST) perspective, one might further speculate that acute threats to bodily resources such as oxygen scarcity could temporarily narrow future time perspective and shift priorities toward emotionally meaningful information (Mather & Carstensen, 2005; Scheibe & Carstensen, 2010). To date, however, the only experimental study that explicitly drew on this framework in a hypoxic context, using small samples under normobaric and hypobaric hypoxia, did not find the predicted preferential retention of positive over negative material (Gatti et al., 2024), leaving the status of such motivational shifts under acute hypoxia uncertain. Any convergence in coherence patterns across older adults and hypoxic younger adults will therefore be interpreted primarily as evidence that perturbing the precision and salience of bodily signals is one plausible pathway to reduced subjective–physiological coupling, while acknowledging that motivational and cognitive mechanisms highlighted in constructionist and SST accounts are likely to make additional, independent contributions (Lindquist, 2013; Scheibe & Carstensen, 2010; Mauss et al., 2024). The present study uses a three-group design to test whether aging and acute hypoxia converge on reduced emotional coherence, thereby probing a core mechanism proposed by PHEA. We compare younger adults under normoxia, younger adults under acute normobaric hypoxia (FiO₂ < 12%), and older adults under normoxia while they view emotional pictures and provide trial-wise ratings of valence, arousal, and approach motivation. Emotional coherence is operationalized as the trial-by-trial covariation between subjective bodily intensity and autonomic responses (heart rate and skin conductance). Our primary hypotheses are that (H1, Aging) older adults will show reduced subjective–physiological coherence compared with younger adults in normoxia, consistent with PHEA; (H2, Hypoxia effect) acute hypoxia will reduce coherence in younger adults relative to normoxia; and (H3, Aging-like pattern) coherence in hypoxic younger adults will approximate that of older adults, consistent with an “aging-like” profile of subjective–physiological coupling. In secondary, mechanism-oriented analyses, we will examine whether group differences in coherence are statistically related to resting vagal activity and objective cardiac interoceptive accuracy under each group’s oxygen condition. Beyond these primary, valence-collapsed hypotheses, we will exploratorily examine whether age- and hypoxia-related differences in coherence vary by stimulus valence. Previous work suggests that age effects on coherence and reactivity can be emotion-specific, with relatively preserved or even heightened experience–physiology coherence for sadness in older adults when loss-related films are used (Lohani et al., 2018; Seider et al., 2011) and more heterogeneous patterns for other emotions. Given these mixed findings, we do not formulate strong directional predictions by valence and treat these analyses as exploratory. Methods Open Science and Stage 1 Status This article is a Stage 1 Registered Report. The full protocol, analysis scripts, and anonymized data will be made available on the Open Science Framework upon completion of data collection and initial analyses. All hypotheses, inclusion/exclusion criteria, preprocessing steps, and statistical models described below are preregistered and will be implemented as specified. Any deviations will be transparently documented and labeled as exploratory. Participants We will recruit 90 community-dwelling adults: 60 younger adults (18–35 years) and 30 older adults (60–80 years). Younger adults will be randomly assigned to a normoxia group ( n = 30) or a hypoxia group ( n = 30). Older adults will be tested only under normoxia ( n = 30). Within each group, we will aim for balanced gender representation. Participants will be recruited via university mailing lists, local advertisements, and community organizations. Eligible participants will be community-dwelling adults aged 18–35 years (younger group) or 60–80 years (older group), fluent in French, with normal or corrected-to-normal vision and right-handed. All participants must provide written informed consent and must not have stayed more than 48 hours above 2,000 m in the preceding 6 months, in order to limit interindividual variability related to recent altitude acclimatization, which can persist after return to lower altitude and modulate physiological responses to hypoxia (e.g., Burtscher et al., 2022; Treml et al., 2020). For older adults, global cognition will be screened with the French version of the Telephone Interview for Cognitive Status (F-TICS-m/TICS-F; Lacoste & Trivalle, 2009; Vercambre et al., 2010). The TICS-m family of instruments is a brief, validated measure of global cognitive functioning designed for epidemiological screening (Knopman et al., 2010). We will include only older participants with a score > 27 (on the 0–41 scale used here), a conservative threshold informed by prior validation work showing that scores at or below 27 on TICS-m/F-TICS-m are indicative of dementia or clinically relevant cognitive impairment (Knopman et al., 2010). Exclusion criteria focus on minimizing risk under acute normobaric hypoxia and ensuring interpretable autonomic recordings. Potential participants will be excluded if they report past or current cardiovascular, respiratory, neurological, metabolic, or major psychiatric disorders; uncontrolled hypertension; a history of altitude-related illness requiring medical attention; diagnosed sleep apnea, chronic obstructive pulmonary disease, or other chronic pulmonary disease incompatible with hypoxic exposure; conditions incompatible with repeated non-invasive blood pressure monitoring; current use of medications known to substantially affect cardiovascular, respiratory, autonomic, or neuroendocrine function (e.g., β-blockers, non-stabilised antihypertensives, systemic corticosteroids, psychostimulants); current smoking or vaping; or body-mass index ≥ 32. These criteria are consistent with safety recommendations for short-term exposure to normobaric hypoxia in research and therapeutic contexts, which emphasize careful screening for cardiopulmonary and vascular disease and close monitoring of at-risk individuals (e.g., Burtscher et al., 2012; Millet et al., 2016). Pregnancy or suspected pregnancy is a contraindication to hypoxic exposure; women of childbearing potential will therefore be screened for pregnancy status at inclusion and re-checked on the test day, and any reported or suspected pregnancy will lead to exclusion from the study. Sample size justification The sample size of 90 participants (30 per group) was determined primarily by resource constraints, consistent with a feasibility-based approach to sample size justification (Lakens, 2022). Recruitment of older adults for multi-hour physiological testing combined with hypoxia safety protocols is logistically demanding, and the hypoxia facility has limited availability within the project timeline. These constraints make it infeasible to recruit substantially larger samples. Following recommendations for resource-constrained designs, we complement this feasibility-based justification with a sensitivity analysis to clarify which effect sizes the study can detect with reasonable power, allowing readers and reviewers to judge the informativeness of the design given its constraints (Lakens, 2022). Sensitivity analyses Because closed-form power calculations for generalized estimating equations (GEE) with non-zero within-subject correlation are not generally available in simple form and depend on design-specific assumptions about the correlation structure (e.g., Gastañaga et al., 2006; Li & McKeague, 2013), we based our sensitivity analyses on two-sample t tests applied to person-level coherence indices. This follows a pragmatic approach in which power is approximated for a simpler model that uses the same outcome and contrasts as the planned analysis (Frison & Pocock, 1992). The resulting thresholds provide a transparent estimate of the smallest standardized between-group differences that the design can detect. In the actual analyses, GEE models will use all available trials and account for within-person dependence; modeling the correlation structure generally improves efficiency relative to analyses that ignore correlation (e.g., Li & McKeague, 2013), so the t-test–based sensitivity estimates can be regarded as conservative. For H1 (older vs . younger adults in normoxia) and H2 (younger adults in hypoxia vs. normoxia), we treated the group difference in coherence as an independent-samples t test on Cohen’s d and used the pwr package (Champely, 2023) in R with α = .05, n = 30 per group, and 80% power. Under these assumptions, the minimal detectable standardized difference is d = 0.74 for a two-sided test. This effect size falls in the medium-to-large range (Cohen, 1988) and indicates that only relatively pronounced group differences in coherence can be detected with reasonable power. For H3 (aging-like pattern), we are concerned with whether coherence in hypoxic younger adults is sufficiently similar to that of older adults to support an “aging-like” interpretation. Given the planned sample size of n = 30 per group, standard equivalence tests on Cohen’s d with substantively reasonable bounds in the small-to-moderate range (e.g., | d | ≤ 0.30–0.50) are expected to have very low power in independent-samples designs (Lakens, 2017; Lakens et al., 2018). We therefore do not treat H3 as a high-powered equivalence test. Instead, as detailed in the Statistical analysis plan, H3 will be addressed in an evidence-quantification framework that focuses on the magnitude and uncertainty of the group difference and complements these estimates with Bayes factors, which provide graded evidence for similarity vs . difference between hypoxic younger and older adults (Rouder et al., 2009). Hypoxia apparatus and safety monitoring Testing will take place in a normobaric hypoxic chamber at [xxx] (University of [xxx]). Inspired oxygen fraction (FiO₂) will be controlled by a gas-mixing hypoxic system (Altitrainer®, SMTEC SA, Nyon, Switzerland), which generates normobaric hypoxia by mixing nitrogen into ambient air and continuously regulates FiO₂ via an inline oxygen sensor and PO₂ probe. This system allows stable delivery of room air (FiO₂ ≈ 21%) for normoxic conditions and hypoxic air with a target FiO₂ between 11.5% and 12.0% for the hypoxia condition (see Lichtblau et al., 2025). Arterial oxygen saturation (SpO₂) and heart rate will be monitored continuously via finger pulse oximetry throughout the session. Non-invasive blood pressure will be measured at baseline and at regular intervals (every 10 minutes during acclimatization, then at midpoint and end of the task phase) using an automated cuff. Acute mountain sickness (AMS) symptoms (headache, nausea, dizziness) will be assessed repeatedly using a brief standardized checklist derived from the Lake Louise Score. These monitoring procedures and thresholds are consistent with current recommendations for short-term normobaric hypoxia in healthy participants (e.g., Burtscher et al., 2022). For younger adults assigned to the hypoxia condition, FiO₂ will be gradually reduced during the acclimatization phase until SpO₂ stabilizes between 75% and 85%, corresponding to a simulated altitude of approximately 3,500–4,000 m that is generally well tolerated under close monitoring. In the overall experimental session, this hypoxic (or normoxic) condition will first be established for a separate memory task from a companion project; the emotional image-viewing task and the interoceptive accuracy task of the present study will then be administered within the same oxygen condition immediately afterward, without altering FiO₂. Younger adults assigned to the normoxia condition and older adults tested under normoxia will remain at FiO₂ ≈ 21% throughout, with identical physiological and symptom monitoring but no hypoxic exposure. The session will be immediately interrupted and the chamber returned to normoxia (FiO₂ ≈ 21%) if any of the following occur: SpO₂ 180 mmHg or diastolic blood pressure > 105 mmHg on two consecutive readings; sustained heart rate > 150 bpm for more than 2 minutes; or the participant reports severe headache, nausea, dizziness, chest pain, or marked distress. The session will also be stopped immediately if the participant requests to discontinue or if the experimenter judges continuation to be unsafe. A physician affiliated with the facility will be available on call and can be summoned if symptoms fail to resolve promptly after return to normoxia. For younger participants assigned to the hypoxia condition, total hypoxic exposure duration (from SpO₂ stabilization to the end of the task phase) is expected to be approximately 60-70 minutes. As a manipulation check, we will verify that mean SpO₂ during the emotional task is below 85% for hypoxia participants; individuals who do not reach or maintain this threshold will be flagged and retained for planned sensitivity analyses. Stimuli Participants will view 120 images (40 negative, 40 neutral, 40 positive) taken from the stimulus set developed by Laulan and Rimmele (2024). All images originate from the International Affective Image System (IAPS; Lang et al., 2005). Normative valence and arousal ratings (1–9 scales) were taken from Leclerc and Kensinger (2011), who collected ratings from 50 younger and 50 older adults. Based on the normative ratings, negative images have mean valence scores between 1 and 3.5 in both age groups, neutral images between 4 and 6, and positive images between 6.5 and 9. One-way ANOVAs confirmed the expected effect of valence in younger adults, F (2,117) = 1076.49, p < .001, and in older adults, F(2,117) = 930.64, p < .001, indicating that negative, neutral, and positive images are clearly differentiated by valence in both groups. Arousal ratings show the typical U-shaped pattern: in younger adults, negative and positive images are more arousing than neutral images, F (2,117) = 83.85, p < .001, and the same pattern holds in older adults, F (2,117) = 55.18, p < .001. Within each valence category, paired t tests on the per-image differences between younger and older raters revealed no reliable age differences in valence or arousal (all | ts (39)| ≤ 1.56, ps ≥ .12), ensuring that any age- or condition-related effects in our task are not trivially explained by baseline differences in normative ratings. To reduce potential confounding from low-level visual properties, we ensured that negative, neutral, and positive images were matched on basic physical features using the quantitative image descriptors provided by Laulan and Rimmele (2024). In the present 120-image subset, luminance, contrast, entropy, and the L*, a*, and b* dimensions of CIELAB color space do not differ reliably across valence categories, all Fs (2,117) ≤ 1.65, ps ≥ .20 (see Table 1 for descriptive statistics). We also verified that images are matched on semantic content: each image was coded as depicting people, faces, animals, objects, or landscapes, and a chi-square test indicated that the distribution of content types does not differ across valence categories, χ² (8) = 3.62, p = .89. Measure Negative Neutral Positive F (2,117) p Valence (younger) 2.59 (0.47) 5.03 (0.36) 7.35 (0.53) 1076.49 < .001 Valence (older) 2.56 (0.50) 5.07 (0.47) 7.23 (0.48) 930.64 < .001 Arousal (younger) 5.20 (0.67) 3.38 (0.52) 5.03 (0.85) 83.85 < .001 Arousal (older) 5.07 (0.96) 3.27 (0.69) 4.95 (0.90) 55.18 < .001 Luminance 91.55 (33.95) 87.26 (35.09) 96.48 (39.87) 0.64 .528 Contrast 70.10 (14.91) 72.12 (14.99) 67.67 (15.37) 0.87 .420 Entropy 6.73 (1.10) 6.44 (1.18) 6.69 (1.23) 0.74 .482 L* 37.97 (13.86) 36.34 (14.46) 40.43 (16.02) 0.77 .463 a* 5.21 (6.60) 4.95 (7.05) 5.44 (8.87) 0.04 .960 b* 9.77 (8.59) 14.30 (11.92) 12.59 (12.82) 1.65 .196 Note. N = 120 images (40 negative, 40 neutral, 40 positive). Values are means with standard deviations in parentheses. F and p values correspond to one-way ANOVAs testing the effect of valence category on each measure. Emotional image task Trial structure The emotional image task consists of 120 trials (40 negative, 40 neutral, 40 positive). Each trial follows a fixed sequence designed to elicit reliable autonomic responses while allowing for precise trial-by-trial ratings, in line with standard image-viewing paradigms combining physiology and self-report (e.g., Pastor et al., 2007; van Dongen et al., 2016). On each trial, a central fixation cross is presented for 1,000 ms, followed by the image for 5,000 ms. Heart rate and skin conductance are recorded continuously throughout the fixation and image epochs. Immediately after image offset, participants complete three self-report ratings presented sequentially on the screen: valence, arousal, and approach motivation. Each rating is given on a visual analog scale (VAS) ranging from 0 to 100, displayed as a horizontal line with verbal anchors at the endpoints (and midpoint when appropriate). For valence, the scale is anchored at “very negative” (0), “neutral” (50), and “very positive” (100), similar to prior work using VAS-based affect ratings in image-viewing and reappraisal tasks (e.g., Pollatos et al., 2007). Arousal is rated from “not at all activated” (0) to “very activated” (100). Approach motivation is rated from “strong tendency to avoid” (0), through “no tendency either way” (50), to “strong tendency to approach” (100), consistent with conceptualizations of approach–avoidance motivation as graded tendencies to move toward vs . away from stimuli (Elliot, 2006; Harmon-Jones et al., 2013). Participants move a cursor along the VAS using response keys and confirm their choice with a button press; each scale remains on screen until response, up to a maximum of 9,000 ms. If no response is made within this window, the trial advances and the missing value is recorded. After the rating phase, a blank screen is presented for a jittered inter-trial interval (ITI) uniformly sampled between 1,000 and 2,000 ms. This jitter reduces temporal predictability and minimizes overlap between autonomic responses to successive trials, as recommended in psychophysiological work with rapid image presentation (e.g., Hollandt et al., 2023; Pastor et al., 2007). Stimuli are presented in pseudo-random order with the constraint that no more than three images of the same valence appear consecutively. The task is divided into three blocks of 40 trials each, with brief self-paced breaks between blocks. Total task duration is approximately 25-30 minutes. Subjective indices For each trial, we will extract three primary subjective indices (valence, arousal, approach motivation). For the main coherence analyses, we will derive a composite “subjective bodily intensity” index by averaging z-scored arousal ratings and the absolute deviation of approach ratings from the neutral point (|approach − 50|), reflecting perceived bodily activation and action tendency toward or away from the stimulus. This composite is motivated by theoretical and empirical work emphasizing coherence between affective arousal, action readiness, and autonomic activation as a key dimension of subjective–physiological alignment (Mauss et al., 2005; Brown et al., 2020; Biddell et al., 2024) and is used formally in the Statistical analysis plan. The individual scales (arousal and approach), as well as valence ratings, will be retained and analyzed separately in exploratory and follow-up analyses. Trait interoceptive sensibility In addition to trial-wise ratings, participants will also complete the Multidimensional Assessment of Interoceptive Awareness, Version 2 (MAIA-2; Mehling et al., 2018; French validation: Willem et al., 2022). MAIA-2 assesses several dimensions of self-reported interoceptive sensibility, including noticing bodily sensations, sustaining attention to the body, linking bodily sensations to emotional experience, listening to the body for self-regulation, and trusting bodily signals. We will compute a global MAIA-2 score (averaging subscales with acceptable internal consistency) and use it primarily to characterize interoceptive beliefs and style. In the main confirmatory analyses, MAIA-2 will not be treated as a central predictor of coherence; instead, it will be included, when relevant, as a covariate in secondary models to ensure that condition effects on state interoception and coherence are not reducible to stable interoceptive sensibility. Analyses of individual MAIA-2 subscales will be clearly labeled as exploratory. Interoceptive accuracy task To obtain an objective state measure of cardiac interoception under each oxygen condition, participants will complete a heartbeat discrimination task adapted from Garfinkel et al. (2015). During each trial, a sequence of 10 auditory tones (50 ms; 800 Hz) will be presented over headphones while ECG is recorded. On half the trials, tones will be time-locked to the R-peaks of the ECG (synchronous condition; delay ≈ 0 ms). On the other half, tones will be delayed relative to R-peaks by 300 ms (asynchronous condition). After each tone sequence, participants will indicate whether the tones were ”in sync” or ”out of sync” with their heartbeat. The task will comprise 80 trials (40 synchronous, 40 asynchronous) presented in randomized order, with brief breaks after every 20 trials. Each trial will begin with a fixation cross (1,000 ms), followed by the tone sequence (approximately 8–10 s, depending on heart rate) and a response screen (up to 4,000 ms). A jittered inter-trial interval (1,000–2,000 ms) will separate trials. Heartbeat discrimination accuracy will be quantified using signal detection theory indices, with d′ computed separately for each participant based on hits (correct ”in sync” responses on synchronous trials) and false alarms (incorrect ”in sync” responses on asynchronous trials). Trials with missing responses or technical artifacts (e.g., ECG dropout preventing accurate R-peak locking) will be excluded. This d′ index will serve as our primary measure of interoceptive accuracy under each group’s oxygen condition and will be used in mechanism-oriented analyses of emotional coherence. As emphasized in contemporary interoception frameworks, heartbeat discrimination performance reflects not only the fidelity of cardiac afferent processing but also attentional, decisional, and metacognitive factors (Garfinkel et al., 2015; Murphy et al., 2017). We therefore interpret d′ as an operational state marker of cardiac interoceptive accuracy in this task context, rather than as a pure measure of interoceptive ability. Physiological recording and preprocessing Cardiac measures (ECG, event-related heart rate, resting HRV) Electrocardiogram (ECG) will be recorded continuously throughout the session using a BIOPAC MP160 system (BIOPAC Systems Inc., Goleta, CA) with a three-lead modified Lead II configuration and a sampling rate ≥ 1,000 Hz. Signals will be acquired with AcqKnowledge software, band-pass filtered using standard ECG settings, and stored for offline analysis. R-peaks will be detected with automated algorithms and visually inspected by trained staff; artifacts and ectopic beats will be corrected manually or, when necessary, excluded from analysis. Inter-beat intervals (IBIs) will be exported for heart-rate (HR) and heart-rate variability (HRV) analyses. Event-related heart-rate change For each trial of the emotional image task, we will compute event-related HR change (ΔHR) as the mean HR during the 0–4 s interval after image onset (i.e., most of the image presentation period) minus the mean HR in a 1-s pre-stimulus baseline (−1 to 0 s). This window is chosen to capture the short-latency orienting-related deceleration and early modulation of HR during picture viewing while avoiding contamination by the subsequent rating phase, in line with prior work examining early cardiac responses to emotional pictures (e.g., Pastor et al., 2007). Trials with excessive artifacts in the analysis window (e.g., > 20% interpolated IBIs) will be discarded. ΔHR will serve as the cardiac component of subjective–physiological coherence; in sensitivity analyses, we will examine whether alternative response windows (e.g., 1–4 s) yield convergent patterns. Resting heart-rate variability Resting HRV will be assessed in two standardized 5-minute periods: (a) a seated normoxic baseline before entering the hypoxia chamber and (b) a seated resting period under each participant’s assigned oxygen condition (normoxia vs . hypoxia) immediately before the emotional and interoception tasks. During these recordings, participants will sit quietly, breathe spontaneously, and keep their eyes open. HRV will be derived from artifact-corrected IBIs following current recommendations for experimental and clinical HRV assessment (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996; Laborde, Mosley, & Thayer, 2017). The primary index will be the natural-log transformed root mean square of successive differences (lnRMSSD), a widely used proxy of vagally mediated cardiac control in psychophysiological research, while acknowledging ongoing debates about the specificity and reliability of HRV metrics (Laborde et al., 2017; Nicolini et al., 2022; Pinna et al., 2007). For mechanism-oriented analyses, we will focus on lnRMSSD recorded under the assigned oxygen condition as a state-dependent estimate of autonomic capacity in the context in which emotional coherence is assessed. Baseline normoxic lnRMSSD will be reported descriptively and used in sensitivity analyses to test whether group differences in coherence are better explained by state- versus trait-like autonomic factors. Electrodermal activity (skin conductance responses) Electrodermal activity (EDA) will be recorded using a BIOPAC EDA amplifier module connected to the MP160 system, with data sampled at ≥ 1,000 Hz and downsampled offline as needed. Two Ag/AgCl electrodes filled with isotonic paste will be attached to the thenar and hypothenar eminences of the non-dominant hand, following standard psychophysiological guidelines (Dawson, Schell, & Filion, 2007). EDA signals will be low-pass filtered at 5 Hz, visually inspected for artifacts, and segmented into trial-wise epochs time-locked to image onset. For each trial of the emotional image task, we will compute the skin-conductance response (SCR) as the peak-to-baseline amplitude within 1–4 s after image onset relative to a 1-s pre-stimulus baseline (−1 to 0 s). This latency window lies within the commonly recommended range for stimulus-evoked SCRs (onset latencies ≈ 1–4 s, peaks ≈ 3–6 s; Dawson et al., 2007; see also Benedek & Kaernbach, 2010) and, in the present design, remains fully contained within the image presentation period, thereby minimizing contamination by the subsequent rating phase. Trials with technical artifacts (e.g., electrode detachment, extreme noise) will be excluded. Following common practice, non-responses (peak amplitude < 0.02 µS) will be coded as zero rather than treated as missing, to retain information about trials that failed to elicit a detectable SCR (Dawson et al., 2007). Trial-wise SCR amplitudes will be used as the electrodermal component of subjective–physiological coherence. Procedure Eligibility screening will take place before the laboratory session, via telephone or secure videoconference. Potential participants complete a standardized health questionnaire covering the inclusion and exclusion criteria (cardiovascular, respiratory, neurological, psychiatric, and medication status; smoking; BMI; recent altitude exposure). Older adults additionally complete the TICS-F to screen global cognitive functioning. Only candidates who meet all inclusion criteria and none of the exclusion criteria are invited to the laboratory. On the testing day, participants attend a single session lasting approximately 3 hours. Upon arrival at the laboratory, they provide written informed consent and complete a brief update of their medical status (e.g., recent illnesses, medications, altitude exposure) to confirm continued eligibility. They are then prepared for physiological recording (ECG, electrodermal activity, pulse oximetry, and automated blood-pressure monitoring). Participants are next seated in the normobaric hypoxia chamber with the fraction of inspired oxygen (FiO₂) set to ≈ 21%. They first complete a 5-min seated resting baseline under normoxia. Following this baseline, an acclimatization phase of approximately 40 minutes begins. During acclimatization, all participants watch the same neutral documentary film with minimal emotional content, allowing them to become accustomed to the chamber environment while limiting strong affective reactions. For younger adults assigned to the hypoxia condition, FiO₂ is gradually reduced during the acclimatization phase until arterial oxygen saturation (SpO₂) stabilizes around 75–85%, consistent with prior cognitive hypoxia work in healthy adults (e.g., McMorris et al., 2017; Ramírez-delaCruz et al., 2024) and with ranges typically described as moderate normobaric hypoxia (SpO₂ ≈ 75–85%; Timon et al., 2023). SpO₂, heart rate, blood pressure, and acute mountain sickness symptoms are monitored continuously according to the predefined safety protocol. Younger adults in the normoxia condition and older adults remain at FiO₂ ≈ 21% throughout acclimatization, with identical monitoring and film viewing. Thus, total time spent in the chamber and exposure to the documentary are matched across groups, and only the oxygen fraction differs between hypoxic and normoxic conditions. Once the acclimatization phase is complete and SpO₂ has remained within the target range for at least 5 minutes, participants complete a second 5-min seated resting period under their assigned oxygen condition (normoxia vs. hypoxia). This condition-specific resting period is used to derive resting HRV in the assigned oxygen context. Under the stabilized oxygen condition, participants first complete an experimental task from a related project examining the effects of hypoxia on associative emotional memory; this task uses the same hypoxia protocol and physiological monitoring but is reported elsewhere. Immediately after the memory task, and still under the assigned oxygen condition, participants perform the emotional image task followed by the interoceptive accuracy task, in a fixed order as described above. Short self-paced breaks are provided between blocks and between tasks to limit fatigue and maintain data quality. At the end of the experimental tasks, the chamber is returned to normoxia (FiO₂ ≈ 21%). Participants remain seated under observation until SpO₂ and symptoms have returned to baseline levels and a final safety check (SpO₂, blood pressure, subjective symptoms) is completed. Physiological sensors are then removed, the MAIA-2 questionnaire is completed outside the chamber, and participants are debriefed, compensated, and free to leave the laboratory. Statistical analysis plan Software and general principles All analyses will be conducted in R (version 4.3.2; R Core Team, 2024). Trial-level generalized estimating equations (GEEs) will be fitted with the geepack package (Højsgaard et al., 2006). Regression-based mediation models, where used, will be implemented with lavaan (Rosseel, 2012) or the mediation package (Tingley et al., 2014). Figures will be created with ggplot2 (Wickham, 2016), and data wrangling will rely on the tidyverse suite (Wickham et al., 2019). For H3, Bayes factors for group differences in coherence will be computed using standard Bayesian t-test implementations with default Cauchy priors on standardized effect sizes (Rouder et al., 2009). All scripts for data cleaning and analysis will be deposited on OSF at Stage 2 together with an anonymized dataset and codebook to ensure full reproducibility. Data quality checks and exclusions Data quality checks will be performed before computing coherence and interoception indices. At the trial level, we will exclude trials with missing or invalid subjective ratings (e.g., no response on one of the VAS scales) or unusable physiological data according to the modality-specific criteria described above (e.g., excessive ECG artifacts preventing reliable IBI estimation, electrode failures in EDA). For the interoception task, trials with missing responses or ECG artifacts preventing reliable R-peak detection will be excluded from d’ computation. For the primary coherence indices (computed across all emotional images), participants will be retained for a given physiological modality if at least 80 valid emotional trials remain after cleaning (across valence categories combined). For valence-specific coherence indices, a participant will contribute to the analysis for a given valence only if at least 20 valid trials remain in that valence category (out of 40 planned); sample sizes may therefore differ slightly across valence-specific analyses, but no participant will be excluded from all coherence analyses solely on this basis. For the interoceptive accuracy task, participants with fewer than 60 usable trials (out of 80 planned) will be excluded from analyses involving d’ . These thresholds are defined a priori to balance data quality with retention of participants. For participant-level indices (global coherence across all images, valence-specific coherence, lnRMSSD, interoceptive d’ , MAIA-2 total), we will implement a robust outlier rule based on the median absolute deviation (MAD; Leys et al., 2013): values more extreme than ±3 MAD from the group median will be flagged separately within each age/condition group and for each outcome. Outlier handling will be outcome-specific: participants flagged as outliers for a given index will be excluded from the primary analysis of that index but retained for all other outcomes and for predefined sensitivity analyses including all cases. For valence-specific coherence, outliers will be identified separately within each valence category so that participants can still contribute valid data for other valences. The ±3 MAD rule and its outcome-specific application are fixed a priori and will not be tuned based on the observed data. Operationalization of emotional coherence For each participant, ΔHR and SCR values will be standardized (z-scored) within person across trials. Subjective bodily intensity scores (composite of arousal and approach) will also be z-scored within person. Z-scoring within person does not change Pearson correlation coefficients but facilitates comparability across individuals and use in regression-based models. We will compute two main coherence indices per participant: (a) HR-based coherence: Pearson correlation between z-scored subjective bodily intensity and z-scored ΔHR across all valid emotional trials. (b) SCR-based coherence: Pearson correlation between z-scored subjective bodily intensity and z-scored SCR across all valid emotional trials. These correlations will be Fisher z-transformed for group-level analyses. To explore valence specificity, we will also compute parallel coherence indices within each valence category (negative, neutral, positive), subject to the minimum-trials rule described above. Valence-stratified indices will be treated as secondary outcomes given reduced trial numbers per cell. Primary person-level analyses (H1–H3) For each coherence index (HR-based, SCR-based), we will fit linear models predicting Fisher z-transformed coherence from Group (younger–normoxia, younger–hypoxia, older–normoxia). Pre-registered contrasts will test the three primary hypotheses: H1 (aging effect): older–normoxia vs . younger–normoxia. We predict lower coherence in older adults. H2 (hypoxia effect): younger–hypoxia vs . younger–normoxia. We predict lower coherence in younger adults under hypoxia than under normoxia. H3 (aging-like pattern): younger–hypoxia vs. older–normoxia. We predict that coherence in hypoxic younger adults will be similar to that of older adults within a small-to-moderate range. For H1 and H2, we will conduct two-sided null-hypothesis tests with α = .05 and report unstandardized mean differences, standardized effect sizes (Cohen’s d or Hedges’ g if sample sizes differ due to exclusion), and 95% confidence intervals. For H3, we will not treat similarity as a high-powered equivalence test; instead, we will evaluate the aging-like pattern in an evidence-quantification framework by reporting the observed effect size and its confidence interval and by computing Bayes factors comparing a point-null model (no group difference) to an alternative model allowing non-zero differences (Rouder et al., 2009). These analyses will quantify the relative evidence for similarity versus difference in coherence between hypoxic younger adults and older adults without relying solely on non-significant p values. Trial-level generalized estimating equations (GEE) To complement person-level coherence indices, we will model trial-level data using generalized estimating equations (GEEs) with participant as the clustering factor, an exchangeable working correlation, and robust (sandwich) standard errors (Halekoh et al., 2006). The primary dependent variable will be z-scored subjective bodily intensity. Main predictors will include: (a) the z-scored physiological index (ΔHR or SCR, modeled in separate GEE models), (b) Group (younger–normoxia, younger–hypoxia, older–normoxia), (c) Valence (negative, neutral, positive), (d) their interactions, and (e) trial number (centered) to capture potential habituation or fatigue. We will focus on Physiology × Group and Physiology × Group × Valence interactions as convergent tests of group differences in within-person subjective–physiological coupling. For example, a weaker slope relating physiology to subjective bodily intensity in older adults or hypoxic younger adults compared with normoxic younger adults would be consistent with reduced coherence. Valence will be dummy-coded to allow planned contrasts (e.g., negative vs. neutral, positive vs. neutral). These GEE models will be treated as secondary, convergent confirmatory tests of H1–H3 at the trial level, complementing the person-level coherence indices. Mechanism-oriented analyses: resting HRV and interoceptive accuracy In secondary, mechanism-oriented analyses inspired by PHEA, we will examine whether group differences in emotional coherence are statistically related to differences in autonomic capacity and interoceptive accuracy under the assigned oxygen condition. For each primary coherence index, we will fit regression models including Group and either lnRMSSD (condition-specific) or interoceptive d′ as predictors. We will first test whether Group predicts lnRMSSD and d′ (path a) and whether lnRMSSD and d′ predict coherence over and above Group (path b). If both conditions are met, we will estimate indirect effects of Group on coherence via lnRMSSD and via d′ using regression-based mediation models with non-parametric bootstrapped confidence intervals (10,000 resamples). An indirect effect will be considered statistically supported if the 95% bias-corrected bootstrap CI excludes zero. Because these data are cross-sectional and temporal ordering is not experimentally manipulated, we will interpret such indirect paths as statistical evidence consistent with, but not definitive proof of, the hypothesized mechanisms. These mediation analyses will not be treated as tests of additional primary hypotheses; rather, they will be used to assess whether the pattern of group differences in coherence aligns with the idea that reduced autonomic capacity and degraded interoceptive precision under hypoxia and aging contribute to weaker subjective–physiological coupling. Exploratory role of trait interoceptive sensibility Trait interoceptive sensibility (MAIA-2 total score) will be examined exploratorily. Specifically, we may include MAIA-2 as a covariate in the mediation models above to test whether any association between lnRMSSD or interoceptive d′ and coherence persists after accounting for stable interoceptive style. 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Resting heart rate variability predicts self-reported difficulties in emotion regulation: A focus on different facets of emotion regulation. Frontiers in Psychology , 6 . https://doi.org/10.3389/fpsyg.2015.00261 Information & Authors Information Version history V1 Version 1 05 January 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Pierrick Laulan 0000-0003-3033-7782 [email protected] University of Geneva View all articles by this author Grégoire Millet Universite de Lausanne Institut des Sciences du Sport View all articles by this author Ulrike Rimmele Universite de Lausanne Institut de Psychologie View all articles by this author Metrics & Citations Metrics Article Usage 200 views 102 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Pierrick Laulan, Grégoire Millet, Ulrike Rimmele. Emotional Coherence under Hypoxia and Aging: A Registered Report on Subjective–Physiological Coupling. 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