Cognitive Biotypes Identified Through ECG-Derived Workload and Behavioral Accuracy

Cognitive Biotypes Identified Through ECG-Derived Workload and Behavioral Accuracy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Cognitive Biotypes Identified Through ECG-Derived Workload and Behavioral Accuracy Sarah Conklin, Golshan Kargosha, Jenny Tu, Kanika Bansal, Quang Dang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7071787/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Individual differences in physiological effort during cognitive workload, which we define as mental demand during task execution, are well established, yet self-reports often fail to reflect actual physiological effort. We hypothesized that combining cognitive performance with ECG-derived workload would reveal distinct biotypes of performance and physiological effort and that these biotypes would differ in how closely subjective appraisals align with objective measures. A sample of 100 participants completed cognitive tasks while ECG data were analyzed in real time using a validated workload classification algorithm. Clustering based on standardized performance accuracy and workload revealed three biotypes: (1) high performers with low workload, (2) average-to-high performers with high workload, and (3) low performers with variable workload. These biotypes exhibited distinct patterns of perceptual bias: Clusters 1 and 3 showed smaller discrepancies between subjective and objective workload, while Cluster 1 notably underestimated task success relative to their actual performance. These findings demonstrate that clustering behavioral and physiological data can reveal meaningful cognitive stress response profiles and suggest that subjective-objective misalignment may serve as a potential marker of cognitive resilience or vulnerability. This taxonomy may aid future efforts to personalize assessments or interventions aimed at optimizing performance under stress. Biological sciences/Neuroscience Biological sciences/Physiology Biological sciences/Psychology Social science/Psychology Cognitive workload ECG Cognitive Biotypes Perceptual bias Clustering Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Individual differences in the reflexive physiological reactivity to cognitive challenges, perceived threats, and stressors contribute to disease risk and resilience. 1 – 5 Acutely, enhanced stress reactions may lead to symptoms such as fatigue, anxiety, or stress, yet can also enhance task performance in the short term. 6 Over time, however, exaggerated reactions, whether in magnitude or duration, compromise performance and increase the subsequent risk for high blood pressure, burnout, heart disease, and other indices of poor health. 1 , 7 – 10 In laboratory research, exaggerated reactions to the paced serial addition test (PASAT), a cognitive challenge task, has been shown to predict coronary heart disease (CHD) mortality over a 16-year period. 11 These findings highlight the need to better characterize individual reactivity profiles that span both behavioral performance and autonomic responses, particularly under conditions of increasing cognitive demand. Measurement of stress is a challenge and is approached by a variety of methods. In laboratory settings, paradigms such as the Trier Social Stress Task, the cold pressor test, and the PASAT are used to induce social, physical, or cognitive stress, respectively. 12 – 14 Physiological responses to these laboratory stress-inducing tasks are typically recorded and analyzed in phases (baseline, stress exposure, recovery). 15 , 16 Both exaggerated and blunted reactions to laboratory stress have been related to psychopathology, cardiovascular morbidity and mortality, traumatic experiences, patterns of health behavior, and social and demographic variables. 1 , 2 , 11 , 17 In medical settings, serum cortisol is commonly used as a purely physiological measure of stress. 18 Cortisol is often assessed as an awakening response with the area under the curve calculated across several morning measurements. 18 , 19 Finally, in the wild, wearable devices use proprietary algorithms on photoplethysmography sensor data to estimate stress load or periods of stress. Although these two different methods provide valuable snapshots of measuring stress and are meaningfully associated with risky health outcomes, they do not capture how a person performs under stress, how they appraise the challenge, and how their physiology meets the challenge with reflexive responses facilitated partly by the autonomic nervous system (ANS). Understanding how a person performs while under active stress or challenge, beyond just measurement, could inform precision medicine or lifestyle changes to influence health outcomes associated with heightened stress reactivity. 2 , 20 It is well established that cognitive demand induces a reflexive physiologic response (e.g. change in heart rate) via the ANS, providing a causal mechanism that relates rapid decision-making, frequent task switching, and continuous shifts in attention (ubiquitous in the modern workplace and home) to fundamental changes in physiology. 21 – 23 Using this known relationship, Brooks et al. developed an algorithm using ECG and EEG data that generalizes across multiple cognitive tasks and participant groups. The algorithm makes second-by-second predictions of cognitive depletion, a construct that is sensitive to multitasking, mental and temporal demand, and task difficulty. 24 We explored the utility of the ECG-based workload algorithm to identify patterns in behavioral and physiological response to cognitive demand. Individual differences in physiological responses to cognitive challenges and stressors are crucial in psychological research, as it provides insight into why people react differently to similar situations. 3 , 7 , 25 Stress response research is used in our study to better understand these individual differences. Genetic, psychological, and sociocultural factors can influence these differences. For example, some individuals may have a heightened stress response due to genetic predispositions, leading to increased magnitude and or duration of physiological reactions (blood pressure, cortisol, heart rate dynamics, skin conductance). 26 Others might exhibit more resilient physiological responses, which can buffer against the negative effects of stress. 27 These variations can impact mental health outcomes, such as susceptibility to anxiety, depression, and other stress-related disorders. 28 Despite advancements in neuroimaging technologies, the gold standard assessment of psychological experience remains self-reports, typically gathered through structured or semi-structured interviews, questionnaires, or state probes. 29 , 30 However, recent research has highlighted the need for more integrative frameworks that connect subjective state appraisals with objective measurements of physiological and/or behavioral function. 31 Studies have shown that healthy individuals and those with psychiatric conditions–such as depression, insomnia, schizophrenia, and fibromyalgia–often exhibit significant discrepancy between their subjective reports and objective assessments. 32 – 34 For example, in insomnia sleep-wake state discrepancy is a common phenomenon where patients report frustratingly long sleep onset latency or duration in their sleep, however, on polysomnography their sleep metrics are typical. This discrepancy may prevent proper diagnosis and treatment effectiveness. 34 Conversely, in other work, discordant, positively biased perceptions were linked to more positive outcomes, such as reduced morbidity and mortality. 3 , 35 , 36 Therefore, identifying the subjective-objective discrepancy (the difference between perceived workload and ECG-measured workload) for cognitive ability may help develop interventions to improve performance under cognitive strain or stress. The concept of “biotypes” has emerged in psychology and psychiatry as a promising approach to provide a more global assessment of functional type, capturing the interplay between cognitive, physiological, and/or psychological processes that less integrated approaches would not capture. This framework is increasingly being applied to inform precision medicine approaches to treatment. For example, in psychiatry, biotypes have been used to identify subgroups within conditions like depression, addiction, insomnia, and schizophrenia, improving the prediction of treatment outcomes with a personalized approach of prevention and intervention strategies. 37 , 38 Integrating perception, cognition, and physiological measures, this approach identifies patterns linking subjective experiences with physiological and behavioral responses, making the biotype framework a powerful tool. We expected that a workload-integrated approach to cognitive challenge would reveal distinct cognitive biotypes, characterized by both performance and associated physiological responses. We anticipated observing systematic variation in cognitive task accuracy and workload, leading to four distinct phenotype clusters: 1) average-to-high accuracy, low workload; 2) average-to-high accuracy, high workload; 3) average to low accuracy, low workload; and 4) average to low accuracy and high workload. This expectation is grounded in the continuous nature of the workload algorithm. Objectively measured workload varies along a spectrum from high to low, reinforcing the idea that both accuracy and workload serve as primary determinants of biotypical differentiation. Although four clusters were hypothesized, analysis revealed a stable three-cluster solution, indicating that low-performing individuals did not stratify by workload. We also expected that accurate appraisal of cognitive function, based on self-report state probes completed during tasks when the workload measure from ECG is used as ground truth, would covary significantly with the performance: workload phenotypes described above. In particular, we expected Cluster 1 to show the least discrepancy between subjective ratings and objective measures, reflecting a more accurate or self-aware phenotype. Results Workload and Accuracy Patterns Across Tasks Figure 1 shows the variability among participants for task accuracy, workload, and each of the state probes. As can be seen in Fig. 1B, average workload increase remained relatively stable across difficulty levels, with only a slight rise at the highest level. The implications of this pattern are explored in the Discussion. Cluster Analysis using K Medoids: Evaluating Accuracy and Workload Figure 2 shows standardized accuracy and workload metrics plotted with points colored according to their assigned clusters. Convex hulls outline each cluster's extent, and average distances to the cluster centers (Centroid Proximity Index, CPI) are labeled to indicate cohesion. Distinct Accuracy-Workload Phenotypes We hypothesized that four distinct phenotypes would emerge, defined by combinations of high or low task accuracy and high or low physiological workload. Each participant's nine performances (three tasks × three difficulty levels) were plotted (Fig. 3 a ) . However, clustering using the K-Medoids algorithm revealed three distinct functional groups: (1) high accuracy, low workload, (2) average-to-high accuracy, high workload, and (3) low accuracy, low-to-moderate workload. Participants were assigned to the cluster that appeared most frequently across their nine tasks (mode cluster; ( Fig. 3 d ) . To further refine the classification, we calculated the average cluster assignment within each participant’s dominant cluster. This method ensured that classification captured the participant’s dominant accuracy-workload profile across tasks, reducing the influence of individual task fluctuations. Further analysis based on mode clusters using a chi-square test of independence revealed significant differences in demographic distributions across clusters. Specifically, Cluster 1 (high accuracy, low workload) was predominantly composed of Asian and White individuals, Cluster 2 (average-high accuracy, high workload) had an over-representation of Black individuals, and Cluster 3 (low accuracy, low/moderate workload) included a higher proportion of women ( X 2 (2, 100) = 11.31, p = < .05 for gender; ( X 2 (2, 100) = 37.76, p = < .05 for race; see Fig. 3 e and 3 f). An important observation from this clustering approach is that Cluster 3 was overrepresented in tasks with hard difficulty levels. However, participants in this cluster did not perform poorly only on hard tasks; they also exhibited low accuracy across easy and medium tasks, suggesting a broader performance issue. This pattern suggests that participants in Cluster 3 may have adopted a disengagement strategy, particularly as task difficulty increased, leading to their overall lower workload values. We used the Coefficient of Variation (CV) to assess variability in three key metrics: accuracy, workload, and their interaction (calculated as accuracy × workload). CV was computed as the standard deviation divided by the mean, multiplied by 100. The CV values were 30.13% for accuracy, 68.55% for workload, and 85.79% for the interaction term. The higher CV for the interaction indicates that combining accuracy and workload introduces greater variability than either metric alone. Subjective-Objective Comparisons will Vary by Cluster To test the hypothesis that Cluster 1 would show less discrepancy between subjective state probes and objective measures than the other clusters we conducted a 1-way ANOVA. The independent factor was modal cluster (Clusters 1, 2, and 3) and the dependent variables were the calculated discrepancy metrics for mental demand, temporal demand, and success. The results revealed a significant effect of Biotype on the Subjective Temporal Demand-Objective Workload discrepancy metric, F(2,882) = 10.47, p < 0.001, η2 = 0.09, 95% CI [0.07-1.00]. Post hoc comparisons using the Tukey HSD indicated that Cluster 2 had more discrepancy than Cluster 1 and 3 (see Fig. 4 ). Similarly, the main effect for Biotype on Subjective Mental Demand - Objective Workload discrepancy metric was significant (F(2,882) = 46.4, p < 0.001, η2 = 0.10, 95% CI [0.07-1.00]). Post hoc comparisons using the Tukey HSD showed the same pattern, Cluster 2 had more discrepancy than Cluster 1 and 3. Finally, the main effect of Biotype on Subjective Success - Objective Accuracy discrepancy metric was significant (F(2,884) = 10.07, p < 0.001, η2 = 0.02, 95% CI [0.01-1.00]). Post hoc comparisons using the Tukey HSD indicated that Cluster 1 had significantly more discrepancy on the success metric than both clusters 2 and 3. Discussion To explore evidence for cognitive biotypes we used a clustering approach to group participants based on performance and workload dynamics derived from an ECG-based workload algorithm and investigated the discrepancy between subjective and objective assessments of success, temporal demand, and cognitive workload during the tasks. We found support for the concept of cognitive biotypes through the identification of three, not four, distinct performance x workload groups. The hypothesis that the highest-performing and most efficient cluster would show the least discrepancy was partially supported. The findings provide novel insights into cognitive task appraisal, physiological cost, and perceptual bias, offering both theoretical and practical implications for stress and workload assessment and research. The results generally supported the hypothesis that distinct phenotypes emerge based on cognitive performance and physiological workload. Using K-Medoids clustering, three clusters were identified, reflecting variability in accuracy and workload profiles across participants. 39 These clusters demonstrate the value of integrating behavioral and physiological measurements in addition to subjective and objective comparisons of the same phenomena in understanding cognitive task performance and associated stress. For example, Cluster 1 participants exhibited high accuracy and low workload, aligning with a more efficient, or “cognitively fit”, phenotype. 40 – 42 This is in contrast to Cluster 3 participants who displayed average to lower accuracy with a range of high and low workload, indicative of a potentially maladaptive and less efficient response to cognitive challenges. The finding of three clusters, rather than the expected four, suggests that low-performing individuals do not split into distinct subgroups based on workload levels. This finding highlights the possibility that below-average performance might reflect a general difficulty in adapting to cognitive challenges, regardless of workload intensity that future work will be required to better understand. 43 Research on cognitive phenotyping has suggested that maladaptive responses may coalesce around factors like limited performance adaptability or resource allocation strategies, which could explain the absence of further differentiation among low-performing individuals. 41 – 44 The observed workload stabilization across difficulty levels suggests that participants may have reached a self-imposed effort threshold, limiting additional cognitive resources as tasks became harder. This suggests that interventions targeting low performers may benefit from addressing broader cognitive and physiological adaptability rather than workload-specific patterns. 45 – 49 Additionally, the findings revealed significant objective-subjective discrepancies in workload appraisals across clusters. Specifically, participants in Cluster 1 reported a large discrepancy between the subjective state probe for task success and the objective accuracy performance, indicating they underestimated their success. This finding is consistent with the Dunning-Kruger effect where low and high performing individuals over and underestimate their performance capabilities respectively. 50 – 52 Clusters 2 and 3 did this as well but to a lesser extent. This negative perception may reflect higher cognitive strain, lower efficiency, or other factors like mood or poor body awareness. These findings contribute to a growing body of evidence on individual differences in performance under stress. 1 , 53 – 57 The identification of functionally distinct phenotypes highlights the importance of personalized approaches to studying workload and task performance. By integrating ECG-based workload estimation with subjective state probes, this study addresses the limitations of unitary measures of stress or performance, offering a more nuanced understanding of task appraisal dynamics. For example, a recent meta-analysis examined cognitive flexibility in aging, highlighting that age-related reductions in cognitive flexibility were associated with both decreased and increased activation in several functional brain networks. 58 This suggests that variability in cognitive flexibility could contribute to differences in task performance and workload management among individuals and warrants further investigation. The current study underscores the utility of physiological workload models, particularly ECG-based algorithms, in assessing cognitive demand. Compared to more invasive or logistically complex methods like EEG, ECG data provide a practical and scalable approach to monitoring real-time cognitive load in various settings, including workplace, military, and educational environments. Our approach has potential applications in designing personalized interventions to enhance performance and mitigate stress-related health risks over time. For example, a recent study by Tozzi et al. identified a cognitive biotype of depression marked by treatment resistance, impaired cognitive control, and dysfunction in cognitive control brain circuits. 59 People with weak brain connectivity at baseline improved after transcranial magnetic stimulation (TMS), while those with strong connectivity did not change, suggesting that different cognitive biotypes may respond differently to the same intervention. Our Cluster 3 performers, characterized by low performance with a range of low to moderate workload, reflect patterns identified by Tozzi et al. and warrant further investigation for more targeted performance-enhancing interventions. Emerging research suggests that the future of psychological interventions aimed at improving performance and maximizing functionality may lie in the application of neurostimulation techniques. 60 For example, studies have demonstrated that transcranial direct current stimulation (tDCS) can improve cognitive flexibility 61 and working memory, 62 while TMS has shown promise in enhancing attention and reducing symptoms of depression 63 and insomnia. 64 Future investigation could explore the effectiveness of TMS and the three identified biotypes of cognitive workload. The relationship between cognitive workload and health outcomes has revealed both exaggerated and blunted physiological reactions were linked to adverse health effects, including psychopathology, cardiovascular morbidity, and mortality. 1 , 11 , 65 By characterizing how individuals respond to cognitive challenges at varying levels of difficulty, this study provides a foundation for understanding how workload-related phenotypes or biotypes may inform precision health interventions. 37 , 38 , 57 , 66 , 67 For example, Cluster 3 participants may benefit from targeted strategies to improve task efficacy and reduce workload-related stress, whereas participants in Cluster 2 would benefit mostly from workload reduction under challenge. 9 , 68 Conversely, Cluster 1 participants, who demonstrate efficient performance and accurate self-appraisal for mental and temporal demand but underestimate success, could serve as a model for interventions aimed at enhancing resilience and adaptive task engagement. These findings align with prior research showing discrepancies in healthy samples and in a range of pathological conditions like depression, insomnia, and pain suggesting a potential pathway for health promotion through improved cognitive workload management and awareness of perceptual discrepancies. 32 – 34 A major strength of this study is the integration of subjective and objective measures, which provides a comprehensive framework for evaluating cognitive workload. The use of ECG-based workload estimation offers a practical and non-invasive approach to assessing physiological responses, while state probes capture subjective perceptions of stress, challenge, and success. This integrated approach allows for a more nuanced understanding of the interplay between perceived and actual levels of workload. However, several limitations warrant consideration. First, the study sample consists of college students who were predominantly young, and male which may limit the generalizability of the findings to broader populations. Future research should explore these dynamics across more diverse demographic groups across the lifespan with equal representation across genders. 69 Additionally, while the K-Medoids clustering methods proved effective in identifying distinct biotypes, the stability of these over time remains to be determined. Future research should build on these findings by examining the longitudinal implications of workload-related biotypes on health and performance outcomes. 37 , 38 For instance, studies could investigate whether individuals in Clusters 2 and 3, characterized in part by a higher autonomic cost for cognitive effort (i.e., greater physiological activation required to sustain performance, as estimated via ECG-derived workload), are at greater risk for stress-related health conditions like hypertension, heart disease, stroke, diabetes, irritable bowel, depression, anxiety, autoimmune or chronic pain conditions over time than Cluster 1. 9 , 28 , 70 , 71 Similarly, interventions aimed at reducing workload under challenge like exercise, sleep, mindfulness, paced breathing under load could be differentially “prescribed” based on cluster assignment. 37 , 42 , 72 – 74 Similarly interventions aimed at reducing perceptual discrepancies such as biofeedback, cognitive behavioral therapy could be prescribed for individuals with notable perceptual bias. 33 , 75 – 79 Finally, for individuals with Cluster 3 performances that fall below average interventions such as neurostimulation including photobiomodulation could be evaluated for their effectiveness in “resetting” brain networks involved with cognitive control and flexibility. 67 , 80 – 83 Another promising direction is the application of these findings to real-world settings. For example, integrating ECG-based workload monitoring into wearable devices could enable continuous assessment of cognitive demands, providing insights for individuals in high-stress and high-demand occupational environments. 23 , 25 , 84 – 86 Method Participants Participants were young adults (N = 100; M Age = 21.38, SD = 3.27), racially diverse (37% Asian, 31% White/Caucasian, 21% Black/African, 8% Hispanic), and mostly male (76%). An overview of the study design and analysis pipeline is presented in Fig. 5 . Participants who did not follow study instructions, such as failing to complete tasks (n = 1), not remaining seated (n = 1), or falling asleep (n = 3) during testing, were excluded from the analysis. Participants were recruited from the University of Maryland, Baltimore County (UMBC) through flyers and social media platforms, targeting individuals with prior experience in e-sports. Although performance data related to e-sports was collected, it will be presented in a separate publication. All participants were at least 18 years old and fluent in speaking, reading, and writing English. This study was approved by the Institutional Review Board (IRB) at the University of Maryland, Baltimore County. All study procedures involving human participants complied with the ethical standards set by the IRB and the principles of the Belmont Report. All methods were performed in accordance with the relevant guidelines and regulations. Written informed consent was obtained from all participants prior to participation. Participants were given the opportunity to ask questions, and all inquiries were addressed. Participation was voluntary, and individuals were free to withdraw at any time without penalty. Participants received monetary compensation for their time. Procedure After providing consent, participants completed a 60-minute laboratory session (Fig. 5 ). First, brief study questionnaires (demographic information, and questions about video game history (not reported here)) were completed on a desktop computer. Following this, participants were instructed to remain seated, at the same computer, for a 5-minute, baseline recording. Following the baseline period, participants completed three cognitive tasks (< 15 minutes each) on the same computer, in random order, while cardiovascular activity was recorded. For the first round of each cognitive task, there was a practice round that could be repeated to ensure each participant understood the task. Each cognitive task was organized into blocks that consisted of three varying levels of difficulty (easy, medium, and hard). Task difficulty was manipulated by reducing the amount of time to view the stimulus or increasing the number of stimuli to remember. More detail on difficulty manipulation for each task is explained in their corresponding section below. Participants completed a set of state probes that asked them to separately indicate how challenging, successful, stressful, mentally demanding, and hurried the level or block was after completion. Participants repeated this until all difficulty levels were completed for the one task. They repeated these steps until all three tasks were completed. Participants completed nine total levels, three for each task, in randomized order. Equipment Electrocardiography Sensors Heart activity was recorded using a custom-built ECG device created by our team. The device consisted of an Arduino box (Lakewood, NJ, USA) connected to a set of wired electrodes with three leads. Each lead was attached to a disposable sticker placed below the left and right clavicles (near the shoulders) and on the lower left abdomen. The box was installed under the table, allowing participants to manually connect the electrode wires after placing the adhesive stickers. This setup provided a secure and minimally invasive connection for data collection. Recording, Signal Processing, Workload Calculation The ECG signals were collected using a 3-channel configuration, with a sampling rate of 250 Hz. The total collected data was about 62.5 hours. Data was segmented into 30-second samples with a 1-second increment between each sample. During preprocessing, we filtered the data and calculated the signal-to-noise ratio (SNR) for each sample. Samples with an SNR below 4 dB were classified as invalid and removed. Approximately 3.5% of the data were removed as noise. We calculated heart rate metrics for each 30-second ECG sample to estimate workload changes relative to baseline, referred to as workload increase. To account for individual differences in baseline physiology, heart rate features were first standardized within each participant. Workload estimates were then generated for both the baseline period and each of the three cognitive tasks. The change in workload was computed by subtracting each participant’s baseline workload from their task-related workload, providing a personalized measure of physiological effort across tasks. Workload Model Development Previously, we developed models to detect cognitive resource depletion or workload using physiological features extracted from tasks such as mental arithmetic, simulated navigation and decision-making, and visuospatial/sensorimotor processing in our prior study. 24 Cognitive depletion is defined as a cognitive construct that changes continuously and is sensitive to changes in multitasking, mental and temporal demand, and task difficulty. 24 The models demonstrated strong generalizability across diverse tasks and participant samples. Two versions of the model were evaluated: one integrating both EEG and ECG data and another relying solely on ECG data. For this study, the ECG-only model was selected for workload predictions due to its practicality, ease of data collection, and reliable performance in accurately detecting cognitive depletion. The model architecture consists of two fully connected hidden layers, each with 20 nodes, designed to capture meaningful representations of ECG features for binary workload classification. To prevent overfitting, the model employs L1 and L2 regularization with values of 0.003 and 0.01, respectively. These regularization techniques enhance model generalization and robustness. Key ECG features, including mean heart rate, maximum and minimum heart rates, and heart rate variability, are calculated every 30 seconds using the NeuroKit2 Python package. 87 Signal quality was assessed by calculating the signal-to-noise ratio (SNR) over each 30-second window with a 1-second shift. Only windows with SNR greater than 4 dB were retained. R-peaks were detected using the Engzeemod2012 algorithm implemented in NeuroKit2 (v0.2.2). Filtered ECG signals were processed using a zero-phase bandpass filter (0.25–30 Hz), implemented via butter and filtfilt from scipy.signal in Python 3.11. 88 Physiological features from these high-quality windows were standardized relative to each participant’s baseline and used for workload predictions across tasks. This ensured that model input reflected reliable and physiologically valid data. Baseline Acclimation Period and ECG To collect resting baseline data, 5 minutes was recorded while the participant watched a relaxing video of a lava lamp available on YouTube ( https://youtu.be/h_lQ2tMgLVM ). Before the recording, participants were instructed to adhere three adhesive ECG electrodes: one below the clavicle near the right shoulder, one below the clavicle near the left shoulder, and one on the lower left abdomen. Participants were asked to keep both feet on the floor and remain seated. Cognitive Tasks and Physiologic Cost Assessment The cognitive tasks were developed from an open-source cognitive battery published by Dr. Helen Sauzeon and her colleagues at the University of Bordeaux. 89 We adapted their battery into an abbreviated set integrated with an ECG sensor and expanded their code so that task difficulty was manipulated into blocks of easy, medium, and hard levels randomized on each administration. The ECG signal was processed by a machine learning algorithm developed by our group that estimates cognitive workload from ECG data and predicts NASA Task Load Index (NASA-TLX) workload and temporal demand. 24 Completion of each of the three tasks generated a raw accuracy score for each level of task difficulty (easy, medium, and hard for Tasks 1, 2, and 3). Cognitive Biotypes were generated for overall performance that characterizes the physiologic cost of performance (ECG reaction/cognitive depletion) on the cognitive battery across all tasks and difficulty levels. Proportional variation in gender and race across Cognitive Biotypes were examined with chi-square analysis computed using an online calculator available at: https://www.socscistatistics.com/ . Enumeration Task Enumeration measures counting ability. 89 , 90 Trials began with a fixation cross in the center of the screen. Participants were instructed to look at the center of the screen, count the number of circles (varied between 5 and 9), and answer using a mouse click. Difficulty was manipulated across three conditions (easy, medium, and hard) by decreasing the display time and increasing the number of dots to count. Each trial resulted in a binary outcome: success or failure. A trial was considered successful if the participant's response matched the number of circles presented. Accuracy was computed as the number of successful trials divided by the total number of trials. Each participant completed 20 trials for each stimulus condition. Task Switching (TS) Cognitive Task TS measures the flexibility of selective attention and shifting attention between different goals. 89 , 91 In this task, participants were asked to indicate if the target digit was odd/even or if the target digit was higher/lower than five as a function of the color of the task cue. Digits 1–4 and 6–9 were used for target stimuli. Tasks changed as a function of the color and shape of the task cue. Trials were either an even/odd task or a high/low task. When task cues were red and square, participants answered whether a target digit was odd/even by pressing a specific key. When cues were blue and diamond-shaped, a different key was pressed. Task difficulty was manipulated across three conditions (easy, medium, and hard) by reducing the time participants had to respond. Participants had a longer response window in the easy condition, a moderate window in the medium condition, and a very limited window in the hard condition, making it increasingly difficult to process the cue and respond accurately before the deadline. Each trial resulted in a binary outcome: success or failure. A trial was considered successful if the participant provided the correct response of the tasks. A failure outcome occurred either due to an incorrect response or no response within the reaction time deadline. Accuracy was computed by dividing the number of successful trials by the total number of trials. Each participant completed 80 trials for each stimulus condition. Spatial Working Memory Task This task assesses visual-spatial short-term memory capacity. 89 , 92 , 93 For each trial, 1 of 16 squares were colored red briefly (other squares are gray) in a sequential pattern. A new grid of non-colored squares was shown and participants were asked to “replay” the pattern of red squares by clicking on the squares colored red in order on the prior display.Task difficulty was manipulated across three conditions (easy, medium, and hard) by increasing the number of red squares in the sequence, 4 for easy, 6 for medium, and 8 for hard, which made it progressively more difficult to recall and accurately reproduce the full sequence Each trial had a percentage outcome ranging from 0 to 1, represented by the number of squares answered in the correct order divided by the total number of squares. Accuracy was calculated as the average score across all trials. A score of 0 indicated that all responses were incorrect, while a score of 1 indicated that all responses were correct. Each participant completed 20 trials for each stimulus condition. State Probes To capture individual differences in state appraisal during the tasks, at the end of each level, the screen paused, and the participant was prompted to complete several brief questions about their level of perceived stress and frustration, challenge, temporal, and mental demand that best represented their state during the level immediately preceding the prompt. These dimensions are derived from the NASA-TLX to assess subjective experience of workload. 24 , 94 The response option for each state probe was a Likert scale of 0–20 with 0 conveying no stress, challenge, or workload and 20 conveying the highest level. State probe responses were used to generate metrics of subjective-objective discrepancy/perceptual bias to test the second hypothesis. To test the hypothesis that discrepancies between subjective-objective assessments during tasking would covary with biotypes we generated the following metrics using the difference between normalized (subjective) state probe responses and the corresponding (more objective) measure for three comparisons 1) the success discrepancy metric was the state success probe minus objective task accuracy, 2) the workload discrepancy metric was calculated as the subjective workload state probe minus workload from the ECG algorithm, and 3) the temporal demand discrepancy metric included the temporal demand probe minus workload from the algorithm. For each metric, positive values indicated over-estimation. Negative values indicated underestimation. We decided not to calculate discrepancy metrics for the stress or challenge state probes to avoid overlapping analyses of related measures, which can make the results harder to interpret. Instead, we focused on mental and temporal demand, as these items have consistently been linked to cognitive fatigue during task performance in previous research. 24 Brooks et al. found that higher perceived mental and temporal demand were associated with reduced task accuracy and signs of cognitive depletion. Focusing on these measures helped keep the analysis aligned with our goal. Modeling Individual Workload Dynamics To analyze workload dynamics, we computed baseline ECG statistics for each participant and applied z-score standardization to the entire dataset using each participant’s baseline mean and standard deviation. We calculated the mean workload change from the baseline period and the mean accuracy for each task for each participant across nine data points (three cognitive tasks × three difficulty levels). These metrics were standardized across participants to facilitate meaningful comparisons. The analysis revealed the expected variations in accuracy, showing that as task difficulty increased, accuracy decreased. This standardized approach served as the foundation for our workload classifier model, enabling a personalized assessment of workload changes relative to each participant’s baseline. By addressing within-subject variability, it effectively captured individual characteristics and performance dynamics. Clustering Approach We employed the K-Medoids algorithm for clustering due to its robustness in handling noise and outliers. Unlike K-Means, K-Medoids identifies actual data points as cluster centers, providing more interpretable outcomes by minimizing dissimilarities rather than squared distances. 39 Clustering was implemented using the scikit-learn-extra package 95 in Python 3.11. Determining Optimal Number of Clusters To determine the optimal number of clusters, we conducted: Silhouette Analysis: Revealed three distinct clusters, with a high silhouette score indicating well-separated and cohesive clusters. The best silhouette score is 1, and the worst is -1. Calinski-Harabasz Index: This metric evaluates clustering quality by comparing between-cluster and within-cluster variance. Higher scores indicate better clustering performance. Both analyses confirmed three clusters as optimal (see Supplemental material). Centroid Proximity Index (CPI) CPI measures the average distance of points within a cluster to its centroid, with smaller distances indicating stronger clusters. Among the three clusters, the one in the upper-left quadrant of Fig. 2 , characterized by higher accuracy and lower workload, had the smallest CPI. Statistical Analysis: ANOVA Each participant was assigned to a mode cluster based on their most frequently occurring cluster assignment. However, their ratings for mental demand, temporal demand, and success were recorded separately for each of the three games and three difficulty levels, leading to multiple data points per participant. For analysis, we used the mode cluster to process all data points, ensuring that each rating was categorized according to the participant’s dominant cluster while preserving the variability across task conditions. All statistical tests were performed in R (version 4.4.1), using the aov() function from the base stats package. Post hoc pairwise comparisons were conducted using Tukey’s Honest Significant Difference (HSD) test via the TukeyHSD() function. Effect sizes (η²) were calculated using the eta_squared() function from the effectsize package, with 95% confidence intervals. Declarations Competing Interests Justin Brooks is the shareholder of D-Prime LLC. All other authors have no competing interests. Funding This study was supported by the UMBC MIPS0022 Award No: 002186-00001. The funding agency was not involved in the study design, data collection, data analysis, decision to publish, or preparation of the manuscript. Author Contribution Sarah Conklin, Justin Brooks, and Murat Kucukosmanoglu conceptualized the research. Sarah Conklin, Murat Kucukosmanoglu, Golshan Kargosha, Jenny Tu, and Quang Dang carried out the research. Sarah Conklin, Murat Kucukosmanoglu, Golshan Kargosha, Jenny Tu, Quang Dang, and Kanika Bansal prepared the figures and wrote the manuscript. All authors reviewed and edited the final manuscript. Acknowledgements This research was supported by the UMBC MIPS0022 Award No: 002186-00001. We thank Hima Poojitha Sai Sree Myla for assisting in developing the cognitive battery tasks. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. Data Availability All data generated and analyzed during the current study are not publicly available but are available from Justin Brooks upon reasonable request and with approval from the University of Maryland, Baltimore County. References Brindle, R. C., Ginty, A. T., Phillips, A. C. & Carroll, D. A tale of two mechanisms: A meta-analytic approach toward understanding the autonomic basis of cardiovascular reactivity to acute psychological stress. Psychophysiology 51 (10), 964–976. 10.1111/psyp.12248 (2014). Turner, A. I. et al. Psychological stress reactivity and future health and disease outcomes: A systematic review of prospective evidence. Psychoneuroendocrinology 114 , 104599. 10.1016/j.psyneuen.2020.104599 (2020). Seery, M. D. Challenge or threat? Cardiovascular indexes of resilience and vulnerability to potential stress in humans. Neurosci. Biobehavioral Reviews . 35 (7), 1603–1610. 10.1016/j.neubiorev.2011.03.003 (2011). Pourmand, V. et al. Multi-ethnic variation in the ties that bind rumination and heart rate variability: Implications for health disparities. Stress Health Published online January . 11 , smi3365. 10.1002/smi.3365 (2024). Kupper, N., Denollet, J., Widdershoven, J. & Kop, W. J. Cardiovascular Reactivity to Mental Stress and Mortality in Patients With Heart Failure. JACC: Heart Fail. 3 (5), 373–382. 10.1016/j.jchf.2014.12.016 (2015). von Känel, R. et al. Sympathetic nervous system responses to acute psychosocial stress in male physicians with clinical burnout. Biol. Psychol. 183 , 108687. 10.1016/j.biopsycho.2023.108687 (2023). Rush, J., Ong, A. D., Piazza, J. R., Charles, S. T. & Almeida, D. M. Too little, too much, and just right: Exploring the goldilocks zone of daily stress reactivity. Emotion. Published online 2024:No Pagination Specified-No Pagination Specified. 10.1037/emo0001333 Steptoe, A., Cropley, M. & Joekes, K. Task demands and the pressures of everyday life: Associations between cardiovascular reactivity and work blood pressure and heart rate. Health Psychol. 19 (1), 46–54. 10.1037/0278-6133.19.1.46 (2000). Whittaker, A. C., Ginty, A., Hughes, B. M., Steptoe, A. & Lovallo, W. R. Cardiovascular Stress Reactivity and Health: Recent Questions and Future Directions. Psychosom. Med. 83 (7), 756. 10.1097/PSY.0000000000000973 (2021). Ginty, A. T. & Conklin, S. M. High perceived stress in relation to life events is associated with blunted cardiac reactivity. Biol. Psychol. 86 (3), 383–385 (2011). Carroll, D. et al. Increased blood pressure reactions to acute mental stress are associated with 16-year cardiovascular disease mortality. Psychophysiology 49 (10), 1444–1448. 10.1111/j.1469-8986.2012.01463.x (2012). Pouwels, S. et al. Utility of the cold pressor test to predict future cardiovascular events. Expert Rev. Cardiovasc. Ther. 17 (4), 305–318. 10.1080/14779072.2019.1598262 (2019). Tombaugh, T. N. A comprehensive review of the Paced Auditory Serial Addition Test (PASAT). Arch. Clin. Neuropsychol. 21 (1), 53–76. 10.1016/j.acn.2005.07.006 (2006). Kirschbaum, C., Pirke, K. M. & Hellhammer, D. H. Trier Social Stress Test. Published online November . 7 10.1037/t05570-000 (2011). Hoffing, R. A. C. et al. Dissociable mappings of tonic and phasic pupillary features onto cognitive processes involved in mental arithmetic. PLOS ONE . 15 (3), e0230517. 10.1371/journal.pone.0230517 (2020). Usui, H. & Nishida, Y. The very low-frequency band of heart rate variability represents the slow recovery component after a mental stress task. PLoS One . 12 (8), e0182611. 10.1371/journal.pone.0182611 (2017). Ginty, A. T. & Conklin, S. M. High perceived stress in relation to life events is associated with blunted cardiac reactivity. Biol. Psychol. 86 (3), 383–385 (2011). Zorn, J. V. et al. Cortisol stress reactivity across psychiatric disorders: A systematic review and meta-analysis. Psychoneuroendocrinology 77 , 25–36. 10.1016/j.psyneuen.2016.11.036 (2017). Dahlgren, A., Kecklund, G. & Akerstedt, T. Different levels of work-related stress and the effects on sleep, fatigue and cortisol. Scand. J. Work Environ. Health . 31 (4), 277–285. 10.5271/sjweh.883 (2005). Kiecolt-Glaser, J. K., Renna, M. E., Shrout, M. R. & Madison, A. A. Stress Reactivity: What Pushes Us Higher, Faster, and Longer—and Why It Matters. Curr. Dir. Psychol. Sci. 29 (5), 492–498. 10.1177/0963721420949521 (2020). Sazuka, N., Katsumata, K., Komoriya, Y., Oba, T. & Ohira, H. Association of brain–autonomic activities and task accuracy under cognitive load: a pilot study using electroencephalogram, autonomic activity measurements, and arousal level estimated by machine learning. Front. Hum. Neurosci. 18 10.3389/fnhum.2024.1272121 (2024). Cheng, S. et al. Acute combined effects of concurrent physical activities on autonomic nervous activation during cognitive tasks. Front. Physiol. 15 10.3389/fphys.2024.1340061 (2024). Nilsson, E. J., Bärgman, J., Ljung Aust, M., Matthews, G. & Svanberg, B. Let Complexity Bring Clarity: A Multidimensional Assessment of Cognitive Load Using Physiological Measures. Front. Neuroergonomics . 3 , 787295. 10.3389/fnrgo.2022.787295 (2022). Brooks, J. et al. Predicting Rapid Shifts in Cognitive Resource Allocation. Manser, P., Thalmann, M., Adcock, M., Knols, R. H. & de Bruin, E. D. Can Reactivity of Heart Rate Variability Be a Potential Biomarker and Monitoring Tool to Promote Healthy Aging? A Systematic Review With Meta-Analyses. Front. Physiol. 12 10.3389/fphys.2021.686129 (2021). Wu, T., Snieder, H. & de Geus, E. Genetic influences on cardiovascular stress reactivity. Neurosci. Biobehavioral Reviews . 35 (1), 58–68. 10.1016/j.neubiorev.2009.12.001 (2010). Oken, B. S., Chamine, I. & Wakeland, W. A systems approach to stress, stressors and resilience in humans. Behav. Brain. Res. 282 , 144–154. 10.1016/j.bbr.2014.12.047 (2015). Wang, R., Hartman, C. A. & Snieder, H. Stress-related exposures amplify the effects of genetic susceptibility on depression and anxiety. Transl Psychiatry . 13 (1), 1–10. 10.1038/s41398-023-02327-3 (2023). Nelson, C. J., Cho, C., Berk, A. R., Holland, J. & Roth, A. J. Are Gold Standard Depression Measures Appropriate for Use in Geriatric Cancer Patients? A Systematic Evaluation of Self-Report Depression Instruments Used With Geriatric, Cancer, and Geriatric Cancer Samples. JCO 28 (2), 348–356. 10.1200/JCO.2009.23.0201 (2010). Diener, E., Oishi, S. & Tay, L. Handbook of Wellbeing.; (2018). Viljanen, A. et al. Subjective and objective health predicting mortality and institutionalization: an 18-year population-based follow-up study among community-dwelling Finnish older adults. BMC Geriatr. 21 (1), 358. 10.1186/s12877-021-02311-w (2021). Harris, M., Blanco, E., Howie, H. & Rempfer, M. The Discrepancy between Subjective and Objective Evaluations of Cognitive and Functional Ability among People with Schizophrenia: A Systematic Review. Behav. Sci. 14 (1), 30. 10.3390/bs14010030 (2024). Srisurapanont, M., Suttajit, S., Eurviriyanukul, K. & Varnado, P. Discrepancy between objective and subjective cognition in adults with major depressive disorder. Sci. Rep. 7 (1), 3901. 10.1038/s41598-017-04353-w (2017). Kawai, K. et al. A Study of Factors Causing Sleep State Misperception in Patients with Depression. Nat. Sci. Sleep. 14 , 1273–1283. 10.2147/NSS.S366774 (2022). Kim, E. S. et al. Optimism and Cause-Specific Mortality: A Prospective Cohort Study. Am. J. Epidemiol. 185 (1), 21–29. 10.1093/aje/kww182 (2017). Tully-Wilson, C. et al. Self-perceptions of aging: A systematic review of longitudinal studies. Psychol. Aging . 36 (7), 773–789. 10.1037/pag0000638 (2021). Roubinov, D. S. & Belger, A. Multisystem stress response biotypes: a precision psychiatry approach to identifying and treating multidimensional risk factors for anhedonia in adolescence. Neuropsychopharmacology 50 (1), 318–319. 10.1038/s41386-024-01953-9 (2024). Shukla, R. Two Distinct Biotypes in Major Depression Unveiled. Biol. Psychiatry . 95 (5), 382–384. 10.1016/j.biopsych.2023.11.026 (2024). Arora, P. & Deepali, Varshney, S. Analysis of K-Means and K-Medoids Algorithm For Big Data. Procedia Comput. Sci. ; 78 :507–512. doi: 10.1016/j.procs.2016.02.095 (2016). Bassett, D. S. et al. Cognitive fitness of cost-efficient brain functional networks. Proceedings of the National Academy of Sciences. ;106(28):11747–11752. (2009). 10.1073/pnas.0903641106 Taylor, P., Walker, F. R., Heathcote, A. & Aidman, E. Effects of Multimodal Physical and Cognitive Fitness Training on Sustaining Mental Health and Job Readiness in a Military Cohort. Sustainability 15 (11), 9016. 10.3390/su15119016 (2023). Aidman, E. Cognitive Fitness Framework: Towards Assessing, Training and Augmenting Individual-Difference Factors Underpinning High-Performance Cognition. Front. Hum. Neurosci. 13 10.3389/fnhum.2019.00466 (2020). Stasielowicz, L. How important is cognitive ability when adapting to changes? A meta-analysis of the performance adaptation literature. Pers. Indiv. Differ. 166 , 110178. 10.1016/j.paid.2020.110178 (2020). Ryan, M. & Ryznar, R. The Molecular Basis of Resilience: A Narrative Review. Front. Psychiatry . 13 , 856998. 10.3389/fpsyt.2022.856998 (2022). Arjmand, G., Abbas-Zadeh, M. & Eftekhari, M. H. Effect of MIND diet intervention on cognitive performance and brain structure in healthy obese women: a randomized controlled trial. Sci. Rep. 12 (1), 2871. 10.1038/s41598-021-04258-9 (2022). Flood, A. & Keegan, R. J. Cognitive Resilience to Psychological Stress in Military Personnel. Frontiers in Psychology. ;13. Accessed November 1, 2023. https://www.frontiersin.org/articles/ (2022). 10.3389/fpsyg.2022.809003 Matthews, G., Panganiban, A. R., Wells, A., Wohleber, R. W. & Reinerman-Jones, L. E. Metacognition, Hardiness, and Grit as Resilience Factors in Unmanned Aerial Systems (UAS) Operations: A Simulation Study. Frontiers in Psychology. ;10. Accessed November 17, 2023. https://www.frontiersin.org/articles/ (2019). 10.3389/fpsyg.2019.00640 Yang, J. S., Jeon, Y. J., Lee, G. B., Kim, H. C. & Jung, S. J. The association between psychological resilience and cognitive function in longitudinal data: Results from the community follow-up survey. J. Affect. Disord. 290 , 109–116. 10.1016/j.jad.2021.04.062 (2021). Hoorelbeke, K., Koster, E. H. W., Vanderhasselt, M. A., Callewaert, S. & Demeyer, I. The influence of cognitive control training on stress reactivity and rumination in response to a lab stressor and naturalistic stress. Behav. Res. Ther. 69 , 1–10. 10.1016/j.brat.2015.03.010 (2015). Kruger, J. & Dunning, D. Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments. J. Personal. Soc. Psychol. 77 (6), 1121–1134. 10.1037/0022-3514.77.6.1121 (1999). Dunning, D. Chapter five - The Dunning–Kruger Effect: On Being Ignorant of One’s Own Ignorance. In: Olson JM, Zanna MP, eds. Advances in Experimental Social Psychology. Vol 44. Academic Press; :247–296. (2011). 10.1016/B978-0-12-385522-0.00005-6 Dunning, D. On Identifying Human Capital: Flawed Knowledge Leads to Faulty Judgments of Expertise by Individuals and Groups. In: Advances in Group Processes. Vol 32. Emerald Group Publishing Limited; :149–176. doi: 10.1108/S0882-614520150000032006 (2015). Sandström, A., Rhodin, I. N., Lundberg, M., Olsson, T. & Nyberg, L. Impaired cognitive performance in patients with chronic burnout syndrome. Biol. Psychol. 69 (3), 271–279. 10.1016/j.biopsycho.2004.08.003 (2005). Patel, A. N. et al. Mental State Assessment and Validation Using Personalized Physiological Biometrics. Frontiers in Human Neuroscience. ;12. Accessed December 21, 2023. https://www.frontiersin.org/articles/ (2018). 10.3389/fnhum.2018.00221 Leis, O. & Lautenbach, F. Psychological and physiological stress in non-competitive and competitive esports settings: A systematic review. Psychol. Sport Exerc. 51 , 101738. 10.1016/j.psychsport.2020.101738 (2020). Winslow, B., Carroll, M. B., Martin, J. W., Surpris, G. & Chadderdon, G. L. Identification of resilient individuals and those at risk for performance deficits under stress. Frontiers in Neuroscience. ;9. Accessed February 22, 2024. https://www.frontiersin.org/journals/neuroscience/articles/ (2015). 10.3389/fnins.2015.00328 Kim, S. Y., Park, H., Kim, H., Kim, J. & Seo, K. Technostress causes cognitive overload in high-stress people: Eye tracking analysis in a virtual kiosk test. Inf. Process. Manag. 59 (6), 103093. 10.1016/j.ipm.2022.103093 (2022). Xia, H., Hou, Y., Li, Q. & Chen, A. A meta-analysis of cognitive flexibility in aging: Perspective from functional network and lateralization. Hum. Brain. Mapp. 45 (14), e70031. 10.1002/hbm.70031 (2024). Tozzi, L. et al. A cognitive neural circuit biotype of depression showing functional and behavioral improvement after transcranial magnetic stimulation in the B-SMART-fMRI trial. Nat. Mental Health . 2 (8), 987–998. 10.1038/s44220-024-00271-9 (2024). Carè, M., Chiappalone, M. & Cota, V. R. Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function. Front. Neurosci. 18 10.3389/fnins.2024.1363128 (2024). Rahmanian, M. & Lobnia, E. Effectiveness of Transcranial Direct Current Stimulation (tDCS) on Cognitive Flexibility, Problem Solving and Brain Processing Speed in Students. Neuropsychology 9 (35), 75–88. 10.30473/clpsy.2024.71218.1744 (2024). Satorres, E. et al. Enhancing Immediate Memory, Potential Learning, and Working Memory with Transcranial Direct Current Stimulation in Healthy Older Adults. Int. J. Environ. Res. Public Health . 19 (19), 12716. 10.3390/ijerph191912716 (2022). Cash, R. F. H., Cocchi, L., Lv, J., Fitzgerald, P. B. & Zalesky, A. Functional Magnetic Resonance Imaging–Guided Personalization of Transcranial Magnetic Stimulation Treatment for Depression. JAMA Psychiatry . 78 (3), 337–339. 10.1001/jamapsychiatry.2020.3794 (2021). Sun, N., He, Y., Wang, Z., Zou, W. & Liu, X. The effect of repetitive transcranial magnetic stimulation for insomnia: a systematic review and meta-analysis. Sleep Med. 77 , 226–237. 10.1016/j.sleep.2020.05.020 (2021). Carroll, D., Ginty, A. T., Whittaker, A. C., Lovallo, W. R. & de Rooij, S. R. The behavioural, cognitive, and neural corollaries of blunted cardiovascular and cortisol reactions to acute psychological stress. Neurosci. Biobehavioral Reviews . 77 , 74–86. 10.1016/j.neubiorev.2017.02.025 (2017). Pettorruso, M. et al. Addiction biotypes: a paradigm shift for future treatment strategies? Mol. Psychiatry . 29 (5), 1450–1452. 10.1038/s41380-024-02423-w (2024). Cappon, D. B. & Pascual-Leone, A. Toward Precision Noninvasive Brain Stimulation. AJP 181 (9), 795–805. 10.1176/appi.ajp.20240643 (2024). Dehais, F., Lafont, A., Roy, R. & Fairclough, S. A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance. Frontiers in Neuroscience. ;14. Accessed December 7, 2022. https://www.frontiersin.org/articles/ (2020). 10.3389/fnins.2020.00268 Abbasi, A. M., Darvishi, E., Rodrigues, M. A. & Sayehmiri, K. Gender differences in cognitive performance and psychophysiological responses during noise exposure and different workloads. Appl. Acoust. 189 , 108602. 10.1016/j.apacoust.2021.108602 (2022). Geronimus, A. T. et al. Do US Black Women Experience Stress-Related Accelerated Biological Aging? Hum. Nat. 21 (1), 19–38. 10.1007/s12110-010-9078-0 (2010). Cohen, S. & Janicki-Deverts, D. Who’s Stressed? Distributions of Psychological Stress in the United States in Probability Samples from 1983, 2006, and 20091. J. Appl. Soc. Psychol. 42 (6), 1320–1334. 10.1111/j.1559-1816.2012.00900.x (2012). Masmoudi, K., Chaari, F., Ben Waer, F., Rebai, H. & Sahli, S. A single session of slow-paced breathing improved cognitive functions and postural control among middle-aged women: a randomized single blinded controlled trial. Menopause Published online May . 28 10.1097/GME.0000000000002470 (2024). Mitsea, E., Drigas, A. & Skianis, C. Artificial Intelligence, Immersive Technologies, and Neurotechnologies in Breathing Interventions for Mental and Emotional Health: A Systematic Review. Electronics 13 (12), 2253. 10.3390/electronics13122253 (2024). Gardi, C., Fazia, T., Stringa, B. & Giommi, F. A short Mindfulness retreat can improve biological markers of stress and inflammation. Psychoneuroendocrinology 135 , 105579. 10.1016/j.psyneuen.2021.105579 (2022). Walton, T. F., Ree, M. J., Fueggle, S. N. & Bucks, R. S. A scoping review of sleep discrepancy methodology: What are we measuring and what does it mean? Sleep Med. 126 , 32–66. 10.1016/j.sleep.2024.11.016 (2025). Tang, N. K. Y. & Harvey, A. G. Altering misperception of sleep in insomnia: Behavioral experiment versus verbal feedback. J. Consult. Clin. Psychol. 74 , 767–776. 10.1037/0022-006X.74.4.767 (2006). Tang, N. K. Y. & Harvey, A. G. Correcting distorted perception of sleep in insomnia: a novel behavioural experiment? Behav. Res. Ther. 42 (1), 27–39. 10.1016/S0005-7967(03)00068-8 (2004). Tang, N. K. Y., Goodchild, C. E. & Salkovskis, P. M. Hybrid cognitive-behaviour therapy for individuals with insomnia and chronic pain: A pilot randomised controlled trial. Behav. Res. Ther. 50 (12), 814–821. 10.1016/j.brat.2012.08.006 (2012). Laborde, S. et al. The Influence of Slow-Paced Breathing on Executive Function. J. Psychophysiol. 36 (1), 13–27. 10.1027/0269-8803/a000279 (2022). Liebert, A. et al. Photophysical Mechanisms of Photobiomodulation Therapy as Precision Medicine. Biomedicines 11 (2), 237. 10.3390/biomedicines11020237 (2023). Machado, L. Understanding cognition and how it changes with aging, brain disease, and lifestyle choices. J. Royal Soc. New. Z. 51 (1), 128–142. 10.1080/03036758.2020.1796102 (2021). Cao, H. et al. Neuromodulation techniques for modulating cognitive function: enhancing stimulation precision and intervention effects. Neural Regeneration Res. .:10.4103/NRR.NRR. 10.4103/NRR.NRR-D-24-00836 Rue, A. L. Healthy Brain Aging: Role of Cognitive Reserve, Cognitive Stimulation, and Cognitive Exercises. Clin. Geriatr. Med. 26 (1), 99–111. 10.1016/j.cger.2009.11.003 (2010). Stapel, J., Mullakkal-Babu, F. A. & Happee, R. Automated driving reduces perceived workload, but monitoring causes higher cognitive load than manual driving. Transp. Res. Part. F: Traffic Psychol. Behav. 60 , 590–605. 10.1016/j.trf.2018.11.006 (2019). Canali, S., Schiaffonati, V. & Aliverti, A. Challenges and recommendations for wearable devices in digital health: Data quality, interoperability, health equity, fairness. PLOS Digit. Health . 1 (10), e0000104. 10.1371/journal.pdig.0000104 (2022). Kim, N., Kim, J. & Ahn, C. R. Predicting workers’ inattentiveness to struck-by hazards by monitoring biosignals during a construction task: A virtual reality experiment. Adv. Eng. Inform. 49 , 101359. 10.1016/j.aei.2021.101359 (2021). Makowski, D. et al. NeuroKit2: A Python toolbox for neurophysiological signal processing. Behav. Res. 53 (4), 1689–1696. 10.3758/s13428-020-01516-y (2021). Gommers, R. et al. scipy/scipy: SciPy 1.9.0. Zenodo. Published online July . 29 10.5281/zenodo.6940349 (2022). Adolphe, M. et al. An Open-Source Cognitive Test Battery to Assess Human Attention and Memory. Frontiers in Psychology. ;13. Accessed January 2, 2023. https://www.frontiersin.org/articles/ (2022). 10.3389/fpsyg.2022.880375 Bavelier, D. & Green, C. S. Enhancing Attentional Control: Lessons from Action Video Games. Neuron 104 (1), 147–163. 10.1016/j.neuron.2019.09.031 (2019). Lapierre, M. D., Cropper, S. J. & Howe, P. D. L. Shared processing in multiple object tracking and visual working memory in the absence of response order and task order confounds. PLoS One . 12 (4), e0175736. 10.1371/journal.pone.0175736 (2017). Berch, D. B., Krikorian, R. & Huha, E. M. The Corsi Block-Tapping Task: Methodological and Theoretical Considerations. Brain Cogn. 38 (3), 317–338. 10.1006/brcg.1998.1039 (1998). Soreq, E., Violante, I. R., Daws, R. E. & Hampshire, A. Neuroimaging evidence for a network sampling theory of individual differences in human intelligence test performance. Nat. Commun. 12 (1), 2072. 10.1038/s41467-021-22199-9 (2021). Hart, S. G. & Staveland, L. E. Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. In: Hancock PA, Meshkati N, eds. Advances in Psychology. Vol 52. Human Mental Workload. North-Holland; :139–183. (1988). 10.1016/S0166-4115(08)62386-9 Pedregosa, F. Scikit-learn: Machine learning in Python. Published online 2011. Accessed April 30, (2024). https://cir.nii.ac.jp/crid/1370005891170856713 Additional Declarations Competing interest reported. Justin Brooks is the shareholder of D-Prime LLC. All other authors have no competing interests. Supplementary Files SupplementalFigure.docx Cite Share Download PDF Status: Published Journal Publication published 06 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 13 Nov, 2025 Reviews received at journal 10 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviews received at journal 10 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviews received at journal 02 Nov, 2025 Reviewers agreed at journal 30 Oct, 2025 Reviewers agreed at journal 24 Oct, 2025 Reviewers invited by journal 23 Oct, 2025 Editor assigned by journal 17 Jul, 2025 Submission checks completed at journal 10 Jul, 2025 First submitted to journal 10 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7071787","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":539426605,"identity":"e80adb12-e8a6-46f9-a5f5-da8bbcb22d77","order_by":0,"name":"Sarah Conklin","email":"","orcid":"","institution":"Center for Women's Biobehavioral Health Research, Department of Psychiatry, University of Pittsburgh Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Conklin","suffix":""},{"id":539426609,"identity":"c4181145-6594-44c2-926b-a9d1f72c89d8","order_by":1,"name":"Golshan Kargosha","email":"","orcid":"","institution":"D-Prime LLC","correspondingAuthor":false,"prefix":"","firstName":"Golshan","middleName":"","lastName":"Kargosha","suffix":""},{"id":539426612,"identity":"1d3a767a-3c94-4e27-870f-c828bbe0feb1","order_by":2,"name":"Jenny Tu","email":"","orcid":"","institution":"University of Maryland Institute for Health Computing","correspondingAuthor":false,"prefix":"","firstName":"Jenny","middleName":"","lastName":"Tu","suffix":""},{"id":539426615,"identity":"b41ceda7-2538-43e7-b6a6-459b431a1bc5","order_by":3,"name":"Kanika Bansal","email":"","orcid":"","institution":"US Army DEVCOM Army Research Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Kanika","middleName":"","lastName":"Bansal","suffix":""},{"id":539426616,"identity":"ed24a039-60e2-40f0-a9e5-3e017400b342","order_by":4,"name":"Quang Dang","email":"","orcid":"","institution":"University of Maryland, Baltimore County","correspondingAuthor":false,"prefix":"","firstName":"Quang","middleName":"","lastName":"Dang","suffix":""},{"id":539426617,"identity":"ac7be68c-819c-425f-a44e-79ba6e05eebd","order_by":5,"name":"Justin Brooks","email":"","orcid":"","institution":"D-Prime LLC","correspondingAuthor":false,"prefix":"","firstName":"Justin","middleName":"","lastName":"Brooks","suffix":""},{"id":539426618,"identity":"06bd76af-66dd-4acc-b1dd-fb8a2d1d77d2","order_by":6,"name":"Murat Kucukosmanoglu","email":"data:image/png;base64,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","orcid":"","institution":"D-Prime LLC","correspondingAuthor":true,"prefix":"","firstName":"Murat","middleName":"","lastName":"Kucukosmanoglu","suffix":""}],"badges":[],"createdAt":"2025-07-08 07:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7071787/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7071787/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-37107-8","type":"published","date":"2026-03-06T15:58:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":95172651,"identity":"bc1b37e8-d902-4fa2-b4dc-546f73cf1333","added_by":"auto","created_at":"2025-11-05 06:39:55","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6243652,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReportNeurometryxDiscrepancyManuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7071787/v1/09f2de03594e893ce9503185.docx"},{"id":95172642,"identity":"4755fd1c-d603-4f0d-9d23-27181a82b657","added_by":"auto","created_at":"2025-11-05 06:39:54","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8481,"visible":true,"origin":"","legend":"","description":"","filename":"2fbd0be13d7f4539a1f2e3f974885120.json","url":"https://assets-eu.researchsquare.com/files/rs-7071787/v1/593a253717297e260ffa9073.json"},{"id":95172658,"identity":"8fbf8537-73a8-4113-a326-3f8599d40409","added_by":"auto","created_at":"2025-11-05 06:39:55","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2707383,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-7071787/v1/0bcd34e384c2d00b32b787a2.docx"},{"id":95228554,"identity":"7579a152-d9fd-4a3c-b78e-6e46d98c5961","added_by":"auto","created_at":"2025-11-05 16:33:55","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":200726,"visible":true,"origin":"","legend":"","description":"","filename":"2fbd0be13d7f4539a1f2e3f9748851201enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7071787/v1/ad3ed01f538ddbbc7c4ea7a2.xml"},{"id":95172659,"identity":"df7b9de1-0600-486a-9f2f-e45d7b6215ee","added_by":"auto","created_at":"2025-11-05 06:39:56","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":237635,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7071787/v1/2c88210e09e931ecaa8253a1.png"},{"id":95227259,"identity":"dbacd1ca-a9b5-4a06-a07b-30e642201b04","added_by":"auto","created_at":"2025-11-05 16:32:18","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":131008,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7071787/v1/b89be1c74f22d7febcc59b84.png"},{"id":95172654,"identity":"7c85a5c7-04d0-40f5-98bd-ea5f10b1e4a5","added_by":"auto","created_at":"2025-11-05 06:39:55","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":120953,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7071787/v1/f14b09a01d881c8443738eb8.png"},{"id":95226432,"identity":"f203a612-826a-4021-b14b-88f8a9667232","added_by":"auto","created_at":"2025-11-05 16:31:07","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":137265,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7071787/v1/e04ded8bb842fa45ebc550c1.png"},{"id":95172647,"identity":"fa8fb6a5-7ee8-4d82-b819-7f6fc95ab9cf","added_by":"auto","created_at":"2025-11-05 06:39:54","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":34375,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7071787/v1/edb1f7de01a493f0fdf9e46d.png"},{"id":95172656,"identity":"d848f19a-e822-4562-a0fe-62459d3d9cd9","added_by":"auto","created_at":"2025-11-05 06:39:55","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":196681,"visible":true,"origin":"","legend":"","description":"","filename":"2fbd0be13d7f4539a1f2e3f9748851201structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7071787/v1/99efdd40ff4af13610bce399.xml"},{"id":95172655,"identity":"1a573e5e-1de1-46cc-be89-811572daa027","added_by":"auto","created_at":"2025-11-05 06:39:55","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":219637,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7071787/v1/53f8e015034bc196e4fe6a26.html"},{"id":95226493,"identity":"057ba97e-fdd1-43b9-8c15-c5b320608e3a","added_by":"auto","created_at":"2025-11-05 16:31:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2098770,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eParticipant variability in performance, workload, and subjective experience across cognitive task difficulty levels. \u003c/strong\u003ePanels \u003cstrong\u003ea.\u003c/strong\u003e accuracy and \u003cstrong\u003eb.\u003c/strong\u003e workload probability shows violin plots across Easy, Medium, and Hard tasks, with red dots indicating median values and black dots representing individual participants. Panels \u003cstrong\u003ec.\u003c/strong\u003e through \u003cstrong\u003eg.\u003c/strong\u003e display participant responses to state probes: \u003cstrong\u003ec.\u003c/strong\u003e perceived challenge (“How challenging was it? How hard did you have to work?”), \u003cstrong\u003ed. \u003c/strong\u003eperceived success (“How successful were you in accomplishing the task goals?”), \u003cstrong\u003ee.\u003c/strong\u003e stress appraisal (“How stressful, discouraging, and frustrating was the task?”), \u003cstrong\u003ef.\u003c/strong\u003e mental demand (“How mentally demanding was the task?”), and \u003cstrong\u003eg. \u003c/strong\u003etemporal demand (“How hurried or rushed was the pace of the task?”).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7071787/v1/4e04f80f7a1fbada2bf7a017.png"},{"id":95172645,"identity":"98efaba9-5986-48c7-9f5a-7547a0a59cc0","added_by":"auto","created_at":"2025-11-05 06:39:54","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":636376,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCluster Analysis of Workload and Accuracy Using K-Medoids.\u003c/strong\u003e Three clusters of workload probability and accuracy were identified using K-Medoids. The average distance from all points within each cluster to its centroid is indicated in black text.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7071787/v1/5ccae62202c1e22a93f9f99c.jpeg"},{"id":95227726,"identity":"2976e358-f63b-4d87-a6fb-1df3b7ce9306","added_by":"auto","created_at":"2025-11-05 16:32:49","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":520514,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCluster analysis of workload and accuracy across participants\u003c/strong\u003e. \u003cstrong\u003ea.\u003c/strong\u003eScatter plot of standardized workload probability versus cognitive task accuracy for all data points (3 tasks × 3 difficulty levels), colored by cluster (Cluster 1 = blue, Cluster 2 = yellow, Cluster 3 = red) with centroids (×). \u003cstrong\u003eb.\u003c/strong\u003e Stacked bar chart of cluster proportions within each racial group (A = Asian, B = Black/African-American, H = Hispanic/Latino, W = White/Caucasian). \u003cstrong\u003ec.\u003c/strong\u003eStacked bar chart of cluster proportions within each gender (F = Female, M = Male). \u003cstrong\u003ed.\u003c/strong\u003e Scatter plot of each subject’s mode cluster membership (most frequent cluster across nine points) with centroids. \u003cstrong\u003ee.\u003c/strong\u003e Stacked bar chart of mode-cluster proportions by race. \u003cstrong\u003ef\u003c/strong\u003e. Stacked bar chart of mode-cluster proportions by gender.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7071787/v1/c26a35a395ca509ffffda4ee.jpeg"},{"id":95226779,"identity":"06e94f02-084f-4bf7-9409-c6ea14d3663d","added_by":"auto","created_at":"2025-11-05 16:31:43","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":648538,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCluster-level discrepancies between subjective and objective measures. \u003c/strong\u003ePanels (\u003cstrong\u003ea-c\u003c/strong\u003e) display box-and-whisker plots of the difference between subjective ratings and objective indices for Cluster 1 (blue), Cluster 2 (yellow) and Cluster 3 (red): \u003cstrong\u003ea.\u003c/strong\u003e temporal-demand discrepancy (subjective temporal demand − ECG-based workload), \u003cstrong\u003eb.\u003c/strong\u003e mental-demand discrepancy (subjective mental demand − workload), and \u003cstrong\u003ec.\u003c/strong\u003esuccess discrepancy (subjective success − task accuracy); black lines mark the medians. Panels (\u003cstrong\u003ed-f\u003c/strong\u003e) show the corresponding scatter-plots: \u003cstrong\u003ed.\u003c/strong\u003e workload probability vs temporal-demand discrepancy, \u003cstrong\u003ee.\u003c/strong\u003e workload probability vs mental-demand discrepancy, and \u003cstrong\u003ef.\u003c/strong\u003etask accuracy vs success discrepancy. Clusters 1 and 3 have smaller mental- and temporal- demand discrepancies than Cluster 2, while Cluster 1 shows the largest discrepancy on the success metric.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7071787/v1/217b5eb1c83f1d4f074a645a.jpeg"},{"id":95172643,"identity":"17812ec8-0436-4e3e-bf1c-cb1ba76a4987","added_by":"auto","created_at":"2025-11-05 06:39:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":120352,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy Design and Analysis Pipeline. \u003c/strong\u003eThe illustrated study protocol shows participants following the baseline phase to the testing phase in the same session where objective workload (ECG data) was measured in both phases. In the analysis phase, objective task accuracy and workload performance metrics were clustered into three distinct biotypes. The perceptual discrepancy metric assessed the difference in estimation between subjective state probes and objective measures of the same phenomena. ECG = Electrocardiogram.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7071787/v1/4b3f1060a7413514dcc2b7c0.png"},{"id":104250712,"identity":"78acb448-d464-4bee-8710-7cfef56c4bdd","added_by":"auto","created_at":"2026-03-09 16:06:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4908581,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7071787/v1/3c9fe2cb-0b32-44a7-b543-5eb65f8e432b.pdf"},{"id":95172649,"identity":"2832496f-8899-457b-9586-ddc23bd24fa7","added_by":"auto","created_at":"2025-11-05 06:39:55","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2707383,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-7071787/v1/47ab7d990df345f1790a95cc.docx"}],"financialInterests":"Competing interest reported. Justin Brooks is the shareholder of D-Prime LLC. All other authors have no competing interests.","formattedTitle":"Cognitive Biotypes Identified Through ECG-Derived Workload and Behavioral Accuracy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIndividual differences in the reflexive physiological reactivity to cognitive challenges, perceived threats, and stressors contribute to disease risk and resilience.\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Acutely, enhanced stress reactions may lead to symptoms such as fatigue, anxiety, or stress, yet can also enhance task performance in the short term.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Over time, however, exaggerated reactions, whether in magnitude or duration, compromise performance and increase the subsequent risk for high blood pressure, burnout, heart disease, and other indices of poor health.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e In laboratory research, exaggerated reactions to the paced serial addition test (PASAT), a cognitive challenge task, has been shown to predict coronary heart disease (CHD) mortality over a 16-year period.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e These findings highlight the need to better characterize individual reactivity profiles that span both behavioral performance and autonomic responses, particularly under conditions of increasing cognitive demand.\u003c/p\u003e\u003cp\u003eMeasurement of stress is a challenge and is approached by a variety of methods. In laboratory settings, paradigms such as the Trier Social Stress Task, the cold pressor test, and the PASAT are used to induce social, physical, or cognitive stress, respectively.\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Physiological responses to these laboratory stress-inducing tasks are typically recorded and analyzed in phases (baseline, stress exposure, recovery).\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Both exaggerated and blunted reactions to laboratory stress have been related to psychopathology, cardiovascular morbidity and mortality, traumatic experiences, patterns of health behavior, and social and demographic variables.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIn medical settings, serum cortisol is commonly used as a purely physiological measure of stress.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Cortisol is often assessed as an awakening response with the area under the curve calculated across several morning measurements.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Finally, in the wild, wearable devices use proprietary algorithms on photoplethysmography sensor data to estimate stress load or periods of stress. Although these two different methods provide valuable snapshots of measuring stress and are meaningfully associated with risky health outcomes, they do not capture how a person performs under stress, how they appraise the challenge, and how their physiology meets the challenge with reflexive responses facilitated partly by the autonomic nervous system (ANS). Understanding how a person performs while under active stress or challenge, beyond just measurement, could inform precision medicine or lifestyle changes to influence health outcomes associated with heightened stress reactivity.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIt is well established that cognitive demand induces a reflexive physiologic response (e.g. change in heart rate) via the ANS, providing a causal mechanism that relates rapid decision-making, frequent task switching, and continuous shifts in attention (ubiquitous in the modern workplace and home) to fundamental changes in physiology.\u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Using this known relationship, Brooks et al. developed an algorithm using ECG and EEG data that generalizes across multiple cognitive tasks and participant groups. The algorithm makes second-by-second predictions of cognitive depletion, a construct that is sensitive to multitasking, mental and temporal demand, and task difficulty.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e We explored the utility of the ECG-based workload algorithm to identify patterns in behavioral and physiological response to cognitive demand.\u003c/p\u003e\u003cp\u003eIndividual differences in physiological responses to cognitive challenges and stressors are crucial in psychological research, as it provides insight into why people react differently to similar situations.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Stress response research is used in our study to better understand these individual differences. Genetic, psychological, and sociocultural factors can influence these differences. For example, some individuals may have a heightened stress response due to genetic predispositions, leading to increased magnitude and or duration of physiological reactions (blood pressure, cortisol, heart rate dynamics, skin conductance).\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Others might exhibit more resilient physiological responses, which can buffer against the negative effects of stress.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e These variations can impact mental health outcomes, such as susceptibility to anxiety, depression, and other stress-related disorders.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eDespite advancements in neuroimaging technologies, the gold standard assessment of psychological experience remains self-reports, typically gathered through structured or semi-structured interviews, questionnaires, or state probes.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e However, recent research has highlighted the need for more integrative frameworks that connect subjective state appraisals with objective measurements of physiological and/or behavioral function.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Studies have shown that healthy individuals and those with psychiatric conditions\u0026ndash;such as depression, insomnia, schizophrenia, and fibromyalgia\u0026ndash;often exhibit significant discrepancy between their subjective reports and objective assessments.\u003csup\u003e\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e For example, in insomnia sleep-wake state discrepancy is a common phenomenon where patients report frustratingly long sleep onset latency or duration in their sleep, however, on polysomnography their sleep metrics are typical. This discrepancy may prevent proper diagnosis and treatment effectiveness.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Conversely, in other work, discordant, positively biased perceptions were linked to more positive outcomes, such as reduced morbidity and mortality.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e Therefore, identifying the subjective-objective discrepancy (the difference between perceived workload and ECG-measured workload) for cognitive ability may help develop interventions to improve performance under cognitive strain or stress.\u003c/p\u003e\u003cp\u003eThe concept of \u0026ldquo;biotypes\u0026rdquo; has emerged in psychology and psychiatry as a promising approach to provide a more global assessment of functional type, capturing the interplay between cognitive, physiological, and/or psychological processes that less integrated approaches would not capture. This framework is increasingly being applied to inform precision medicine approaches to treatment. For example, in psychiatry, biotypes have been used to identify subgroups within conditions like depression, addiction, insomnia, and schizophrenia, improving the prediction of treatment outcomes with a personalized approach of prevention and intervention strategies.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e Integrating perception, cognition, and physiological measures, this approach identifies patterns linking subjective experiences with physiological and behavioral responses, making the biotype framework a powerful tool.\u003c/p\u003e\u003cp\u003eWe expected that a workload-integrated approach to cognitive challenge would reveal distinct cognitive biotypes, characterized by both performance and associated physiological responses. We anticipated observing systematic variation in cognitive task accuracy and workload, leading to four distinct phenotype clusters: 1) average-to-high accuracy, low workload; 2) average-to-high accuracy, high workload; 3) average to low accuracy, low workload; and 4) average to low accuracy and high workload. This expectation is grounded in the continuous nature of the workload algorithm. Objectively measured workload varies along a spectrum from high to low, reinforcing the idea that both accuracy and workload serve as primary determinants of biotypical differentiation. Although four clusters were hypothesized, analysis revealed a stable three-cluster solution, indicating that low-performing individuals did not stratify by workload. We also expected that accurate appraisal of cognitive function, based on self-report state probes completed during tasks when the workload measure from ECG is used as ground truth, would covary significantly with the performance: workload phenotypes described above. In particular, we expected Cluster 1 to show the least discrepancy between subjective ratings and objective measures, reflecting a more accurate or self-aware phenotype.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eWorkload and Accuracy Patterns Across Tasks\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFigure 1 shows the variability among participants for task accuracy, workload, and each of the state probes. As can be seen in Fig. 1B, average workload increase remained relatively stable across difficulty levels, with only a slight rise at the highest level. The implications of this pattern are explored in the Discussion.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCluster Analysis using K Medoids: Evaluating Accuracy and Workload\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows standardized accuracy and workload metrics plotted with points colored according to their assigned clusters. Convex hulls outline each cluster's extent, and average distances to the cluster centers (Centroid Proximity Index, CPI) are labeled to indicate cohesion.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDistinct Accuracy-Workload Phenotypes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe hypothesized that four distinct phenotypes would emerge, defined by combinations of high or low task accuracy and high or low physiological workload. Each participant's nine performances (three tasks \u0026times; three difficulty levels) were plotted (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. However, clustering using the K-Medoids algorithm revealed three distinct functional groups: (1) high accuracy, low workload, (2) average-to-high accuracy, high workload, and (3) low accuracy, low-to-moderate workload. Participants were assigned to the cluster that appeared most frequently across their nine tasks (mode cluster; \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e. To further refine the classification, we calculated the average cluster assignment within each participant\u0026rsquo;s dominant cluster. This method ensured that classification captured the participant\u0026rsquo;s dominant accuracy-workload profile across tasks, reducing the influence of individual task fluctuations.\u003c/p\u003e\u003cp\u003eFurther analysis based on mode clusters using a chi-square test of independence revealed significant differences in demographic distributions across clusters. Specifically, Cluster 1 (high accuracy, low workload) was predominantly composed of Asian and White individuals, Cluster 2 (average-high accuracy, high workload) had an over-representation of Black individuals, and Cluster 3 (low accuracy, low/moderate workload) included a higher proportion of women (\u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e(2, 100)\u0026thinsp;=\u0026thinsp;11.31, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;.05 for gender; (\u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e(2, 100)\u0026thinsp;=\u0026thinsp;37.76, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;.05 for race; \u003cb\u003esee\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAn important observation from this clustering approach is that Cluster 3 was overrepresented in tasks with hard difficulty levels. However, participants in this cluster did not perform poorly only on hard tasks; they also exhibited low accuracy across easy and medium tasks, suggesting a broader performance issue. This pattern suggests that participants in Cluster 3 may have adopted a disengagement strategy, particularly as task difficulty increased, leading to their overall lower workload values.\u003c/p\u003e\u003cp\u003eWe used the Coefficient of Variation (CV) to assess variability in three key metrics: accuracy, workload, and their interaction (calculated as accuracy \u0026times; workload). CV was computed as the standard deviation divided by the mean, multiplied by 100. The CV values were 30.13% for accuracy, 68.55% for workload, and 85.79% for the interaction term. The higher CV for the interaction indicates that combining accuracy and workload introduces greater variability than either metric alone.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSubjective-Objective Comparisons will Vary by Cluster\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo test the hypothesis that Cluster 1 would show less discrepancy between subjective state probes and objective measures than the other clusters we conducted a 1-way ANOVA. The independent factor was modal cluster (Clusters 1, 2, and 3) and the dependent variables were the calculated discrepancy metrics for mental demand, temporal demand, and success. The results revealed a significant effect of Biotype on the Subjective Temporal Demand-Objective Workload discrepancy metric, F(2,882)\u0026thinsp;=\u0026thinsp;10.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η2\u0026thinsp;=\u0026thinsp;0.09, 95% CI [0.07-1.00]. Post hoc comparisons using the Tukey HSD indicated that Cluster 2 had more discrepancy than Cluster 1 and 3 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, the main effect for Biotype on Subjective Mental Demand - Objective Workload discrepancy metric was significant (F(2,882)\u0026thinsp;=\u0026thinsp;46.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η2\u0026thinsp;=\u0026thinsp;0.10, 95% CI [0.07-1.00]). Post hoc comparisons using the Tukey HSD showed the same pattern, Cluster 2 had more discrepancy than Cluster 1 and 3. Finally, the main effect of Biotype on Subjective Success - Objective Accuracy discrepancy metric was significant (F(2,884)\u0026thinsp;=\u0026thinsp;10.07, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η2\u0026thinsp;=\u0026thinsp;0.02, 95% CI [0.01-1.00]). Post hoc comparisons using the Tukey HSD indicated that Cluster 1 had significantly more discrepancy on the success metric than both clusters 2 and 3.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo explore evidence for cognitive biotypes we used a clustering approach to group participants based on performance and workload dynamics derived from an ECG-based workload algorithm and investigated the discrepancy between subjective and objective assessments of success, temporal demand, and cognitive workload during the tasks. We found support for the concept of cognitive biotypes through the identification of three, not four, distinct performance x workload groups. The hypothesis that the highest-performing and most efficient cluster would show the least discrepancy was partially supported. The findings provide novel insights into cognitive task appraisal, physiological cost, and perceptual bias, offering both theoretical and practical implications for stress and workload assessment and research.\u003c/p\u003e\u003cp\u003eThe results generally supported the hypothesis that distinct phenotypes emerge based on cognitive performance and physiological workload. Using K-Medoids clustering, three clusters were identified, reflecting variability in accuracy and workload profiles across participants.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e These clusters demonstrate the value of integrating behavioral and physiological measurements in addition to subjective and objective comparisons of the same phenomena in understanding cognitive task performance and associated stress. For example, Cluster 1 participants exhibited high accuracy and low workload, aligning with a more efficient, or “cognitively fit”, phenotype.\u003csup\u003e\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e–\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e This is in contrast to Cluster 3 participants who displayed average to lower accuracy with a range of high and low workload, indicative of a potentially maladaptive and less efficient response to cognitive challenges. The finding of three clusters, rather than the expected four, suggests that low-performing individuals do not split into distinct subgroups based on workload levels. This finding highlights the possibility that below-average performance might reflect a general difficulty in adapting to cognitive challenges, regardless of workload intensity that future work will be required to better understand.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eResearch on cognitive phenotyping has suggested that maladaptive responses may coalesce around factors like limited performance adaptability or resource allocation strategies, which could explain the absence of further differentiation among low-performing individuals.\u003csup\u003e\u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e–\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e The observed workload stabilization across difficulty levels suggests that participants may have reached a self-imposed effort threshold, limiting additional cognitive resources as tasks became harder. This suggests that interventions targeting low performers may benefit from addressing broader cognitive and physiological adaptability rather than workload-specific patterns.\u003csup\u003e\u003cspan additionalcitationids=\"CR46 CR47 CR48\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e–\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAdditionally, the findings revealed significant objective-subjective discrepancies in workload appraisals across clusters. Specifically, participants in Cluster 1 reported a large discrepancy between the subjective state probe for task success and the objective accuracy performance, indicating they underestimated their success. This finding is consistent with the Dunning-Kruger effect where low and high performing individuals over and underestimate their performance capabilities respectively.\u003csup\u003e\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e–\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e Clusters 2 and 3 did this as well but to a lesser extent. This negative perception may reflect higher cognitive strain, lower efficiency, or other factors like mood or poor body awareness.\u003c/p\u003e\u003cp\u003eThese findings contribute to a growing body of evidence on individual differences in performance under stress.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan additionalcitationids=\"CR54 CR55 CR56\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e–\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e The identification of functionally distinct phenotypes highlights the importance of personalized approaches to studying workload and task performance. By integrating ECG-based workload estimation with subjective state probes, this study addresses the limitations of unitary measures of stress or performance, offering a more nuanced understanding of task appraisal dynamics. For example, a recent meta-analysis examined cognitive flexibility in aging, highlighting that age-related reductions in cognitive flexibility were associated with both decreased and increased activation in several functional brain networks.\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e This suggests that variability in cognitive flexibility could contribute to differences in task performance and workload management among individuals and warrants further investigation.\u003c/p\u003e\u003cp\u003eThe current study underscores the utility of physiological workload models, particularly ECG-based algorithms, in assessing cognitive demand. Compared to more invasive or logistically complex methods like EEG, ECG data provide a practical and scalable approach to monitoring real-time cognitive load in various settings, including workplace, military, and educational environments. Our approach has potential applications in designing personalized interventions to enhance performance and mitigate stress-related health risks over time. For example, a recent study by Tozzi et al. identified a cognitive biotype of depression marked by treatment resistance, impaired cognitive control, and dysfunction in cognitive control brain circuits.\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e People with weak brain connectivity at baseline improved after transcranial magnetic stimulation (TMS), while those with strong connectivity did not change, suggesting that different cognitive biotypes may respond differently to the same intervention. Our Cluster 3 performers, characterized by low performance with a range of low to moderate workload, reflect patterns identified by Tozzi et al. and warrant further investigation for more targeted performance-enhancing interventions. Emerging research suggests that the future of psychological interventions aimed at improving performance and maximizing functionality may lie in the application of neurostimulation techniques.\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e For example, studies have demonstrated that transcranial direct current stimulation (tDCS) can improve cognitive flexibility\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e and working memory,\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e while TMS has shown promise in enhancing attention and reducing symptoms of depression\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e and insomnia.\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e Future investigation could explore the effectiveness of TMS and the three identified biotypes of cognitive workload.\u003c/p\u003e\u003cp\u003eThe relationship between cognitive workload and health outcomes has revealed both exaggerated and blunted physiological reactions were linked to adverse health effects, including psychopathology, cardiovascular morbidity, and mortality.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e By characterizing how individuals respond to cognitive challenges at varying levels of difficulty, this study provides a foundation for understanding how workload-related phenotypes or biotypes may inform precision health interventions.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e For example, Cluster 3 participants may benefit from targeted strategies to improve task efficacy and reduce workload-related stress, whereas participants in Cluster 2 would benefit mostly from workload reduction under challenge.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eConversely, Cluster 1 participants, who demonstrate efficient performance and accurate self-appraisal for mental and temporal demand but underestimate success, could serve as a model for interventions aimed at enhancing resilience and adaptive task engagement. These findings align with prior research showing discrepancies in healthy samples and in a range of pathological conditions like depression, insomnia, and pain suggesting a potential pathway for health promotion through improved cognitive workload management and awareness of perceptual discrepancies.\u003csup\u003e\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e–\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eA major strength of this study is the integration of subjective and objective measures, which provides a comprehensive framework for evaluating cognitive workload. The use of ECG-based workload estimation offers a practical and non-invasive approach to assessing physiological responses, while state probes capture subjective perceptions of stress, challenge, and success. This integrated approach allows for a more nuanced understanding of the interplay between perceived and actual levels of workload. However, several limitations warrant consideration. First, the study sample consists of college students who were predominantly young, and male which may limit the generalizability of the findings to broader populations. Future research should explore these dynamics across more diverse demographic groups across the lifespan with equal representation across genders.\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e Additionally, while the K-Medoids clustering methods proved effective in identifying distinct biotypes, the stability of these over time remains to be determined.\u003c/p\u003e\u003cp\u003eFuture research should build on these findings by examining the longitudinal implications of workload-related biotypes on health and performance outcomes.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e For instance, studies could investigate whether individuals in Clusters 2 and 3, characterized in part by a higher autonomic cost for cognitive effort (i.e., greater physiological activation required to sustain performance, as estimated via ECG-derived workload), are at greater risk for stress-related health conditions like hypertension, heart disease, stroke, diabetes, irritable bowel, depression, anxiety, autoimmune or chronic pain conditions over time than Cluster 1.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e Similarly, interventions aimed at reducing workload under challenge like exercise, sleep, mindfulness, paced breathing under load could be differentially “prescribed” based on cluster assignment.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan additionalcitationids=\"CR73\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e–\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e Similarly interventions aimed at reducing perceptual discrepancies such as biofeedback, cognitive behavioral therapy could be prescribed for individuals with notable perceptual bias.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan additionalcitationids=\"CR76 CR77 CR78\" citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e–\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e Finally, for individuals with Cluster 3 performances that fall below average interventions such as neurostimulation including photobiomodulation could be evaluated for their effectiveness in “resetting” brain networks involved with cognitive control and flexibility.\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan additionalcitationids=\"CR81 CR82\" citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e–\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAnother promising direction is the application of these findings to real-world settings. For example, integrating ECG-based workload monitoring into wearable devices could enable continuous assessment of cognitive demands, providing insights for individuals in high-stress and high-demand occupational environments.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan additionalcitationids=\"CR85\" citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e–\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cb\u003eParticipants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eParticipants were young adults (N = 100; M Age = 21.38, SD = 3.27), racially diverse (37% Asian, 31% White/Caucasian, 21% Black/African, 8% Hispanic), and mostly male (76%). An overview of the study design and analysis pipeline is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Participants who did not follow study instructions, such as failing to complete tasks (n = 1), not remaining seated (n = 1), or falling asleep (n = 3) during testing, were excluded from the analysis.\u003c/p\u003e\u003cp\u003eParticipants were recruited from the University of Maryland, Baltimore County (UMBC) through flyers and social media platforms, targeting individuals with prior experience in e-sports. Although performance data related to e-sports was collected, it will be presented in a separate publication. All participants were at least 18 years old and fluent in speaking, reading, and writing English. This study was approved by the Institutional Review Board (IRB) at the University of Maryland, Baltimore County. All study procedures involving human participants complied with the ethical standards set by the IRB and the principles of the Belmont Report. All methods were performed in accordance with the relevant guidelines and regulations. Written informed consent was obtained from all participants prior to participation. Participants were given the opportunity to ask questions, and all inquiries were addressed. Participation was voluntary, and individuals were free to withdraw at any time without penalty. Participants received monetary compensation for their time.\u003c/p\u003e\u003cp\u003e\u003cb\u003eProcedure\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter providing consent, participants completed a 60-minute laboratory session (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). First, brief study questionnaires (demographic information, and questions about video game history (not reported here)) were completed on a desktop computer. Following this, participants were instructed to remain seated, at the same computer, for a 5-minute, baseline recording.\u003c/p\u003e\u003cp\u003eFollowing the baseline period, participants completed three cognitive tasks (\u0026lt; 15 minutes each) on the same computer, in random order, while cardiovascular activity was recorded. For the first round of each cognitive task, there was a practice round that could be repeated to ensure each participant understood the task. Each cognitive task was organized into blocks that consisted of three varying levels of difficulty (easy, medium, and hard). Task difficulty was manipulated by reducing the amount of time to view the stimulus or increasing the number of stimuli to remember. More detail on difficulty manipulation for each task is explained in their corresponding section below. Participants completed a set of state probes that asked them to separately indicate how challenging, successful, stressful, mentally demanding, and hurried the level or block was after completion. Participants repeated this until all difficulty levels were completed for the one task. They repeated these steps until all three tasks were completed. Participants completed nine total levels, three for each task, in randomized order.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEquipment\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eElectrocardiography Sensors\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHeart activity was recorded using a custom-built ECG device created by our team. The device consisted of an Arduino box (Lakewood, NJ, USA) connected to a set of wired electrodes with three leads. Each lead was attached to a disposable sticker placed below the left and right clavicles (near the shoulders) and on the lower left abdomen. The box was installed under the table, allowing participants to manually connect the electrode wires after placing the adhesive stickers. This setup provided a secure and minimally invasive connection for data collection.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRecording, Signal Processing, Workload Calculation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe ECG signals were collected using a 3-channel configuration, with a sampling rate of 250 Hz. The total collected data was about 62.5 hours. Data was segmented into 30-second samples with a 1-second increment between each sample. During preprocessing, we filtered the data and calculated the signal-to-noise ratio (SNR) for each sample. Samples with an SNR below 4 dB were classified as invalid and removed. Approximately 3.5% of the data were removed as noise.\u003c/p\u003e\u003cp\u003eWe calculated heart rate metrics for each 30-second ECG sample to estimate workload changes relative to baseline, referred to as workload increase. To account for individual differences in baseline physiology, heart rate features were first standardized within each participant. Workload estimates were then generated for both the baseline period and each of the three cognitive tasks. The change in workload was computed by subtracting each participant’s baseline workload from their task-related workload, providing a personalized measure of physiological effort across tasks.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWorkload Model Development\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePreviously, we developed models to detect cognitive resource depletion or workload using physiological features extracted from tasks such as mental arithmetic, simulated navigation and decision-making, and visuospatial/sensorimotor processing in our prior study.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Cognitive depletion is defined as a cognitive construct that changes continuously and is sensitive to changes in multitasking, mental and temporal demand, and task difficulty.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e The models demonstrated strong generalizability across diverse tasks and participant samples. Two versions of the model were evaluated: one integrating both EEG and ECG data and another relying solely on ECG data. For this study, the ECG-only model was selected for workload predictions due to its practicality, ease of data collection, and reliable performance in accurately detecting cognitive depletion.\u003c/p\u003e\u003cp\u003eThe model architecture consists of two fully connected hidden layers, each with 20 nodes, designed to capture meaningful representations of ECG features for binary workload classification. To prevent overfitting, the model employs L1 and L2 regularization with values of 0.003 and 0.01, respectively. These regularization techniques enhance model generalization and robustness.\u003c/p\u003e\u003cp\u003eKey ECG features, including mean heart rate, maximum and minimum heart rates, and heart rate variability, are calculated every 30 seconds using the NeuroKit2 Python package.\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e Signal quality was assessed by calculating the signal-to-noise ratio (SNR) over each 30-second window with a 1-second shift. Only windows with SNR greater than 4 dB were retained. R-peaks were detected using the Engzeemod2012 algorithm implemented in NeuroKit2 (v0.2.2). Filtered ECG signals were processed using a zero-phase bandpass filter (0.25–30 Hz), implemented via butter and filtfilt from scipy.signal in Python 3.11.\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e Physiological features from these high-quality windows were standardized relative to each participant’s baseline and used for workload predictions across tasks. This ensured that model input reflected reliable and physiologically valid data.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBaseline Acclimation Period and ECG\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo collect resting baseline data, 5 minutes was recorded while the participant watched a relaxing video of a lava lamp available on YouTube (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://youtu.be/h_lQ2tMgLVM\u003c/span\u003e\u003cspan address=\"https://youtu.be/h_lQ2tMgLVM\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Before the recording, participants were instructed to adhere three adhesive ECG electrodes: one below the clavicle near the right shoulder, one below the clavicle near the left shoulder, and one on the lower left abdomen. Participants were asked to keep both feet on the floor and remain seated.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCognitive Tasks and Physiologic Cost Assessment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe cognitive tasks were developed from an open-source cognitive battery published by Dr. Helen Sauzeon and her colleagues at the University of Bordeaux.\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e We adapted their battery into an abbreviated set integrated with an ECG sensor and expanded their code so that task difficulty was manipulated into blocks of easy, medium, and hard levels randomized on each administration. The ECG signal was processed by a machine learning algorithm developed by our group that estimates cognitive workload from ECG data and predicts NASA Task Load Index (NASA-TLX) workload and temporal demand.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Completion of each of the three tasks generated a raw accuracy score for each level of task difficulty (easy, medium, and hard for Tasks 1, 2, and 3). Cognitive Biotypes were generated for overall performance that characterizes the physiologic cost of performance (ECG reaction/cognitive depletion) on the cognitive battery across all tasks and difficulty levels. Proportional variation in gender and race across Cognitive Biotypes were examined with chi-square analysis computed using an online calculator available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.socscistatistics.com/\u003c/span\u003e\u003cspan address=\"https://www.socscistatistics.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEnumeration Task\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEnumeration measures counting ability.\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e,\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e Trials began with a fixation cross in the center of the screen. Participants were instructed to look at the center of the screen, count the number of circles (varied between 5 and 9), and answer using a mouse click. Difficulty was manipulated across three conditions (easy, medium, and hard) by decreasing the display time and increasing the number of dots to count. Each trial resulted in a binary outcome: success or failure. A trial was considered successful if the participant's response matched the number of circles presented. Accuracy was computed as the number of successful trials divided by the total number of trials. Each participant completed 20 trials for each stimulus condition.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTask Switching (TS) Cognitive Task\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTS measures the flexibility of selective attention and shifting attention between different goals.\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e,\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e In this task, participants were asked to indicate if the target digit was odd/even or if the target digit was higher/lower than five as a function of the color of the task cue. Digits 1–4 and 6–9 were used for target stimuli. Tasks changed as a function of the color and shape of the task cue. Trials were either an even/odd task or a high/low task. When task cues were red and square, participants answered whether a target digit was odd/even by pressing a specific key. When cues were blue and diamond-shaped, a different key was pressed. Task difficulty was manipulated across three conditions (easy, medium, and hard) by reducing the time participants had to respond. Participants had a longer response window in the easy condition, a moderate window in the medium condition, and a very limited window in the hard condition, making it increasingly difficult to process the cue and respond accurately before the deadline. Each trial resulted in a binary outcome: success or failure. A trial was considered successful if the participant provided the correct response of the tasks. A failure outcome occurred either due to an incorrect response or no response within the reaction time deadline. Accuracy was computed by dividing the number of successful trials by the total number of trials. Each participant completed 80 trials for each stimulus condition.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpatial Working Memory Task\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis task assesses visual-spatial short-term memory capacity.\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e,\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e,\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e For each trial, 1 of 16 squares were colored red briefly (other squares are gray) in a sequential pattern. A new grid of non-colored squares was shown and participants were asked to “replay” the pattern of red squares by clicking on the squares colored red in order on the prior display.Task difficulty was manipulated across three conditions (easy, medium, and hard) by increasing the number of red squares in the sequence, 4 for easy, 6 for medium, and 8 for hard, which made it progressively more difficult to recall and accurately reproduce the full sequence Each trial had a percentage outcome ranging from 0 to 1, represented by the number of squares answered in the correct order divided by the total number of squares. Accuracy was calculated as the average score across all trials. A score of 0 indicated that all responses were incorrect, while a score of 1 indicated that all responses were correct. Each participant completed 20 trials for each stimulus condition.\u003c/p\u003e\u003cp\u003e\u003cb\u003eState Probes\u003c/b\u003e\u003c/p\u003e\u003cp\u003e To capture individual differences in state appraisal during the tasks, at the end of each level, the screen paused, and the participant was prompted to complete several brief questions about their level of perceived stress and frustration, challenge, temporal, and mental demand that best represented their state during the level immediately preceding the prompt. These dimensions are derived from the NASA-TLX to assess subjective experience of workload.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e The response option for each state probe was a Likert scale of 0–20 with 0 conveying no stress, challenge, or workload and 20 conveying the highest level. State probe responses were used to generate metrics of subjective-objective discrepancy/perceptual bias to test the second hypothesis.\u003c/p\u003e\u003cp\u003eTo test the hypothesis that discrepancies between subjective-objective assessments during tasking would covary with biotypes we generated the following metrics using the difference between normalized (subjective) state probe responses and the corresponding (more objective) measure for three comparisons 1) the success discrepancy metric was the state success probe minus objective task accuracy, 2) the workload discrepancy metric was calculated as the subjective workload state probe minus workload from the ECG algorithm, and 3) the temporal demand discrepancy metric included the temporal demand probe minus workload from the algorithm. For each metric, positive values indicated over-estimation. Negative values indicated underestimation. We decided not to calculate discrepancy metrics for the stress or challenge state probes to avoid overlapping analyses of related measures, which can make the results harder to interpret. Instead, we focused on mental and temporal demand, as these items have consistently been linked to cognitive fatigue during task performance in previous research.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Brooks et al. found that higher perceived mental and temporal demand were associated with reduced task accuracy and signs of cognitive depletion. Focusing on these measures helped keep the analysis aligned with our goal.\u003c/p\u003e\u003cp\u003e\u003cb\u003eModeling Individual Workload Dynamics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo analyze workload dynamics, we computed baseline ECG statistics for each participant and applied z-score standardization to the entire dataset using each participant’s baseline mean and standard deviation. We calculated the mean workload change from the baseline period and the mean accuracy for each task for each participant across nine data points (three cognitive tasks × three difficulty levels). These metrics were standardized across participants to facilitate meaningful comparisons. The analysis revealed the expected variations in accuracy, showing that as task difficulty increased, accuracy decreased. This standardized approach served as the foundation for our workload classifier model, enabling a personalized assessment of workload changes relative to each participant’s baseline. By addressing within-subject variability, it effectively captured individual characteristics and performance dynamics.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClustering Approach\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe employed the K-Medoids algorithm for clustering due to its robustness in handling noise and outliers. Unlike K-Means, K-Medoids identifies actual data points as cluster centers, providing more interpretable outcomes by minimizing dissimilarities rather than squared distances.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e Clustering was implemented using the scikit-learn-extra package \u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e in Python 3.11.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDetermining Optimal Number of Clusters\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo determine the optimal number of clusters, we conducted:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eSilhouette Analysis: Revealed three distinct clusters, with a high silhouette score indicating well-separated and cohesive clusters. The best silhouette score is 1, and the worst is -1.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCalinski-Harabasz Index: This metric evaluates clustering quality by comparing between-cluster and within-cluster variance. Higher scores indicate better clustering performance. Both analyses confirmed three clusters as optimal (see Supplemental material).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003cb\u003eCentroid Proximity Index (CPI)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCPI measures the average distance of points within a cluster to its centroid, with smaller distances indicating stronger clusters. Among the three clusters, the one in the upper-left quadrant of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, characterized by higher accuracy and lower workload, had the smallest CPI.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical Analysis: ANOVA\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEach participant was assigned to a mode cluster based on their most frequently occurring cluster assignment. However, their ratings for mental demand, temporal demand, and success were recorded separately for each of the three games and three difficulty levels, leading to multiple data points per participant. For analysis, we used the mode cluster to process all data points, ensuring that each rating was categorized according to the participant’s dominant cluster while preserving the variability across task conditions. All statistical tests were performed in R (version 4.4.1), using the aov() function from the base stats package. Post hoc pairwise comparisons were conducted using Tukey’s Honest Significant Difference (HSD) test via the TukeyHSD() function. Effect sizes (η²) were calculated using the eta_squared() function from the effectsize package, with 95% confidence intervals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eJustin Brooks is the shareholder of D-Prime LLC. All other authors have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was supported by the UMBC MIPS0022 Award No: 002186-00001. The funding agency was not involved in the study design, data collection, data analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSarah Conklin, Justin Brooks, and Murat Kucukosmanoglu conceptualized the research. Sarah Conklin, Murat Kucukosmanoglu, Golshan Kargosha, Jenny Tu, and Quang Dang carried out the research. Sarah Conklin, Murat Kucukosmanoglu, Golshan Kargosha, Jenny Tu, Quang Dang, and Kanika Bansal prepared the figures and wrote the manuscript. All authors reviewed and edited the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThis research was supported by the UMBC MIPS0022 Award No: 002186-00001. We thank Hima Poojitha Sai Sree Myla for assisting in developing the cognitive battery tasks. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated and analyzed during the current study are not publicly available but are available from Justin Brooks upon reasonable request and with approval from the University of Maryland, Baltimore County.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBrindle, R. C., Ginty, A. T., Phillips, A. C. \u0026amp; Carroll, D. A tale of two mechanisms: A meta-analytic approach toward understanding the autonomic basis of cardiovascular reactivity to acute psychological stress. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cb\u003e51\u003c/b\u003e (10), 964\u0026ndash;976. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/psyp.12248\u003c/span\u003e\u003cspan address=\"10.1111/psyp.12248\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTurner, A. I. et al. Psychological stress reactivity and future health and disease outcomes: A systematic review of prospective evidence. \u003cem\u003ePsychoneuroendocrinology\u003c/em\u003e \u003cb\u003e114\u003c/b\u003e, 104599. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.psyneuen.2020.104599\u003c/span\u003e\u003cspan address=\"10.1016/j.psyneuen.2020.104599\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeery, M. D. Challenge or threat? Cardiovascular indexes of resilience and vulnerability to potential stress in humans. \u003cem\u003eNeurosci. Biobehavioral Reviews\u003c/em\u003e. \u003cb\u003e35\u003c/b\u003e (7), 1603\u0026ndash;1610. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neubiorev.2011.03.003\u003c/span\u003e\u003cspan address=\"10.1016/j.neubiorev.2011.03.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePourmand, V. et al. Multi-ethnic variation in the ties that bind rumination and heart rate variability: Implications for health disparities. \u003cem\u003eStress Health Published online January\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e, smi3365. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/smi.3365\u003c/span\u003e\u003cspan address=\"10.1002/smi.3365\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKupper, N., Denollet, J., Widdershoven, J. \u0026amp; Kop, W. J. Cardiovascular Reactivity to Mental Stress and Mortality in Patients With Heart Failure. \u003cem\u003eJACC: Heart Fail.\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e (5), 373\u0026ndash;382. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jchf.2014.12.016\u003c/span\u003e\u003cspan address=\"10.1016/j.jchf.2014.12.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evon K\u0026auml;nel, R. et al. Sympathetic nervous system responses to acute psychosocial stress in male physicians with clinical burnout. \u003cem\u003eBiol. Psychol.\u003c/em\u003e \u003cb\u003e183\u003c/b\u003e, 108687. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.biopsycho.2023.108687\u003c/span\u003e\u003cspan address=\"10.1016/j.biopsycho.2023.108687\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRush, J., Ong, A. D., Piazza, J. R., Charles, S. T. \u0026amp; Almeida, D. M. Too little, too much, and just right: Exploring the goldilocks zone of daily stress reactivity. Emotion. Published online 2024:No Pagination Specified-No Pagination Specified. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/emo0001333\u003c/span\u003e\u003cspan address=\"10.1037/emo0001333\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSteptoe, A., Cropley, M. \u0026amp; Joekes, K. Task demands and the pressures of everyday life: Associations between cardiovascular reactivity and work blood pressure and heart rate. \u003cem\u003eHealth Psychol.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e (1), 46\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/0278-6133.19.1.46\u003c/span\u003e\u003cspan address=\"10.1037/0278-6133.19.1.46\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2000).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWhittaker, A. C., Ginty, A., Hughes, B. M., Steptoe, A. \u0026amp; Lovallo, W. R. Cardiovascular Stress Reactivity and Health: Recent Questions and Future Directions. \u003cem\u003ePsychosom. Med.\u003c/em\u003e \u003cb\u003e83\u003c/b\u003e (7), 756. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/PSY.0000000000000973\u003c/span\u003e\u003cspan address=\"10.1097/PSY.0000000000000973\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGinty, A. T. \u0026amp; Conklin, S. M. High perceived stress in relation to life events is associated with blunted cardiac reactivity. \u003cem\u003eBiol. Psychol.\u003c/em\u003e \u003cb\u003e86\u003c/b\u003e (3), 383\u0026ndash;385 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarroll, D. et al. Increased blood pressure reactions to acute mental stress are associated with 16-year cardiovascular disease mortality. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cb\u003e49\u003c/b\u003e (10), 1444\u0026ndash;1448. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1469-8986.2012.01463.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1469-8986.2012.01463.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePouwels, S. et al. Utility of the cold pressor test to predict future cardiovascular events. \u003cem\u003eExpert Rev. Cardiovasc. Ther.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e (4), 305\u0026ndash;318. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/14779072.2019.1598262\u003c/span\u003e\u003cspan address=\"10.1080/14779072.2019.1598262\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTombaugh, T. N. A comprehensive review of the Paced Auditory Serial Addition Test (PASAT). \u003cem\u003eArch. Clin. Neuropsychol.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e (1), 53\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.acn.2005.07.006\u003c/span\u003e\u003cspan address=\"10.1016/j.acn.2005.07.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKirschbaum, C., Pirke, K. M. \u0026amp; Hellhammer, D. H. Trier Social Stress Test. \u003cem\u003ePublished online November\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/t05570-000\u003c/span\u003e\u003cspan address=\"10.1037/t05570-000\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoffing, R. A. C. et al. Dissociable mappings of tonic and phasic pupillary features onto cognitive processes involved in mental arithmetic. \u003cem\u003ePLOS ONE\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e (3), e0230517. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0230517\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0230517\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUsui, H. \u0026amp; Nishida, Y. The very low-frequency band of heart rate variability represents the slow recovery component after a mental stress task. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e (8), e0182611. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0182611\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0182611\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGinty, A. T. \u0026amp; Conklin, S. M. High perceived stress in relation to life events is associated with blunted cardiac reactivity. \u003cem\u003eBiol. Psychol.\u003c/em\u003e \u003cb\u003e86\u003c/b\u003e (3), 383\u0026ndash;385 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZorn, J. V. et al. Cortisol stress reactivity across psychiatric disorders: A systematic review and meta-analysis. \u003cem\u003ePsychoneuroendocrinology\u003c/em\u003e \u003cb\u003e77\u003c/b\u003e, 25\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.psyneuen.2016.11.036\u003c/span\u003e\u003cspan address=\"10.1016/j.psyneuen.2016.11.036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDahlgren, A., Kecklund, G. \u0026amp; Akerstedt, T. Different levels of work-related stress and the effects on sleep, fatigue and cortisol. \u003cem\u003eScand. J. Work Environ. Health\u003c/em\u003e. \u003cb\u003e31\u003c/b\u003e (4), 277\u0026ndash;285. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5271/sjweh.883\u003c/span\u003e\u003cspan address=\"10.5271/sjweh.883\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKiecolt-Glaser, J. K., Renna, M. E., Shrout, M. R. \u0026amp; Madison, A. A. Stress Reactivity: What Pushes Us Higher, Faster, and Longer\u0026mdash;and Why It Matters. \u003cem\u003eCurr. Dir. Psychol. Sci.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e (5), 492\u0026ndash;498. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0963721420949521\u003c/span\u003e\u003cspan address=\"10.1177/0963721420949521\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSazuka, N., Katsumata, K., Komoriya, Y., Oba, T. \u0026amp; Ohira, H. Association of brain\u0026ndash;autonomic activities and task accuracy under cognitive load: a pilot study using electroencephalogram, autonomic activity measurements, and arousal level estimated by machine learning. \u003cem\u003eFront. Hum. Neurosci.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnhum.2024.1272121\u003c/span\u003e\u003cspan address=\"10.3389/fnhum.2024.1272121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheng, S. et al. Acute combined effects of concurrent physical activities on autonomic nervous activation during cognitive tasks. \u003cem\u003eFront. Physiol.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fphys.2024.1340061\u003c/span\u003e\u003cspan address=\"10.3389/fphys.2024.1340061\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNilsson, E. J., B\u0026auml;rgman, J., Ljung Aust, M., Matthews, G. \u0026amp; Svanberg, B. Let Complexity Bring Clarity: A Multidimensional Assessment of Cognitive Load Using Physiological Measures. \u003cem\u003eFront. Neuroergonomics\u003c/em\u003e. \u003cb\u003e3\u003c/b\u003e, 787295. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnrgo.2022.787295\u003c/span\u003e\u003cspan address=\"10.3389/fnrgo.2022.787295\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrooks, J. et al. Predicting Rapid Shifts in Cognitive Resource Allocation.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eManser, P., Thalmann, M., Adcock, M., Knols, R. H. \u0026amp; de Bruin, E. D. Can Reactivity of Heart Rate Variability Be a Potential Biomarker and Monitoring Tool to Promote Healthy Aging? A Systematic Review With Meta-Analyses. \u003cem\u003eFront. Physiol.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fphys.2021.686129\u003c/span\u003e\u003cspan address=\"10.3389/fphys.2021.686129\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu, T., Snieder, H. \u0026amp; de Geus, E. Genetic influences on cardiovascular stress reactivity. \u003cem\u003eNeurosci. Biobehavioral Reviews\u003c/em\u003e. \u003cb\u003e35\u003c/b\u003e (1), 58\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neubiorev.2009.12.001\u003c/span\u003e\u003cspan address=\"10.1016/j.neubiorev.2009.12.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOken, B. S., Chamine, I. \u0026amp; Wakeland, W. A systems approach to stress, stressors and resilience in humans. \u003cem\u003eBehav. Brain. Res.\u003c/em\u003e \u003cb\u003e282\u003c/b\u003e, 144\u0026ndash;154. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbr.2014.12.047\u003c/span\u003e\u003cspan address=\"10.1016/j.bbr.2014.12.047\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, R., Hartman, C. A. \u0026amp; Snieder, H. Stress-related exposures amplify the effects of genetic susceptibility on depression and anxiety. \u003cem\u003eTransl Psychiatry\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e (1), 1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41398-023-02327-3\u003c/span\u003e\u003cspan address=\"10.1038/s41398-023-02327-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNelson, C. J., Cho, C., Berk, A. R., Holland, J. \u0026amp; Roth, A. J. Are Gold Standard Depression Measures Appropriate for Use in Geriatric Cancer Patients? A Systematic Evaluation of Self-Report Depression Instruments Used With Geriatric, Cancer, and Geriatric Cancer Samples. \u003cem\u003eJCO\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (2), 348\u0026ndash;356. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/JCO.2009.23.0201\u003c/span\u003e\u003cspan address=\"10.1200/JCO.2009.23.0201\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiener, E., Oishi, S. \u0026amp; Tay, L. Handbook of Wellbeing.; (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eViljanen, A. et al. Subjective and objective health predicting mortality and institutionalization: an 18-year population-based follow-up study among community-dwelling Finnish older adults. \u003cem\u003eBMC Geriatr.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e (1), 358. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12877-021-02311-w\u003c/span\u003e\u003cspan address=\"10.1186/s12877-021-02311-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarris, M., Blanco, E., Howie, H. \u0026amp; Rempfer, M. The Discrepancy between Subjective and Objective Evaluations of Cognitive and Functional Ability among People with Schizophrenia: A Systematic Review. \u003cem\u003eBehav. Sci.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (1), 30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/bs14010030\u003c/span\u003e\u003cspan address=\"10.3390/bs14010030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSrisurapanont, M., Suttajit, S., Eurviriyanukul, K. \u0026amp; Varnado, P. Discrepancy between objective and subjective cognition in adults with major depressive disorder. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (1), 3901. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-017-04353-w\u003c/span\u003e\u003cspan address=\"10.1038/s41598-017-04353-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKawai, K. et al. A Study of Factors Causing Sleep State Misperception in Patients with Depression. \u003cem\u003eNat. Sci. Sleep.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 1273\u0026ndash;1283. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/NSS.S366774\u003c/span\u003e\u003cspan address=\"10.2147/NSS.S366774\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim, E. S. et al. Optimism and Cause-Specific Mortality: A Prospective Cohort Study. \u003cem\u003eAm. J. Epidemiol.\u003c/em\u003e \u003cb\u003e185\u003c/b\u003e (1), 21\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/aje/kww182\u003c/span\u003e\u003cspan address=\"10.1093/aje/kww182\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTully-Wilson, C. et al. Self-perceptions of aging: A systematic review of longitudinal studies. \u003cem\u003ePsychol. Aging\u003c/em\u003e. \u003cb\u003e36\u003c/b\u003e (7), 773\u0026ndash;789. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/pag0000638\u003c/span\u003e\u003cspan address=\"10.1037/pag0000638\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoubinov, D. S. \u0026amp; Belger, A. Multisystem stress response biotypes: a precision psychiatry approach to identifying and treating multidimensional risk factors for anhedonia in adolescence. \u003cem\u003eNeuropsychopharmacology\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e (1), 318\u0026ndash;319. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41386-024-01953-9\u003c/span\u003e\u003cspan address=\"10.1038/s41386-024-01953-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShukla, R. Two Distinct Biotypes in Major Depression Unveiled. \u003cem\u003eBiol. Psychiatry\u003c/em\u003e. \u003cb\u003e95\u003c/b\u003e (5), 382\u0026ndash;384. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.biopsych.2023.11.026\u003c/span\u003e\u003cspan address=\"10.1016/j.biopsych.2023.11.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArora, P. \u0026amp; Deepali, Varshney, S. Analysis of K-Means and K-Medoids Algorithm For Big Data. \u003cem\u003eProcedia Comput. Sci.\u003c/em\u003e ;\u003cb\u003e78\u003c/b\u003e:507\u0026ndash;512. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.procs.2016.02.095\u003c/span\u003e\u003cspan address=\"10.1016/j.procs.2016.02.095\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBassett, D. S. et al. Cognitive fitness of cost-efficient brain functional networks. Proceedings of the National Academy of Sciences. ;106(28):11747\u0026ndash;11752. (2009). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.0903641106\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0903641106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaylor, P., Walker, F. R., Heathcote, A. \u0026amp; Aidman, E. Effects of Multimodal Physical and Cognitive Fitness Training on Sustaining Mental Health and Job Readiness in a Military Cohort. \u003cem\u003eSustainability\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (11), 9016. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/su15119016\u003c/span\u003e\u003cspan address=\"10.3390/su15119016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAidman, E. Cognitive Fitness Framework: Towards Assessing, Training and Augmenting Individual-Difference Factors Underpinning High-Performance Cognition. \u003cem\u003eFront. Hum. Neurosci.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnhum.2019.00466\u003c/span\u003e\u003cspan address=\"10.3389/fnhum.2019.00466\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStasielowicz, L. How important is cognitive ability when adapting to changes? A meta-analysis of the performance adaptation literature. \u003cem\u003ePers. Indiv. Differ.\u003c/em\u003e \u003cb\u003e166\u003c/b\u003e, 110178. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.paid.2020.110178\u003c/span\u003e\u003cspan address=\"10.1016/j.paid.2020.110178\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRyan, M. \u0026amp; Ryznar, R. The Molecular Basis of Resilience: A Narrative Review. \u003cem\u003eFront. Psychiatry\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e, 856998. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpsyt.2022.856998\u003c/span\u003e\u003cspan address=\"10.3389/fpsyt.2022.856998\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArjmand, G., Abbas-Zadeh, M. \u0026amp; Eftekhari, M. H. Effect of MIND diet intervention on cognitive performance and brain structure in healthy obese women: a randomized controlled trial. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (1), 2871. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-021-04258-9\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-04258-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFlood, A. \u0026amp; Keegan, R. J. Cognitive Resilience to Psychological Stress in Military Personnel. Frontiers in Psychology. ;13. Accessed November 1, 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/articles/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpsyg.2022.809003\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2022.809003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMatthews, G., Panganiban, A. R., Wells, A., Wohleber, R. W. \u0026amp; Reinerman-Jones, L. E. Metacognition, Hardiness, and Grit as Resilience Factors in Unmanned Aerial Systems (UAS) Operations: A Simulation Study. Frontiers in Psychology. ;10. Accessed November 17, 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/articles/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpsyg.2019.00640\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2019.00640\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang, J. S., Jeon, Y. J., Lee, G. B., Kim, H. C. \u0026amp; Jung, S. J. The association between psychological resilience and cognitive function in longitudinal data: Results from the community follow-up survey. \u003cem\u003eJ. Affect. Disord.\u003c/em\u003e \u003cb\u003e290\u003c/b\u003e, 109\u0026ndash;116. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2021.04.062\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2021.04.062\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoorelbeke, K., Koster, E. H. W., Vanderhasselt, M. A., Callewaert, S. \u0026amp; Demeyer, I. The influence of cognitive control training on stress reactivity and rumination in response to a lab stressor and naturalistic stress. \u003cem\u003eBehav. Res. Ther.\u003c/em\u003e \u003cb\u003e69\u003c/b\u003e, 1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.brat.2015.03.010\u003c/span\u003e\u003cspan address=\"10.1016/j.brat.2015.03.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKruger, J. \u0026amp; Dunning, D. Unskilled and unaware of it: How difficulties in recognizing one\u0026rsquo;s own incompetence lead to inflated self-assessments. \u003cem\u003eJ. Personal. Soc. Psychol.\u003c/em\u003e \u003cb\u003e77\u003c/b\u003e (6), 1121\u0026ndash;1134. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/0022-3514.77.6.1121\u003c/span\u003e\u003cspan address=\"10.1037/0022-3514.77.6.1121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1999).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDunning, D. Chapter five - The Dunning\u0026ndash;Kruger Effect: On Being Ignorant of One\u0026rsquo;s Own Ignorance. In: Olson JM, Zanna MP, eds. Advances in Experimental Social Psychology. Vol 44. Academic Press; :247\u0026ndash;296. (2011). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/B978-0-12-385522-0.00005-6\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12-385522-0.00005-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDunning, D. On Identifying Human Capital: Flawed Knowledge Leads to Faulty Judgments of Expertise by Individuals and Groups. In: Advances in Group Processes. Vol 32. Emerald Group Publishing Limited; :149\u0026ndash;176. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1108/S0882-614520150000032006\u003c/span\u003e\u003cspan address=\"10.1108/S0882-614520150000032006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSandstr\u0026ouml;m, A., Rhodin, I. N., Lundberg, M., Olsson, T. \u0026amp; Nyberg, L. Impaired cognitive performance in patients with chronic burnout syndrome. \u003cem\u003eBiol. Psychol.\u003c/em\u003e \u003cb\u003e69\u003c/b\u003e (3), 271\u0026ndash;279. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.biopsycho.2004.08.003\u003c/span\u003e\u003cspan address=\"10.1016/j.biopsycho.2004.08.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePatel, A. N. et al. Mental State Assessment and Validation Using Personalized Physiological Biometrics. Frontiers in Human Neuroscience. ;12. Accessed December 21, 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/articles/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnhum.2018.00221\u003c/span\u003e\u003cspan address=\"10.3389/fnhum.2018.00221\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeis, O. \u0026amp; Lautenbach, F. Psychological and physiological stress in non-competitive and competitive esports settings: A systematic review. \u003cem\u003ePsychol. Sport Exerc.\u003c/em\u003e \u003cb\u003e51\u003c/b\u003e, 101738. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.psychsport.2020.101738\u003c/span\u003e\u003cspan address=\"10.1016/j.psychsport.2020.101738\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWinslow, B., Carroll, M. B., Martin, J. W., Surpris, G. \u0026amp; Chadderdon, G. L. Identification of resilient individuals and those at risk for performance deficits under stress. Frontiers in Neuroscience. ;9. Accessed February 22, 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/journals/neuroscience/articles/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/journals/neuroscience/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnins.2015.00328\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2015.00328\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim, S. Y., Park, H., Kim, H., Kim, J. \u0026amp; Seo, K. Technostress causes cognitive overload in high-stress people: Eye tracking analysis in a virtual kiosk test. \u003cem\u003eInf. Process. Manag.\u003c/em\u003e \u003cb\u003e59\u003c/b\u003e (6), 103093. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ipm.2022.103093\u003c/span\u003e\u003cspan address=\"10.1016/j.ipm.2022.103093\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXia, H., Hou, Y., Li, Q. \u0026amp; Chen, A. A meta-analysis of cognitive flexibility in aging: Perspective from functional network and lateralization. \u003cem\u003eHum. Brain. Mapp.\u003c/em\u003e \u003cb\u003e45\u003c/b\u003e (14), e70031. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hbm.70031\u003c/span\u003e\u003cspan address=\"10.1002/hbm.70031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTozzi, L. et al. A cognitive neural circuit biotype of depression showing functional and behavioral improvement after transcranial magnetic stimulation in the B-SMART-fMRI trial. \u003cem\u003eNat. Mental Health\u003c/em\u003e. \u003cb\u003e2\u003c/b\u003e (8), 987\u0026ndash;998. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s44220-024-00271-9\u003c/span\u003e\u003cspan address=\"10.1038/s44220-024-00271-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCar\u0026egrave;, M., Chiappalone, M. \u0026amp; Cota, V. R. Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function. \u003cem\u003eFront. Neurosci.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnins.2024.1363128\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2024.1363128\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRahmanian, M. \u0026amp; Lobnia, E. Effectiveness of Transcranial Direct Current Stimulation (tDCS) on Cognitive Flexibility, Problem Solving and Brain Processing Speed in Students. \u003cem\u003eNeuropsychology\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (35), 75\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.30473/clpsy.2024.71218.1744\u003c/span\u003e\u003cspan address=\"10.30473/clpsy.2024.71218.1744\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSatorres, E. et al. Enhancing Immediate Memory, Potential Learning, and Working Memory with Transcranial Direct Current Stimulation in Healthy Older Adults. \u003cem\u003eInt. J. Environ. Res. Public Health\u003c/em\u003e. \u003cb\u003e19\u003c/b\u003e (19), 12716. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph191912716\u003c/span\u003e\u003cspan address=\"10.3390/ijerph191912716\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCash, R. F. H., Cocchi, L., Lv, J., Fitzgerald, P. B. \u0026amp; Zalesky, A. Functional Magnetic Resonance Imaging\u0026ndash;Guided Personalization of Transcranial Magnetic Stimulation Treatment for Depression. \u003cem\u003eJAMA Psychiatry\u003c/em\u003e. \u003cb\u003e78\u003c/b\u003e (3), 337\u0026ndash;339. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamapsychiatry.2020.3794\u003c/span\u003e\u003cspan address=\"10.1001/jamapsychiatry.2020.3794\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun, N., He, Y., Wang, Z., Zou, W. \u0026amp; Liu, X. The effect of repetitive transcranial magnetic stimulation for insomnia: a systematic review and meta-analysis. \u003cem\u003eSleep Med.\u003c/em\u003e \u003cb\u003e77\u003c/b\u003e, 226\u0026ndash;237. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.sleep.2020.05.020\u003c/span\u003e\u003cspan address=\"10.1016/j.sleep.2020.05.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarroll, D., Ginty, A. T., Whittaker, A. C., Lovallo, W. R. \u0026amp; de Rooij, S. R. The behavioural, cognitive, and neural corollaries of blunted cardiovascular and cortisol reactions to acute psychological stress. \u003cem\u003eNeurosci. Biobehavioral Reviews\u003c/em\u003e. \u003cb\u003e77\u003c/b\u003e, 74\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neubiorev.2017.02.025\u003c/span\u003e\u003cspan address=\"10.1016/j.neubiorev.2017.02.025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePettorruso, M. et al. Addiction biotypes: a paradigm shift for future treatment strategies? \u003cem\u003eMol. Psychiatry\u003c/em\u003e. \u003cb\u003e29\u003c/b\u003e (5), 1450\u0026ndash;1452. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41380-024-02423-w\u003c/span\u003e\u003cspan address=\"10.1038/s41380-024-02423-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCappon, D. B. \u0026amp; Pascual-Leone, A. Toward Precision Noninvasive Brain Stimulation. \u003cem\u003eAJP\u003c/em\u003e \u003cb\u003e181\u003c/b\u003e (9), 795\u0026ndash;805. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1176/appi.ajp.20240643\u003c/span\u003e\u003cspan address=\"10.1176/appi.ajp.20240643\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDehais, F., Lafont, A., Roy, R. \u0026amp; Fairclough, S. A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance. Frontiers in Neuroscience. ;14. Accessed December 7, 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/articles/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnins.2020.00268\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2020.00268\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbbasi, A. M., Darvishi, E., Rodrigues, M. A. \u0026amp; Sayehmiri, K. Gender differences in cognitive performance and psychophysiological responses during noise exposure and different workloads. \u003cem\u003eAppl. Acoust.\u003c/em\u003e \u003cb\u003e189\u003c/b\u003e, 108602. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.apacoust.2021.108602\u003c/span\u003e\u003cspan address=\"10.1016/j.apacoust.2021.108602\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeronimus, A. T. et al. Do US Black Women Experience Stress-Related Accelerated Biological Aging? \u003cem\u003eHum. Nat.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e (1), 19\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12110-010-9078-0\u003c/span\u003e\u003cspan address=\"10.1007/s12110-010-9078-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCohen, S. \u0026amp; Janicki-Deverts, D. Who\u0026rsquo;s Stressed? Distributions of Psychological Stress in the United States in Probability Samples from 1983, 2006, and 20091. \u003cem\u003eJ. Appl. Soc. Psychol.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e (6), 1320\u0026ndash;1334. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1559-1816.2012.00900.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1559-1816.2012.00900.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMasmoudi, K., Chaari, F., Ben Waer, F., Rebai, H. \u0026amp; Sahli, S. A single session of slow-paced breathing improved cognitive functions and postural control among middle-aged women: a randomized single blinded controlled trial. \u003cem\u003eMenopause Published online May\u003c/em\u003e. \u003cb\u003e28\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/GME.0000000000002470\u003c/span\u003e\u003cspan address=\"10.1097/GME.0000000000002470\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMitsea, E., Drigas, A. \u0026amp; Skianis, C. Artificial Intelligence, Immersive Technologies, and Neurotechnologies in Breathing Interventions for Mental and Emotional Health: A Systematic Review. \u003cem\u003eElectronics\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (12), 2253. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/electronics13122253\u003c/span\u003e\u003cspan address=\"10.3390/electronics13122253\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGardi, C., Fazia, T., Stringa, B. \u0026amp; Giommi, F. A short Mindfulness retreat can improve biological markers of stress and inflammation. \u003cem\u003ePsychoneuroendocrinology\u003c/em\u003e \u003cb\u003e135\u003c/b\u003e, 105579. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.psyneuen.2021.105579\u003c/span\u003e\u003cspan address=\"10.1016/j.psyneuen.2021.105579\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalton, T. F., Ree, M. J., Fueggle, S. N. \u0026amp; Bucks, R. S. A scoping review of sleep discrepancy methodology: What are we measuring and what does it mean? \u003cem\u003eSleep Med.\u003c/em\u003e \u003cb\u003e126\u003c/b\u003e, 32\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.sleep.2024.11.016\u003c/span\u003e\u003cspan address=\"10.1016/j.sleep.2024.11.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang, N. K. Y. \u0026amp; Harvey, A. G. Altering misperception of sleep in insomnia: Behavioral experiment versus verbal feedback. \u003cem\u003eJ. Consult. Clin. Psychol.\u003c/em\u003e \u003cb\u003e74\u003c/b\u003e, 767\u0026ndash;776. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/0022-006X.74.4.767\u003c/span\u003e\u003cspan address=\"10.1037/0022-006X.74.4.767\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang, N. K. Y. \u0026amp; Harvey, A. G. Correcting distorted perception of sleep in insomnia: a novel behavioural experiment? \u003cem\u003eBehav. Res. Ther.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e (1), 27\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0005-7967(03)00068-8\u003c/span\u003e\u003cspan address=\"10.1016/S0005-7967(03)00068-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang, N. K. Y., Goodchild, C. E. \u0026amp; Salkovskis, P. M. Hybrid cognitive-behaviour therapy for individuals with insomnia and chronic pain: A pilot randomised controlled trial. \u003cem\u003eBehav. Res. Ther.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e (12), 814\u0026ndash;821. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.brat.2012.08.006\u003c/span\u003e\u003cspan address=\"10.1016/j.brat.2012.08.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaborde, S. et al. The Influence of Slow-Paced Breathing on Executive Function. \u003cem\u003eJ. Psychophysiol.\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e (1), 13\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1027/0269-8803/a000279\u003c/span\u003e\u003cspan address=\"10.1027/0269-8803/a000279\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiebert, A. et al. Photophysical Mechanisms of Photobiomodulation Therapy as Precision Medicine. \u003cem\u003eBiomedicines\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (2), 237. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/biomedicines11020237\u003c/span\u003e\u003cspan address=\"10.3390/biomedicines11020237\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMachado, L. Understanding cognition and how it changes with aging, brain disease, and lifestyle choices. \u003cem\u003eJ. Royal Soc. New. Z.\u003c/em\u003e \u003cb\u003e51\u003c/b\u003e (1), 128\u0026ndash;142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/03036758.2020.1796102\u003c/span\u003e\u003cspan address=\"10.1080/03036758.2020.1796102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCao, H. et al. Neuromodulation techniques for modulating cognitive function: enhancing stimulation precision and intervention effects. \u003cem\u003eNeural Regeneration Res.\u003c/em\u003e .:10.4103/NRR.NRR. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/NRR.NRR-D-24-00836\u003c/span\u003e\u003cspan address=\"10.4103/NRR.NRR-D-24-00836\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRue, A. L. Healthy Brain Aging: Role of Cognitive Reserve, Cognitive Stimulation, and Cognitive Exercises. \u003cem\u003eClin. Geriatr. Med.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e (1), 99\u0026ndash;111. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cger.2009.11.003\u003c/span\u003e\u003cspan address=\"10.1016/j.cger.2009.11.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStapel, J., Mullakkal-Babu, F. A. \u0026amp; Happee, R. Automated driving reduces perceived workload, but monitoring causes higher cognitive load than manual driving. \u003cem\u003eTransp. Res. Part. F: Traffic Psychol. Behav.\u003c/em\u003e \u003cb\u003e60\u003c/b\u003e, 590\u0026ndash;605. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.trf.2018.11.006\u003c/span\u003e\u003cspan address=\"10.1016/j.trf.2018.11.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCanali, S., Schiaffonati, V. \u0026amp; Aliverti, A. Challenges and recommendations for wearable devices in digital health: Data quality, interoperability, health equity, fairness. \u003cem\u003ePLOS Digit. Health\u003c/em\u003e. \u003cb\u003e1\u003c/b\u003e (10), e0000104. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pdig.0000104\u003c/span\u003e\u003cspan address=\"10.1371/journal.pdig.0000104\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim, N., Kim, J. \u0026amp; Ahn, C. R. Predicting workers\u0026rsquo; inattentiveness to struck-by hazards by monitoring biosignals during a construction task: A virtual reality experiment. \u003cem\u003eAdv. Eng. Inform.\u003c/em\u003e \u003cb\u003e49\u003c/b\u003e, 101359. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.aei.2021.101359\u003c/span\u003e\u003cspan address=\"10.1016/j.aei.2021.101359\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMakowski, D. et al. NeuroKit2: A Python toolbox for neurophysiological signal processing. \u003cem\u003eBehav. Res.\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e (4), 1689\u0026ndash;1696. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3758/s13428-020-01516-y\u003c/span\u003e\u003cspan address=\"10.3758/s13428-020-01516-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGommers, R. et al. scipy/scipy: SciPy 1.9.0. Zenodo. \u003cem\u003ePublished online July\u003c/em\u003e. \u003cb\u003e29\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.6940349\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.6940349\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdolphe, M. et al. An Open-Source Cognitive Test Battery to Assess Human Attention and Memory. Frontiers in Psychology. ;13. Accessed January 2, 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/articles/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpsyg.2022.880375\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2022.880375\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBavelier, D. \u0026amp; Green, C. S. Enhancing Attentional Control: Lessons from Action Video Games. \u003cem\u003eNeuron\u003c/em\u003e \u003cb\u003e104\u003c/b\u003e (1), 147\u0026ndash;163. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuron.2019.09.031\u003c/span\u003e\u003cspan address=\"10.1016/j.neuron.2019.09.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLapierre, M. D., Cropper, S. J. \u0026amp; Howe, P. D. L. Shared processing in multiple object tracking and visual working memory in the absence of response order and task order confounds. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e (4), e0175736. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0175736\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0175736\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerch, D. B., Krikorian, R. \u0026amp; Huha, E. M. The Corsi Block-Tapping Task: Methodological and Theoretical Considerations. \u003cem\u003eBrain Cogn.\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e (3), 317\u0026ndash;338. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1006/brcg.1998.1039\u003c/span\u003e\u003cspan address=\"10.1006/brcg.1998.1039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1998).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSoreq, E., Violante, I. R., Daws, R. E. \u0026amp; Hampshire, A. Neuroimaging evidence for a network sampling theory of individual differences in human intelligence test performance. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (1), 2072. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-021-22199-9\u003c/span\u003e\u003cspan address=\"10.1038/s41467-021-22199-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHart, S. G. \u0026amp; Staveland, L. E. Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. In: Hancock PA, Meshkati N, eds. Advances in Psychology. Vol 52. Human Mental Workload. North-Holland; :139\u0026ndash;183. (1988). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0166-4115(08)62386-9\u003c/span\u003e\u003cspan address=\"10.1016/S0166-4115(08)62386-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePedregosa, F. Scikit-learn: Machine learning in Python. Published online 2011. Accessed April 30, (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cir.nii.ac.jp/crid/1370005891170856713\u003c/span\u003e\u003cspan address=\"https://cir.nii.ac.jp/crid/1370005891170856713\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cognitive workload, ECG, Cognitive Biotypes, Perceptual bias, Clustering","lastPublishedDoi":"10.21203/rs.3.rs-7071787/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7071787/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndividual differences in physiological effort during cognitive workload, which we define as mental demand during task execution, are well established, yet self-reports often fail to reflect actual physiological effort. We hypothesized that combining cognitive performance with ECG-derived workload would reveal distinct biotypes of performance and physiological effort and that these biotypes would differ in how closely subjective appraisals align with objective measures. A sample of 100 participants completed cognitive tasks while ECG data were analyzed in real time using a validated workload classification algorithm. Clustering based on standardized performance accuracy and workload revealed three biotypes: (1) high performers with low workload, (2) average-to-high performers with high workload, and (3) low performers with variable workload. These biotypes exhibited distinct patterns of perceptual bias: Clusters 1 and 3 showed smaller discrepancies between subjective and objective workload, while Cluster 1 notably underestimated task success relative to their actual performance. These findings demonstrate that clustering behavioral and physiological data can reveal meaningful cognitive stress response profiles and suggest that subjective-objective misalignment may serve as a potential marker of cognitive resilience or vulnerability. This taxonomy may aid future efforts to personalize assessments or interventions aimed at optimizing performance under stress.\u003c/p\u003e","manuscriptTitle":"Cognitive Biotypes Identified Through ECG-Derived Workload and Behavioral Accuracy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-05 06:39:49","doi":"10.21203/rs.3.rs-7071787/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-13T16:28:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-10T21:23:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177032957744261106497966921293084854326","date":"2025-11-10T18:06:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272238776469374420597975661861876359578","date":"2025-11-10T15:07:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-10T13:54:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212957007712305079316381597398101852410","date":"2025-11-10T13:42:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-02T07:46:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277110082031799680127396360601044328191","date":"2025-10-30T15:21:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316359506184798360223545842718424551053","date":"2025-10-24T07:02:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-23T22:59:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-18T03:28:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-10T18:33:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-10T18:28:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f51d2b9d-0da0-492b-ac1f-90211af42b76","owner":[],"postedDate":"November 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":57370779,"name":"Biological sciences/Neuroscience"},{"id":57370780,"name":"Biological sciences/Physiology"},{"id":57370781,"name":"Biological sciences/Psychology"},{"id":57370782,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-03-09T16:02:44+00:00","versionOfRecord":{"articleIdentity":"rs-7071787","link":"https://doi.org/10.1038/s41598-026-37107-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-06 15:58:11","publishedOnDateReadable":"March 6th, 2026"},"versionCreatedAt":"2025-11-05 06:39:49","video":"","vorDoi":"10.1038/s41598-026-37107-8","vorDoiUrl":"https://doi.org/10.1038/s41598-026-37107-8","workflowStages":[]},"version":"v1","identity":"rs-7071787","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7071787","identity":"rs-7071787","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}