Physiological Manifestations in Strategic Decision-Making under Conditions of Uncertainty: Insights from the Iowa Gambling Task

Physiological Manifestations in Strategic Decision-Making under Conditions of Uncertainty: Insights from the Iowa Gambling Task | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Physiological Manifestations in Strategic Decision-Making under Conditions of Uncertainty: Insights from the Iowa Gambling Task Ali Moghimi, Elahe Yaghoubian, Morteza Izadifar, Hamidreza Kobravi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8464219/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Decision-making is a key topic in cognitive sciences and neuroeconomics. While many studies have analyzed individual performance under conditions of uncertainty within the framework of the Iowa Gambling Task (IGT), few have examined the physiological manifestations during the task and their alignment with participants’ strategies, particularly in the context of healthy dynamics, free from limitations. This study aimed to investigate decision-making under uncertainty in a stress-free environment (free from time constraints or emotional interventions) using a modified IGT. Twenty-eight participants (12 men, 16 women; 21.96 ± 3.82 years) completed the task in five stages, with 50 trials in each stage. Four physiological signals were recorded: Blood Volume Pulse (BVP), temperature, respiration rate, and Skin Conductance Response (SCR). At first, participants preferred disadvantageous cards (A and B) but gradually shifted to advantageous cards (C and D). Initial stages showed a slight increase in body temperature and an LF/HF ratio above one, indicating increased sympathetic activity and physiological stress. As the task progressed, individuals adapted to the rules and adopted more optimal strategies. Stage 4 marked a turning point with significant decreases in SCR and an LF/HF ratio below one, reflecting reduced stress. In the final stage, decreased BVP and an LF/HF ratio of one indicated a balance between the sympathetic and parasympathetic systems. Finally, it can be acknowledged that the task structure was useful in identifying the roles of the cards. Additionally, advancing in conditions of uncertainty through decision-making reflects a dynamic balance between neural systems related to reward and stress. Decision-making Uncertainty Physiological manifestations Gambling strategies Iowa Gambling Task Adaptation Dynamic balance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1.Introduction Decision-making, which involves the process of selecting among alternatives or, more precisely, choosing the most appropriate action from a range of possible options, is a fundamental human behavior studied across various domains, ranging from cognitive psychology to economics. Like other executive processes, decision-making entails the integration of diverse types of information: multimodal sensory inputs, automatic and emotional responses, past experiences, and future goals. In other words, decision-making refers to situations where decision-makers evaluate available options, understand the risks associated with each, and then select the best course of action based on their values and beliefs. For instance, under conditions of uncertainty and risk, the factors typically considered include risk assessment, reward evaluation, loss aversion, regret anticipation, and value estimation [ 1 – 5 ].Decision-making can be divided into three stages: evaluating options, executing actions, and assessing outcomes [ 6 , 7 ]. Since the aim of this study is to investigate individuals' performance under uncertainty, it was necessary to use a cognitive task that allows for the assessment of decision-making within a controlled and standardized laboratory setting. For this purpose, the Iowa Gambling Task (IGT), which will be introduced in the next section, was selected. This task is suitable because it captures two essential aspects of real-life decision-making: the conflict between immediate rewards and long-term consequences, and making decisions under conditions of uncertainty[ 8 ]. The Iowa Gambling Task specifically designed to study decision-making strategies under uncertainty and how individuals should decide between short-term choices (quick gains) and long-term choices (more sustainable rewards). In this card game, participants select cards from four different decks (A, B, C, and D). With each card, they can win or lose a specified amount of money. Two decks are more advantageous than the others: decks A and B ("bad decks") contain cards that can lead to significant gains (e.g., $ 100), but also cards that can result in substantial losses (e.g., - $ 1250). Decks C and D ("good decks") contain cards that yield smaller gains (e.g., $ 50) but also smaller losses (e.g., - $ 250). Thus, although initially choosing from decks A and B may lead to quicker financial gains, over the long term, the better strategy is to select cards from decks C and D [ 9 , 10 ]. Extensive research has been conducted on decision-making using the Iowa Gambling Task (IGT). For instance, studies have investigated the performance of individuals with various disorders, such as gambling disorder, brain injuries, schizophrenia, Parkinson’s disease, and others [ 11 – 18 ]. These studies often utilize diverse tools, including electrophysiological signal recording to identify various biological markers that elucidate the mechanisms involved in decision-making under uncertainty and the factors influencing these processes. Research has also examined strategic performance under different conditions, such as emotional interventions [ 19 , 20 ] and various time constraints [ 21 , 22 ]. Since the present study focuses on healthy individuals, the following sections will highlight a brief overview of some approaches and findings of research in this field. Some research has focused on individual personality traits and their influence on healthy individuals’ performance in the IGT. Bechara et al. (2000) demonstrated that this decision-making pattern results from the interplay between reward and punishment over time, confirming that individuals with poor performance in the IGT often possess high cognitive inhibition, a trait associated with "short-termism" regarding future consequences, which can explain their performance [ 23 ]. In addition to Franken and Muris (2005) suggested that the way people make decisions, especially how sensitive they are to rewards, can be somewhat predicted by individual differences. However, it is not linked to impulsivity as measured by personality questionnaires [ 24 ]. In a study aimed at investigating how personality traits (such as impulsivity and sensation seeking) and mood influence individuals’ choices in the Iowa Gambling Task (IGT), 91 students completed questionnaires assessing personality and mood, as well as a computerized version of the IGT. According to the results, negative mood, drive, impulsivity, and sensation seeking were positively correlated with increased selections from Deck B (which involves large but infrequent losses) and negatively correlated with selections from Deck D (which involves small but consistent gains). Therefore, IGT data should be analyzed at the individual deck level, as combining decks may lead to misinterpretation of personality traits and mood states as pathological risk-taking behaviors [ 25 ]. Also, Kornilov et al. (2015) indicated that tolerance for uncertainty predicts the initial propensity for risk, while the opposite condition reflects exploratory learning, evidenced by frequent switching between card decks after a loss [ 26 ]. Additionally, the study conducted by Zana Hasan Babakr & Nabi Fatahi investigated the impact of the Big Five personality traits on risky decision-making [ 27 ]. The findings revealed that Agreeableness, Neuroticism, and Conscientiousness are significantly correlated with decision-making in risky situations. In contrast, Extraversion and Openness did not demonstrate a notable association with such decision-making processes. Furthermore, this research highlighted that, men are more prone to engaging in risk-taking behaviors compared to women. This is while, scientists found that Extraversion is systematically associated with engaging in various risk-taking behaviors [ 28 ]. Heilman et al. (2010) showed that better emotional regulation could lead to more rational and less risky decisions under uncertainty, suggesting that cognitive reappraisal as a positive emotion regulation strategy can reduce risk avoidance [ 29 ]. In general, studies of this nature highlight the importance of considering personality traits in the investigation of risk-taking behaviors. Moreover, Brand et al. (2007) found a correlation between improved decision-making strategies and cognitive abilities such as working memory and executive skills, which enhance performance over time [ 30 ]. From a gender differences perspective, Zanini et al., (2025) found that men generally perform better on the IGT compared to women. This could be attributed to structural and functional differences in the brain, particularly in the ventromedial prefrontal cortex and amygdala, as well as greater sensitivity of women to wins and losses [ 5 ]. Weller et al. (2013) emphasized that beyond individual and gender differences, performance in the IGT is more closely associated with the aversion to losses than the desire to acquire gains [ 31 ]. These studies affirm that factors such as emotional regulation strategies, personality traits, individual/gender differences, and cognitive abilities play crucial roles in enhancing risk-related decision-making. Recent research has also highlighted the impact of emotional cues and physiological responses on risky decision-making under uncertainty. A study has examined how temporal pressure and information ambiguity affect risk-taking behaviors and the relationship between these behaviors and physiological responses such as skin conductance response (SCR). According to the results, individuals with high anxiety made more risky choices and acted more riskily under pressure [ 32 ]. Priolo et al. (2021) employed the IGT and recorded physiological responses such as SCR and heart rate variability (HRV), revealing that unrelated emotional cues could influence risky choices. Their findings indicated that disproportionate manipulation of good decks with negative sounds reduced selections from these decks, although SCR did show significant changes in this condition. This underscores the complexities of the decision-making process and emphasizes the importance of understanding emotional responses in financial and strategic choices [ 33 ]. Similarly, Simonovic et al. (2019), in a meta-analysis focused on the relationship between SCR and decision-making under uncertainty, particularly the predictive nature of this measure, demonstrated that changes in SCR can serve as indicators of emotional and cognitive responses during the decision-making process, influencing risk-taking choices [ 34 ]. HRV, as an indicator of the brain–body connection, may be associated with decision-making. A study by Forte et al. (2021) examined the relationship between HRV and decision-making in 130 healthy students using the Iowa Gambling Task (IGT). The results showed that individuals with better decision-making abilities exhibited higher vagally mediated HRV (vmHRV) during rest, task performance, and recovery phases. This finding suggests that HRV reflects the functioning of inhibitory circuits in the prefrontal cortex and supports more effective decision-making[ 35 ]. Other studies have further explored the physiological impacts on decision-making. Agren et al. (2018) analyzed SCR responses at different decision-making stages, highlighting their significance as indicators of emotions and physiological reactions in risk conditions. They defined gambling in three stages: decision-making, anticipation, and outcome, observing that the greatest arousal was in the anticipation stage [ 36 ]. Crone et al. (2004) investigated the effects of heart rate and SCR changes on decision-making processes, noting that heightened SCR responses before outcomes, particularly in undesirable choices, indicated a cohesive emotional reaction regardless of performance [ 37 ]. Colombetti (2008) addresses Damasio’s somatic marker hypothesis and presents two key points: (a) bodily states execute inclinations and are essential for decision-making, and (b) they contribute to the decision-making process by predicting the long-term outcomes of available options [ 38 ]. Clavellino et al. (2019) utilized Damasio’s somatic marker hypothesis to establish two profiles—positive and negative SCR profiles—to examine their performance in the IGT. The group with the positive profile, exhibiting deeper feedback sensitivity to emotional or environmental stimuli made better decisions than their counterparts, illustrating the influence of SCR characteristics on gambling behavior [ 39 ]. In an experimental study (2020), researchers examined whether physiological responses such as SCR and heart rate deceleration following outcome feedback could predict performance in the Iowa Gambling Task (IGT). The findings showed that these responses, especially after negative outcomes and selections from bad decks, were more pronounced. SCR was significantly associated with overall task performance. Using a reinforcement learning model, it was found that individuals who showed stronger SCRs to bad decks tended to exhibit greater loss aversion and lower learning rates. These feedback-related physiological responses reflect individual differences in outcome evaluation and learning mechanisms that shape decision-making strategies[ 40 ]. In a recent study, Priolo et al. (2024) investigated the effect of irrelevant emotional reactions—particularly disgust—on decision-making under uncertainty. By associating disgust-eliciting images with either advantageous or disadvantageous card decks in the IGT, the authors found that linking negative emotions to advantageous decks impaired decision-making performance during the early stages of the task. Furthermore, physiological responses such as SCR, heart rate, and pupil dilation were assessed as indicators of autonomic arousal. While no significant effects were observed for SCR and heart rate, gradual changes in pupil dilation suggested that even irrelevant emotional cues can influence both decision-making and physiological regulation[ 41 ]. The autonomic nervous system (ANS) consists of two branches: the sympathetic and parasympathetic systems. The sympathetic system is activated in response to challenging situations, while the parasympathetic system functions during rest. For example, when an individual begins the Iowa Gambling Task in a laboratory setting, they may experience various psychological states. To assess these states, physiological indicators such as blood volume pulse (BVP), skin temperature, respiration rate, and skin conductance response can be measured to evaluate an individual’s cognitive-emotional status. Blood Volume Pulse (BVP) reflects changes in peripheral blood volume and can indicate reactions to stress or excitement, as the ANS regulates vascular tone. The high-frequency component of BVP (BVP HF) indicates parasympathetic activity, whereas the low-frequency component (BVP LF) indicates sympathetic activity. The LF/HF ratio serves as a significant index in HRV analysis, representing the balance between sympathetic and parasympathetic activities [ 42 ]. Skin Temperature is influenced by superficial blood flow, which can be altered by the sympathetic system through vasoconstriction or vasodilation; a decrease in skin temperature is typically associated with stress. Respiratory Rate (or Resp) are controlled by respiratory centers in the brainstem and can be directly influenced by a person’s emotions (such as stress or relaxation). Skin Conductance Response (SCR) is linked to sweating, where an increase in SCR is commonly associated with sympathetic activity and responses to stress or excitement. Elevated stress levels can result in increased SCR, alterations in respiration rate, and decreased skin temperature [ 43 , 44 ]. The aim of this study was to examine individuals’ performance under conditions of uncertainty using the Iowa Gambling Task with a specific configuration and a total of 250 selections. The significance of the research lies in the detailed investigation of the physiological manifestations of individuals’ strategic performance throughout the task. The research question is whether a synergistic trend exists between individuals’ performance in terms of the card selection pattern and the activity of the autonomic nervous system (ANS). The research hypotheses in this study are as follows: The number of selections provided is sufficient to identify the roles of the cards. Furthermore, offering these selections across five stages can be beneficial in recognizing the roles of the cards. Significant physiological changes will be observed at the point where participants achieve recognition of the card categories during the task. 2.Materials and Methods 2.1.Data Collection 2.1.1.Participants For this study, 28 individuals (12 men and 16 women, M age = 21.96, SD = 3.81; age range: 18 to 35 years; with educational levels in undergraduate or graduate studies; right-handed) were assessed through a comprehensive questionnaire. The questionnaire examined various criteria, including demographic characteristics (age, gender, education, handedness (Edinburgh [ 45 ]), history of psychological disorders, substance and medication use, and current health status. It is noteworthy that participants’ consent to participate was documented through an informed consent form before entering the experimental environment. Subsequently, these individuals completed the DASS-21 [ 46 ], and ERQ questionnaires [ 47 ] in order to measure symptoms of depression, anxiety and stress, and to assess strategic approaches in coping with challenging situations. The whole research process is briefly illustrated in Fig. 1 . 2.1.2.Experimental Environment participants were seated on height-adjustable chairs tailored to their height in front of a 19-inch monitor (with a resolution of 1366 × 768, an aspect ratio of 16:9, brightness of 250 cd/m², and a response time of 5 ms) positioned on a standard-height table (such that the top of the screen was aligned with their eye level) to provide standardized testing conditions. All sessions were conducted in a quiet room with a moderate temperature (25–27°C) and appropriate lighting to minimize environmental distractions. One of the aims of this study was to conduct the experiment in conditions as free as possible from any stressors or intrusive factors. 2.1.3.Task Design For the implementation of the Iowa Gambling Task in this study, PsychoPy software was utilized. The task was structured into five blocks of 50 trials each, without any time constraints throughout the game or for each click, allowing participants the option to rest between stages at their discretion. The gaming environment consisted of four categories of cards, presenting the number of selections from each category, the total selections made, the winnings and losses associated with each click, and the final outcome of each choice. To reduce emotional arousal and prevent unwanted feelings, the dominant color of the gaming environment was chosen to be gray, with texts and numbers displayed in black. The cards in this task served the following roles. The standard format has been used here [ 9 ]: • Card A: Bad card with a high number of losses • Card B: Bad card with a significant loss amount • Card C: Good card with moderate gain and moderate loss • Card D: Good card with low gain and low loss 2.1.4.Measurement Tools and Experiment Protocol In this study, physiological signals were recorded using the ProComp5 Infiniti system (Thought Technology, Canada) with a sampling rate of 256 Hz. Data acquisition and processing were performed using the BioGraph Infiniti software. The device includes built-in hardware filters, such as a 50 Hz notch filter, to minimize power-line interference and ensure high-quality recordings. The overall procedure of the experimental protocol is illustrated in Fig. 2 . In the second stage, the Self-Assessment Manikin (SAM) scale—a visual tool used to assess emotional states—was employed [ 48 ]. Participants evaluated and selected their emotional states (pleasure and arousal) using graphical images, and these two indices are presented in Fig. 3 . In the final stage, the post-Iowa Gambling Task questionnaire developed by Heilman et al. (2010) was administered [ 29 ]. 2.2.Data Analysis 2.2.1.Data Preprocessing and Statistical Analysis To preserve meaningful stage-related variations and avoid eliminating genuine physiological effects, a light preprocessing procedure was applied. For BVP and SCR, only high-frequency noise was filtered to remove sharp artifacts while maintaining the natural variability of the signals. For Resp, a band-pass filter (0.18–0.5 Hz) was used to extract the respiratory component, and Temp remained unfiltered due to its very slow changes, which make filtering unnecessary. The LF/HF ratio was directly obtained from the software outputs because of its standardized and validated calculation method. The system’s built-in hardware filters—including a fifth-order low-pass Butterworth anti-aliasing filter and a notch filter—along with the use of an optical cable, reduced environmental and electrical noise, ensuring clean and high-quality physiological recordings before digital preprocessing. Table 1 summarizes the filtering steps applied to each physiological signal. After preprocessing, the mean value of each signal was computed for every participant and stage. Data normality was verified using the Lilliefors test. Depending on the results, paired t -tests were used for normally distributed data, and Wilcoxon signed-rank tests were performed when normality was not met. Effect sizes (Cohen’s d for t -tests and r for Wilcoxon) were calculated along with the direction of change (increase or decrease) to provide a clear representation of stage-related dynamics. Table 1 Appropriate ranges for filtering physiological data in normal laboratory conditions for this study Signal Filter Range Reason BVP([ 49 – 51 ]) Low-pass 40 Hz To reduce high-frequency noise and preserve the main physiological waveform. Resp([ 52 ]) Band-pass 0.18–0.5 Hz To isolate respiratory activity within the natural breathing frequency range. SCR([ 53 ]) Low-pass 5 Hz To remove high-frequency artifacts while maintaining signal integrity. 2.2.2.Analysis of Card Selection Data As previously discussed, the Iowa Gambling Task features four decks of cards, each with different roles. In this study, the main strategy is outlined as follows: Step One : Identify the roles of the cards with a minimum of selections from each deck (ideally between 20 to 25 selections). Step Two : Avoid greater losses in favor of maintaining the initial loan granted. Step Three : Choose cards with the least risk of loss to ensure a consistent profit that compensates for losses, repays the initial loan, and progresses toward a win. The LTC parameter, representing long-term consequences, is derived by subtracting the sum of bad cards from good cards, emphasizing long-term rewards. On the other hand, the IFL parameter, which stands for infrequent losses, is calculated by subtracting the sum of immediate gains from long-term losses, reflecting the tendency to prioritize immediate outcomes [ 54 ]. Table 2 presents different conditions of these two parameters, along with their corresponding interpretations. Table 2 Terminology of factors related to card selection and different states of their arrangements LTC IFL Choice Pattern Interpretation Positive Positive Good Cards The participant prefers decks C and D, focusing on long-term positive outcomes with aligned choices. Positive Negative Good Cards The participant chooses decks C and D but may not fully consider the balance of gains and losses. Negative Positive Bad Cards The participant tends towards decks A and B, likely drawn by their immediate gains. Negative Negative Bad Cards The participant focuses on decks A and B, showing a preference for long-term losses despite immediate gains. zero Positive Mixed Cards The participant selects a mix of decks C, D, A, and B, with a tendency towards positive long-term outcomes. zero Negative Mixed Cards The participant mixes card selections, but overall long-term outcomes are negative. Note:When LTC is high and IFL is low, an individual tends to make stable choices that lead to long-term gains. Conversely, when LTC is low and IFL is high, the individual focuses more on short-term rewards, even if these choices result in long-term losses. 2.2.3.Analysis of Individuals’ Performance After the IGT In this questionnaire, individuals were asked to rate each card on a scale from − 10 to 10 based on their experience. They were then asked to provide a numerical report as a percentage regarding their confidence in their knowledge about the game (evaluating subjective knowledge) and how certain they were about knowing the optimal strategy for the task. Finally, they were asked which single deck they would choose if they had to use only one for the remainder of the task. According to the research from which this questionnaire was derived, three levels of Declarative knowledge can be conceptualized during the task: Level 0—the participant has no knowledge of identifying the best decks. Level 1—the individual has knowledge but cannot provide verbal explanations about the results of those decks that would justify their preference. Level 2—the individual has knowledge and is also able to provide verbal explanations that justify. Based on individuals’ responses, performance after the task was assessed. 3.Results 3.1.Pre-Task Questionnaire Results The questionnaires used in this study were described in detail in the Materials and Methods section. Figure 4 illustrates the mean scores for each subscale. Based on the DASS-21 results, participants showed moderate levels of stress (M = 21.86, SD = 11.49) and anxiety (M = 13.50, SD = 11.49), as well as mild to moderate depression (M = 16.00, SD = 11.96). Regarding the ERQ, the mean score for cognitive reappraisal was 26.21 (SD = 6.99), while expressive suppression had a mean of 13.64 (SD = 5.44). The reliability analysis showed that Cronbach’s alpha values were 0.802 for reappraisal and 0.795 for suppression in the ERQ. For the DASS-21, Cronbach’s alpha values were 0.881 for stress, 0.896 for anxiety, and 0.909 for depression, indicating high internal consistency. 3.2.Physiological Responses During the Task Based on the procedures described in the Materials and Methods section regarding preprocessing and statistical analysis, stage-related changes in physiological signals are shown in Fig. 5 . Each cell’s color represents the p-value for comparisons between consecutive stages. For significant comparisons (p < 0.05), the test type is indicated as t for paired t-test or w for Wilcoxon signed-rank test, along with the effect size (Cohen’s d for t-test, r for Wilcoxon) and the direction of change (increase ↑ or decrease ↓). For non-significant comparisons (p ≥ 0.05), only the p-value is displayed. Effect sizes are interpreted using the following benchmarks: d: small ≈ 0.2, medium ≈ 0.5 and large ≈ 0.8 also for r: small ≈ 0.1, medium ≈ 0.3, and large ≈ 0.5. It is worth noting that the progression of the LF/HF ratio across consecutive stages is as follows: Stage 2 relative to Stage 1 is 1.14, Stage 3 relative to Stage 2 is 0.94, Stage 4 relative to Stage 3 is 0.93, and Stage 5 relative to Stage 4 is 1[ 55 ]. 3.3.Card Selection Patterns As previously explained, the Iowa Gambling Task includes four card categories: two advantageous decks (C and D) and two disadvantageous decks (A and B). Participants must choose cards from among these four categories to progress through the task. Figure 6 illustrates the participants’ selection patterns from these four decks across five stages, showing how their decision-making developed during the task. In analyzing the group’s performance across 250 selections, several parameters are examined for understanding changes in participants’ gameplay across the 5 stages. The definitions and calculations of these parameters are detailed in the Methods and Materials section. Table 3 presents the mean values of the parameters related to card selection (IFL and LTC) and the card selection patterns at each stage. Table 3 The card selection process & values of parameters related to card selection at each stage Stage Card Selection Pattern Mean LTC (C + D)-(A + B) Mean IFL (A + D)-(C + B) 1 B > A > D > C -1.5714 -0.7857 2 B > A > D > C -3.2143 -1.6429 3 A > D > B > C -2.0714 5.9286 4 D > C > A > B 3.9286 3.4286 5 C > D > A > B 4.2143 1.1429 3.4.Post-Task Questionnaire and Emotional Ratings Before and after the Iowa Gambling Task, participants’ emotional satisfaction and arousal levels were measured using the SAM scale. Additionally, questions related to the IGT were asked using a specific questionnaire [ 29 ]. As shown in Fig. 7 , the average percentage of participants’ subjective knowledge of the game was 55.71%, and the average confidence in their optimal strategy was 38.07%. The average "pleasure" score decreased from 6.2143 to 5.7857, while the average "arousal" score increased from 5.2143 to 5.5. The scores assigned to the cards were as follows: Card A = 123, Card B = 9, Card C = 120, and Card D = 125. In response to the question about which card category would be chosen exclusively until the end of the game, 8 participants chose Card A, 6 chose Card B, and 7 chose each of cards C and D. 4.Discussion The aim of the present study was to investigate individuals’ performance under conditions of uncertainty using a modified version of the Iowa Gambling Task. From a strategic perspective, in the initial stages of the game, participants exhibited a focus on immediate gains and a preference for selecting disadvantageous decks, particularly during the first two stages. Individuals displayed a prominent preference for Deck B due to their strong inclination toward immediate rewards. This behavior led to losses for most participants. In other words, LTC and IFL values were negative during the first two stages, further confirming this behavioral tendency. From the third stage onward, a gradual shift in strategy was observed. Although LTC remained negative, IFL turned positive, indicating a reduced tendency to select decks associated with infrequent but large losses. In the subsequent stages, both indices became positive, accompanied by an increased selection of advantageous decks, reflecting improved decision-making and a strategic switch from focusing on immediate rewards to pursuing long-term gains. Physiologically, this pattern was accompanied by a significant increase in body temperature and an LF/HF ratio above one. In the early stages of the task (Stage 2 compared to Stage 1), the increase in the LF/HF ratio (1.14) and body temperature reflects heightened sympathetic activity and physiological arousal, which are typical when facing cognitive challenges and uncertainty. However, in stages 4 and 5, attention shifted toward long-term losses and a preference for advantageous decks, resulting in the development of an optimal card selection pattern in stage 4, which was maintained through stage 5. Stage 4, in comparison to stage 3, marked a turning point in strategic performance. This shift was reflected in a significant reduction in SCR and an LF/HF ratio below one, indicating a strategic adjustment in participants' behavior. In stages 4 and 5, the decrease in LF/HF ratio (0.94 and then 0.93) and the significant reduction in SCR indicate a gradual decrease in stress and an increase in parasympathetic control, which is associated with more deliberate and focused decision-making strategies. In stage 5, the brief decrease in BVP and the return of the LF/HF ratio to 1 signify balance in the autonomic nervous system and cardiovascular regulation following complex decision-making stages, which aligns with the improvement of long-term decision-making strategies. Additionally, post-task evaluations revealed that while participants’ ratings for the card decks aligned with an optimal card selection pattern, the ratings for deck A were not different from those of the advantageous decks. This finding corresponded with participants’ overall performance up to stage 3. Moreover, declarative knowledge remained at a zero level, as no substantial distinction was found between their preference ratings and their consistent choice of a single deck throughout the game. In summary, during the initial stages of the game, participants experienced stress due to unfamiliarity with the environment, rules, and resulting losses. As the game progressed, increased familiarity with the conditions and rules enabled participants to develop an optimal strategy (a partial understanding of the roles of the decks) and effectively manage the situation. This progression may be associated with participants’ high scores on emotional regulation, particularly cognitive reappraisal. Finally, it can be concluded that the number of trials set for this task (250 trials) facilitated the identification of deck roles. The significant changes observed at the game’s turning point highlight synergistic changes in the components of the autonomic nervous system during the progression of the IGT. Various studies have been conducted in the field of IGT that have yielded similar results to the present study or have confirmed its findings. However, some studies may present different outcomes .Similar findings have been reported in other researches; for instance, Lin et al. (2007), Lin et al. (2012), and Lee et al. (2020) demonstrated that in decision-making under uncertainty, individuals are more influenced by the frequency of small gains and losses than by the overall outcome. Consequently, they tend to favor the disadvantageous Deck B because of its small immediate rewards. In other words, individuals prefer to avoid immediate losses, even at the cost of forfeiting greater long-term benefits [ 56 – 58 ]. Also, researchers found that Deck B, due to its high frequency of large rewards, can lead to myopic decision-making, where individuals focus on immediate gains and neglect long-term consequences, reinforcing short-term incorrect choices [ 59 ]. In the present study, this phenomenon was observed as well, with participants initially showing an increase in selections from Deck B in the first and second stages, followed by a decrease from the third stage onwards. From a physiological perspective, the present study also yielded findings, which will be compared and interpreted in relation to other comparable studies. For example, Fernie and Tunney (2013) concluded that physiological activities related to decision-making only become significant once individuals have acquired sufficient knowledge of the task structure. In their study, only participants who reached this level of knowledge showed physiological responses after significant punishments and rewards [ 60 ]. In the present study, individuals, after showing a preference for the disadvantageous card decks, particularly Deck B after 100 choices and Deck A after 150 choices, which led to significant losses, reached the optimal selection pattern in stage 4. They also showed significant values in the LFHF ratio components and skin conductance response. Similar to the study by Clavellino et al. (2019), participants in the present study also exhibited positive physiological responses, including relaxation and, consequently, stability in autonomic nervous system activity (LF/HF ratio component). Additionally, a reduction in stress was evident through a significant decrease in skin conductance and blood volume pulse parameters in the later stages. Similar to this study, they reduced their risky choices[ 39 ]. A similar argument can be made for the study by Wagar et al. (2006), where they argued that when an individual is about to make a selection from the bad deck, the GSR response significantly increases, whereas this response is lower when selecting from the good deck [ 61 ]. Moreover, according to the study by Lee et al. (2010), in the current research, individuals in stages 4 and 5, where they reached the optimal pattern, showed a significant reduction in blood volume pulse, which aligns with the findings of the mentioned paper, where a decrease in heart rate is associated with advantageous decisions [ 62 ]. Although, Carter and Pasqualini, (2004) found that the skin conductance responses they recorded in their study were progressively associated with better performance in the IGT task, supporting the concept that higher autonomic nervous system activity corresponds to improved decision-making, a finding that contradicts the results of the present paper [ 63 ], the study by Drucaroff et al. (2011) suggests that high autonomic system activity is beneficial for the decision-making process. In contrast, in the present study, achieving accurate knowledge of the cards occurred under conditions where the sympathetic and parasympathetic systems were balanced in favor of the relaxation component, and the changes in skin conductance response were also reduced [ 64 ]. Finally, Forte et al. (2022) found that higher cardiac activity levels are associated with better decision-making performance. In line with this, the present study observed higher blood volume pulse in Stage 4 (the individuals’ strategic turning point in the task) compared to Stage 5, as well as a higher selection rate from Deck D in Stage 4 than in Stage 5, thus supporting this research finding[ 65 ]. 4.1.Strengths, Limitations, and Future Directions This study provides deeper insights into the nature and mechanisms of the Iowa Gambling Task (IGT). The structured and carefully controlled design of the task in this research allowed for evaluating individuals' decision-making under uncertainty, while minimizing the influence of external factors, especially mental fatigue. The task was specifically designed to reduce fatigue, ensuring that participants' performance more accurately reflected cognitive processes rather than exhaustion. Nevertheless, this study has certain limitations. The absence of EEG and eye-tracking data limited our ability to explore neural activity and attentional dynamics during decision-making. Future studies integrating these methods could provide more comprehensive insights into the underlying cognitive and neural mechanisms. Additionally, the relatively small sample size, particularly in terms of age and gender diversity, restricts the generalizability of the findings. Future research could benefit from applying diverse data-driven approaches, integrating multimodal datasets, and examining independent groups to enhance the robustness of analyses. Moreover, comparing the original IGT with the modified version used in this study, particularly regarding the influence of time constraints, could provide a clearer understanding of this factor's role in decision-making. Designing cognitive and behavioral interventions, improving the temporal resolution of physiological data, and analyzing the outcomes of each choice in detail may contribute to identifying the healthy dynamics of decision-making. Finally, implementing advanced data processing techniques such as machine learning, deep learning, and computational modeling could be an effective step toward more practical applications and a deeper understanding of both the IGT and human decision-making performance. 5.Conclusion All in all, the results of the study showed that in the early stages of the game, individuals tended to select disadvantageous cards and focused on immediate gains, which led to losses and physiological stress. This stress was accompanied by a slight but significant increase in body temperature and heightened sympathetic activity. However, as the game progressed, familiarity with the rules and conditions—or, in other words, increased adaptation—enabled participants to adopt an optimal strategy. This was associated with a significant reduction in SCR and a decrease in sympathetic activity in Stage 4, leading to physiological balance in Stage 5. Post-task evaluations also revealed that, despite participants’ scores being close to the optimal pattern, their declarative knowledge remained at a low level. Significant changes at the game’s turning point—the achievement of the optimal card selection pattern—reflected physiological signs of relaxation. Therefore, it can be concluded that the number of selections and stages in this task contributed to participants’ identification of the roles of the cards. Additionally, advancing in conditions of uncertainty through decision-making reflects a dynamic balance between neural systems related to reward and stress. Declarations Competing Interests The authors declare no competing interests. Ethics Approval The study was conducted in accordance with the Declaration of Helsinki and approved by the Biomedical Ethics Committee of Ferdowsi University of Mashhad (Code: IR.UM.REC.1401.175). Consent to Participate Informed consent was obtained from all individual participants included in the study. Consent for Publication Participants provided informed consent for the publication of anonymized data. Funding This research was supported by Ferdowsi University of Mashhad, Research Affairs (Grant No. 3/57322). Author Contribution • Elahe Yaghoubian: Conceptualization, Methodology, Task Development, Data Collection, Formal Analysis, Writing – Original Draft, Writing – Review & Editing, Project Administration, Resources• Ali Moghimi: Conceptualization, Methodology, Supervision, Funding Acquisition, Writing – Review & Editing• Morteza Izadifar: Conceptualization, Methodology, Supervision, Writing – Review & Editing• Hamidreza Kobravi: Conceptualization, Methodology, Supervision, Writing – Review & Editing• Nilufar Totonchi: Conceptualization, Task Development, Data Collection Data Availability The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request, subject to institutional policies. References Fellows LK (2004) The Cognitive Neuroscience of Human Decision Making: A Review and Conceptual Framework. 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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-8464219","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":597776191,"identity":"f7c6f59c-316a-4106-bfda-820df8d5caa5","order_by":0,"name":"Ali Moghimi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYHACNoYEBgk5KEeCeC3GJGoBgsQGol2l295+7cHDHIv0DdcOMH74wWCRT1CL2Zkz5QaJ2yRyN9xOYJbsYZCwJGid2Y2cNAmoFgZpoF8MCNsC1ZJuALTlN5Fa0o+BtCQAtbARacuZM2wgLYYzbye2WfYYEKPlePszyZ/b6uT5bicfvvGjoo6wFgYGHpgixgYGBmI0MDCwPyBK2SgYBaNgFIxgAABarzfw8sKfjQAAAABJRU5ErkJggg==","orcid":"","institution":"Ferdowsi University of Mashhad","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"","lastName":"Moghimi","suffix":""},{"id":597776192,"identity":"de48f68c-ffc7-4d49-95f0-559ae4bc5846","order_by":1,"name":"Elahe Yaghoubian","email":"","orcid":"","institution":"Ferdowsi University of Mashhad","correspondingAuthor":false,"prefix":"","firstName":"Elahe","middleName":"","lastName":"Yaghoubian","suffix":""},{"id":597776193,"identity":"3e8b75d6-8adb-4064-8dac-62949b5c0fab","order_by":2,"name":"Morteza Izadifar","email":"","orcid":"","institution":"Ludwig-Maximilians-Universität München","correspondingAuthor":false,"prefix":"","firstName":"Morteza","middleName":"","lastName":"Izadifar","suffix":""},{"id":597776194,"identity":"4900105c-81f7-400e-bb77-f1e3e2e1d344","order_by":3,"name":"Hamidreza Kobravi","email":"","orcid":"","institution":"Islamic Azad University, Mashhad","correspondingAuthor":false,"prefix":"","firstName":"Hamidreza","middleName":"","lastName":"Kobravi","suffix":""},{"id":597776195,"identity":"623b0464-d1fd-4951-9a82-10a9fb1443f0","order_by":4,"name":"Nilufar Totonchi","email":"","orcid":"","institution":"Ferdowsi University of Mashhad","correspondingAuthor":false,"prefix":"","firstName":"Nilufar","middleName":"","lastName":"Totonchi","suffix":""}],"badges":[],"createdAt":"2025-12-28 06:23:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8464219/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8464219/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104403959,"identity":"36d1c5cd-462d-4f1e-ad38-b6a382df3c19","added_by":"auto","created_at":"2026-03-11 12:19:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1623187,"visible":true,"origin":"","legend":"\u003cp\u003eResearch flowchart\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-8464219/v1/e88b795bcc2b977fdbb34ba3.png"},{"id":104173121,"identity":"44c3f891-2efb-40d1-b9a2-6212b8657ab6","added_by":"auto","created_at":"2026-03-08 15:26:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1399289,"visible":true,"origin":"","legend":"\u003cp\u003eThe process of protocol execution\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-8464219/v1/d586d6aa4f61ff3a37202760.png"},{"id":104173123,"identity":"c22fb38d-f1ee-4eec-b3eb-552256bd7283","added_by":"auto","created_at":"2026-03-08 15:26:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":747078,"visible":true,"origin":"","legend":"\u003cp\u003ePleasure: (from very sad to very happy) (top). Arousal (from calm to excited) (bottom)\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-8464219/v1/6ab34e6e6f7936baa22e71a1.png"},{"id":104403856,"identity":"9242ac97-30f2-48aa-9fa1-dab43b6ee73b","added_by":"auto","created_at":"2026-03-11 12:19:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1619106,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the mean scores (± SD) for each subscale of the DASS-21 and ERQ questionnaires\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-8464219/v1/ae797b154466c9eb180b83f1.png"},{"id":104173128,"identity":"d921c89b-f2da-4187-adb9-8c83d56e096a","added_by":"auto","created_at":"2026-03-08 15:26:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2001178,"visible":true,"origin":"","legend":"\u003cp\u003eStage-related changes in physiological signals during IGT for all participants\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-8464219/v1/7770b9dff0e38c79e3def455.png"},{"id":104404745,"identity":"b387f208-16db-40c5-b81d-bf119674c237","added_by":"auto","created_at":"2026-03-11 12:21:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2548029,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency of card selections by participants across 5 stages (Rounds) in the IGT\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-8464219/v1/e27137522dfe36334e8dc7fc.png"},{"id":104173127,"identity":"a7c53456-3264-4396-8e80-88d4d7716bd5","added_by":"auto","created_at":"2026-03-08 15:26:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2345942,"visible":true,"origin":"","legend":"\u003cp\u003eParticipants’ post-task responses: (a) Mean scores, (b) Sum scores\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-8464219/v1/3aee74350c5fa1d73bbb33cf.png"},{"id":104409508,"identity":"e01bf765-df4e-4622-96fe-96f19a9194e2","added_by":"auto","created_at":"2026-03-11 12:45:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13160427,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8464219/v1/c4be7e39-4f48-4607-a4f9-02d1a50e2228.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Physiological Manifestations in Strategic Decision-Making under Conditions of Uncertainty: Insights from the Iowa Gambling Task","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eDecision-making, which involves the process of selecting among alternatives or, more precisely, choosing the most appropriate action from a range of possible options, is a fundamental human behavior studied across various domains, ranging from cognitive psychology to economics. Like other executive processes, decision-making entails the integration of diverse types of information: multimodal sensory inputs, automatic and emotional responses, past experiences, and future goals. In other words, decision-making refers to situations where decision-makers evaluate available options, understand the risks associated with each, and then select the best course of action based on their values and beliefs. For instance, under conditions of uncertainty and risk, the factors typically considered include risk assessment, reward evaluation, loss aversion, regret anticipation, and value estimation [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].Decision-making can be divided into three stages: evaluating options, executing actions, and assessing outcomes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince the aim of this study is to investigate individuals' performance under uncertainty, it was necessary to use a cognitive task that allows for the assessment of decision-making within a controlled and standardized laboratory setting. For this purpose, the Iowa Gambling Task (IGT), which will be introduced in the next section, was selected. This task is suitable because it captures two essential aspects of real-life decision-making: the conflict between immediate rewards and long-term consequences, and making decisions under conditions of uncertainty[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Iowa Gambling Task specifically designed to study decision-making strategies under uncertainty and how individuals should decide between short-term choices (quick gains) and long-term choices (more sustainable rewards). In this card game, participants select cards from four different decks (A, B, C, and D). With each card, they can win or lose a specified amount of money. Two decks are more advantageous than the others: decks A and B (\"bad decks\") contain cards that can lead to significant gains (e.g., \u003cspan\u003e$\u003c/span\u003e100), but also cards that can result in substantial losses (e.g., -\u003cspan\u003e$\u003c/span\u003e1250). Decks C and D (\"good decks\") contain cards that yield smaller gains (e.g., \u003cspan\u003e$\u003c/span\u003e50) but also smaller losses (e.g., -\u003cspan\u003e$\u003c/span\u003e250). Thus, although initially choosing from decks A and B may lead to quicker financial gains, over the long term, the better strategy is to select cards from decks C and D [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExtensive research has been conducted on decision-making using the Iowa Gambling Task (IGT). For instance, studies have investigated the performance of individuals with various disorders, such as gambling disorder, brain injuries, schizophrenia, Parkinson\u0026rsquo;s disease, and others [\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These studies often utilize diverse tools, including electrophysiological signal recording to identify various biological markers that elucidate the mechanisms involved in decision-making under uncertainty and the factors influencing these processes.\u003c/p\u003e \u003cp\u003eResearch has also examined strategic performance under different conditions, such as emotional interventions [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and various time constraints [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Since the present study focuses on healthy individuals, the following sections will highlight a brief overview of some approaches and findings of research in this field.\u003c/p\u003e \u003cp\u003eSome research has focused on individual personality traits and their influence on healthy individuals\u0026rsquo; performance in the IGT. Bechara et al. (2000) demonstrated that this decision-making pattern results from the interplay between reward and punishment over time, confirming that individuals with poor performance in the IGT often possess high cognitive inhibition, a trait associated with \"short-termism\" regarding future consequences, which can explain their performance [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In addition to Franken and Muris (2005) suggested that the way people make decisions, especially how sensitive they are to rewards, can be somewhat predicted by individual differences. However, it is not linked to impulsivity as measured by personality questionnaires [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In a study aimed at investigating how personality traits (such as impulsivity and sensation seeking) and mood influence individuals\u0026rsquo; choices in the Iowa Gambling Task (IGT), 91 students completed questionnaires assessing personality and mood, as well as a computerized version of the IGT. According to the results, negative mood, drive, impulsivity, and sensation seeking were positively correlated with increased selections from Deck B (which involves large but infrequent losses) and negatively correlated with selections from Deck D (which involves small but consistent gains).\u003c/p\u003e \u003cp\u003eTherefore, IGT data should be analyzed at the individual deck level, as combining decks may lead to misinterpretation of personality traits and mood states as pathological risk-taking behaviors [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Also, Kornilov et al. (2015) indicated that tolerance for uncertainty predicts the initial propensity for risk, while the opposite condition reflects exploratory learning, evidenced by frequent switching between card decks after a loss [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, the study conducted by Zana Hasan Babakr \u0026amp; Nabi Fatahi investigated the impact of the Big Five personality traits on risky decision-making [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The findings revealed that Agreeableness, Neuroticism, and Conscientiousness are significantly correlated with decision-making in risky situations. In contrast, Extraversion and Openness did not demonstrate a notable association with such decision-making processes.\u003c/p\u003e \u003cp\u003eFurthermore, this research highlighted that, men are more prone to engaging in risk-taking behaviors compared to women. This is while, scientists found that Extraversion is systematically associated with engaging in various risk-taking behaviors [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Heilman et al. (2010) showed that better emotional regulation could lead to more rational and less risky decisions under uncertainty, suggesting that cognitive reappraisal as a positive emotion regulation strategy can reduce risk avoidance [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In general, studies of this nature highlight the importance of considering personality traits in the investigation of risk-taking behaviors.\u003c/p\u003e \u003cp\u003eMoreover, Brand et al. (2007) found a correlation between improved decision-making strategies and cognitive abilities such as working memory and executive skills, which enhance performance over time [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. From a gender differences perspective, Zanini et al., (2025) found that men generally perform better on the IGT compared to women. This could be attributed to structural and functional differences in the brain, particularly in the ventromedial prefrontal cortex and amygdala, as well as greater sensitivity of women to wins and losses [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Weller et al. (2013) emphasized that beyond individual and gender differences, performance in the IGT is more closely associated with the aversion to losses than the desire to acquire gains [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These studies affirm that factors such as emotional regulation strategies, personality traits, individual/gender differences, and cognitive abilities play crucial roles in enhancing risk-related decision-making.\u003c/p\u003e \u003cp\u003eRecent research has also highlighted the impact of emotional cues and physiological responses on risky decision-making under uncertainty. A study has examined how temporal pressure and information ambiguity affect risk-taking behaviors and the relationship between these behaviors and physiological responses such as skin conductance response (SCR). According to the results, individuals with high anxiety made more risky choices and acted more riskily under pressure [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Priolo et al. (2021) employed the IGT and recorded physiological responses such as SCR and heart rate variability (HRV), revealing that unrelated emotional cues could influence risky choices. Their findings indicated that disproportionate manipulation of good decks with negative sounds reduced selections from these decks, although SCR did show significant changes in this condition. This underscores the complexities of the decision-making process and emphasizes the importance of understanding emotional responses in financial and strategic choices [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSimilarly, Simonovic et al. (2019), in a meta-analysis focused on the relationship between SCR and decision-making under uncertainty, particularly the predictive nature of this measure, demonstrated that changes in SCR can serve as indicators of emotional and cognitive responses during the decision-making process, influencing risk-taking choices [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. HRV, as an indicator of the brain\u0026ndash;body connection, may be associated with decision-making. A study by Forte et al. (2021) examined the relationship between HRV and decision-making in 130 healthy students using the Iowa Gambling Task (IGT). The results showed that individuals with better decision-making abilities exhibited higher vagally mediated HRV (vmHRV) during rest, task performance, and recovery phases. This finding suggests that HRV reflects the functioning of inhibitory circuits in the prefrontal cortex and supports more effective decision-making[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOther studies have further explored the physiological impacts on decision-making. Agren et al. (2018) analyzed SCR responses at different decision-making stages, highlighting their significance as indicators of emotions and physiological reactions in risk conditions. They defined gambling in three stages: decision-making, anticipation, and outcome, observing that the greatest arousal was in the anticipation stage [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Crone et al. (2004) investigated the effects of heart rate and SCR changes on decision-making processes, noting that heightened SCR responses before outcomes, particularly in undesirable choices, indicated a cohesive emotional reaction regardless of performance [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eColombetti (2008) addresses Damasio\u0026rsquo;s somatic marker hypothesis and presents two key points: (a) bodily states execute inclinations and are essential for decision-making, and (b) they contribute to the decision-making process by predicting the long-term outcomes of available options [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Clavellino et al. (2019) utilized Damasio\u0026rsquo;s somatic marker hypothesis to establish two profiles\u0026mdash;positive and negative SCR profiles\u0026mdash;to examine their performance in the IGT. The group with the positive profile, exhibiting deeper feedback sensitivity to emotional or environmental stimuli made better decisions than their counterparts, illustrating the influence of SCR characteristics on gambling behavior [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn an experimental study (2020), researchers examined whether physiological responses such as SCR and heart rate deceleration following outcome feedback could predict performance in the Iowa Gambling Task (IGT). The findings showed that these responses, especially after negative outcomes and selections from bad decks, were more pronounced. SCR was significantly associated with overall task performance. Using a reinforcement learning model, it was found that individuals who showed stronger SCRs to bad decks tended to exhibit greater loss aversion and lower learning rates. These feedback-related physiological responses reflect individual differences in outcome evaluation and learning mechanisms that shape decision-making strategies[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn a recent study, Priolo et al. (2024) investigated the effect of irrelevant emotional reactions\u0026mdash;particularly disgust\u0026mdash;on decision-making under uncertainty. By associating disgust-eliciting images with either advantageous or disadvantageous card decks in the IGT, the authors found that linking negative emotions to advantageous decks impaired decision-making performance during the early stages of the task. Furthermore, physiological responses such as SCR, heart rate, and pupil dilation were assessed as indicators of autonomic arousal. While no significant effects were observed for SCR and heart rate, gradual changes in pupil dilation suggested that even irrelevant emotional cues can influence both decision-making and physiological regulation[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe autonomic nervous system (ANS) consists of two branches: the sympathetic and parasympathetic systems. The sympathetic system is activated in response to challenging situations, while the parasympathetic system functions during rest. For example, when an individual begins the Iowa Gambling Task in a laboratory setting, they may experience various psychological states. To assess these states, physiological indicators such as blood volume pulse (BVP), skin temperature, respiration rate, and skin conductance response can be measured to evaluate an individual\u0026rsquo;s cognitive-emotional status.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBlood Volume Pulse (BVP)\u003c/b\u003e reflects changes in peripheral blood volume and can indicate reactions to stress or excitement, as the ANS regulates vascular tone. The high-frequency component of BVP (BVP HF) indicates parasympathetic activity, whereas the low-frequency component (BVP LF) indicates sympathetic activity. The LF/HF ratio serves as a significant index in HRV analysis, representing the balance between sympathetic and parasympathetic activities [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eSkin Temperature\u003c/b\u003e is influenced by superficial blood flow, which can be altered by the sympathetic system through vasoconstriction or vasodilation; a decrease in skin temperature is typically associated with stress.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRespiratory Rate (or Resp)\u003c/b\u003e are controlled by respiratory centers in the brainstem and can be directly influenced by a person\u0026rsquo;s emotions (such as stress or relaxation).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSkin Conductance Response (SCR)\u003c/b\u003e is linked to sweating, where an increase in SCR is commonly associated with sympathetic activity and responses to stress or excitement. Elevated stress levels can result in increased SCR, alterations in respiration rate, and decreased skin temperature [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe aim of this study was to examine individuals\u0026rsquo; performance under conditions of uncertainty using the Iowa Gambling Task with a specific configuration and a total of 250 selections. The significance of the research lies in the detailed investigation of the physiological manifestations of individuals\u0026rsquo; strategic performance throughout the task. The research question is whether a synergistic trend exists between individuals\u0026rsquo; performance in terms of the card selection pattern and the activity of the autonomic nervous system (ANS). The research hypotheses in this study are as follows:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe number of selections provided is sufficient to identify the roles of the cards. Furthermore, offering these selections across five stages can be beneficial in recognizing the roles of the cards.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSignificant physiological changes will be observed at the point where participants achieve recognition of the card categories during the task.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"2.Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1.Data Collection\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1.Participants\u003c/h2\u003e \u003cp\u003eFor this study, 28 individuals (12 men and 16 women, \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eage\u003c/em\u003e\u003c/sub\u003e= 21.96, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.81; age range: 18 to 35 years; with educational levels in undergraduate or graduate studies; right-handed) were assessed through a comprehensive questionnaire. The questionnaire examined various criteria, including demographic characteristics (age, gender, education, handedness (Edinburgh [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]), history of psychological disorders, substance and medication use, and current health status. It is noteworthy that participants\u0026rsquo; consent to participate was documented through an informed consent form before entering the experimental environment.\u003c/p\u003e \u003cp\u003eSubsequently, these individuals completed the DASS-21 [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and ERQ questionnaires [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] in order to measure symptoms of depression, anxiety and stress, and to assess strategic approaches in coping with challenging situations. The whole research process is briefly illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2.Experimental Environment\u003c/h2\u003e \u003cp\u003eparticipants were seated on height-adjustable chairs tailored to their height in front of a 19-inch monitor (with a resolution of 1366 \u0026times; 768, an aspect ratio of 16:9, brightness of 250 cd/m\u0026sup2;, and a response time of 5 ms) positioned on a standard-height table (such that the top of the screen was aligned with their eye level) to provide standardized testing conditions. All sessions were conducted in a quiet room with a moderate temperature (25\u0026ndash;27\u0026deg;C) and appropriate lighting to minimize environmental distractions. One of the aims of this study was to conduct the experiment in conditions as free as possible from any stressors or intrusive factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3.Task Design\u003c/h2\u003e \u003cp\u003eFor the implementation of the Iowa Gambling Task in this study, PsychoPy software was utilized. The task was structured into five blocks of 50 trials each, without any time constraints throughout the game or for each click, allowing participants the option to rest between stages at their discretion. The gaming environment consisted of four categories of cards, presenting the number of selections from each category, the total selections made, the winnings and losses associated with each click, and the final outcome of each choice. To reduce emotional arousal and prevent unwanted feelings, the dominant color of the gaming environment was chosen to be gray, with texts and numbers displayed in black. The cards in this task served the following roles. The standard format has been used here [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e\u0026bull; Card A: Bad card with a high number of losses\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e\u0026bull; Card B: Bad card with a significant loss amount\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e\u0026bull; Card C: Good card with moderate gain and moderate loss\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e\u0026bull; Card D: Good card with low gain and low loss\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.1.4.Measurement Tools and Experiment Protocol\u003c/h2\u003e \u003cp\u003eIn this study, physiological signals were recorded using the ProComp5 Infiniti system (Thought Technology, Canada) with a sampling rate of 256 Hz. Data acquisition and processing were performed using the BioGraph Infiniti software. The device includes built-in hardware filters, such as a 50 Hz notch filter, to minimize power-line interference and ensure high-quality recordings. The overall procedure of the experimental protocol is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In the second stage, the Self-Assessment Manikin (SAM) scale\u0026mdash;a visual tool used to assess emotional states\u0026mdash;was employed [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Participants evaluated and selected their emotional states (pleasure and arousal) using graphical images, and these two indices are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In the final stage, the post-Iowa Gambling Task questionnaire developed by Heilman et al. (2010) was administered [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.2.Data Analysis\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1.Data Preprocessing and Statistical Analysis\u003c/h2\u003e \u003cp\u003eTo preserve meaningful stage-related variations and avoid eliminating genuine physiological effects, a light preprocessing procedure was applied. For BVP and SCR, only high-frequency noise was filtered to remove sharp artifacts while maintaining the natural variability of the signals. For Resp, a band-pass filter (0.18\u0026ndash;0.5 Hz) was used to extract the respiratory component, and Temp remained unfiltered due to its very slow changes, which make filtering unnecessary. The LF/HF ratio was directly obtained from the software outputs because of its standardized and validated calculation method.\u003c/p\u003e \u003cp\u003eThe system\u0026rsquo;s built-in hardware filters\u0026mdash;including a fifth-order low-pass Butterworth anti-aliasing filter and a notch filter\u0026mdash;along with the use of an optical cable, reduced environmental and electrical noise, ensuring clean and high-quality physiological recordings before digital preprocessing. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the filtering steps applied to each physiological signal. After preprocessing, the mean value of each signal was computed for every participant and stage. Data normality was verified using the Lilliefors test. Depending on the results, paired \u003cem\u003et\u003c/em\u003e-tests were used for normally distributed data, and Wilcoxon signed-rank tests were performed when normality was not met. Effect sizes (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e for \u003cem\u003et\u003c/em\u003e-tests and r for Wilcoxon) were calculated along with the direction of change (increase or decrease) to provide a clear representation of stage-related dynamics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAppropriate ranges for filtering physiological data in normal laboratory conditions for this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFilter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReason\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBVP([\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow-pass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 Hz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTo reduce high-frequency noise and preserve the main physiological waveform.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResp([\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBand-pass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u0026ndash;0.5 Hz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTo isolate respiratory activity within the natural breathing frequency range.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCR([\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow-pass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 Hz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTo remove high-frequency artifacts while maintaining signal integrity.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2.Analysis of Card Selection Data\u003c/h2\u003e \u003cp\u003eAs previously discussed, the Iowa Gambling Task features four decks of cards, each with different roles. In this study, the main strategy is outlined as follows:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStep One\u003c/b\u003e: Identify the roles of the cards with a minimum of selections from each deck (ideally between 20 to 25 selections).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStep Two\u003c/b\u003e: Avoid greater losses in favor of maintaining the initial loan granted.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStep Three\u003c/b\u003e: Choose cards with the least risk of loss to ensure a consistent profit that compensates for losses, repays the initial loan, and progresses toward a win.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe LTC parameter, representing long-term consequences, is derived by subtracting the sum of bad cards from good cards, emphasizing long-term rewards. On the other hand, the IFL parameter, which stands for infrequent losses, is calculated by subtracting the sum of immediate gains from long-term losses, reflecting the tendency to prioritize immediate outcomes [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents different conditions of these two parameters, along with their corresponding interpretations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTerminology of factors related to card selection and different states of their arrangements\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLTC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIFL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChoice Pattern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood Cards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe participant prefers decks C and D, focusing on long-term positive outcomes with aligned choices.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood Cards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe participant chooses decks C and D but may not fully consider the balance of gains and losses.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBad Cards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe participant tends towards decks A and B, likely drawn by their immediate gains.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBad Cards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe participant focuses on decks A and B, showing a preference for long-term losses despite immediate gains.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ezero\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMixed Cards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe participant selects a mix of decks C, D, A, and B, with a tendency towards positive long-term outcomes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ezero\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMixed Cards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe participant mixes card selections, but overall long-term outcomes are negative.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote:When LTC is high and IFL is low, an individual tends to make stable choices that lead to long-term gains. Conversely, when LTC is low and IFL is high, the individual focuses more on short-term rewards, even if these choices result in long-term losses.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3.Analysis of Individuals\u0026rsquo; Performance After the IGT\u003c/h2\u003e \u003cp\u003eIn this questionnaire, individuals were asked to rate each card on a scale from \u0026minus;\u0026thinsp;10 to 10 based on their experience. They were then asked to provide a numerical report as a percentage regarding their confidence in their knowledge about the game (evaluating subjective knowledge) and how certain they were about knowing the optimal strategy for the task. Finally, they were asked which single deck they would choose if they had to use only one for the remainder of the task. According to the research from which this questionnaire was derived, three levels of Declarative knowledge can be conceptualized during the task: Level 0\u0026mdash;the participant has no knowledge of identifying the best decks. Level 1\u0026mdash;the individual has knowledge but cannot provide verbal explanations about the results of those decks that would justify their preference. Level 2\u0026mdash;the individual has knowledge and is also able to provide verbal explanations that justify. Based on individuals\u0026rsquo; responses, performance after the task was assessed.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3.Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1.Pre-Task Questionnaire Results\u003c/h2\u003e \u003cp\u003eThe questionnaires used in this study were described in detail in the Materials and Methods section. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the mean scores for each subscale. Based on the DASS-21 results, participants showed moderate levels of stress (M\u0026thinsp;=\u0026thinsp;21.86, SD\u0026thinsp;=\u0026thinsp;11.49) and anxiety (M\u0026thinsp;=\u0026thinsp;13.50, SD\u0026thinsp;=\u0026thinsp;11.49), as well as mild to moderate depression (M\u0026thinsp;=\u0026thinsp;16.00, SD\u0026thinsp;=\u0026thinsp;11.96). Regarding the ERQ, the mean score for cognitive reappraisal was 26.21 (SD\u0026thinsp;=\u0026thinsp;6.99), while expressive suppression had a mean of 13.64 (SD\u0026thinsp;=\u0026thinsp;5.44). The reliability analysis showed that Cronbach\u0026rsquo;s alpha values were 0.802 for reappraisal and 0.795 for suppression in the ERQ. For the DASS-21, Cronbach\u0026rsquo;s alpha values were 0.881 for stress, 0.896 for anxiety, and 0.909 for depression, indicating high internal consistency.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2.Physiological Responses During the Task\u003c/h2\u003e \u003cp\u003eBased on the procedures described in the Materials and Methods section regarding preprocessing and statistical analysis, stage-related changes in physiological signals are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Each cell\u0026rsquo;s color represents the p-value for comparisons between consecutive stages. For significant comparisons (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the test type is indicated as t for paired t-test or w for Wilcoxon signed-rank test, along with the effect size (Cohen\u0026rsquo;s d for t-test, r for Wilcoxon) and the direction of change (increase \u0026uarr; or decrease \u0026darr;). For non-significant comparisons (p\u0026thinsp;\u0026ge;\u0026thinsp;0.05), only the p-value is displayed. Effect sizes are interpreted using the following benchmarks: d: small\u0026thinsp;\u0026asymp;\u0026thinsp;0.2, medium\u0026thinsp;\u0026asymp;\u0026thinsp;0.5 and large\u0026thinsp;\u0026asymp;\u0026thinsp;0.8 also for r: small\u0026thinsp;\u0026asymp;\u0026thinsp;0.1, medium\u0026thinsp;\u0026asymp;\u0026thinsp;0.3, and large\u0026thinsp;\u0026asymp;\u0026thinsp;0.5. It is worth noting that the progression of the LF/HF ratio across consecutive stages is as follows: Stage 2 relative to Stage 1 is 1.14, Stage 3 relative to Stage 2 is 0.94, Stage 4 relative to Stage 3 is 0.93, and Stage 5 relative to Stage 4 is 1[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3.Card Selection Patterns\u003c/h2\u003e \u003cp\u003eAs previously explained, the Iowa Gambling Task includes four card categories: two advantageous decks (C and D) and two disadvantageous decks (A and B). Participants must choose cards from among these four categories to progress through the task. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the participants\u0026rsquo; selection patterns from these four decks across five stages, showing how their decision-making developed during the task. In analyzing the group\u0026rsquo;s performance across 250 selections, several parameters are examined for understanding changes in participants\u0026rsquo; gameplay across the 5 stages. The definitions and calculations of these parameters are detailed in the Methods and Materials section. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the mean values of the parameters related to card selection (IFL and LTC) and the card selection patterns at each stage.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe card selection process \u0026amp; values of parameters related to card selection at each stage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCard Selection Pattern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean LTC\u003c/p\u003e \u003cp\u003e(C\u0026thinsp;+\u0026thinsp;D)-(A\u0026thinsp;+\u0026thinsp;B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean IFL\u003c/p\u003e \u003cp\u003e(A\u0026thinsp;+\u0026thinsp;D)-(C\u0026thinsp;+\u0026thinsp;B)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u0026thinsp;\u0026gt;\u0026thinsp;A\u0026thinsp;\u0026gt;\u0026thinsp;D\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.5714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.7857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u0026thinsp;\u0026gt;\u0026thinsp;A\u0026thinsp;\u0026gt;\u0026thinsp;D\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.2143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.6429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u0026thinsp;\u0026gt;\u0026thinsp;D\u0026thinsp;\u0026gt;\u0026thinsp;B\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.0714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.9286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD\u0026thinsp;\u0026gt;\u0026thinsp;C\u0026thinsp;\u0026gt;\u0026thinsp;A\u0026thinsp;\u0026gt;\u0026thinsp;B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.9286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.4286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u0026thinsp;\u0026gt;\u0026thinsp;D\u0026thinsp;\u0026gt;\u0026thinsp;A\u0026thinsp;\u0026gt;\u0026thinsp;B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.2143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.1429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4.Post-Task Questionnaire and Emotional Ratings\u003c/h2\u003e \u003cp\u003e Before and after the Iowa Gambling Task, participants\u0026rsquo; emotional satisfaction and arousal levels were measured using the SAM scale. Additionally, questions related to the IGT were asked using a specific questionnaire [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the average percentage of participants\u0026rsquo; subjective knowledge of the game was 55.71%, and the average confidence in their optimal strategy was 38.07%. The average \"pleasure\" score decreased from 6.2143 to 5.7857, while the average \"arousal\" score increased from 5.2143 to 5.5. The scores assigned to the cards were as follows: Card A\u0026thinsp;=\u0026thinsp;123, Card B\u0026thinsp;=\u0026thinsp;9, Card C\u0026thinsp;=\u0026thinsp;120, and Card D\u0026thinsp;=\u0026thinsp;125. In response to the question about which card category would be chosen exclusively until the end of the game, 8 participants chose Card A, 6 chose Card B, and 7 chose each of cards C and D.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4.Discussion","content":"\u003cp\u003eThe aim of the present study was to investigate individuals\u0026rsquo; performance under conditions of uncertainty using a modified version of the Iowa Gambling Task.\u003c/p\u003e \u003cp\u003eFrom a strategic perspective, in the initial stages of the game, participants exhibited a focus on immediate gains and a preference for selecting disadvantageous decks, particularly during the first two stages. Individuals displayed a prominent preference for Deck B due to their strong inclination toward immediate rewards. This behavior led to losses for most participants. In other words, LTC and IFL values were negative during the first two stages, further confirming this behavioral tendency. From the third stage onward, a gradual shift in strategy was observed. Although LTC remained negative, IFL turned positive, indicating a reduced tendency to select decks associated with infrequent but large losses. In the subsequent stages, both indices became positive, accompanied by an increased selection of advantageous decks, reflecting improved decision-making and a strategic switch from focusing on immediate rewards to pursuing long-term gains.\u003c/p\u003e \u003cp\u003ePhysiologically, this pattern was accompanied by a significant increase in body temperature and an LF/HF ratio above one. In the early stages of the task (Stage 2 compared to Stage 1), the increase in the LF/HF ratio (1.14) and body temperature reflects heightened sympathetic activity and physiological arousal, which are typical when facing cognitive challenges and uncertainty.\u003c/p\u003e \u003cp\u003eHowever, in stages 4 and 5, attention shifted toward long-term losses and a preference for advantageous decks, resulting in the development of an optimal card selection pattern in stage 4, which was maintained through stage 5. Stage 4, in comparison to stage 3, marked a turning point in strategic performance.\u003c/p\u003e \u003cp\u003eThis shift was reflected in a significant reduction in SCR and an LF/HF ratio below one, indicating a strategic adjustment in participants' behavior. In stages 4 and 5, the decrease in LF/HF ratio (0.94 and then 0.93) and the significant reduction in SCR indicate a gradual decrease in stress and an increase in parasympathetic control, which is associated with more deliberate and focused decision-making strategies.\u003c/p\u003e \u003cp\u003eIn stage 5, the brief decrease in BVP and the return of the LF/HF ratio to 1 signify balance in the autonomic nervous system and cardiovascular regulation following complex decision-making stages, which aligns with the improvement of long-term decision-making strategies.\u003c/p\u003e \u003cp\u003eAdditionally, post-task evaluations revealed that while participants\u0026rsquo; ratings for the card decks aligned with an optimal card selection pattern, the ratings for deck A were not different from those of the advantageous decks. This finding corresponded with participants\u0026rsquo; overall performance up to stage 3. Moreover, declarative knowledge remained at a zero level, as no substantial distinction was found between their preference ratings and their consistent choice of a single deck throughout the game. In summary, during the initial stages of the game, participants experienced stress due to unfamiliarity with the environment, rules, and resulting losses.\u003c/p\u003e \u003cp\u003eAs the game progressed, increased familiarity with the conditions and rules enabled participants to develop an optimal strategy (a partial understanding of the roles of the decks) and effectively manage the situation. This progression may be associated with participants\u0026rsquo; high scores on emotional regulation, particularly cognitive reappraisal. Finally, it can be concluded that the number of trials set for this task (250 trials) facilitated the identification of deck roles. The significant changes observed at the game\u0026rsquo;s turning point highlight synergistic changes in the components of the autonomic nervous system during the progression of the IGT.\u003c/p\u003e \u003cp\u003eVarious studies have been conducted in the field of IGT that have yielded similar results to the present study or have confirmed its findings. However, some studies may present different outcomes .Similar findings have been reported in other researches; for instance, Lin et al. (2007), Lin et al. (2012), and Lee et al. (2020) demonstrated that in decision-making under uncertainty, individuals are more influenced by the frequency of small gains and losses than by the overall outcome. Consequently, they tend to favor the disadvantageous Deck B because of its small immediate rewards. In other words, individuals prefer to avoid immediate losses, even at the cost of forfeiting greater long-term benefits [\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Also, researchers found that Deck B, due to its high frequency of large rewards, can lead to myopic decision-making, where individuals focus on immediate gains and neglect long-term consequences, reinforcing short-term incorrect choices [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. In the present study, this phenomenon was observed as well, with participants initially showing an increase in selections from Deck B in the first and second stages, followed by a decrease from the third stage onwards.\u003c/p\u003e \u003cp\u003eFrom a physiological perspective, the present study also yielded findings, which will be compared and interpreted in relation to other comparable studies. For example, Fernie and Tunney (2013) concluded that physiological activities related to decision-making only become significant once individuals have acquired sufficient knowledge of the task structure. In their study, only participants who reached this level of knowledge showed physiological responses after significant punishments and rewards [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. In the present study, individuals, after showing a preference for the disadvantageous card decks, particularly Deck B after 100 choices and Deck A after 150 choices, which led to significant losses, reached the optimal selection pattern in stage 4. They also showed significant values in the LFHF ratio components and skin conductance response.\u003c/p\u003e \u003cp\u003eSimilar to the study by Clavellino et al. (2019), participants in the present study also exhibited positive physiological responses, including relaxation and, consequently, stability in autonomic nervous system activity (LF/HF ratio component). Additionally, a reduction in stress was evident through a significant decrease in skin conductance and blood volume pulse parameters in the later stages. Similar to this study, they reduced their risky choices[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. A similar argument can be made for the study by Wagar et al. (2006), where they argued that when an individual is about to make a selection from the bad deck, the GSR response significantly increases, whereas this response is lower when selecting from the good deck [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Moreover, according to the study by Lee et al. (2010), in the current research, individuals in stages 4 and 5, where they reached the optimal pattern, showed a significant reduction in blood volume pulse, which aligns with the findings of the mentioned paper, where a decrease in heart rate is associated with advantageous decisions [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough, Carter and Pasqualini, (2004) found that the skin conductance responses they recorded in their study were progressively associated with better performance in the IGT task, supporting the concept that higher autonomic nervous system activity corresponds to improved decision-making, a finding that contradicts the results of the present paper [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], the study by Drucaroff et al. (2011) suggests that high autonomic system activity is beneficial for the decision-making process. In contrast, in the present study, achieving accurate knowledge of the cards occurred under conditions where the sympathetic and parasympathetic systems were balanced in favor of the relaxation component, and the changes in skin conductance response were also reduced [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, Forte et al. (2022) found that higher cardiac activity levels are associated with better decision-making performance. In line with this, the present study observed higher blood volume pulse in Stage 4 (the individuals\u0026rsquo; strategic turning point in the task) compared to Stage 5, as well as a higher selection rate from Deck D in Stage 4 than in Stage 5, thus supporting this research finding[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1.Strengths, Limitations, and Future Directions\u003c/h2\u003e \u003cp\u003eThis study provides deeper insights into the nature and mechanisms of the Iowa Gambling Task (IGT). The structured and carefully controlled design of the task in this research allowed for evaluating individuals' decision-making under uncertainty, while minimizing the influence of external factors, especially mental fatigue. The task was specifically designed to reduce fatigue, ensuring that participants' performance more accurately reflected cognitive processes rather than exhaustion.\u003c/p\u003e \u003cp\u003eNevertheless, this study has certain limitations. The absence of EEG and eye-tracking data limited our ability to explore neural activity and attentional dynamics during decision-making. Future studies integrating these methods could provide more comprehensive insights into the underlying cognitive and neural mechanisms. Additionally, the relatively small sample size, particularly in terms of age and gender diversity, restricts the generalizability of the findings.\u003c/p\u003e \u003cp\u003eFuture research could benefit from applying diverse data-driven approaches, integrating multimodal datasets, and examining independent groups to enhance the robustness of analyses. Moreover, comparing the original IGT with the modified version used in this study, particularly regarding the influence of time constraints, could provide a clearer understanding of this factor's role in decision-making.\u003c/p\u003e \u003cp\u003eDesigning cognitive and behavioral interventions, improving the temporal resolution of physiological data, and analyzing the outcomes of each choice in detail may contribute to identifying the healthy dynamics of decision-making. Finally, implementing advanced data processing techniques such as machine learning, deep learning, and computational modeling could be an effective step toward more practical applications and a deeper understanding of both the IGT and human decision-making performance.\u003c/p\u003e \u003c/div\u003e"},{"header":"5.Conclusion","content":"\u003cp\u003eAll in all, the results of the study showed that in the early stages of the game, individuals tended to select disadvantageous cards and focused on immediate gains, which led to losses and physiological stress. This stress was accompanied by a slight but significant increase in body temperature and heightened sympathetic activity. However, as the game progressed, familiarity with the rules and conditions\u0026mdash;or, in other words, increased adaptation\u0026mdash;enabled participants to adopt an optimal strategy. This was associated with a significant reduction in SCR and a decrease in sympathetic activity in Stage 4, leading to physiological balance in Stage 5. Post-task evaluations also revealed that, despite participants\u0026rsquo; scores being close to the optimal pattern, their declarative knowledge remained at a low level. Significant changes at the game\u0026rsquo;s turning point\u0026mdash;the achievement of the optimal card selection pattern\u0026mdash;reflected physiological signs of relaxation. Therefore, it can be concluded that the number of selections and stages in this task contributed to participants\u0026rsquo; identification of the roles of the cards. Additionally, advancing in conditions of uncertainty through decision-making reflects a dynamic balance between neural systems related to reward and stress.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics Approval\u003c/strong\u003e \u003cp\u003e The study was conducted in accordance with the Declaration of Helsinki and approved by the Biomedical Ethics Committee of Ferdowsi University of Mashhad (Code: IR.UM.REC.1401.175).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Participate\u003c/strong\u003e \u003cp\u003e Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for Publication\u003c/strong\u003e \u003cp\u003eParticipants provided informed consent for the publication of anonymized data.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was supported by Ferdowsi University of Mashhad, Research Affairs (Grant No. 3/57322).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\u0026bull; Elahe Yaghoubian: Conceptualization, Methodology, Task Development, Data Collection, Formal Analysis, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing, Project Administration, Resources\u0026bull; Ali Moghimi: Conceptualization, Methodology, Supervision, Funding Acquisition, Writing \u0026ndash; Review \u0026amp; Editing\u0026bull; Morteza Izadifar: Conceptualization, Methodology, Supervision, Writing \u0026ndash; Review \u0026amp; Editing\u0026bull; Hamidreza Kobravi: Conceptualization, Methodology, Supervision, Writing \u0026ndash; Review \u0026amp; Editing\u0026bull; Nilufar Totonchi: Conceptualization, Task Development, Data Collection\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request, subject to institutional policies.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFellows LK (2004) The Cognitive Neuroscience of Human Decision Making: A Review and Conceptual Framework. 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Appl Cogn Psychol 36:100\u0026ndash;110. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/acp.3901\u003c/span\u003e\u003cspan address=\"10.1002/acp.3901\" targettype=\"DOI\" 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":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Decision-making, Uncertainty, Physiological manifestations, Gambling strategies, Iowa Gambling Task, Adaptation, Dynamic balance","lastPublishedDoi":"10.21203/rs.3.rs-8464219/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8464219/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDecision-making is a key topic in cognitive sciences and neuroeconomics. While many studies have analyzed individual performance under conditions of uncertainty within the framework of the Iowa Gambling Task (IGT), few have examined the physiological manifestations during the task and their alignment with participants\u0026rsquo; strategies, particularly in the context of healthy dynamics, free from limitations. This study aimed to investigate decision-making under uncertainty in a stress-free environment (free from time constraints or emotional interventions) using a modified IGT. Twenty-eight participants (12 men, 16 women; 21.96\u0026thinsp;\u0026plusmn;\u0026thinsp;3.82 years) completed the task in five stages, with 50 trials in each stage. Four physiological signals were recorded: Blood Volume Pulse (BVP), temperature, respiration rate, and Skin Conductance Response (SCR). At first, participants preferred disadvantageous cards (A and B) but gradually shifted to advantageous cards (C and D). Initial stages showed a slight increase in body temperature and an LF/HF ratio above one, indicating increased sympathetic activity and physiological stress. As the task progressed, individuals adapted to the rules and adopted more optimal strategies. Stage 4 marked a turning point with significant decreases in SCR and an LF/HF ratio below one, reflecting reduced stress. In the final stage, decreased BVP and an LF/HF ratio of one indicated a balance between the sympathetic and parasympathetic systems. Finally, it can be acknowledged that the task structure was useful in identifying the roles of the cards. Additionally, advancing in conditions of uncertainty through decision-making reflects a dynamic balance between neural systems related to reward and stress.\u003c/p\u003e","manuscriptTitle":"Physiological Manifestations in Strategic Decision-Making under Conditions of Uncertainty: Insights from the Iowa Gambling Task","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 15:26:10","doi":"10.21203/rs.3.rs-8464219/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7b626b27-a5e1-483d-b719-15f9996f84f6","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-08T15:26:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 15:26:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8464219","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8464219","identity":"rs-8464219","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}