Paradox of Time: How Constraints and Abundance Shape Decision-Making and Cognition

Paradox of Time: How Constraints and Abundance Shape Decision-Making and Cognition | 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 Paradox of Time: How Constraints and Abundance Shape Decision-Making and Cognition Yan-Bang Zhou, Shun-Jie Ruan, Ya-Ru Bu, Qing Bao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6887021/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 Background Intertemporal decision-making involves balancing immediate benefits against future outcomes, affecting crucial domains such as financial planning and health behaviors. Time pressure substantially shapes these decisions: constrained time typically encourages intuitive reasoning and decision biases, whereas sufficient time facilitates more deliberative processing. Nevertheless, existing research has largely overlooked conditions involving delayed responses, limiting a comprehensive understanding of how varying temporal constraints influence intertemporal choice. Methods Sixty-five participants (30 males; mean age 22.5 years) completed intertemporal choice tasks under three conditions: time-constrained (decision within 3 seconds), baseline (unlimited decision time), and delayed response (decision after 5 seconds). Behavioral measures, including reaction times and choices (short-term vs. long-term options), were recorded. Additionally, eye-tracking data—Outcome Gaze Proportion (OGP) and pupil diameter (PD)—were collected to assess attention allocation and cognitive-emotional load during decision-making. Results Reaction times differed significantly across conditions, with the shortest times observed in the time-constrained condition and the longest in the delayed response condition. Participants showed an increased preference for short-term rewards as decision time increased, suggesting a paradoxical rise in impatience under extended deliberation. Eye-tracking data indicated that Outcome Gaze Proportion (OGP) increased with longer decision windows, reflecting more attention to outcome attributes, whereas pupil diameter (PD) was largest under time pressure, signaling heightened cognitive and emotional load. Mediation analyses confirmed that both OGP and PD significantly mediated the relationship between time condition and choice behavior, suggesting that attention allocation and physiological arousal jointly shape intertemporal preferences. Conclusion Time shapes not only the speed but also the quality of intertemporal choices by modulating attention and emotional arousal. Our findings reveal that both time pressure and delay systematically influence decision preferences, highlighting the need to consider temporal context in models of self-control and decision optimization. Time pressure intertemporal decision-making cognitive load eye-tracking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Squirrels gather acorns to endure winter; investors allocate capital for future returns. Both actions exemplify intertemporal decision-making—choices made in the present with consequences unfolding over time [ 1 – 3 ]. In humans, such decisions are not merely practical—they are psychologically and neurologically mediated acts shaped by dynamic interactions between cognition, emotion, and context [ 4 ]. The human capacity to delay gratification, manage future uncertainty, and regulate trade-offs between immediacy and long-term benefit is a central topic in decision science, with broad implications for financial planning, health behavior, and public policy. The Role of Time in Shaping Decision Strategies A key contextual factor is time itself. Objective constraints on decision time significantly alter the trajectory of cognition [ 5 – 7 ]. When time is limited, individuals tend to rely on heuristic or intuitive reasoning rather than deliberative computation, as described by dual-process theories [ 8 – 10 ]. Under time pressure, intuitive responses dominate, often amplifying framing effects and decision biases [ 11 ]. Conversely, when more time is available, deliberative processes may regain control, potentially reducing these biases. Notably, research has also documented paradoxical increases in patience under acute time pressure, suggesting that temporal scarcity may sometimes promote future-oriented reasoning via attentional refocusing or affective override [ 4 , 12 ]. Capturing Cognitive and Emotional Dynamics Physiological and cognitive signatures of these shifts can be captured via eye-tracking and pupillometry [ 13 – 14 ]. Metrics such as Outcome-Gaze-Proportion (OGP) reflect how attention is allocated between decision attributes, while pupil diameter indexes both cognitive effort and emotional arousal [ 15 – 17 ]. These tools allow researchers to investigate not just what decision is made, but how it is constructed. Importantly, pupil dilation is a compound signal: it reflects both working memory load and affective states [ 18 – 19 ]. Recent theoretical syntheses emphasize that pupil-based measures offer a “preconscious” window into decision processes, sensitive to both cognitive and emotional perturbations [ 20 – 21 ]. The attentional narrowing hypothesis, formalized in the Attentional Myopia Model, proposes that cognitive load reduces the breadth of cue integration, making decision-makers focus disproportionately on salient attributes [ 22 ]. In the intertemporal domain, this may translate into excessive focus on immediate or visually dominant outcomes. Zhou et al. [ 23 – 24 ] extended this framework, showing that both attention distribution (OGP) and pupil dynamics predict temporal preference reversals. However, most extant studies rely on binary manipulations—comparing time pressure with baseline conditions—thereby neglecting delayed response states. This asymmetry in design limits our understanding of how cognitive and affective systems respond when individuals are granted surplus time to decide. Gaps in Existing Literature Furthermore, intertemporal choice research has often emphasized behavioral outputs at the expense of decision dynamics. Studies typically quantify patience or impulsivity via discounting curves, but rarely interrogate the underlying attentional and physiological trajectories that produce such curves. By integrating eye-tracking and pupillometry into decision-making paradigms, researchers can directly observe the micro-mechanisms of trade-off processing under variable temporal regimes. This is especially relevant given that cognitive and emotional factors do not operate in isolation—they interact in real time to shape preferences, salience encoding, and effort mobilization. Present study This study addresses two persistent gaps in the literature: (1) the limited exploration of delayed decision conditions as a counterpoint to time pressure, and (2) the underutilization of multimodal process data to explain intertemporal choice variability. Building upon prior findings and guided by dual-process models, attentional narrowing frameworks, and recent pupillometry-based insights, we explore how the structure of temporal opportunity—whether constrained, neutral, or abundant—shapes intertemporal decision architecture. In this context, we proposed the hypotheses of the research as follows: H1 The probability of choosing the smaller-sooner (SS) option will be lowest under time-constrained conditions, intermediate under baseline conditions, and highest under delayed response conditions. H2 Outcome-Gaze-Proportion (OGP) will increase with time abundance, showing lowest values under time pressure and highest values in delayed conditions. H3 Pupil diameter will inversely vary with time availability, peaking under time constraints and minimizing under delay. H4 Task condition effects on SS choices will be mediated positively by OGP (attention to outcomes) and negatively by pupil diameter (cognitive/emotional load). H5 The relative contribution of attentional versus physiological processes will vary across conditions, with attentional mediation stronger under time abundance and physiological mediation stronger under time pressure. Method Participant The necessary sample size was determined using G*Power software [ 25 ] through linear regression analysis, assuming a medium effect size (Cohen's f² = 0.15), which indicates a moderate level of practical significance. The analysis indicated that 55 participants were required to achieve a power of 0.80 at a significance level of 0.05. To ensure robustness and account for potential attrition, 65 participants were recruited, including 30 males, with an average age of 22.5 years. Participants were given 10 Yuan basic payment (approximately USD $ 2) as a decision-making reward. At the end of the experiment, three trials were randomly selected from their real choices, and discounted based on their performance. On average, participants’ reward included basic payment and extra reward, totaling 25 yuan (approximately USD $ 4). The study was approved by the Ethics Committee of Ningxia University, and all participants provided written informed consent prior to participation. The research was conducted in accordance with the Declaration of Helsinki. Apparatus During the experiment, eye movement data from participants were captured using an EyeLink1000 Plus eye tracker (SR Research, Ontario, Canada). The stimuli were displayed on a 20-inch Dell monitor with a resolution of 1280 × 1024 pixels and a refresh rate of 60 Hz, offering a viewing angle of 36° horizontally and 29° vertically. Participants were seated approximately 58 cm from the screen and instructed to minimize head movements by utilizing a chin rest. Although participants observed the stimuli with both eyes, data collection was restricted to the right eye only. Responses to the stimuli were recorded through keyboard input. Stimuli The experiment employed a computer to generate 10,000 intertemporal options. Each option consisted of a delay and an outcome, representing a certain amount of money that would be gained after a certain time. These options were then randomly paired into sets of small sooner (SS) and large later (LL) options. Following the removal of strongly dominant option pairs, 50 pairs of SS and LL options were randomly chosen from the remaining pairs. The options were presented using Arial font, ensuring a viewing angle of 2.89° and a minimum distance of 5° between any two stimulus presentations [ 26 – 27 ]. Additionally, to encourage conscientious participation, five pairs of strongly dominant options were included in the task. To ensure a balanced presentation of the experimental material, half of the participants viewed the delayed option above the outcome, while the remaining half observed the outcome positioned above the delay. The order of the experiments was also balanced for each participant. Task We employed a within-participant design, with time manipulation as the independent variable, and choice outcomes as well as eye-tracking measures as the dependent variables. Participants were required to choose their preferred option between SS and LL options. Each participant completed choice tasks under three different time conditions: baseline condition, constraint condition and delayed response condition. The order of the three conditions was balanced across participants. In the time constraint condition, participants were required to make decision within three seconds of the stimulus presentation [ 12 , 28 ]. In the baseline condition, participants had unlimited time to make decision. In the delayed response condition, participants were required to make decision after five seconds of the stimulus presentation. The durations of 3 seconds (time constraint) and 5 seconds (delayed response) were selected based on previous studies that demonstrated their efficacy in modulating decision behavior under time pressure [ 12 , 28 ]. In addition, pilot testing confirmed that participants could complete the tasks reliably within these windows without excessive errors or omissions. To incentivize full engagement with the experiment, upon completion of the experiment, one trial was randomly selected from each condition. Depending on the participant's choice in the selected trial, the delay and outcome were discounted as the actual gain [ 23 , 29 ]. The expected variation in response time across conditions (i.e., shortest in the time-constrained condition and longest in the delayed response condition) was used as a manipulation check to confirm the effectiveness of our temporal constraint design, rather than as a central hypothesis. Experimental Procedures Initially, participants were directed to the laboratory and instructed to read and sign an informed consent form. Then, experimental instructions were displayed on the laboratory computer screen for the participants to review. An experimenter then assisted participants in adjusting their seat height and fixing their head position to ensure comfort. Subsequently, a 5-point calibration was performed to achieve eye fixation, followed by a practice session to familiarize participants with the experimental tasks. Each condition formal experiment comprised two blocks of 55 trials each. Participants completed the blocks in a random order, with a brief two-minute interval in between. In the baseline condition, a fixation point appeared in the center of the screen, prompting participants to fixate and press the space bar. Following this, the word "Choice" was displayed at the center of the screen for 1000 milliseconds, allowing participants unlimited time to make their selection. Two options then appeared on the left and right sides of the screen, with the F key used to select the left option and the J key for the right option. After a key press, the term "Finish" appeared in the center of the screen for 1000ms, signifying the end of the trial (see Fig. 1 a). In the time constraint condition, participants fixated on a focal point displayed at the center of the screen and pressed the space bar key. This action triggered the appearance of the word "Choice" for one second. Following this, participants were given 3000ms to browse, and press a key to select one of the options. Failure to respond within the time limit resulted in the word "Warning" being displayed at the center of the screen for 1000ms, indicating trial failure. Conversely, if a key was pressed, the word "Finished" appeared for 1000ms, indicating the end of the trial (see Fig. 1 b). In the delayed response condition, participants fixated on a focal point displayed at the center of the screen and pressed the space bar key. This action triggered the appearance of the word "Choice" for one second. Following this, participants need browse the presentation for 5000 ms, then need press a key to select one of the options. When a key was pressed, the word "Finished" appeared for 1000ms, indicating the end of the trial. (see Fig. 1 c). Results Three participants who failed to meet engagement criteria on dominant control trials were excluded, leaving a final sample of 62 participants. Additionally, 94 trials where participants failed to respond within the 3000ms limit in the time constraint condition were removed from the analysis. All data supporting the findings of this study have been shared on the Open Science Framework and are available at: https://osf.io/mtdc9 . Response time Response time (RT) was defined as the duration between the presentation of the option and the key press response. A linear mixed-effects model was conducted to examine the effect of task conditions on RT, including a fixed effect of task (baseline = 0, constraint = 1; delayed = 2), and random intercepts for participants, and stimuli. The analysis revealed significant differences in RT across the three task conditions F (2, 18597) = 8072.5, p < .0001. Specifically, the mean RT for the baseline condition was 2803ms ( SE = 77.5, CI95% = [2651, 2955]), for the time constraint condition was 1348ms ( SE = 77.5, CI95% = [1196, 1500]) and for the delayed response condition was 5781ms ( SE = 77.5, CI95% = [5629, 5933]). Post-hoc pairwise comparisons using the Tukey method indicated significant differences between all pairs of task conditions. Participants took significantly more time in the baseline condition than in the time constraint condition ( z = 66.017, SE = 22.0, p < .0001). They also took significantly more time in the delayed response condition compared to the baseline condition ( z = -135.079, SE = 22.0, p < .0001). Lastly, participants took significantly more time in the delayed response condition compared to the time constraint condition ( z = -202.284, SE = 21.9, p < .0001) (Fig. 2 about here). Choice Choice was defined as the proportion of participants choosing the SS option in the both tasks. The generalized linear mixed model (GLMM) analysis revealed significant differences in the probability of choosing SS option across the three task conditions (F (2, 18597) = 62.19, p < 0.0001). The estimated marginal means (EMMs) and pairwise comparisons showed that the probability of choosing SS option was highest in the delayed response condition (M = 0.837, SE = 0.0268), followed by the baseline condition (M = 0.782, SE = 0.0334) and lowest in the time constraint condition (M = 0.751, SE = 0.0365). Pairwise comparisons indicated that the probability of choosing SS option in the baseline condition was significantly higher than in the time constraint condition ( β = 0.0302, SE = 0.00891, z = 3.390, p = 0.0020). Additionally, the delayed response condition showed a significantly higher probability of choosing SS option compared to the baseline condition ( β = 0.0553, SE = 0.00997, z = 5.548, p < 0.0001) and the time constraint condition ( β = 0.0855, SE = 0.01240, z = 6.897, p < 0.0001). (see Fig. 3 ). Outcome-Gaze-Proportion The Outcome-Gaze-Proportion (OGP) reflects the proportion of total gaze duration allocated to the outcome attributes relative to all attributes [ 23 ]. In this study, OGP was calculated as OGP = gaze duration on outcome / total gaze duration. An OGP greater than 0.5 indicates a preference for attending to outcome attributes, while an OGP less than 0.5 reflects greater focus on delay attributes. This measure provides critical insights into how individuals prioritize information under different decision-making conditions and serves as an index of attentional shifts observed in our experimental tasks. A linear mixed-effects model was conducted to examine the effect of task conditions on Outcome-Gaze-Proportion (OGP), including a fixed effect of task and random intercepts for participants and stimuli. The analysis revealed significant differences in OGP across the three task conditions, F (2, 18597) = 124.53, p < .0001. The mean OGP for the Baseline Condition was 0.510 ( SE = 0.0146, CI95% = [0.481, 0.539]), for the Delayed Response Condition was 0.520 ( SE = 0.0146, CI95% = [0.491, 0.548]) and for the Time Constraint Condition was 0.475 ( SE = 0.0146, CI95% = [0.446, 0.503]). Post-hoc pairwise comparisons using the Tukey method indicated significant differences between all pairs of task conditions. Participants exhibited a significantly higher OGP in the Delayed Response Condition compared to the Baseline Condition ( z = 3.486, SE = 0.0027, p = 0.0014). Conversely, participants showed a significantly lower OGP in the Time Constraint Condition compared to the Baseline Condition ( z = -12.885, SE = 0.0027, p < .0001). Additionally, the OGP in the Delayed Response Condition was significantly higher than in the Time Constraint Condition ( z = 16.371, SE = 0.0027, p < .0001) (Fig. 4 about here). Pupil diameter Pupil diameter (PD) was measured as the size of the pupil in response to different task conditions. A linear mixed-effects model was conducted to examine the effect of task conditions on pupil diameter, including a fixed effect of task and random intercepts for participants and stimuli. The analysis revealed significant differences in pupil diameter across the three task conditions, F (2, 18597) = 235.58, p < .0001. Specifically, the mean pupil diameter for the Baseline Condition was 1032 ( SE = 26.7, CI95% = [979, 1084]), for the Time Constraint Condition was 1059 ( SE = 26.7, CI95% = [1007, 1111]) and for the Delayed Response Condition was 1015 ( SE = 26.7, CI95% = [963, 1068]). Post-hoc pairwise comparisons using the Tukey method indicated significant differences between all pairs of task conditions. Participants had a significantly larger pupil diameter in the Time Constraint Condition compared to the Baseline Condition ( z = 7.965, SE = 2.04, p < .0001). They also had a significantly smaller pupil diameter in the Delayed Response Condition compared to the Baseline Condition ( z = -13.505, SE = 2.04, p < .0001). Lastly, participants had a significantly smaller pupil diameter in the Delayed Response Condition compared to the Time Constraint Condition ( z = -21.469, SE = 2.04, p < .0001) (Fig. 5 ). Although this study used mean pupil diameter without baseline correction, the consistent fixation period and visual conditions likely reduced intertrial noise. Future research should incorporate trial-based baseline correction (e.g., subtracting pre-trial fixation values) to enhance the sensitivity of pupillometric analyses [ 30 ]. Mediating effect To investigate whether task type influences the probability of choosing the SS option through OGP and Pupil, we conducted a mediation analysis using R software. The independent variable was task type (0 = baseline condition; 1 = time constraint condition; 2 = delayed response condition), the mediating variables were OGP and Pupil and the dependent variable was the probability of choosing the SS option. The results revealed significant indirect effects of task type on the probability of choosing the SS option through OGP ( a 1 b 1 = 0.014832, CI 95% = = [0.01152, 0.01814], SE = 0.00174, p < 0.05) and through Pupil ( a 2 b 2 = -0.000013765, CI 95% = [-0.00002, -0.00001], SE = 0.000001, p < 0.05). The total effect of task type on the probability of choosing the SS option was significant ( c = 0.075, CI 95% = [0.051, 0.099], SE = 0.012, p < 0.05). After controlling for the mediators OGP and Pupil, the direct effect of task type on the probability of choosing the SS option was 0.065 (CI 95% = [0.041, 0.089], SE = 0.012, p < 0.05) (see Fig. 6 ). Discussion Behavioral and Cognitive Adaptations Under Time Pressure Using eye-tracking techniques, this study examined decision-making processes across three conditions: time-constrained, baseline, and delayed response. The findings revealed significant differences in reaction times across these conditions, with the longest reaction time observed in the delayed response condition, intermediate times in the baseline condition, and the shortest reaction times in the time-constrained condition. Similarly, the probability of choosing short-term (SS) options varied across conditions, increasing progressively with greater time availability. Participants were least likely to choose SS options under time constraints, more likely under baseline conditions, and most likely in the delayed response condition. These results highlight the dual effects of time on decision-making. Under time constraints, individuals prioritized long-term (LL) options, aligning with theories suggesting that time pressure narrows attention to goal-relevant information and suppresses impulsive tendencies [ 5 , 7 , 24 ]. This attentional focusing is thought to suppress peripheral cues and promote reliance on high-value attributes, even in the absence of extended deliberation. Conversely, under delayed conditions, extended decision-making periods appeared to heighten impatience, resulting in a preference for immediate rewards. This tendency likely reflects not deeper cognitive processing, but rather the affective cost associated with waiting. Research suggests that prolonged anticipation can evoke discomfort, uncertainty, and negative affect, which function as psychological costs that discount future rewards [ 19 , 31 ]. Supporting this interpretation, pupil-based evidence shows that individuals exhibit increased emotional reactivity and discomfort during extended wait periods, even when task difficulty remains unchanged [ 32 ]. This paradox—where too little time fosters cognitive simplification and too much time increases emotional aversion—points to a non-linear model of temporal influence on decision-making. Rather than a linear relationship between time and self-control, our data support a curvature: moderate time allows balance, but either extreme shifts decision architecture. This aligns with recent findings that emotional arousal and time anticipation jointly influence intertemporal preference, independent of reward value [ 33 ]. From a theoretical perspective, the Delay Discounting effect provides a foundational explanation for these findings. Yet it may be insufficient without accounting for emotional processes that emerge under delayed response. When waiting incurs affective costs—such as anticipatory anxiety or frustration—individuals may engage in “emotional discounting,” whereby they devalue delayed rewards not due to preference instability but due to aversive present-moment experience [ 18 , 33 ]. The Role of OGP and PD in Decision-Making To further uncover the mechanisms behind the behavioral patterns observed, we examined two eye-tracking indices: Outcome Gaze Proportion (OGP) and pupil diameter (PD). The findings demonstrated that OGP progressively increased with time abundance, reflecting more distributed attention to outcome attributes when deliberation time was extended. In contrast, PD was largest under time pressure, indicating elevated cognitive and emotional strain in that condition. Lower OGP in the time-constrained condition suggests a rapid prioritization of essential cues. This aligns with dual-process theories, which argue that intuitive, heuristic processing dominates under high cognitive load or temporal scarcity [ 9 ]. In contrast, the delayed condition promoted wider gaze dispersion and deeper exploration, consistent with reflective, System 2 processing. These findings mirror research in moral decision-making and cooperation, where time pressure induces intuitive responses and time extension facilitates moral reasoning [ 34 – 35 ]. The role of PD adds a physiological dimension to these interpretations. Larger pupil diameters under time constraints indicate higher cognitive load and sympathetic arousal [ 18 ], corroborating the interpretation that individuals experience greater mental effort and emotional tension when forced to decide quickly. Recent studies further show that PD is not merely a stress indicator but also reflects anticipatory biases and upcoming choice commitment [ 32 – 33 ]. Therefore, PD fluctuations offer a real-time index of both cognitive effort and affective cost in temporal decision-making. Theoretical Implications and Future Directions This study contributes to understanding how temporal framing shapes intertemporal preferences through both attentional and affective mechanisms. By integrating behavioral, eye-tracking, and pupillometric data, we offer a framework in which time is not merely a contextual variable, but a dynamic regulator of cognitive architecture. Our findings refine the Attention Myopia Model [ 22 ], suggesting that attentional narrowing under pressure can be either adaptive or maladaptive depending on task structure and reward salience. Practically, the results have implications for domains such as financial planning, public messaging, and time-sensitive interventions. The widely held belief that “more time leads to better decisions” may overlook the affective burden induced by delay. Conversely, well-calibrated time pressure can help individuals prioritize long-term outcomes by reducing noise and limiting impulsive distraction. Nonetheless, several limitations must be acknowledged. First, while our laboratory design ensured control over timing and presentation, it lacks the ecological complexity of real-life decision environments. Future research should explore more naturalistic contexts using mobile eye-tracking or immersive simulations. Second, although OGP and pupil diameter are well-established proxies for attentional focus and arousal, they remain indirect measures of internal cognitive processes. Additional physiological markers—such as heart rate variability or galvanic skin response-may provide converging evidence for the observed mechanisms. Overall, this study demonstrates that time constraints and delays exert asymmetric effects on decision-making not merely through rational trade-offs, but by modulating how people attend to information and how they feel while choosing. These insights open space for targeted experimental manipulations and decision-support designs that make time work with, rather than against, human cognition. Conclusion Time is both a constraint and a constructor of preference. Our results demonstrate that variations in available time alter not only the pace of decisions but their direction—shifting the balance between cognition and emotion, attention and arousal, patience and impulse. Understanding this interplay offers a deeper lens into intertemporal choice and expands the boundary conditions for self-control. Abbreviations Not applicable. Declarations Ethics approval and consent to participate: The study was approved by the Ethics Committee of Ningxia University. All participants provided written informed consent prior to participation. The research was conducted in accordance with the Declaration of Helsinki. Consent for publication: Not applicable. Availability of data and materials: All data supporting the findings of this study are publicly available on the Open Science Framework at: https://osf.io/mtdc9 Competing interests: The authors declare that they have no competing interests. Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors’ contributions: Yan-Bang Zhou conceived the study, designed the experimental paradigm, and drafted the manuscript. Shun-Jie Ruan and Ya-Ru Bu contributed to data collection, preprocessing, and analysis. Qing Bao provided theoretical guidance, assisted in the interpretation of results, and revised the manuscript. All authors read and approved the final manuscript. Acknowledgements: Not applicable. 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Frontiers in Psychology, 15 , 1451674. https://doi.org/10.3389/fpsyg.2024.1451674 Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39 (2), 175–191. https://doi.org/10.3758/bf03193146 Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124 (3), 372–422. https://doi.org/10.1037/0033-2909.124.3.372 Rayner, K. (2009). Eye movements and attention in reading, scene perception, and visual search. Quarterly Journal of Experimental Psychology, 62 (8), 1457–1506. https://doi.org/10.1080/17470210902816461 Svenson, O., & Edland, A. (1987). Change of preferences under time pressure: Choices and judgements. Scandinavian Journal of Psychology, 28 (4), 322–330. https://doi.org/10.1111/j.1467-9450.1987.tb00769.x Zhou, Y. B., Li, Q., Li, Q. Y., & Liu, H. Z. (2022). Evaluation scale or output format: The attentional mechanism underpinning time preference reversal. Frontiers in Psychology, 13 , 865598. https://doi.org/10.3389/fpsyg.2022.865598 Rubaltelli, E., Dickert, S., & Slovic, P. (2012). Response mode, compatibility, and dual-processes in the evaluation of simple gambles: An eye-tracking investigation. Judgment and Decision Making, 7 (4), 427–440. Wang, Y., Liu, Y., Li, Q., & Luo, Y. (2021). Emotion and intertemporal choice: A neurocognitive perspective. Frontiers in Neuroscience, 15 , 638989. https://doi.org/10.3389/fnins.2021.638989 Gross, M. P., & Dobbins, I. G. (2021). Pupil dilation during memory encoding reflects time pressure rather than depth of processing. Cognition, 213 , 104701. https://doi.org/10.1016/j.cognition.2021.104701 Lempert, K. M., Speer, M. E., Delgado, M. R., & Phelps, E. A. (2023). Emotional arousal predicts individual differences in intertemporal choice. Nature Human Behaviour, 7 (3), 412–420. https://doi.org/10.1038/s41562-022-01501-7 Tinghög, G., Andersson, D., Bonn, C., Böttiger, H., Josephson, C., Lundgren, G., Västfjäll, D., Kirchler, M., & Johannesson, M. (2013). Intuition and cooperation reconsidered. Nature, 498 (7452), E1–E2. https://doi.org/10.1038/nature12194 Persson, E., & Tinghög, G. (2023). The effect of fast and slow decision-making on equity-efficiency tradeoffs and moral repugnance. Royal Society Open Science, 10 (9), 230558. https://doi.org/10.1098/rsos.230558 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6887021","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481876983,"identity":"580338ee-1e31-4977-be4d-e73da1ec36cc","order_by":0,"name":"Yan-Bang Zhou","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yan-Bang","middleName":"","lastName":"Zhou","suffix":""},{"id":481876984,"identity":"e948979d-5142-43ae-a9e0-875739ef2e1d","order_by":1,"name":"Shun-Jie Ruan","email":"","orcid":"","institution":"Ningxia University","correspondingAuthor":false,"prefix":"","firstName":"Shun-Jie","middleName":"","lastName":"Ruan","suffix":""},{"id":481876985,"identity":"40455c07-e338-4e8f-a110-a0722da7b64b","order_by":2,"name":"Ya-Ru Bu","email":"","orcid":"","institution":"Ningxia University","correspondingAuthor":false,"prefix":"","firstName":"Ya-Ru","middleName":"","lastName":"Bu","suffix":""},{"id":481876986,"identity":"744836a9-e17e-45ce-bc6f-a49d3128069a","order_by":3,"name":"Qing Bao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBACAxCRwMDAw8bMfvBBQkUN8Vpk+Nl5kg0enDlGpBYgsJHsZzCTfNjCTFiLOfvhgzce7qjlMTjMkFaR2MDGwN/enYBXi2VPWrJF4pnjQC2Mx24k7pBhkDhzdgN+hx3IMZNIbDsGtuVG4hk2BgOJXAJazr+BazErSGxjJkLLDbAtNTySzQxmDERpsZzxDOiXtgM8/Mw8yRIJZ47xEPSLOX/ywZs/2+rs2fiPH/z4o6JGjr+9F78WEJBgYDgM5/AQVA7VUkeUwlEwCkbBKBihAAA/U0jf9HqGWgAAAABJRU5ErkJggg==","orcid":"","institution":"Ningxia University","correspondingAuthor":true,"prefix":"","firstName":"Qing","middleName":"","lastName":"Bao","suffix":""}],"badges":[],"createdAt":"2025-06-13 09:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6887021/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6887021/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86329331,"identity":"53dfcc77-5f00-4b84-9749-78e725ca70aa","added_by":"auto","created_at":"2025-07-09 11:43:18","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":143317,"visible":true,"origin":"","legend":"\u003cp\u003ea figure depicts the trial flow with no time constraint; b figure depicts the trial flow in the time constraint condition; c figure depicts the trial flow in the delayed response condition.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6887021/v1/1fe5c00a954087dcdcc1b98b.jpeg"},{"id":86328646,"identity":"c2fa7377-5254-49a8-9db5-e3b7e7ea24af","added_by":"auto","created_at":"2025-07-09 11:35:18","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45345,"visible":true,"origin":"","legend":"\u003cp\u003eBar chart showing the effect of task on reaction time.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6887021/v1/8325397e04cc08a440ac768f.jpeg"},{"id":86328649,"identity":"5e7e1d89-cd11-4edf-82b9-198e7fb6bd8a","added_by":"auto","created_at":"2025-07-09 11:35:18","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54967,"visible":true,"origin":"","legend":"\u003cp\u003eBar chart showing the effect of task on probability of choosing the SS option.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6887021/v1/c17ea94576f12fd8336f4c16.jpeg"},{"id":86329335,"identity":"db245088-53ec-4b9f-9f3c-69847dd28560","added_by":"auto","created_at":"2025-07-09 11:43:18","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53114,"visible":true,"origin":"","legend":"\u003cp\u003eBar chart showing the effect of task on Outcome-Gaze-Proportion.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6887021/v1/bc0b8f9b6bcdb03b8aa0ca7d.jpeg"},{"id":86328656,"identity":"5c2ed13c-9b02-469a-9c05-38fdbdc7204f","added_by":"auto","created_at":"2025-07-09 11:35:18","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":46262,"visible":true,"origin":"","legend":"\u003cp\u003eBar chart showing the effect of task on pupil diameter.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6887021/v1/ccb8f04b2b0bdf33612c79c9.jpeg"},{"id":86328652,"identity":"525ad8ab-7044-48b8-af88-78effdeaaacb","added_by":"auto","created_at":"2025-07-09 11:35:18","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":65841,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the parallel multiple mediator model analysis of the OGP and the Pupil.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6887021/v1/2cab801de3bd80a16b83dfe6.jpeg"},{"id":92078993,"identity":"15f50cc4-7995-4bf6-a74f-6e654ce9322b","added_by":"auto","created_at":"2025-09-24 11:24:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1008456,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6887021/v1/c658dd5e-d7e0-496b-b785-beaafd34ff48.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Paradox of Time: How Constraints and Abundance Shape Decision-Making and Cognition","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSquirrels gather acorns to endure winter; investors allocate capital for future returns. Both actions exemplify intertemporal decision-making—choices made in the present with consequences unfolding over time [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In humans, such decisions are not merely practical—they are psychologically and neurologically mediated acts shaped by dynamic interactions between cognition, emotion, and context [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The human capacity to delay gratification, manage future uncertainty, and regulate trade-offs between immediacy and long-term benefit is a central topic in decision science, with broad implications for financial planning, health behavior, and public policy.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe Role of Time in Shaping Decision Strategies\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA key contextual factor is time itself. Objective constraints on decision time significantly alter the trajectory of cognition [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e–\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. When time is limited, individuals tend to rely on heuristic or intuitive reasoning rather than deliberative computation, as described by dual-process theories [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e–\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Under time pressure, intuitive responses dominate, often amplifying framing effects and decision biases [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Conversely, when more time is available, deliberative processes may regain control, potentially reducing these biases. Notably, research has also documented paradoxical increases in patience under acute time pressure, suggesting that temporal scarcity may sometimes promote future-oriented reasoning via attentional refocusing or affective override [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eCapturing Cognitive and Emotional Dynamics\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePhysiological and cognitive signatures of these shifts can be captured via eye-tracking and pupillometry [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e–\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Metrics such as Outcome-Gaze-Proportion (OGP) reflect how attention is allocated between decision attributes, while pupil diameter indexes both cognitive effort and emotional arousal [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e–\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These tools allow researchers to investigate not just what decision is made, but how it is constructed. Importantly, pupil dilation is a compound signal: it reflects both working memory load and affective states [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e–\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Recent theoretical syntheses emphasize that pupil-based measures offer a “preconscious” window into decision processes, sensitive to both cognitive and emotional perturbations [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e–\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe attentional narrowing hypothesis, formalized in the Attentional Myopia Model, proposes that cognitive load reduces the breadth of cue integration, making decision-makers focus disproportionately on salient attributes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In the intertemporal domain, this may translate into excessive focus on immediate or visually dominant outcomes. Zhou et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e–\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] extended this framework, showing that both attention distribution (OGP) and pupil dynamics predict temporal preference reversals. However, most extant studies rely on binary manipulations—comparing time pressure with baseline conditions—thereby neglecting delayed response states. This asymmetry in design limits our understanding of how cognitive and affective systems respond when individuals are granted surplus time to decide.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGaps in Existing Literature\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFurthermore, intertemporal choice research has often emphasized behavioral outputs at the expense of decision dynamics. Studies typically quantify patience or impulsivity via discounting curves, but rarely interrogate the underlying attentional and physiological trajectories that produce such curves. By integrating eye-tracking and pupillometry into decision-making paradigms, researchers can directly observe the micro-mechanisms of trade-off processing under variable temporal regimes. This is especially relevant given that cognitive and emotional factors do not operate in isolation—they interact in real time to shape preferences, salience encoding, and effort mobilization.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePresent study\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study addresses two persistent gaps in the literature: (1) the limited exploration of delayed decision conditions as a counterpoint to time pressure, and (2) the underutilization of multimodal process data to explain intertemporal choice variability. Building upon prior findings and guided by dual-process models, attentional narrowing frameworks, and recent pupillometry-based insights, we explore how the structure of temporal opportunity—whether constrained, neutral, or abundant—shapes intertemporal decision architecture. In this context, we proposed the hypotheses of the research as follows:\u003c/p\u003e\u003cp\u003e\u003cb\u003eH1\u003c/b\u003e The probability of choosing the smaller-sooner (SS) option will be lowest under time-constrained conditions, intermediate under baseline conditions, and highest under delayed response conditions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eH2\u003c/b\u003e Outcome-Gaze-Proportion (OGP) will increase with time abundance, showing lowest values under time pressure and highest values in delayed conditions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eH3\u003c/b\u003e Pupil diameter will inversely vary with time availability, peaking under time constraints and minimizing under delay.\u003c/p\u003e\u003cp\u003e\u003cb\u003eH4\u003c/b\u003e Task condition effects on SS choices will be mediated positively by OGP (attention to outcomes) and negatively by pupil diameter (cognitive/emotional load).\u003c/p\u003e\u003cp\u003e\u003cb\u003eH5\u003c/b\u003e The relative contribution of attentional versus physiological processes will vary across conditions, with attentional mediation stronger under time abundance and physiological mediation stronger under time pressure.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cb\u003eParticipant\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe necessary sample size was determined using G*Power software [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] through linear regression analysis, assuming a medium effect size (Cohen's f² = 0.15), which indicates a moderate level of practical significance. The analysis indicated that 55 participants were required to achieve a power of 0.80 at a significance level of 0.05. To ensure robustness and account for potential attrition, 65 participants were recruited, including 30 males, with an average age of 22.5 years. Participants were given 10 Yuan basic payment (approximately USD \u003cspan\u003e$\u003c/span\u003e2) as a decision-making reward. At the end of the experiment, three trials were randomly selected from their real choices, and discounted based on their performance. On average, participants’ reward included basic payment and extra reward, totaling 25 yuan (approximately USD \u003cspan\u003e$\u003c/span\u003e4). The study was approved by the Ethics Committee of Ningxia University, and all participants provided written informed consent prior to participation. The research was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003e\u003cb\u003eApparatus\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDuring the experiment, eye movement data from participants were captured using an EyeLink1000 Plus eye tracker (SR Research, Ontario, Canada). The stimuli were displayed on a 20-inch Dell monitor with a resolution of 1280 × 1024 pixels and a refresh rate of 60 Hz, offering a viewing angle of 36° horizontally and 29° vertically. Participants were seated approximately 58 cm from the screen and instructed to minimize head movements by utilizing a chin rest. Although participants observed the stimuli with both eyes, data collection was restricted to the right eye only. Responses to the stimuli were recorded through keyboard input.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStimuli\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe experiment employed a computer to generate 10,000 intertemporal options. Each option consisted of a delay and an outcome, representing a certain amount of money that would be gained after a certain time. These options were then randomly paired into sets of small sooner (SS) and large later (LL) options. Following the removal of strongly dominant option pairs, 50 pairs of SS and LL options were randomly chosen from the remaining pairs. The options were presented using Arial font, ensuring a viewing angle of 2.89° and a minimum distance of 5° between any two stimulus presentations [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e–\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, to encourage conscientious participation, five pairs of strongly dominant options were included in the task. To ensure a balanced presentation of the experimental material, half of the participants viewed the delayed option above the outcome, while the remaining half observed the outcome positioned above the delay. The order of the experiments was also balanced for each participant.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTask\u003c/b\u003e\u003c/p\u003e\u003cp\u003e We employed a within-participant design, with time manipulation as the independent variable, and choice outcomes as well as eye-tracking measures as the dependent variables. Participants were required to choose their preferred option between SS and LL options. Each participant completed choice tasks under three different time conditions: baseline condition, constraint condition and delayed response condition. The order of the three conditions was balanced across participants.\u003c/p\u003e\u003cp\u003eIn the time constraint condition, participants were required to make decision within three seconds of the stimulus presentation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In the baseline condition, participants had unlimited time to make decision. In the delayed response condition, participants were required to make decision after five seconds of the stimulus presentation. The durations of 3 seconds (time constraint) and 5 seconds (delayed response) were selected based on previous studies that demonstrated their efficacy in modulating decision behavior under time pressure [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In addition, pilot testing confirmed that participants could complete the tasks reliably within these windows without excessive errors or omissions.\u003c/p\u003e\u003cp\u003eTo incentivize full engagement with the experiment, upon completion of the experiment, one trial was randomly selected from each condition. Depending on the participant's choice in the selected trial, the delay and outcome were discounted as the actual gain [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe expected variation in response time across conditions (i.e., shortest in the time-constrained condition and longest in the delayed response condition) was used as a manipulation check to confirm the effectiveness of our temporal constraint design, rather than as a central hypothesis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExperimental Procedures\u003c/b\u003e\u003c/p\u003e\u003cp\u003e Initially, participants were directed to the laboratory and instructed to read and sign an informed consent form. Then, experimental instructions were displayed on the laboratory computer screen for the participants to review. An experimenter then assisted participants in adjusting their seat height and fixing their head position to ensure comfort. Subsequently, a 5-point calibration was performed to achieve eye fixation, followed by a practice session to familiarize participants with the experimental tasks.\u003c/p\u003e\u003cp\u003eEach condition formal experiment comprised two blocks of 55 trials each. Participants completed the blocks in a random order, with a brief two-minute interval in between.\u003c/p\u003e\u003cp\u003eIn the baseline condition, a fixation point appeared in the center of the screen, prompting participants to fixate and press the space bar. Following this, the word \"Choice\" was displayed at the center of the screen for 1000 milliseconds, allowing participants unlimited time to make their selection. Two options then appeared on the left and right sides of the screen, with the F key used to select the left option and the J key for the right option. After a key press, the term \"Finish\" appeared in the center of the screen for 1000ms, signifying the end of the trial (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eIn the time constraint condition, participants fixated on a focal point displayed at the center of the screen and pressed the space bar key. This action triggered the appearance of the word \"Choice\" for one second. Following this, participants were given 3000ms to browse, and press a key to select one of the options. Failure to respond within the time limit resulted in the word \"Warning\" being displayed at the center of the screen for 1000ms, indicating trial failure. Conversely, if a key was pressed, the word \"Finished\" appeared for 1000ms, indicating the end of the trial (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eIn the delayed response condition, participants fixated on a focal point displayed at the center of the screen and pressed the space bar key. This action triggered the appearance of the word \"Choice\" for one second. Following this, participants need browse the presentation for 5000 ms, then need press a key to select one of the options. When a key was pressed, the word \"Finished\" appeared for 1000ms, indicating the end of the trial. (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThree participants who failed to meet engagement criteria on dominant control trials were excluded, leaving a final sample of 62 participants. Additionally, 94 trials where participants failed to respond within the 3000ms limit in the time constraint condition were removed from the analysis. All data supporting the findings of this study have been shared on the Open Science Framework and are available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/mtdc9\u003c/span\u003e\u003cspan address=\"https://osf.io/mtdc9\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResponse time\u003c/b\u003e\u003c/p\u003e\u003cp\u003eResponse time (RT) was defined as the duration between the presentation of the option and the key press response. A linear mixed-effects model was conducted to examine the effect of task conditions on RT, including a fixed effect of task (baseline\u0026thinsp;=\u0026thinsp;0, constraint\u0026thinsp;=\u0026thinsp;1; delayed\u0026thinsp;=\u0026thinsp;2), and random intercepts for participants, and stimuli. The analysis revealed significant differences in RT across the three task conditions F (2, 18597)\u0026thinsp;=\u0026thinsp;8072.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001. Specifically, the mean RT for the baseline condition was 2803ms (\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;77.5, CI95% = [2651, 2955]), for the time constraint condition was 1348ms (\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;77.5, CI95% = [1196, 1500]) and for the delayed response condition was 5781ms (\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;77.5, CI95% = [5629, 5933]). Post-hoc pairwise comparisons using the Tukey method indicated significant differences between all pairs of task conditions. Participants took significantly more time in the baseline condition than in the time constraint condition (\u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;66.017, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001). They also took significantly more time in the delayed response condition compared to the baseline condition (\u003cem\u003ez\u003c/em\u003e = -135.079, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Lastly, participants took significantly more time in the delayed response condition compared to the time constraint condition (\u003cem\u003ez\u003c/em\u003e = -202.284, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21.9, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e about here).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eChoice\u003c/b\u003e\u003c/p\u003e\u003cp\u003eChoice was defined as the proportion of participants choosing the SS option in the both tasks. The generalized linear mixed model (GLMM) analysis revealed significant differences in the probability of choosing SS option across the three task conditions (F (2, 18597)\u0026thinsp;=\u0026thinsp;62.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The estimated marginal means (EMMs) and pairwise comparisons showed that the probability of choosing SS option was highest in the delayed response condition (M\u0026thinsp;=\u0026thinsp;0.837, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0268), followed by the baseline condition (M\u0026thinsp;=\u0026thinsp;0.782, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0334) and lowest in the time constraint condition (M\u0026thinsp;=\u0026thinsp;0.751, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0365). Pairwise comparisons indicated that the probability of choosing SS option in the baseline condition was significantly higher than in the time constraint condition (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0302, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00891, \u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.390, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0020). Additionally, the delayed response condition showed a significantly higher probability of choosing SS option compared to the baseline condition (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0553, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00997, \u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.548, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and the time constraint condition (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0855, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01240, \u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.897, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutcome-Gaze-Proportion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Outcome-Gaze-Proportion (OGP) reflects the proportion of total gaze duration allocated to the outcome attributes relative to all attributes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this study, OGP was calculated as OGP\u0026thinsp;=\u0026thinsp;gaze duration on outcome / total gaze duration. An OGP greater than 0.5 indicates a preference for attending to outcome attributes, while an OGP less than 0.5 reflects greater focus on delay attributes. This measure provides critical insights into how individuals prioritize information under different decision-making conditions and serves as an index of attentional shifts observed in our experimental tasks.\u003c/p\u003e\u003cp\u003eA linear mixed-effects model was conducted to examine the effect of task conditions on Outcome-Gaze-Proportion (OGP), including a fixed effect of task and random intercepts for participants and stimuli. The analysis revealed significant differences in OGP across the three task conditions, F (2, 18597)\u0026thinsp;=\u0026thinsp;124.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001. The mean OGP for the Baseline Condition was 0.510 (\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0146, CI95% = [0.481, 0.539]), for the Delayed Response Condition was 0.520 (\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0146, CI95% = [0.491, 0.548]) and for the Time Constraint Condition was 0.475 (\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0146, CI95% = [0.446, 0.503]). Post-hoc pairwise comparisons using the Tukey method indicated significant differences between all pairs of task conditions. Participants exhibited a significantly higher OGP in the Delayed Response Condition compared to the Baseline Condition (\u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.486, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0027, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0014). Conversely, participants showed a significantly lower OGP in the Time Constraint Condition compared to the Baseline Condition (\u003cem\u003ez\u003c/em\u003e = -12.885, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0027, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Additionally, the OGP in the Delayed Response Condition was significantly higher than in the Time Constraint Condition (\u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16.371, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0027, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e about here).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePupil diameter\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePupil diameter (PD) was measured as the size of the pupil in response to different task conditions. A linear mixed-effects model was conducted to examine the effect of task conditions on pupil diameter, including a fixed effect of task and random intercepts for participants and stimuli. The analysis revealed significant differences in pupil diameter across the three task conditions, F (2, 18597)\u0026thinsp;=\u0026thinsp;235.58, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001. Specifically, the mean pupil diameter for the Baseline Condition was 1032 (\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;26.7, CI95% = [979, 1084]), for the Time Constraint Condition was 1059 (\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;26.7, CI95% = [1007, 1111]) and for the Delayed Response Condition was 1015 (\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;26.7, CI95% = [963, 1068]). Post-hoc pairwise comparisons using the Tukey method indicated significant differences between all pairs of task conditions. Participants had a significantly larger pupil diameter in the Time Constraint Condition compared to the Baseline Condition (\u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.965, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001). They also had a significantly smaller pupil diameter in the Delayed Response Condition compared to the Baseline Condition (\u003cem\u003ez\u003c/em\u003e = -13.505, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Lastly, participants had a significantly smaller pupil diameter in the Delayed Response Condition compared to the Time Constraint Condition (\u003cem\u003ez\u003c/em\u003e = -21.469, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Although this study used mean pupil diameter without baseline correction, the consistent fixation period and visual conditions likely reduced intertrial noise. Future research should incorporate trial-based baseline correction (e.g., subtracting pre-trial fixation values) to enhance the sensitivity of pupillometric analyses [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMediating effect\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate whether task type influences the probability of choosing the SS option through OGP and Pupil, we conducted a mediation analysis using R software. The independent variable was task type (0\u0026thinsp;=\u0026thinsp;baseline condition; 1\u0026thinsp;=\u0026thinsp;time constraint condition; 2\u0026thinsp;=\u0026thinsp;delayed response condition), the mediating variables were OGP and Pupil and the dependent variable was the probability of choosing the SS option. The results revealed significant indirect effects of task type on the probability of choosing the SS option through OGP (\u003cem\u003ea\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003cem\u003eb\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.014832, CI\u003csub\u003e95%\u003c/sub\u003e = = [0.01152, 0.01814], \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00174, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and through Pupil (\u003cem\u003ea\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003cem\u003eb\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e = -0.000013765, CI\u003csub\u003e95%\u003c/sub\u003e = [-0.00002, -0.00001], \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000001, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The total effect of task type on the probability of choosing the SS option was significant (\u003cem\u003ec\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.075, CI\u003csub\u003e95%\u003c/sub\u003e = [0.051, 0.099], \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). After controlling for the mediators OGP and Pupil, the direct effect of task type on the probability of choosing the SS option was 0.065 (CI\u003csub\u003e95%\u003c/sub\u003e = [0.041, 0.089], \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cb\u003eBehavioral and Cognitive Adaptations Under Time Pressure\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing eye-tracking techniques, this study examined decision-making processes across three conditions: time-constrained, baseline, and delayed response. The findings revealed significant differences in reaction times across these conditions, with the longest reaction time observed in the delayed response condition, intermediate times in the baseline condition, and the shortest reaction times in the time-constrained condition. Similarly, the probability of choosing short-term (SS) options varied across conditions, increasing progressively with greater time availability. Participants were least likely to choose SS options under time constraints, more likely under baseline conditions, and most likely in the delayed response condition.\u003c/p\u003e\u003cp\u003eThese results highlight the dual effects of time on decision-making. Under time constraints, individuals prioritized long-term (LL) options, aligning with theories suggesting that time pressure narrows attention to goal-relevant information and suppresses impulsive tendencies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This attentional focusing is thought to suppress peripheral cues and promote reliance on high-value attributes, even in the absence of extended deliberation.\u003c/p\u003e\u003cp\u003eConversely, under delayed conditions, extended decision-making periods appeared to heighten impatience, resulting in a preference for immediate rewards. This tendency likely reflects not deeper cognitive processing, but rather the affective cost associated with waiting. Research suggests that prolonged anticipation can evoke discomfort, uncertainty, and negative affect, which function as psychological costs that discount future rewards [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Supporting this interpretation, pupil-based evidence shows that individuals exhibit increased emotional reactivity and discomfort during extended wait periods, even when task difficulty remains unchanged [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis paradox\u0026mdash;where too little time fosters cognitive simplification and too much time increases emotional aversion\u0026mdash;points to a non-linear model of temporal influence on decision-making. Rather than a linear relationship between time and self-control, our data support a curvature: moderate time allows balance, but either extreme shifts decision architecture. This aligns with recent findings that emotional arousal and time anticipation jointly influence intertemporal preference, independent of reward value [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFrom a theoretical perspective, the Delay Discounting effect provides a foundational explanation for these findings. Yet it may be insufficient without accounting for emotional processes that emerge under delayed response. When waiting incurs affective costs\u0026mdash;such as anticipatory anxiety or frustration\u0026mdash;individuals may engage in \u0026ldquo;emotional discounting,\u0026rdquo; whereby they devalue delayed rewards not due to preference instability but due to aversive present-moment experience [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe Role of OGP and PD in Decision-Making\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further uncover the mechanisms behind the behavioral patterns observed, we examined two eye-tracking indices: Outcome Gaze Proportion (OGP) and pupil diameter (PD). The findings demonstrated that OGP progressively increased with time abundance, reflecting more distributed attention to outcome attributes when deliberation time was extended. In contrast, PD was largest under time pressure, indicating elevated cognitive and emotional strain in that condition.\u003c/p\u003e\u003cp\u003eLower OGP in the time-constrained condition suggests a rapid prioritization of essential cues. This aligns with dual-process theories, which argue that intuitive, heuristic processing dominates under high cognitive load or temporal scarcity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In contrast, the delayed condition promoted wider gaze dispersion and deeper exploration, consistent with reflective, System 2 processing. These findings mirror research in moral decision-making and cooperation, where time pressure induces intuitive responses and time extension facilitates moral reasoning [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe role of PD adds a physiological dimension to these interpretations. Larger pupil diameters under time constraints indicate higher cognitive load and sympathetic arousal [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], corroborating the interpretation that individuals experience greater mental effort and emotional tension when forced to decide quickly. Recent studies further show that PD is not merely a stress indicator but also reflects anticipatory biases and upcoming choice commitment [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Therefore, PD fluctuations offer a real-time index of both cognitive effort and affective cost in temporal decision-making.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTheoretical Implications and Future Directions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study contributes to understanding how temporal framing shapes intertemporal preferences through both attentional and affective mechanisms. By integrating behavioral, eye-tracking, and pupillometric data, we offer a framework in which time is not merely a contextual variable, but a dynamic regulator of cognitive architecture. Our findings refine the Attention Myopia Model [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], suggesting that attentional narrowing under pressure can be either adaptive or maladaptive depending on task structure and reward salience.\u003c/p\u003e\u003cp\u003ePractically, the results have implications for domains such as financial planning, public messaging, and time-sensitive interventions. The widely held belief that \u0026ldquo;more time leads to better decisions\u0026rdquo; may overlook the affective burden induced by delay. Conversely, well-calibrated time pressure can help individuals prioritize long-term outcomes by reducing noise and limiting impulsive distraction.\u003c/p\u003e\u003cp\u003eNonetheless, several limitations must be acknowledged. First, while our laboratory design ensured control over timing and presentation, it lacks the ecological complexity of real-life decision environments. Future research should explore more naturalistic contexts using mobile eye-tracking or immersive simulations.\u003c/p\u003e\u003cp\u003eSecond, although OGP and pupil diameter are well-established proxies for attentional focus and arousal, they remain indirect measures of internal cognitive processes. Additional physiological markers\u0026mdash;such as heart rate variability or galvanic skin response-may provide converging evidence for the observed mechanisms.\u003c/p\u003e\u003cp\u003eOverall, this study demonstrates that time constraints and delays exert asymmetric effects on decision-making not merely through rational trade-offs, but by modulating how people attend to information and how they feel while choosing. These insights open space for targeted experimental manipulations and decision-support designs that make time work with, rather than against, human cognition.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTime is both a constraint and a constructor of preference. Our results demonstrate that variations in available time alter not only the pace of decisions but their direction\u0026mdash;shifting the balance between cognition and emotion, attention and arousal, patience and impulse. Understanding this interplay offers a deeper lens into intertemporal choice and expands the boundary conditions for self-control.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate: The study was approved by the Ethics Committee of Ningxia University. All participants provided written informed consent prior to participation. The research was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eConsent for publication: Not applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: All data supporting the findings of this study are publicly available on the Open Science Framework at: https://osf.io/mtdc9\u003c/p\u003e\n\u003cp\u003eCompeting interests: The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions: Yan-Bang Zhou conceived the study, designed the experimental paradigm, and drafted the manuscript. Shun-Jie Ruan and Ya-Ru Bu contributed to data collection, preprocessing, and analysis. Qing Bao provided theoretical guidance, assisted in the interpretation of results, and revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements: Not applicable.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; information: Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThaler, R. (1981). Some empirical evidence on dynamic inconsistency. \u003cem\u003eEconomics Letters, 8\u003c/em\u003e(3), 201\u0026ndash;207. https://doi.org/10.1016/0165-1765(81)90067-7\u003c/li\u003e\n\u003cli\u003ePrelec, D., \u0026amp; Loewenstein, G. (1991). Decision making over time and under uncertainty: A common approach. \u003cem\u003eManagement Science, 37\u003c/em\u003e(7), 770\u0026ndash;786. https://doi.org/10.1287/mnsc.37.7.770\u003c/li\u003e\n\u003cli\u003eFrederick, S., Loewenstein, G., \u0026amp; O\u0026apos;Donoghue, T. (2002). 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The effect of fast and slow decision-making on equity-efficiency tradeoffs and moral repugnance. \u003cem\u003eRoyal Society Open Science, 10\u003c/em\u003e(9), 230558. https://doi.org/10.1098/rsos.230558\u003c/li\u003e\n\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":"Time pressure, intertemporal decision-making, cognitive load, eye-tracking","lastPublishedDoi":"10.21203/rs.3.rs-6887021/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6887021/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eIntertemporal decision-making involves balancing immediate benefits against future outcomes, affecting crucial domains such as financial planning and health behaviors. Time pressure substantially shapes these decisions: constrained time typically encourages intuitive reasoning and decision biases, whereas sufficient time facilitates more deliberative processing. Nevertheless, existing research has largely overlooked conditions involving delayed responses, limiting a comprehensive understanding of how varying temporal constraints influence intertemporal choice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eSixty-five participants (30 males; mean age 22.5 years) completed intertemporal choice tasks under three conditions: time-constrained (decision within 3 seconds), baseline (unlimited decision time), and delayed response (decision after 5 seconds). Behavioral measures, including reaction times and choices (short-term vs. long-term options), were recorded. Additionally, eye-tracking data—Outcome Gaze Proportion (OGP) and pupil diameter (PD)—were collected to assess attention allocation and cognitive-emotional load during decision-making.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eReaction times differed significantly across conditions, with the shortest times observed in the time-constrained condition and the longest in the delayed response condition. Participants showed an increased preference for short-term rewards as decision time increased, suggesting a paradoxical rise in impatience under extended deliberation. Eye-tracking data indicated that Outcome Gaze Proportion (OGP) increased with longer decision windows, reflecting more attention to outcome attributes, whereas pupil diameter (PD) was largest under time pressure, signaling heightened cognitive and emotional load. Mediation analyses confirmed that both OGP and PD significantly mediated the relationship between time condition and choice behavior, suggesting that attention allocation and physiological arousal jointly shape intertemporal preferences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e Time shapes not only the speed but also the quality of intertemporal choices by modulating attention and emotional arousal. Our findings reveal that both time pressure and delay systematically influence decision preferences, highlighting the need to consider temporal context in models of self-control and decision optimization.\u003c/p\u003e","manuscriptTitle":"Paradox of Time: How Constraints and Abundance Shape Decision-Making and Cognition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-09 11:35:13","doi":"10.21203/rs.3.rs-6887021/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":"7c9891ac-7198-483c-89b7-75596e4513d2","owner":[],"postedDate":"July 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-24T11:23:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-09 11:35:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6887021","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6887021","identity":"rs-6887021","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}