A Common Mechanism Behind Time Reproduction and Time-Based Prospective Memory

A Common Mechanism Behind Time Reproduction and Time-Based Prospective Memory | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Common Mechanism Behind Time Reproduction and Time-Based Prospective Memory Giovanni Cantarella, Giulia Stramucci, Patrizia Bisiacchi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7456434/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 The capacity to accurately estimate time is crucial for carrying out intended actions in the future. Prior research on time-based prospective memory (TBPM) has explored the roles of external time monitoring, task characteristics, and time perception abilities in supporting the execution of delayed intentions. The present study examined whether TBPM performance shares common underlying mechanisms with two temporal tasks: time bisection and time reproduction. Participants completed both timing tasks followed by a TBPM task in a single experimental session (within-subject design). In the TBPM task, they were required to press a designated key every two minutes while simultaneously performing a visual search task. Cognitive load was manipulated by varying the number of distractors in the ongoing task. Results showed that performance in the time reproduction task—but not in time bisection—was significantly associated with TBPM accuracy, suggesting the involvement of shared cognitive processes between these two tasks. Participants who demonstrated greater accuracy in time reproduction and engaged in more effective time monitoring (clock-checking) strategies also performed better on the TBPM task. Although increased cognitive load influenced reaction times and monitoring behaviour, it did not significantly affect TBPM accuracy. These findings highlight the importance of temporal abilities in supporting successful performance in TBPM. Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology time estimation time-based prospective memory time discrimination strategic monitoring external time monitoring Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Time-based prospective memory (TBPM) refers to the ability to perform an action at a specific time or after an amount of time has elapsed (Brandimonte et al., 1996 ). TBPM is a highly complex process requiring the encoding of the intention, storage of information in memory and its retrieval, and execution of planned actions at a specific time (McDaniel & Einstein, 2000 ). Prospective memory (PM) abilities are essential for achieving an independent and autonomous lifestyle from childhood to late adulthood. Failures in PM can lead to serious consequences, highlighting the importance of understanding the perceptual and cognitive processes that support it, particularly in the temporal domain. Furthermore, the performance on PM tasks can be influenced by the strategies of external time monitoring, which may involve shared mechanisms between temporal tasks and TBPM (Brewer et al., 2010 ; Martin et al., 2003 ). A typical PM paradigm can be divided into two phases: a monitoring phase, during which the participant performs the ongoing (ONG) task while monitoring the occurrence of the PM target, and a detection phase that involves identifying and responding to the PM target (Brandimonte et al., 2001 ). The presence of a PM task affects the ongoing task performance, resulting in a decrease known as PM cost (Jager & Kliegel, 2008). This phenomenon is characterized by a decrease in the ONG task performance when an individual is simultaneously required to remember to carry out the intended action. Research has shown that increasing the demands of a PM task can disrupt performance (Gan & Guo, 2019 ; Mäntylä et al., 2009 ). Higher cognitive load limits available attentional resources, which in turn affects time monitoring and impairs TBPM performance. The demands of the PM task also influence how participants manage the task: as load increases, they tend to shift from a more automatic response style to one that requires greater attentional effort. This shift affects how individuals allocate mental resources in response to varying levels of memory demand (Cantarella et al., 2023 ; Cona et al., 2013 ). Because TBPM lacks external environmental cues, it requires more self-initiated and deliberate effort compared to other forms of PM, such as event-based PM. Individuals must allocate mental resources to periodically check the time and recall the intended action. A central question in TBPM research concerns the role of time monitoring - specifically, how much successful performance depends on detecting the right moment to act, often referred to as target checking. Some studies suggest that monitoring the passage of time, typically measured by the number of clock checks, plays an important role in successful task performance (Harris & Wilkins, 1982 ). Supporting this, various findings have reported that a greater number of strategically timed clock checks is associated with better TBPM outcomes, even during ongoing tasks that are cognitively demanding. This pattern has been observed in both older adults (Bisiacchi et al., 2008 ; Cona et al., 2012a ; Mäntylä et al., 2009 ; McFarland & Glisky, 2009 ) and children (Ceci & Bronfenbrenner, 1985 ; Mackinlay et al., 2009 ; Mäntylä et al., 2007 ). In this regard, Munaretto et al. ( 2022 ) proposed a framework for understanding external time monitoring in TBPM, dividing it into two stages: an initial loose monitoring phase and a later finer-grained monitoring phase. This distinction reflects how individuals adjust their monitoring behaviour based on the demands of the task. In the early part of the time interval, loose monitoring limits the number of clock checks, helping to preserve attentional resources for the ongoing task. As the target time approaches, however, monitoring becomes more frequent and precise, supporting accurate execution of the intended action. This shift illustrates how individuals manage attention across time, combining external time checks with internal estimation processes. While strategic monitoring has often been linked to better TBPM outcomes, excessive clock checking can interfere with the accuracy of performance on the ongoing task (Mäntylä & Carelli, 2006 ). Effective monitoring depends on maintaining an appropriate balance between attention directed at time tracking and attention required for processing the primary task (Cona et al., 2012b ; Jäger & Kliegel, 2008). Previous research on TBPM has pointed to the importance of identifying the strategies and cognitive functions involved in effective time monitoring and intention execution. One key aspect is the shift from an initial, loose monitoring to more strategic, fine-grained monitoring strategy as the target time approaches (Munaretto et al., 2022 ). This shift appears to be supported by functions such as executive control and time estimation, which allow individuals to maintain performance while managing the competing demands of an ongoing task (Mioni et al., 2020 ; Mioni & Stablum, 2014 ). In addition, some studies suggest that individual differences in neural mechanisms, such as resting-state functional connectivity, may support the use of efficient monitoring strategies, especially in dual-task settings (Zangrossi et al., 2021 ). Although there is general agreement that time perception plays a role in TBPM, relatively few studies have directly examined how these two processes interact (Graf & Grondin, 2006 ; Labelle et al., 2009 ; Mackinlay et al., 2009 ; McFarland & Glisky, 2009 ), and their findings are not entirely consistent. Some researchers have proposed that time perception contributes little to TBPM accuracy, arguing that asking someone to perform an action at a certain time does not necessarily make the task time-based in a strict sense. According to this view, memory processes, rather than timing ability, are the main factor influencing performance (Labelle et al., 2009 ). In TBPM tasks, participants often rely on external cues such as a clock. Each time they check the clock, they align their internal estimate with the actual time, update their internal representation, and estimate how long to wait before checking again (Block & Zakay, 2006 ; Mioni et al., 2012 ; Zakay & Block, 1996 ). However, other findings suggest that timing skills do influence how people monitor time and manage delayed intentions. For example, Labelle et al. ( 2009 ) found that time estimation ability was associated with the frequency of monitoring, especially in the final portion of the waiting period. Similarly, Mioni et al. ( 2012 ) reported a positive association between performance on a time reproduction task and clock-checking frequency in TBPM, indicating a link between temporal accuracy and monitoring behaviour. Notably, individuals with poorer timing ability tended to check the clock more often. This suggests that frequent clock checks may serve as a compensatory response in individuals with less precise internal timing. Vanneste et al. ( 2016 ) also reported that weaker timing skills were linked to more frequent monitoring, further supporting this interpretation. Building on prior findings that timing abilities are related to monitoring behaviour (Labelle et al., 2009 ; Mioni et al., 2012 ; Wang et al., 2023), this study examined whether performance on time bisection and time reproduction tasks reflects the cognitive processes involved in TBPM. These tasks target different aspects of time perception: bisection involves judging whether a given interval is closer to a previously learned short or long duration, while reproduction requires holding a time interval in memory and reproducing it, engaging sustained attention and memory functions (Grondin, 2008 ; Zakay, 1990 ). Therefore, we aimed to determine whether time reproduction, which depends more on internally-driven timing and attention, shows a stronger relation to TBPM performance than time bisection, which is more reliant on perceptual judgments. In addition, since cognitive load influences how attention is distributed and how people manage monitoring, we expected that increasing task demands would clarify the distinct contributions of these two timing tasks to TBPM performance. Materials and Methods Participants Sixty students (20 males and 40 females) from the University of Padua were recruited to participate in the study. The participants were aged between 18 and 35 (mean age = 22.4; SD = 3.21). All participants had normal or corrected-to-normal vision and no history of neurological or psychiatric pathology. They gave their consent to take part in the research and were informed that their participation was voluntary. Participants received monetary compensation or course credits after each session. We determined the sample size of our study through the G*Power software (v. 3.1.9.7; Faul, Erdfelder, Lang, and Buchner, 2007). Based on the results of a previous study (Khan, Sharma and Dixit, 2008 ), we estimated a medium effect size np2 = 16 for obtaining the main effect of time monitoring (Critical F = 3.11), and set the significance level at α = 0.05 and the desired power (1 – β) at 0.95, leading to a minimum sample size of 43. Procedure Participants completed the procedure in a single test session at the Department of General Psychology (Padua, Italy). The tasks were presented on a 15-inch PC monitor, and participants were seated approximately 60 cm from the screen. To assess time perception abilities, each participant undertook two computerized tasks: time bisection and time reproduction. All participants initially completed the bisection and reproduction tasks, without randomizing their order. They were then given the TBPM task, comprising the ongoing task (baseline block) and the PM task (PM block), resulting in a within-subject experimental design. To prevent any possible interference effects, the baseline block was administered prior to the PM block. Time bisection task The time bisection task (Kopec and Brody, 2010) was divided into a learning phase and a testing phase. In the learning phase, participants were administered 5 short (S = 200ms) and 5 long (L = 800ms) standard durations to familiarize with the reference durations. There was only a single learning session at the beginning of the task. As declared in previous studies (Kopec & Brody, 2010; Penney & Cheng, 2018), short standards were presented first in each learning phase. The stimulus consisted of a red circle (size 4.5 cm) displayed for a specific duration against a dark background, for a total of 10 trials. Feedback on response accuracy was given at the end of this session. The testing phase involved comparing the two standards for seven different durations (2000, 3000, 4000, 5000, 6000, 7000, and 8000 ms). Subjects were required to judge the relative durations of new intervals and to determine whether they were closer in duration to the “short” or the “long” standard. Each response was followed by an inter-trial interval of 1000, 2000 or 3000 ms. In the experimental block, each duration (2000, 3000, 4000, 5000, 6000, 7000, and 8000 ms) was presented eight times in randomized order for 56 trials. Subjects were not given any feedback on the accuracy of their responses. Time reproduction task In the time reproduction task (Zakay, 1990 ; Grondin, 2008 ; 2010), participants were instructed to reproduce the duration of a previously viewed stimulus. Once the instructions were given, a white circle with a size of 4.5 cm was displayed at the center of the computer screen, for durations of 400, 900, or 1400 ms. When the stimulus disappeared, a question mark appeared on the computer screen (ISI = 2000 ms), and participants were instructed to press the spacebar (using the index finger of their dominant hand) for a duration that matched the time the stimulus was on the screen as closely as possible. While reproducing the duration of the stimulus by pressing the spacebar, participants were instructed to read aloud a series of numeric digits that appeared in the center of the screen until they released the bar. The numbers ranged from 1 and 9 and were presented randomly, with an interstimulus interval from 400 to 1000 ms. Participants did not receive feedback on the accuracy of their performance. Ongoing task – baseline The ongoing task was a visual search paradigm consisting of 576 trials. Participants were instructed to detect a red circle (target stimulus) among a series of white letters randomly positioned across the screen (distractors). If the target stimulus appeared on the right side of the screen, participants had to press the right red key of the computer keyboard ("Z") with their right index finger. Conversely, if the red circle appeared on the left side of the screen, participants were instructed to press the left red key on the keyboard ("N"). The stimuli were displayed for 1500 ms (ISI = 1000 ms). Participants did not receive feedback on the accuracy of their responses. Time-based prospective memory task Instructions for the TBPM task were provided only after the baseline block had been completed. Participants were instructed to press the ‘X’ key every 2 minutes from the beginning of each block, while continuing to perform the ongoing task (see above). The TBPM task consisted of four blocks, each lasting approximately 7 minutes, for a total of 576 trials (144 trials per block; see Fig. 1 for a graphical representation of the task). Participants were instructed to identify a red circle (the target stimulus) among a series of distractors and to press a designated key every 2 minutes as part of the time-based prospective memory (TBPM) component. Based on the duration of each block, participants completed approximately 3 to 4 TBPM intervals per block, resulting in a total of 12 to 16 intervals across the entire task. Two experimental conditions - high and low memory load - were randomly alternated across blocks. In the high-load condition, the target had to be detected among distractors that shared perceptual features, such as red squares or green circles, increasing the difficulty of visual discrimination. In the low-load condition, the target appeared among distractors (letters and shapes) that did not overlap perceptually with the target, making the task less demanding (see also Pashler, 1987 ). Moreover, participants were told that they had the opportunity to check a digital clock that showed the exact time in minutes and seconds, and that they had to perform this task as accurately as possible. When they pressed the button to check the time (‘C’), the digital clock appeared in the center of the screen for 300 ms. The digital clock could be checked only a fixed number of times (5) every 2 minutes. Participants were not informed about the duration of the TBPM task, which lasted 20 minutes, with the possibility of taking breaks between blocks. Data analysis Time bisection Temporal abilities were first analyzed in terms of the proportion of long responses (raw data), i.e., the proportion of times each subject pressed “long” for each new comparison interval. Consequently, an overall seven-point psychometric function was traced, plotting the seven comparison intervals on the x-axis and the probability of responding “long” (p-long) on the y-axis, for each experimental condition. The bisection point (BP) was calculated for each participant. BP is defined as the stimulus duration for which the participants responded “short” or “long” with equal frequency. The BP is associated with the target duration corresponding to a predicted rate of long responses of 50%, and it is used as a measure of perceived duration: the smaller the BP value, the longer the perceived duration. Temporal abilities were also analyzed in terms of Constant Error (CE), defined as the duration of the mid-point between the two standards (2000 and 8000 ms) minus the BP (Grondin et al. , 2015). CE is a measure of accuracy, positively related to perceived duration. Positive or negative CE values are an index of over- or under-estimation of temporal durations compared to the midpoint. In addition, the WR parameter was implemented. It is defined as the degree of discriminability the subject uses to parse the standard durations into the “short” and “long” categories. This variable measures the participant’s sensitivity to time: a subject with a high degree of discriminability would produce a psychometric curve that appears very step-like, resulting in a low WR, while a poorer discriminability would result in a more gradual psychometric function and a higher WR (Kopec and Brody, 2010). Time reproduction Reproduction abilities were analyzed in terms of the estimated-to-target duration ratio (RATIO), the absolute error (AE) and the coefficient of variation (CV). The RATIO was obtained by dividing each participant’s reproduced duration (R d ) by the target duration (T d ) for that trial [RATIO = R d /T d ]. Coefficients above and below 1.0 were indicative of over-reproduction and under-reproduction, respectively. The AE was calculated as the difference between the reproduced duration and the target duration (in absolute value) divided by the target duration [AE= |R d - T d | / T d ] (Brown, 1985 ; see also Glicksohn and Hadad, 2012). Large AE levels indicate low performance. The CV was obtained by dividing the standard deviation in time reproduction performance by the mean reproduction value, separately for each interval (Brown, 1997 ). This measure indicates the variability of time reproduction performance. Since their distribution violated the assumption of normality ( Shapiro-Wilk test ; p < .001), a non-parametric analysis (Friedman test) was run on these measures, with interval as a within-subject factor. Time-based Prospective Memory task TBPM performance was assessed in terms of accuracy in PM and reaction times (RTs), clock checking (time-monitoring frequency), and ongoing task performance (RTs and accuracy at the visual search paradigm). PM accuracy was scored as correct if participants pressed the button within 10 s (± 5 s) of the target time (2 minutes). Time monitoring frequency was analyzed in terms of the number of clock checks; moreover, we calculated a Strategic Monitoring Index (SMI), consisting of the number of clock checks in the last 40% and the number of clock checks in the remaining 60% of the time elapsed over the total target time of 2 minutes (see Mantyla et al., 2007, Mioni et al., 2012 ; 2014 for a similar procedure). To investigate the TBPM performance trend among blocks, we performed a repeated-measure ANOVA on TBPM accuracy and the number of clock checks, with block (1, 2, 3 and 4) as within-subject factor. Post-hoc analyses were conducted with the Bonferroni test. The effect size was indicated as partial eta square (η 2 p ). To check for possible PM costs, we performed Student’s paired-samples t-tests comparing RTs values at the ongoing task during baseline vs. TBPM conditions, and RTs values at the ongoing vs. the TBPM task. A non-parametric approach (Wilcoxon signed-rank test) was employed instead to compare accuracy values in the baseline vs. the TBPM conditions and the TBPM accuracy with the ongoing accuracy. We also performed a Student’s t-test on RTs values and number of clock checks, comparing blocks under high vs. low cognitive load. The same comparison on the TBPM accuracy was performed by using a non-parametric approach (Wilcoxon signed-rank test). Correlation – Time perception To verify the hypothesis that time perception abilities - as assessed through the time bisection task - could relate to TBPM accuracy, we run a correlation (Pearson’s) analysis between WR values (time sensitivity) and TBPM accuracy. To establish possible relations across different timing abilities, we also run a correlation (Pearson’s) analysis between time perception and time reproduction abilities, as assessed, respectively, through the time bisection (BP, an index of time under- or overestimation) and time reproduction (RATIO, an index of time under- or over-reproduction) tasks. Relation between time perception and TBPM To examine the relation between time estimation/reproduction abilities and TBPM performance, we fitted a linear mixed-effects model using the lmerTest package in R. Prior to modeling, continuous predictors were mean-centered to improve interpretability and reduce multicollinearity. The model included fixed effects for AE, WR, and their two-way interactions with Load (AE*Load, WR*Load). Random intercepts were included for participants and number of clock checks to account for repeated measures and fluctuations in monitoring behavior that might affect TBPM performance. The effect of strategic monitoring To investigate the relation between strategic monitoring and TBPM performance, we run another linear mixed-effects model including SMI, memory load Low vs. High), and their interaction as fixed effects. A random intercept for each participant was also included to account for inter-individual variability. Results Time bisection The seven-point psychometric function plotting each interval with the corresponding proportion of “long” responses (see Fig. 2 ) showed that participants correctly discriminated among durations. Descriptive statistics of the temporal abilities in terms of BP (mean = 4627 ms; st.dev. = 944.76) and CE (mean = 413 ms; st.dev. = 1007. 88) indicated that participants were accurate at estimating durations, with a slight tendency to time overestimation. Participants’ temporal sensitivities, as assessed with WR values, were also relatively high (mean = 0,20; st.dev. = 0,13), confirming their capacity to correctly discriminate durations. Time reproduction The results of the Friedman test on RATIO values revealed a main effect of interval [χ 2 = 29.107; p < .001]. Post-hoc analysis confirmed that RATIOs decreased as the interval increased; with higher values for the shorter interval − 4000 ms (1.13), followed by the medium interval – 9000 ms (0.99; p = .025) and the longer interval – 14000 ms (0.92; p = .002). This analysis showed that participants’ tendency to over-reproduce durations diminished as the duration of intervals increased (see Fig. 3 ). The same analysis on CV [χ 2 = 4.933; p = .09] and AE [χ 2 = 0.041; p = .98] failed to reach statistical significance]. Time-based Prospective Memory Overall, the discrimination accuracy of the ongoing task was high, both for the baseline (98%) and the TBPM (96%) conditions, while accuracy for the TBPM task was relatively lower (76%). The repeated-measure ANOVA on the TBPM accuracy revealed a main effect of block [F (2.349) = 15.372; p < .001; η 2 p = .207]. Post-hoc analyses (Bonferroni’s correction) showed that TBPM accuracy in the first block was significantly lower (.560) than in the subsequent blocks (.787, .833 and .854; all ps. .99). Thus, the execution of the TBPM task was less accurate in the first block but then improved and remained stable in the subsequent blocks. The same ANOVA on the number of clock checks failed to reach statistical significance [F (2.204) = .0.825; p = .451]. Paired-samples t-tests revealed instead significant differences in RTs [t= -16.605; p < .001; d= -2.199] of the ongoing (visual search) task between the baseline (587.48 ms) and the TBPM condition (762.21 ms). Regarding accuracy values, results of the Wilcoxon signed-rank test indicated significantly higher accuracy values [W = 1513; p < .001] at the ongoing task during the baseline condition (.98) compared to the TBPM (.96) condition. Thus, participants were slower and less accurate in the visual search task while concomitantly executing the TBPM intention ( PM cost ). Moreover, the accuracy at the ongoing task in this condition resulted also significantly lower [W = 73.50; p < .001] than the accuracy at the TBPM task (.76). The results of the paired-samples t-tests on RTs of the ongoing task blocks under different conditions of cognitive load revealed significant differences [t= -12.577; p < .001; d= -1.666], with slower RTs in high vs. low-load blocks (835.55 vs. 688.84 ms). The non-parametric analysis on the TBPM accuracy [W = 494; p = .34] and the paired-samples t-test on number of clock checks [t= -0. 402; p = .689] instead failed to reach statistical significance. This set of analyses revealed that increasing cognitive load impacted ongoing task performance but did not affect the execution of delayed intentions at specific time points or the strategic monitoring processes (clock-checking) that are necessary to accomplish them. Correlation with time perception Correlation (Pearson’s) analysis failed to reveal a significant association between WR values and TBPM accuracy (r = -0.001; p = .99), indicating the absence of a link between time perception (in the timescale of seconds) and the ability to carry out intended actions at specific temporal moments in the future (TBPM). However, we found a significant (Pearson’s) correlation (r = − .388; p = .002) between BP and RATIO values, confirming the existence of a relation between these two timing abilities: time overestimation (lower BP) was associated with time over-reproduction (higher RATIO). Relation between time perception and TBPM Model assumptions were assessed by inspecting residual distributions and residuals-vs-fitted plots, which suggested an acceptable fit to the data. The linear mixed-effects model revealed a significant interaction AE * Load (χ²(1) = 4.21, p = .040): post-hoc analyses showed that under high memory load, increases in absolute error rates were associated with a significant reduction in TBPM accuracy (slope = − 0.624, 95% CI [− 1.216, − 0.031]), while under low memory load, the slope was positive but non-significant (slope = 0.103, 95% CI [− 0.496, 0.703]; see Fig. 4 ). No significant main effects were found for AE ( p = .732), WR ( p = .380), and the interaction WR * Load was also not significant ( p = .494). Model fit indices indicated that fixed effects accounted for a small proportion of variance in TBPM accuracy (Marginal R² = 0.023), while the full model, including random participant effects, explained a substantially larger portion (Conditional R² = 0.340), suggesting that individual differences among participants accounted for a significant share of the variability in performance. Although the interaction term explained only a modest share of the variance, these findings indicate that TBPM accuracy is associated with time reproduction abilities (i.e., AE), but selectively under high memory load conditions. The effect of strategic monitoring The linear mixed model revealed a significant main effect of SMI on TBPM accuracy (χ²(1) = 51.55, p < .001): specifically, the slope of SMI was positive and significant (β = 0.058, SE = 0.0097, p < .001), suggesting that TBPM performance improved as SMI increased. Neither the main effect of Load (χ²(1) = 0.19, p = .66) nor the interaction SM*Load(χ²(1) = 0.81, p = .37) reached statistical significance, indicating that the effect of SMI on TBPM accuracy was consistent across levels of memory load. Model fit indices suggesting moderate explanatory power, with fixed effects accounting for 21.5% of the variance in TBPM accuracy (Marginal R² = 0.215) and the full model, including random effects, explaining 30.8% of the variance (Conditional R² = 0.308). Discussion The present study aimed to identify which specific aspects of timing - namely, time bisection and time reproduction - are most relevant for supporting TBPM performance. We hypothesized that accurate time bisection and reproduction would not only support effective monitoring strategies but also associate with overall TBPM accuracy. Results partially supported our initial hypothesis: among the two timing measures, only time reproduction, but not bisection, was related to TBPM performance. Notably, this association was moderated by the cognitive load of the ongoing task. The relationship between absolute error in time reproduction and TBPM accuracy was observed only under high-load conditions, suggesting that when cognitive resources are limited, time reproduction abilities become more influential in the execution of delayed intentions. Moreover, strategic monitoring was positively associated with TBPM accuracy under both load conditions, suggesting that increasing clock checks shortly before the target time supports successful TBPM performance, regardless of task difficulty. Time bisection and time reproduction both require the encoding and retention of temporal information. However, they differ in the type of response they demand. The time bisection task involves a perceptual judgment: participants compare a probe interval to two reference durations (“short” and “long”) stored in memory and make a categorical decision based on this comparison (Grondin, 2008 ; Vatakis et al., 2018 ). A consistent tendency to classify probe intervals as “short” reflects time underestimation, while classifying them as “long” indicates overestimation (Bueti et al., 2008 ). In contrast, the time reproduction task requires participants to encode a target duration and later reproduce an interval of equivalent length, typically while engaged in a concurrent task (Grondin, 2008 ; Zakay, 1990 ). This task not only relies on time estimation abilities but also engages higher-order cognitive functions. Accurate reproduction depends, in fact, on attentional and memory resources to retain and manipulate the duration while performing another task (Baudouin et al., 2006 ). Dividing attention between temporal and non-temporal demands reduces the resources available for timing, thereby affecting performance (Brown, 1997 ). The attentional gate model (Brown, 1997 ; Block & Zakay, 2006 ) explains this by proposing that increased cognitive load reduces the flow of temporal information into working memory, impairing timing accuracy. Moreover, time reproduction requires reliance on an internal clock to guide the response, a process that may resemble internal monitoring mechanisms. This executive-like function is comparable to the self-initiated monitoring required in TBPM. In our study, the overlap between the processes involved in time reproduction and TBPM may account for the observed association between time reproduction accuracy and TBPM performance under high cognitive load. Our results confirmed that time monitoring plays a key role in TBPM. Specifically, we found that strategic monitoring - i.e., more frequent clock-checking - is strongly associated with successful TBPM performance. According to the Test-Wait-Test-Exit model (Harris & Wilkins, 1982 ), accurate TBPM performance depends on a monitoring process involving repeated test-wait cycles until the target time approaches. During this period, individuals progressively align their internal sense of time with an external reference (e.g., a clock). As the deadline nears, they increase the frequency of clock checks, engaging in finer-grained monitoring to respond at the correct moment. To capture this behavior, we used the Strategic Monitoring Index (Mioni & Stablum, 2014 ), which quantifies the number of clock checks made during the final portion of the time interval—i.e., the period closest to the target time. Our findings are also consistent with previous studies (Harris & Wilkins, 1982 ; Munaretto et al., 2022 ), showing that individuals who increase their monitoring frequency as the deadline approaches tend to respond more accurately in TBPM tasks. In other words, more frequent clock checks during the critical window improved timing precision and helped prevent PM failures. We also suggest that individual differences in this strategy may be linked to differences in timing abilities. Participants may rely on an internal timekeeping mechanism to estimate when the critical window is approaching, at which point they shift to relying more on external cues. In the present study, this flexible use of internal and external time information may have contributed to the variability in monitoring strategies - and, in turn, to the differences in TBPM performance - we observed. Our findings also confirmed the presence of a PM cost. Specifically, maintaining a TBPM intention led to reduced performance on the ongoing task, as shown by slower reaction times and decreased accuracy (Hicks et al., 2005 ; Marsh et al., 2006 ). Increasing the cognitive load of the ongoing visual search task further impaired performance on that task, but did not significantly affect accuracy in the TBPM task. Notably, participants showed a higher number of clock checks under high-load conditions compared to low-load conditions, possibly reflecting a compensatory strategy aimed at preserving accuracy in the TBPM task. These findings suggest that, when monitoring demands are fixed, young adults may allocate a greater share of their cognitive resources to time monitoring, which can limit resources available for the ongoing task. While increased cognitive load impaired ongoing task performance, TBPM accuracy remained stable—likely due to this adjustment in strategy. This pattern supports the idea that TBPM performance can be maintained through adaptive monitoring, even under heightened task demands. Previous research has identified various individual factors influencing PM performance (Ball et al., 2013 , 2019 ). PM depends on multiple cognitive processes, and individual differences may reflect the engagement of distinct cognitive and neural systems (Ball et al., 2022 ). Attention and memory are particularly important for the detection and retrieval of PM intentions. Maintaining attention helps prevent mind-wandering and supports the inhibition of ongoing task responses in favor of monitoring for PM cues (Ball et al., 2018 ). In TBPM specifically, where individuals must track elapsed time, differences in time perception abilities may also play a role. TBPM tasks typically require estimating and responding at a specific time interval, resembling prospective time estimation paradigms. Research on time perception (e.g., Brown, 1985 ) has consistently shown that estimated durations shorten as non-temporal task demands increase. Our findings extend this by showing that individual differences in time reproduction abilities contribute to variation in monitoring behavior and help predict accuracy in meeting TBPM deadlines. These results are consistent with Zangrossi et al. (2019), who demonstrated that individual differences in resting-state brain network organization are associated with the use of time monitoring strategies, such as clock-checking frequency. Their study found that better monitoring performance was related to more efficient network integration, suggesting a neurobiological basis for strategic monitoring differences. Together with our behavioral findings, this evidence highlights the value of considering individual variability in timing abilities when examining TBPM performance. However, further research is needed to investigate the neural mechanisms underlying time estimation and monitoring strategies, and how these contribute to performance in prospective memory tasks. Finally, we examined the relationship between individuals’ abilities to estimate and reproduce time intervals within the same timescale (seconds). The results revealed a positive association between the two measures: individuals who tended to overestimate durations in the time bisection task also tended to over-reproduce durations in the time reproduction task. In other words, participants who perceived intervals as longer were also more likely to reproduce them as longer than their actual duration. This finding contrasts with previous results by Cantarella, Vianello, et al. (2023), which showed a different pattern in patients with right hemisphere damage, where time underestimation was linked to time over-reproduction for shorter durations (milliseconds). That pattern was interpreted as a consequence of internal clock slowing following brain damage. In contrast, our study focused on healthy young adults and used longer time intervals (in the range of seconds). The observed association between bisection and reproduction supports the view that these tasks share a common component related to time estimation. Nonetheless, additional processes likely distinguish time reproduction from process-pure estimation tasks, and further research is needed to clarify these differences. One potential limitation of the present study concerns the relatively high number of clock-checking opportunities during each interval (five per two-minute window). Although participants were instructed to respond as accurately as possible at the target time, not all made full use of the available checks. Since different monitoring strategies can influence TBPM performance, the high availability of time cues may have influenced how participants engaged with the task, possibly affecting the observed relationship between time reproduction and TBPM performance. Future studies could manipulate the number of available clock checks to explore how reduced access to external cues alters monitoring strategies and performance. In conclusion, the present findings indicate that adequate temporal abilities are important for successful TBPM performance. Among the two timing measures assessed, only time reproduction was significantly associated with TBPM accuracy. These results point to the involvement of shared mechanisms between time perception and TBPM that go beyond estimation alone. Both time reproduction and TBPM tasks, in fact, require individuals to maintain a representation of elapsed time in working memory while performing a concurrent task and to decide when to act—whether stopping a timed interval or executing a planned action. We propose that this internal tracking of time may serve as a common cognitive process supporting performance across both domains, especially in the absence of reliable external cues. Declarations Ethics approval and consent to participate: This study involving healthy participants was conducted in accordance with the ethical standards of the Declaration of Helsinki (1964). It was reviewed and approved by the Ethics Committee of the University of Padua (Protocol Code: 2116). All participants provided written informed consent prior to participation. Consent for publication: Not applicable. Availability of data and materials: We report how we determined our sample size, all data exclusions, all inclusion/exclusion criteria, whether inclusion/exclusion criteria were established prior to data analysis, all manipulations, and all measures in the study. No part of the study or analyses/ procedures were pre-registered prior to the research being conducted. Behavioral data are available on the public repository OSF ( https://osf.io/ujb7x/?view_only=5c346f904d1f4dcf961b7d7e2c3727f0 ). The research materials (e.g., task, including stimuli and code) for this study will be shared upon reasonable request. Competing interests: The authors declare that they have no competing interests. Funding: This research was supported by the Department of General Psychology (University of Padua) research funding. Authors’ contributions: GC and PB developed the study concept and contributed to the study design. GS recruited the participants and carried out data collection. 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Res. 402 , 113130. https://doi.org/10.1016/J.BBR.2021.113130 (2021). 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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02:13:00","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":123781,"visible":true,"origin":"","legend":"","description":"","filename":"555810d62ed84e5eb092aa27fbf184f11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7456434/v1/409681ce1792c993e2792d62.xml"},{"id":93728786,"identity":"8367e5b0-795b-4956-986e-49e818017e37","added_by":"auto","created_at":"2025-10-17 02:12:59","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":136044,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7456434/v1/b1893b6859732917e510413d.html"},{"id":93728776,"identity":"1014ee86-63b7-4df4-a522-7db3f08de276","added_by":"auto","created_at":"2025-10-17 02:12:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":164323,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTBPM task, stimuli and conditions.\u003c/em\u003eExample of a sequence of trials for the TBPM task, showing the load changes (HIGH/LOW). At the beginning of the block, participants received the instructions concerning the ongoing -visual search - task (ONG), plus the time-based prospective memory (TBPM) task. The ONG task required pressing the right or left button according to the position of the red circle, while concurrently remembering to press another (green) button after 120 secs (TBPM task). For a maximum of 5 times every 120 secs time-range, participants could check for the time elapsed by pressing a different (yellow) button. The end of a block could determine, in counterbalanced order, a shift in the ONG task conditions (e.g., low to high load), by changing the number of distractors appearing on the screen.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7456434/v1/be4413a1d3236e433dd36f2d.png"},{"id":93728774,"identity":"27298388-3a15-40af-ac7b-559407f592a3","added_by":"auto","created_at":"2025-10-17 02:12:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":22327,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of “long” responses as a function of Intervals. Error bars indicate ± standard deviations from the mean\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7456434/v1/82af179b98c984e582b4e1a9.png"},{"id":93728787,"identity":"ddb80168-6ff1-4cb6-815c-104e4aa74c66","added_by":"auto","created_at":"2025-10-17 02:12:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":32473,"visible":true,"origin":"","legend":"\u003cp\u003eRATIO values as a function of Intervals. Error bars indicate ± standard deviations from the mean\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7456434/v1/92e9e157341ced55a2bb4002.png"},{"id":93728789,"identity":"9da1663e-458e-436c-853a-b17c7a9d846f","added_by":"auto","created_at":"2025-10-17 02:12:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":48883,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRelation between time estimation/reproduction and TBPM accuracy.\u003c/em\u003e Left panel: Under high memory load (red), higher absolute errors in time reproduction was associated with lower TBPM accuracy, while no significant association emerged under low load (green). Right panel: No significant association was found between weber ratio and TBPM accuracy, regardless of load. Shaded areas represent 95% confidence intervals. Asterisk (*) indicates a significant effect (\u003cem\u003ep\u003c/em\u003e \u0026lt; .05).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7456434/v1/ce194b9f8133bc295931edc5.png"},{"id":105034359,"identity":"2ac41b10-68da-4163-a337-b30e8f6bba01","added_by":"auto","created_at":"2026-03-20 07:23:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":944379,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7456434/v1/471f1516-eb93-4b2a-baa0-b47b6ca936d0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Common Mechanism Behind Time Reproduction and Time-Based Prospective Memory","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTime-based prospective memory (TBPM) refers to the ability to perform an action at a specific time or after an amount of time has elapsed (Brandimonte et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). TBPM is a highly complex process requiring the encoding of the intention, storage of information in memory and its retrieval, and execution of planned actions at a specific time (McDaniel \u0026amp; Einstein, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Prospective memory (PM) abilities are essential for achieving an independent and autonomous lifestyle from childhood to late adulthood. Failures in PM can lead to serious consequences, highlighting the importance of understanding the perceptual and cognitive processes that support it, particularly in the temporal domain. Furthermore, the performance on PM tasks can be influenced by the strategies of external time monitoring, which may involve shared mechanisms between temporal tasks and TBPM (Brewer et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Martin et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA typical PM paradigm can be divided into two phases: a monitoring phase, during which the participant performs the ongoing (ONG) task while monitoring the occurrence of the PM target, and a detection phase that involves identifying and responding to the PM target (Brandimonte et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The presence of a PM task affects the ongoing task performance, resulting in a decrease known as PM cost (Jager \u0026amp; Kliegel, 2008). This phenomenon is characterized by a decrease in the ONG task performance when an individual is simultaneously required to remember to carry out the intended action. Research has shown that increasing the demands of a PM task can disrupt performance (Gan \u0026amp; Guo, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; M\u0026auml;ntyl\u0026auml; et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Higher cognitive load limits available attentional resources, which in turn affects time monitoring and impairs TBPM performance. The demands of the PM task also influence how participants manage the task: as load increases, they tend to shift from a more automatic response style to one that requires greater attentional effort. This shift affects how individuals allocate mental resources in response to varying levels of memory demand (Cantarella et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Cona et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBecause TBPM lacks external environmental cues, it requires more self-initiated and deliberate effort compared to other forms of PM, such as event-based PM. Individuals must allocate mental resources to periodically check the time and recall the intended action. A central question in TBPM research concerns the role of time monitoring - specifically, how much successful performance depends on detecting the right moment to act, often referred to as target checking. Some studies suggest that monitoring the passage of time, typically measured by the number of clock checks, plays an important role in successful task performance (Harris \u0026amp; Wilkins, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). Supporting this, various findings have reported that a greater number of strategically timed clock checks is associated with better TBPM outcomes, even during ongoing tasks that are cognitively demanding. This pattern has been observed in both older adults (Bisiacchi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Cona et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012a\u003c/span\u003e; M\u0026auml;ntyl\u0026auml; et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; McFarland \u0026amp; Glisky, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and children (Ceci \u0026amp; Bronfenbrenner, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Mackinlay et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; M\u0026auml;ntyl\u0026auml; et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this regard, Munaretto et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) proposed a framework for understanding external time monitoring in TBPM, dividing it into two stages: an initial \u003cb\u003eloose monitoring\u003c/b\u003e phase and a later \u003cb\u003efiner-grained monitoring\u003c/b\u003e phase. This distinction reflects how individuals adjust their monitoring behaviour based on the demands of the task. In the early part of the time interval, loose monitoring limits the number of clock checks, helping to preserve attentional resources for the ongoing task. As the target time approaches, however, monitoring becomes more frequent and precise, supporting accurate execution of the intended action. This shift illustrates how individuals manage attention across time, combining external time checks with internal estimation processes. While strategic monitoring has often been linked to better TBPM outcomes, excessive clock checking can interfere with the accuracy of performance on the ongoing task (M\u0026auml;ntyl\u0026auml; \u0026amp; Carelli, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Effective monitoring depends on maintaining an appropriate balance between attention directed at time tracking and attention required for processing the primary task (Cona et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e; J\u0026auml;ger \u0026amp; Kliegel, 2008).\u003c/p\u003e\u003cp\u003ePrevious research on TBPM has pointed to the importance of identifying the strategies and cognitive functions involved in effective time monitoring and intention execution. One key aspect is the shift from an initial, loose monitoring to more strategic, fine-grained monitoring strategy as the target time approaches (Munaretto et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This shift appears to be supported by functions such as executive control and time estimation, which allow individuals to maintain performance while managing the competing demands of an ongoing task (Mioni et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mioni \u0026amp; Stablum, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In addition, some studies suggest that individual differences in neural mechanisms, such as resting-state functional connectivity, may support the use of efficient monitoring strategies, especially in dual-task settings (Zangrossi et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough there is general agreement that time perception plays a role in TBPM, relatively few studies have directly examined how these two processes interact (Graf \u0026amp; Grondin, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Labelle et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mackinlay et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; McFarland \u0026amp; Glisky, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and their findings are not entirely consistent. Some researchers have proposed that time perception contributes little to TBPM accuracy, arguing that asking someone to perform an action at a certain time does not necessarily make the task time-based in a strict sense. According to this view, memory processes, rather than timing ability, are the main factor influencing performance (Labelle et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In TBPM tasks, participants often rely on external cues such as a clock. Each time they check the clock, they align their internal estimate with the actual time, update their internal representation, and estimate how long to wait before checking again (Block \u0026amp; Zakay, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Mioni et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zakay \u0026amp; Block, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). However, other findings suggest that timing skills do influence how people monitor time and manage delayed intentions. For example, Labelle et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) found that time estimation ability was associated with the frequency of monitoring, especially in the final portion of the waiting period. Similarly, Mioni et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) reported a positive association between performance on a time reproduction task and clock-checking frequency in TBPM, indicating a link between temporal accuracy and monitoring behaviour. Notably, individuals with poorer timing ability tended to check the clock more often. This suggests that frequent clock checks may serve as a compensatory response in individuals with less precise internal timing. Vanneste et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) also reported that weaker timing skills were linked to more frequent monitoring, further supporting this interpretation.\u003c/p\u003e\u003cp\u003eBuilding on prior findings that timing abilities are related to monitoring behaviour (Labelle et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mioni et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wang et al., 2023), this study examined whether performance on time bisection and time reproduction tasks reflects the cognitive processes involved in TBPM. These tasks target different aspects of time perception: bisection involves judging whether a given interval is closer to a previously learned short or long duration, while reproduction requires holding a time interval in memory and reproducing it, engaging sustained attention and memory functions (Grondin, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Zakay, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Therefore, we aimed to determine whether time reproduction, which depends more on internally-driven timing and attention, shows a stronger relation to TBPM performance than time bisection, which is more reliant on perceptual judgments. In addition, since cognitive load influences how attention is distributed and how people manage monitoring, we expected that increasing task demands would clarify the distinct contributions of these two timing tasks to TBPM performance.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eSixty students (20 males and 40 females) from the University of Padua were recruited to participate in the study. The participants were aged between 18 and 35 (mean age\u0026thinsp;=\u0026thinsp;22.4; SD\u0026thinsp;=\u0026thinsp;3.21). All participants had normal or corrected-to-normal vision and no history of neurological or psychiatric pathology. They gave their consent to take part in the research and were informed that their participation was voluntary. Participants received monetary compensation or course credits after each session.\u003c/p\u003e\u003cp\u003eWe determined the sample size of our study through the G*Power software (v. 3.1.9.7; Faul, Erdfelder, Lang, and Buchner, 2007). Based on the results of a previous study (Khan, Sharma and Dixit, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), we estimated a medium effect size np2\u0026thinsp;=\u0026thinsp;16 for obtaining the main effect of time monitoring (Critical F\u0026thinsp;=\u0026thinsp;3.11), and set the significance level at α\u0026thinsp;=\u0026thinsp;0.05 and the desired power (1 \u0026ndash; β) at 0.95, leading to a minimum sample size of 43.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eParticipants completed the procedure in a single test session at the Department of General Psychology (Padua, Italy). The tasks were presented on a 15-inch PC monitor, and participants were seated approximately 60 cm from the screen. To assess time perception abilities, each participant undertook two computerized tasks: time bisection and time reproduction. All participants initially completed the bisection and reproduction tasks, without randomizing their order. They were then given the TBPM task, comprising the ongoing task (baseline block) and the PM task (PM block), resulting in a within-subject experimental design. To prevent any possible interference effects, the baseline block was administered prior to the PM block.\u003c/p\u003e\n\u003ch3\u003eTime bisection task\u003c/h3\u003e\n\u003cp\u003eThe time bisection task (Kopec and Brody, 2010) was divided into a learning phase and a testing phase. In the learning phase, participants were administered 5 short (S\u0026thinsp;=\u0026thinsp;200ms) and 5 long (L\u0026thinsp;=\u0026thinsp;800ms) standard durations to familiarize with the reference durations. There was only a single learning session at the beginning of the task. As declared in previous studies (Kopec \u0026amp; Brody, 2010; Penney \u0026amp; Cheng, 2018), short standards were presented first in each learning phase. The stimulus consisted of a red circle (size 4.5 cm) displayed for a specific duration against a dark background, for a total of 10 trials. Feedback on response accuracy was given at the end of this session. The testing phase involved comparing the two standards for seven different durations (2000, 3000, 4000, 5000, 6000, 7000, and 8000 ms). Subjects were required to judge the relative durations of new intervals and to determine whether they were closer in duration to the \u0026ldquo;short\u0026rdquo; or the \u0026ldquo;long\u0026rdquo; standard. Each response was followed by an inter-trial interval of 1000, 2000 or 3000 ms. In the experimental block, each duration (2000, 3000, 4000, 5000, 6000, 7000, and 8000 ms) was presented eight times in randomized order for 56 trials. Subjects were not given any feedback on the accuracy of their responses.\u003c/p\u003e\n\u003ch3\u003eTime reproduction task\u003c/h3\u003e\n\u003cp\u003eIn the time reproduction task (Zakay, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Grondin, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; 2010), participants were instructed to reproduce the duration of a previously viewed stimulus. Once the instructions were given, a white circle with a size of 4.5 cm was displayed at the center of the computer screen, for durations of 400, 900, or 1400 ms. When the stimulus disappeared, a question mark appeared on the computer screen (ISI\u0026thinsp;=\u0026thinsp;2000 ms), and participants were instructed to press the spacebar (using the index finger of their dominant hand) for a duration that matched the time the stimulus was on the screen as closely as possible. While reproducing the duration of the stimulus by pressing the spacebar, participants were instructed to read aloud a series of numeric digits that appeared in the center of the screen until they released the bar. The numbers ranged from 1 and 9 and were presented randomly, with an interstimulus interval from 400 to 1000 ms. Participants did not receive feedback on the accuracy of their performance.\u003c/p\u003e\n\u003ch3\u003eOngoing task – baseline\u003c/h3\u003e\n\u003cp\u003eThe ongoing task was a visual search paradigm consisting of 576 trials. Participants were instructed to detect a red circle (target stimulus) among a series of white letters randomly positioned across the screen (distractors). If the target stimulus appeared on the right side of the screen, participants had to press the right red key of the computer keyboard (\"Z\") with their right index finger. Conversely, if the red circle appeared on the left side of the screen, participants were instructed to press the left red key on the keyboard (\"N\"). The stimuli were displayed for 1500 ms (ISI\u0026thinsp;=\u0026thinsp;1000 ms). Participants did not receive feedback on the accuracy of their responses.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eTime-based prospective memory task\u003c/h2\u003e\u003cp\u003eInstructions for the TBPM task were provided only after the baseline block had been completed. Participants were instructed to press the \u0026lsquo;X\u0026rsquo; key every 2 minutes from the beginning of each block, while continuing to perform the ongoing task (see above). The TBPM task consisted of four blocks, each lasting approximately 7 minutes, for a total of 576 trials (144 trials per block; see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for a graphical representation of the task). Participants were instructed to identify a red circle (the target stimulus) among a series of distractors and to press a designated key every 2 minutes as part of the time-based prospective memory (TBPM) component. Based on the duration of each block, participants completed approximately 3 to 4 TBPM intervals per block, resulting in a total of 12 to 16 intervals across the entire task.\u003c/p\u003e\u003cp\u003eTwo experimental conditions - high and low memory load - were randomly alternated across blocks. In the high-load condition, the target had to be detected among distractors that shared perceptual features, such as red squares or green circles, increasing the difficulty of visual discrimination. In the low-load condition, the target appeared among distractors (letters and shapes) that did not overlap perceptually with the target, making the task less demanding (see also Pashler, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMoreover, participants were told that they had the opportunity to check a digital clock that showed the exact time in minutes and seconds, and that they had to perform this task as accurately as possible. When they pressed the button to check the time (\u0026lsquo;C\u0026rsquo;), the digital clock appeared in the center of the screen for 300 ms. The digital clock could be checked only a fixed number of times (5) every 2 minutes. Participants were not informed about the duration of the TBPM task, which lasted 20 minutes, with the possibility of taking breaks between blocks.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003eTime bisection\u003c/h2\u003e\u003cp\u003eTemporal abilities were first analyzed in terms of the proportion of long responses (raw data), i.e., the proportion of times each subject pressed \u0026ldquo;long\u0026rdquo; for each new comparison interval. Consequently, an overall seven-point psychometric function was traced, plotting the seven comparison intervals on the x-axis and the probability of responding \u0026ldquo;long\u0026rdquo; (p-long) on the y-axis, for each experimental condition.\u003c/p\u003e\u003cp\u003eThe bisection point (BP) was calculated for each participant. BP is defined as the stimulus duration for which the participants responded \u0026ldquo;short\u0026rdquo; or \u0026ldquo;long\u0026rdquo; with equal frequency. The BP is associated with the target duration corresponding to a predicted rate of long responses of 50%, and it is used as a measure of perceived duration: the smaller the BP value, the longer the perceived duration.\u003c/p\u003e\u003cp\u003eTemporal abilities were also analyzed in terms of Constant Error (CE), defined as the duration of the mid-point between the two standards (2000 and 8000 ms) minus the BP (Grondin \u003cem\u003eet al.\u003c/em\u003e, 2015). CE is a measure of accuracy, positively related to perceived duration. Positive or negative CE values are an index of over- or under-estimation of temporal durations compared to the midpoint.\u003c/p\u003e\u003cp\u003eIn addition, the WR parameter was implemented. It is defined as the degree of discriminability the subject uses to parse the standard durations into the \u0026ldquo;short\u0026rdquo; and \u0026ldquo;long\u0026rdquo; categories. This variable measures the participant\u0026rsquo;s sensitivity to time: a subject with a high degree of discriminability would produce a psychometric curve that appears very step-like, resulting in a low WR, while a poorer discriminability would result in a more gradual psychometric function and a higher WR (Kopec and Brody, 2010).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eTime reproduction\u003c/h2\u003e\u003cp\u003eReproduction abilities were analyzed in terms of the estimated-to-target duration ratio (RATIO), the absolute error (AE) and the coefficient of variation (CV). The RATIO was obtained by dividing each participant\u0026rsquo;s reproduced duration (R\u003csub\u003ed\u003c/sub\u003e) by the target duration (T\u003csub\u003ed\u003c/sub\u003e) for that trial [RATIO\u0026thinsp;=\u0026thinsp;R\u003csub\u003ed\u003c/sub\u003e/T\u003csub\u003ed\u003c/sub\u003e]. Coefficients above and below 1.0 were indicative of over-reproduction and under-reproduction, respectively. The AE was calculated as the difference between the reproduced duration and the target duration (in absolute value) divided by the target duration [AE= |R\u003csub\u003ed\u003c/sub\u003e - T\u003csub\u003ed\u003c/sub\u003e| / T\u003csub\u003ed\u003c/sub\u003e] (Brown, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; see also Glicksohn and Hadad, 2012). Large AE levels indicate low performance. The CV was obtained by dividing the standard deviation in time reproduction performance by the mean reproduction value, separately for each interval (Brown, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). This measure indicates the variability of time reproduction performance.\u003c/p\u003e\u003cp\u003eSince their distribution violated the assumption of normality (\u003cem\u003eShapiro-Wilk test\u003c/em\u003e; p\u0026thinsp;\u0026lt;\u0026thinsp;.001), a non-parametric analysis (Friedman test) was run on these measures, with \u003cem\u003einterval\u003c/em\u003e as a within-subject factor.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eTime-based Prospective Memory task\u003c/h2\u003e\u003cp\u003eTBPM performance was assessed in terms of accuracy in PM and reaction times (RTs), clock checking (time-monitoring frequency), and ongoing task performance (RTs and accuracy at the visual search paradigm). PM accuracy was scored as correct if participants pressed the button within 10 s (\u0026plusmn;\u0026thinsp;5 s) of the target time (2 minutes). Time monitoring frequency was analyzed in terms of the number of clock checks; moreover, we calculated a Strategic Monitoring Index (SMI), consisting of the number of clock checks in the last 40% and the number of clock checks in the remaining 60% of the time elapsed over the total target time of 2 minutes (see Mantyla et al., 2007, Mioni et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; 2014 for a similar procedure).\u003c/p\u003e\u003cp\u003eTo investigate the TBPM performance trend among blocks, we performed a repeated-measure ANOVA on TBPM accuracy and the number of clock checks, with \u003cem\u003eblock\u003c/em\u003e (1, 2, 3 and 4) as within-subject factor. Post-hoc analyses were conducted with the Bonferroni test. The effect size was indicated as partial eta square (η\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e).\u003c/p\u003e\u003cp\u003eTo check for possible PM costs, we performed Student\u0026rsquo;s paired-samples t-tests comparing RTs values at the ongoing task during baseline vs. TBPM conditions, and RTs values at the ongoing vs. the TBPM task. A non-parametric approach (Wilcoxon signed-rank test) was employed instead to compare accuracy values in the baseline vs. the TBPM conditions and the TBPM accuracy with the ongoing accuracy. We also performed a Student\u0026rsquo;s t-test on RTs values and number of clock checks, comparing blocks under high vs. low cognitive load. The same comparison on the TBPM accuracy was performed by using a non-parametric approach (Wilcoxon signed-rank test).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation \u0026ndash; Time perception\u003c/h2\u003e\u003cp\u003eTo verify the hypothesis that time perception abilities - as assessed through the time bisection task - could relate to TBPM accuracy, we run a correlation (Pearson\u0026rsquo;s) analysis between WR values (time sensitivity) and TBPM accuracy. To establish possible relations across different timing abilities, we also run a correlation (Pearson\u0026rsquo;s) analysis between time perception and time reproduction abilities, as assessed, respectively, through the time bisection (BP, an index of time under- or overestimation) and time reproduction (RATIO, an index of time under- or over-reproduction) tasks.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eRelation between time perception and TBPM\u003c/h2\u003e\u003cp\u003eTo examine the relation between time estimation/reproduction abilities and TBPM performance, we fitted a linear mixed-effects model using the lmerTest package in R. Prior to modeling, continuous predictors were mean-centered to improve interpretability and reduce multicollinearity. The model included fixed effects for AE, WR, and their two-way interactions with Load (AE*Load, WR*Load). Random intercepts were included for participants and number of clock checks to account for repeated measures and fluctuations in monitoring behavior that might affect TBPM performance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eThe effect of strategic monitoring\u003c/h2\u003e\u003cp\u003eTo investigate the relation between strategic monitoring and TBPM performance, we run another linear mixed-effects model including SMI, memory load Low vs. High), and their interaction as fixed effects. A random intercept for each participant was also included to account for inter-individual variability.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eTime bisection\u003c/h2\u003e\u003cp\u003eThe seven-point psychometric function plotting each interval with the corresponding proportion of \u0026ldquo;long\u0026rdquo; responses (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) showed that participants correctly discriminated among durations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDescriptive statistics of the temporal abilities in terms of BP (mean\u0026thinsp;=\u0026thinsp;4627 ms; st.dev. = 944.76) and CE (mean\u0026thinsp;=\u0026thinsp;413 ms; st.dev. = 1007. 88) indicated that participants were accurate at estimating durations, with a slight tendency to time overestimation. Participants\u0026rsquo; temporal sensitivities, as assessed with WR values, were also relatively high (mean\u0026thinsp;=\u0026thinsp;0,20; st.dev. = 0,13), confirming their capacity to correctly discriminate durations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eTime reproduction\u003c/h2\u003e\u003cp\u003eThe results of the Friedman test on RATIO values revealed a main effect of \u003cem\u003einterval\u003c/em\u003e [χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;29.107; p\u0026thinsp;\u0026lt;\u0026thinsp;.001]. Post-hoc analysis confirmed that RATIOs decreased as the interval increased; with higher values for the shorter interval \u0026minus;\u0026thinsp;4000 ms (1.13), followed by the medium interval \u0026ndash; 9000 ms (0.99; p\u0026thinsp;=\u0026thinsp;.025) and the longer interval \u0026ndash; 14000 ms (0.92; p\u0026thinsp;=\u0026thinsp;.002). This analysis showed that participants\u0026rsquo; tendency to over-reproduce durations diminished as the duration of intervals increased (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe same analysis on CV [χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.933; p\u0026thinsp;=\u0026thinsp;.09] and AE [χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.041; p\u0026thinsp;=\u0026thinsp;.98] failed to reach statistical significance].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eTime-based Prospective Memory\u003c/h2\u003e\u003cp\u003eOverall, the discrimination accuracy of the ongoing task was high, both for the baseline (98%) and the TBPM (96%) conditions, while accuracy for the TBPM task was relatively lower (76%). The repeated-measure ANOVA on the TBPM accuracy revealed a main effect of \u003cem\u003eblock\u003c/em\u003e [F (2.349)\u0026thinsp;=\u0026thinsp;15.372; p\u0026thinsp;\u0026lt;\u0026thinsp;.001; η\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.207]. Post-hoc analyses (Bonferroni\u0026rsquo;s correction) showed that TBPM accuracy in the first block was significantly lower (.560) than in the subsequent blocks (.787, .833 and .854; all ps. \u0026lt; .001) with no difference among the other blocks (all ps\u0026thinsp;\u0026gt;\u0026thinsp;.99). Thus, the execution of the TBPM task was less accurate in the first block but then improved and remained stable in the subsequent blocks. The same ANOVA on the number of clock checks failed to reach statistical significance [F (2.204)\u0026thinsp;=\u0026thinsp;.0.825; p\u0026thinsp;=\u0026thinsp;.451].\u003c/p\u003e\u003cp\u003ePaired-samples t-tests revealed instead significant differences in RTs [t= -16.605; p\u0026thinsp;\u0026lt;\u0026thinsp;.001; d= -2.199] of the ongoing (visual search) task between the baseline (587.48 ms) and the TBPM condition (762.21 ms). Regarding accuracy values, results of the Wilcoxon signed-rank test indicated significantly higher accuracy values [W\u0026thinsp;=\u0026thinsp;1513; p\u0026thinsp;\u0026lt;\u0026thinsp;.001] at the ongoing task during the baseline condition (.98) compared to the TBPM (.96) condition. Thus, participants were slower and less accurate in the visual search task while concomitantly executing the TBPM intention (\u003cem\u003ePM cost\u003c/em\u003e). Moreover, the accuracy at the ongoing task in this condition resulted also significantly lower [W\u0026thinsp;=\u0026thinsp;73.50; p\u0026thinsp;\u0026lt;\u0026thinsp;.001] than the accuracy at the TBPM task (.76).\u003c/p\u003e\u003cp\u003eThe results of the paired-samples t-tests on RTs of the ongoing task blocks under different conditions of cognitive load revealed significant differences [t= -12.577; p\u0026thinsp;\u0026lt;\u0026thinsp;.001; d= -1.666], with slower RTs in high vs. low-load blocks (835.55 vs. 688.84 ms). The non-parametric analysis on the TBPM accuracy [W\u0026thinsp;=\u0026thinsp;494; p\u0026thinsp;=\u0026thinsp;.34] and the paired-samples t-test on number of clock checks [t= -0. 402; p\u0026thinsp;=\u0026thinsp;.689] instead failed to reach statistical significance. This set of analyses revealed that increasing cognitive load impacted ongoing task performance but did not affect the execution of delayed intentions at specific time points or the strategic monitoring processes (clock-checking) that are necessary to accomplish them.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation with time perception\u003c/h2\u003e\u003cp\u003eCorrelation (Pearson\u0026rsquo;s) analysis failed to reveal a significant association between WR values and TBPM accuracy (r = -0.001; p\u0026thinsp;=\u0026thinsp;.99), indicating the absence of a link between time perception (in the timescale of seconds) and the ability to carry out intended actions at specific temporal moments in the future (TBPM). However, we found a significant (Pearson\u0026rsquo;s) correlation (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.388; p\u0026thinsp;=\u0026thinsp;.002) between BP and RATIO values, confirming the existence of a relation between these two timing abilities: time overestimation (lower BP) was associated with time over-reproduction (higher RATIO).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eRelation between time perception and TBPM\u003c/h2\u003e\u003cp\u003eModel assumptions were assessed by inspecting residual distributions and residuals-vs-fitted plots, which suggested an acceptable fit to the data. The linear mixed-effects model revealed a significant interaction \u003cb\u003eAE * Load\u003c/b\u003e (χ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;4.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.040): post-hoc analyses showed that under high memory load, increases in absolute error rates were associated with a significant reduction in TBPM accuracy (slope\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.624, 95% CI [\u0026minus;\u0026thinsp;1.216, \u0026minus;\u0026thinsp;0.031]), while under low memory load, the slope was positive but non-significant (slope\u0026thinsp;=\u0026thinsp;0.103, 95% CI [\u0026minus;\u0026thinsp;0.496, 0.703]; see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). No significant main effects were found for AE (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.732), WR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.380), and the interaction WR * Load was also not significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.494). Model fit indices indicated that fixed effects accounted for a small proportion of variance in TBPM accuracy (Marginal R\u0026sup2; = 0.023), while the full model, including random participant effects, explained a substantially larger portion (Conditional R\u0026sup2; = 0.340), suggesting that individual differences among participants accounted for a significant share of the variability in performance. Although the interaction term explained only a modest share of the variance, these findings indicate that TBPM accuracy is associated with time reproduction abilities (i.e., AE), but selectively under high memory load conditions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eThe effect of strategic monitoring\u003c/h2\u003e\u003cp\u003eThe linear mixed model revealed a significant main effect of SMI on TBPM accuracy (χ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;51.55, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001): specifically, the slope of SMI was positive and significant (β\u0026thinsp;=\u0026thinsp;0.058, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0097, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting that TBPM performance improved as SMI increased. Neither the main effect of Load (χ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;0.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.66) nor the interaction SM*Load(χ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;0.81, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.37) reached statistical significance, indicating that the effect of SMI on TBPM accuracy was consistent across levels of memory load. Model fit indices suggesting moderate explanatory power, with fixed effects accounting for 21.5% of the variance in TBPM accuracy (Marginal R\u0026sup2; = 0.215) and the full model, including random effects, explaining 30.8% of the variance (Conditional R\u0026sup2; = 0.308).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study aimed to identify which specific aspects of timing - namely, time bisection and time reproduction - are most relevant for supporting TBPM performance. We hypothesized that accurate time bisection and reproduction would not only support effective monitoring strategies but also associate with overall TBPM accuracy. Results partially supported our initial hypothesis: among the two timing measures, only time reproduction, but not bisection, was related to TBPM performance. Notably, this association was moderated by the cognitive load of the ongoing task. The relationship between absolute error in time reproduction and TBPM accuracy was observed only under high-load conditions, suggesting that when cognitive resources are limited, time reproduction abilities become more influential in the execution of delayed intentions. Moreover, strategic monitoring was positively associated with TBPM accuracy under both load conditions, suggesting that increasing clock checks shortly before the target time supports successful TBPM performance, regardless of task difficulty.\u003c/p\u003e\u003cp\u003eTime bisection and time reproduction both require the encoding and retention of temporal information. However, they differ in the type of response they demand. The time bisection task involves a perceptual judgment: participants compare a probe interval to two reference durations (\u0026ldquo;short\u0026rdquo; and \u0026ldquo;long\u0026rdquo;) stored in memory and make a categorical decision based on this comparison (Grondin, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Vatakis et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A consistent tendency to classify probe intervals as \u0026ldquo;short\u0026rdquo; reflects time underestimation, while classifying them as \u0026ldquo;long\u0026rdquo; indicates overestimation (Bueti et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In contrast, the time reproduction task requires participants to encode a target duration and later reproduce an interval of equivalent length, typically while engaged in a concurrent task (Grondin, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Zakay, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). This task not only relies on time estimation abilities but also engages higher-order cognitive functions. Accurate reproduction depends, in fact, on attentional and memory resources to retain and manipulate the duration while performing another task (Baudouin et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Dividing attention between temporal and non-temporal demands reduces the resources available for timing, thereby affecting performance (Brown, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The attentional gate model (Brown, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Block \u0026amp; Zakay, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) explains this by proposing that increased cognitive load reduces the flow of temporal information into working memory, impairing timing accuracy. Moreover, time reproduction requires reliance on an internal clock to guide the response, a process that may resemble internal monitoring mechanisms. This executive-like function is comparable to the self-initiated monitoring required in TBPM. In our study, the overlap between the processes involved in time reproduction and TBPM may account for the observed association between time reproduction accuracy and TBPM performance under high cognitive load.\u003c/p\u003e\u003cp\u003eOur results confirmed that time monitoring plays a key role in TBPM. Specifically, we found that strategic monitoring - i.e., more frequent clock-checking - is strongly associated with successful TBPM performance. According to the Test-Wait-Test-Exit model (Harris \u0026amp; Wilkins, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1982\u003c/span\u003e), accurate TBPM performance depends on a monitoring process involving repeated test-wait cycles until the target time approaches. During this period, individuals progressively align their internal sense of time with an external reference (e.g., a clock). As the deadline nears, they increase the frequency of clock checks, engaging in finer-grained monitoring to respond at the correct moment. To capture this behavior, we used the Strategic Monitoring Index (Mioni \u0026amp; Stablum, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which quantifies the number of clock checks made during the final portion of the time interval\u0026mdash;i.e., the period closest to the target time. Our findings are also consistent with previous studies (Harris \u0026amp; Wilkins, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Munaretto et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), showing that individuals who increase their monitoring frequency as the deadline approaches tend to respond more accurately in TBPM tasks. In other words, more frequent clock checks during the critical window improved timing precision and helped prevent PM failures. We also suggest that individual differences in this strategy may be linked to differences in timing abilities. Participants may rely on an internal timekeeping mechanism to estimate when the critical window is approaching, at which point they shift to relying more on external cues. In the present study, this flexible use of internal and external time information may have contributed to the variability in monitoring strategies - and, in turn, to the differences in TBPM performance - we observed.\u003c/p\u003e\u003cp\u003eOur findings also confirmed the presence of a PM cost. Specifically, maintaining a TBPM intention led to reduced performance on the ongoing task, as shown by slower reaction times and decreased accuracy (Hicks et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Marsh et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Increasing the cognitive load of the ongoing visual search task further impaired performance on that task, but did not significantly affect accuracy in the TBPM task. Notably, participants showed a higher number of clock checks under high-load conditions compared to low-load conditions, possibly reflecting a compensatory strategy aimed at preserving accuracy in the TBPM task. These findings suggest that, when monitoring demands are fixed, young adults may allocate a greater share of their cognitive resources to time monitoring, which can limit resources available for the ongoing task. While increased cognitive load impaired ongoing task performance, TBPM accuracy remained stable\u0026mdash;likely due to this adjustment in strategy. This pattern supports the idea that TBPM performance can be maintained through adaptive monitoring, even under heightened task demands.\u003c/p\u003e\u003cp\u003ePrevious research has identified various individual factors influencing PM performance (Ball et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). PM depends on multiple cognitive processes, and individual differences may reflect the engagement of distinct cognitive and neural systems (Ball et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Attention and memory are particularly important for the detection and retrieval of PM intentions. Maintaining attention helps prevent mind-wandering and supports the inhibition of ongoing task responses in favor of monitoring for PM cues (Ball et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In TBPM specifically, where individuals must track elapsed time, differences in time perception abilities may also play a role. TBPM tasks typically require estimating and responding at a specific time interval, resembling prospective time estimation paradigms. Research on time perception (e.g., Brown, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1985\u003c/span\u003e) has consistently shown that estimated durations shorten as non-temporal task demands increase. Our findings extend this by showing that individual differences in time reproduction abilities contribute to variation in monitoring behavior and help predict accuracy in meeting TBPM deadlines. These results are consistent with Zangrossi et al. (2019), who demonstrated that individual differences in resting-state brain network organization are associated with the use of time monitoring strategies, such as clock-checking frequency. Their study found that better monitoring performance was related to more efficient network integration, suggesting a neurobiological basis for strategic monitoring differences. Together with our behavioral findings, this evidence highlights the value of considering individual variability in timing abilities when examining TBPM performance. However, further research is needed to investigate the neural mechanisms underlying time estimation and monitoring strategies, and how these contribute to performance in prospective memory tasks.\u003c/p\u003e\u003cp\u003eFinally, we examined the relationship between individuals\u0026rsquo; abilities to estimate and reproduce time intervals within the same timescale (seconds). The results revealed a positive association between the two measures: individuals who tended to overestimate durations in the time bisection task also tended to over-reproduce durations in the time reproduction task. In other words, participants who perceived intervals as longer were also more likely to reproduce them as longer than their actual duration. This finding contrasts with previous results by Cantarella, Vianello, et al. (2023), which showed a different pattern in patients with right hemisphere damage, where time underestimation was linked to time over-reproduction for shorter durations (milliseconds). That pattern was interpreted as a consequence of internal clock slowing following brain damage. In contrast, our study focused on healthy young adults and used longer time intervals (in the range of seconds). The observed association between bisection and reproduction supports the view that these tasks share a common component related to time estimation. Nonetheless, additional processes likely distinguish time reproduction from process-pure estimation tasks, and further research is needed to clarify these differences.\u003c/p\u003e\u003cp\u003eOne potential limitation of the present study concerns the relatively high number of clock-checking opportunities during each interval (five per two-minute window). Although participants were instructed to respond as accurately as possible at the target time, not all made full use of the available checks. Since different monitoring strategies can influence TBPM performance, the high availability of time cues may have influenced how participants engaged with the task, possibly affecting the observed relationship between time reproduction and TBPM performance. Future studies could manipulate the number of available clock checks to explore how reduced access to external cues alters monitoring strategies and performance.\u003c/p\u003e\u003cp\u003eIn conclusion, the present findings indicate that adequate temporal abilities are important for successful TBPM performance. Among the two timing measures assessed, only time reproduction was significantly associated with TBPM accuracy. These results point to the involvement of shared mechanisms between time perception and TBPM that go beyond estimation alone. Both time reproduction and TBPM tasks, in fact, require individuals to maintain a representation of elapsed time in working memory while performing a concurrent task and to decide when to act\u0026mdash;whether stopping a timed interval or executing a planned action. We propose that this internal tracking of time may serve as a common cognitive process supporting performance across both domains, especially in the absence of reliable external cues.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/em\u003eThis study involving healthy participants was conducted in accordance with the ethical standards of the Declaration of Helsinki (1964). It was reviewed and approved by the Ethics Committee of the University of Padua (Protocol Code: 2116). All participants provided written informed consent prior to participation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication:\u0026nbsp;\u003c/em\u003eNot applicable.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials:\u003c/em\u003eWe report how we determined our sample size, all data exclusions, all inclusion/exclusion criteria, whether inclusion/exclusion criteria were established prior to data analysis, all manipulations, and all measures in the study. No part of the study or analyses/ procedures were pre-registered prior to the research being conducted. Behavioral data are available on the public repository OSF (\u003cu\u003ehttps://osf.io/ujb7x/?view_only=5c346f904d1f4dcf961b7d7e2c3727f0\u003c/u\u003e). The research materials (e.g., task, including stimuli and code) for this study will be shared upon reasonable request.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests:\u0026nbsp;\u003c/em\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding:\u0026nbsp;\u003c/em\u003eThis research was supported by the Department of General Psychology (University of Padua) research funding.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors’ contributions:\u003c/em\u003e GC and PB developed the study concept and contributed to the study design. GS recruited the participants and carried out data collection. GC and GS conducted data analyses. All authors contributed to the interpretation of the findings. GS drafted the manuscript. GC and PB revised the manuscript, and all authors approved its final version. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements:\u0026nbsp;\u003c/em\u003eWe thank Margherita Bagnoli for helping with data collection.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors’ information:\u0026nbsp;\u003c/em\u003eGiovanni Cantarella and Giulia Stramucci contributed equally to this work and share first authorship.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBall, B. H., Knight, J. B., Dewitt, M. R. \u0026amp; Brewer, G. A. Individual Differences in the Delayed Execution of Prospective Memories, \u003cb\u003e66\u003c/b\u003e(12), 2411\u0026ndash;2425. 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Res.\u003c/em\u003e \u003cb\u003e402\u003c/b\u003e, 113130. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/J.BBR.2021.113130\u003c/span\u003e\u003cspan address=\"10.1016/J.BBR.2021.113130\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\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":"time estimation, time-based prospective memory, time discrimination, strategic monitoring, external time monitoring","lastPublishedDoi":"10.21203/rs.3.rs-7456434/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7456434/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe capacity to accurately estimate time is crucial for carrying out intended actions in the future. Prior research on time-based prospective memory (TBPM) has explored the roles of external time monitoring, task characteristics, and time perception abilities in supporting the execution of delayed intentions. The present study examined whether TBPM performance shares common underlying mechanisms with two temporal tasks: time bisection and time reproduction. Participants completed both timing tasks followed by a TBPM task in a single experimental session (within-subject design). In the TBPM task, they were required to press a designated key every two minutes while simultaneously performing a visual search task. Cognitive load was manipulated by varying the number of distractors in the ongoing task. Results showed that performance in the time reproduction task\u0026mdash;but not in time bisection\u0026mdash;was significantly associated with TBPM accuracy, suggesting the involvement of shared cognitive processes between these two tasks. Participants who demonstrated greater accuracy in time reproduction and engaged in more effective time monitoring (clock-checking) strategies also performed better on the TBPM task. Although increased cognitive load influenced reaction times and monitoring behaviour, it did not significantly affect TBPM accuracy. These findings highlight the importance of temporal abilities in supporting successful performance in TBPM.\u003c/p\u003e","manuscriptTitle":"A Common Mechanism Behind Time Reproduction and Time-Based Prospective Memory","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 02:12:54","doi":"10.21203/rs.3.rs-7456434/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":"554ab32a-528a-432a-8f32-b887b0fafbaf","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56204627,"name":"Biological sciences/Neuroscience"},{"id":56204628,"name":"Biological sciences/Psychology"},{"id":56204629,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-03-18T10:12:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 02:12:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7456434","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7456434","identity":"rs-7456434","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}