Introduction

Procrastination is increasingly becoming a prevalent behavioral problem around the world, which reflects the irrational voluntary postponement of scheduled tasks albeit being worse off for such delays (Blake, 2019; Steel, 2007). In epidemiological investigations, more than 15% of adults were identified as having chronic procrastination problems, and the situation for students was worse as 70-80% of undergraduates engaged in procrastination (American College Health Association, 2022; Ferrari et al., 2005). Moreover, the behavioral genetic evidence indicates a certain heritability of procrastination in human beings as well (Gustavson et al., 2017; Gustavson et al., 2014, 2015). In addition to its prevalence, the undesirable associations between procrastination behavior and health also warrant caution. There is cumulative evidence to show the close associations between procrastination behavior and work performance, financial status, interpersonal relationships, and subjective well-being (Ferrari, 1994; Pychyl & Sirois, 2016; Steel et al., 2021). Further, as prospective cohort studies indicate, many mental health problems emerge alongside procrastination, particularly sleep problems, depression, and anxiety (Hairston & Shpitalni, 2016; Johansson et al., 2023). Even worse, chronic procrastination has been observed to impair general health, as manifested by the close associations with immune system disruption, gastrointestinal disturbance, as well as a high risk of hypertension and cardiovascular disease (Sirois, 2015; Sirois, 2016). Thus, given these critical ramifications, considerable efforts have been devoted to delve into why we procrastinate irrationally.

To probe why we procrastinate irrationally, researchers have built upon theoretical bases of procrastination from different perspectives. For instance, Steel (2007) pioneered a promising temporal motivation theory (TMT) to explicate procrastination as the failure of self-regulation. This theory suggests that individuals would procrastinate as task utility is devalued in the far future (Steel & König, 2006). Based on insights into emotional regulation, the mood repair perspective provides another explanation to elucidate that procrastination is due to the failure of self-regulation to give priority to short-term mood repair caused by doing the task rather than long-term task reward (Sirois & Pychyl, 2013). Recently, the temporal decision model (TDM) of procrastination further provides an integrative framework to explain the procrastination decisions, which highlights that procrastination is contingent on the trade-off between task aversiveness and task-outcome value (Zhang & Feng, 2020; Zhang, Liu, et al., 2019). Task aversiveness reflects how unpleasant individuals perceive tasks to be, with more unpleasant feelings making procrastination more likely (Zhang & Feng, 2020). Task outcome value indicates how much it is worth as we evaluate the benefits it provides us (e.g., keeping body health) once we complete the task before the deadline (e.g., doing scheduled exercise) (Zhang & Feng, 2020). If the task aversiveness is overvalued in this trade-off, the decision to postpone tasks would be made consistently.

It is worthwhile to note that this trade-off and resultant decisions to procrastinate are predominantly influenced by self-control capability, with higher self-control associated with less procrastination behavior (Blake, 2019; Ramzi & Saed, 2019; Zhao et al., 2021). Thus far, there have been three promising pathways attempting to clarify how self-control works in reducing procrastination: one for decreasing task aversiveness, the other one for increasing task-outcome value, and the last one for both (Zhang & Feng, 2020). As identified by both behavioral and neural evidence, procrastinators consistently report high task aversiveness when receiving scheduled tasks, and are more likely to postpone tasks so as to devalue negative task aversiveness (Blunt & Pychyl, 2000; Zhang et al., 2021). Meanwhile, the self-control was observed as powerful for downstream regulation of negative emotional stimuli (Paschke et al., 2016; Tice & Bratslavsky, 2000). Therefore, the first possible pathway is to enhance self-control by facilitating emotional regulation towards task aversiveness (Eckert et al., 2016). On the other hand, as a value-based decision, procrastination behavior is contingent on the evaluation of future outcomes (Rebetez et al., 2016; Zhang, Becker, et al., 2019). Existing evidence has shown that procrastinators generally underestimate the task-outcome value (H. Wu et al., 2016; Zhang et al., 2021). In this vein, it is hard to generate the motivation to take immediate action (Taura et al., 2015). Notably, increasing the value of future rewards has been found effective in making individuals inclined to pursue future outcomes by strengthening self-control (Cho et al., 2015; Kelley et al., 2018). Thus, another pathway worth putting forward is that procrastination behavior could be shaped by increasing future task-outcome value. Also, given the multifaceted roles of self-control, we hypothesize a third pathway whereby both decreased task aversiveness and increased task-outcome value contribute to determining procrastination simultaneously through the exertion of self-control.

Supporting this view, the left dorsolateral prefrontal cortex (DLPFC), responsible for self-control, has been frequently shown to be closely associated with procrastination. As the biomarker of self-control, the neuroanatomical changes of the left DLPFC were revealed to predict procrastination robustly (Chen et al., 2020; Hu et al., 2018; Liu & Feng, 2017). In addition to structural brain hallmarks, the neurofunctional anomalies underlying procrastination were found in DLPFC-involved circuits (Y. Wu et al., 2016; Xu et al., 2021). Furthermore, the triple brain network theory provides network-based insights to highlight the neural signature of procrastination as the self-control neural network (e.g., left DLPFC; anterior cingulate cortex, ACC), emotional regulation network (e.g., insula and orbitofrontal cortex, OFC) and episodic prospection network (e.g., hippocampus and ventromedial prefrontal cortex, vmPFC, amygdala) (Chen & Feng, 2022; Chen et al., 2020; Schlüter et al., 2018; Wypych et al., 2019). In addition to the left lateralization, there is solid evidence indicating significant associations between self-control and the right DLPFC indeed, particularly given that this region specifically functions in top-down regulation, future self-continuity representation and social decisions (Huang et al., 2025; Knoch & Fehr, 2007; Lin & Feng, 2024). Despite this case, Xu and colleagues demonstrated null effects of anodally stimulating the right DLPFC to modulate either value evaluation or emotional regulation for changing procrastination willingness (Xu et al., 2023). Moreover, a substantial amount of neural evidence supports this conclusion that DLPFC is involved in long-term reward evaluation and value encoding via top-down self-control circuits (Frost & McNaughton, 2017; Jimura et al., 2013; Smith et al., 2018). Using a neurocomputational model, Le Bouc & Pessiglione (2022) provided clear evidence indicating that dorsal PFC signaling expected effort values was significantly attenuated in procrastinators compared to healthy controls (Le Bouc & Pessiglione, 2022). In this vein, this evidence supports this conceptualization that the left DLPFC may be a domain-specific neural signature determining one’s procrastination. In light of technical advances, high-resolution transcranial direct current stimulation (HD-tDCS) has been widely used to reveal the causal neurocognitive mechanism of problematic behaviors by modulating cortical excitability, blood-brain barrier permeability and even neuroplasticity (Cirillo et al., 2017; Woods et al., 2016), which is regulated by the NMDA (N-methyl-D-aspartate) system to either bolster LTP (Long-term potentiation) or LTD (Long-term depression) processes (Chrysikou et al., 2022; Shin et al., 2020). For instance, anodic HD-tDCS applied to the left DLPFC was found effective in inhibiting problematic behaviors caused by the lack of self-control (Allenby et al., 2018), showing significantly amplified local neural oscillations (Chrysikou et al., 2022). Beyond regional neuromodulation in a dose-free protocol, cumulative evidence has well-documented that the effects of tDCS for neuromodulation are highly dose-dependent and are involved in network-wise covariance (Sabé et al., 2024; Soleimani et al., 2023; Woodham et al., 2025). Thus, this study aims to provide causal evidence clarifying the brain-behavior association of procrastination and revealing how self-control works to shape one’s procrastination by manipulating left DLPFC activity in a multiple-session (dose) protocol.

To clarify the causal cognitive mechanism of self-control on procrastination, we conducted a double-blind, randomized, multiple-session, placebo-controlled design, with a 2 (active HD-tDCS vs. sham control) × 2 (before first neural stimulation vs. after last neural stimulation) full factorial design (see Fig. 1). This HD-tDCS protocol consisted of 7 sessions spaced over 15 days, and each session was implemented every two days. To ensure sound ecological validity, each procrastinator was informed to report one REAL-LIFE task that he/she should complete the following day (e.g., Day 2) after the current neuromodulation session (e.g., Day 1), and was asked to report the ACTUAL performance for completing this task in that day (Day 2) (see Fig. 1). Based on the temporal decision model of procrastination (Zhang, Liu, et al., 2019), we drew on the experience sampling method (ESM) to estimate the real dynamics of task aversiveness and task-outcome value by using a parameter-free model in each session. More importantly, to clarify which pathway best explains the neurocognitive mechanism of procrastination, we built upon the mixed-effects general linear model and Quasi-Bayesian causal mediation model to test whether the changes in task aversiveness and task outcome value caused by HD-tDCS could predict decreased procrastination. Finally, the follow-up investigation for actual procrastination was conducted for 6 months after the experiment to examine the long-term retention of this neural stimulation effect.

Figure 1

Materials and Methods

This study fully adhered to CONSORT reporting guidelines and was originally preregistered in the OSF repository (10.17605/OSF.IO/Y3EDT). However, due to the technical constraint related to OSF account service (see SM Methods), this OSF page is no longer accessible. For transparency and best practices of open science, based on the original protocol documentations, a preregistration statement has been reconstructed to clarify a prior hypotheses, sample size determinations, and analysis plans for this study (Table S1).

Participants

Due to the lack of diagnostic criteria for clinical procrastinators, we recruited a large-scale sample (n = 1,682) to obtain a stable benchmark distribution. Thus, the procrastinators were captured once their procrastination scores were higher than 66 on General Procrastination Scale (GPS) (see Fig. 2A-B). Following this criterion, a total of 186 participants were included initially, which was in accordance with empirical evidence (i.e., 10 - 15% prevalence of procrastination) (Harriott & Ferrari, 1996). Subsequently, the semi-structured interview was performed to screen those suffering from problematic procrastination and volunteering for this study, thereby enrolling 53 participants (see SM Methods). Seven participants were eventually excluded from the analyses because they voluntarily dropped out before experimental completion. All the included participants were screened for depression and anxiety symptoms (see Tab. 1).

Figure 2

Table 1

A full randomized block design was used to assign participants to both groups (active neuromodulation group, NM; sham-control group, SC) (see Fig. 2C). As the pilot study probing into the effect of single-session tDCS stimulation to change procrastination willingness indicated (t = 2.38, p = .02, 95% CI [0.14, 1.49]; Xu et al., 2023), statistical power was predetermined by G*Power at a relatively medium effect size (1-β err prob = 0.80, f = 0.25), yielding the total sample size at 18 to reach acceptable power (see SM Methods and Figure. S1). All the participants reported no history for HD-tDCS or neuromodulation. No significant differences were found between groups for any demographic characteristics (see Tab. 1). This study and protocol have been fully approved by the Institutional Review Board (IRB) of the School of Psychology, Southwest University (China, IRB200301108).

Measurement

The General Procrastination Scale (GPS) developed by Lay (1986) was used to quantify one’s chronic procrastination symptom here (Lay, 1986). This scale was widely adopted in many cross-cultural contexts, and has been reported to have good psychometric properties (Klein et al., 2019). There were two additional items that we added for lie detection, including description of “the sky is red” and “I have never taken shower”. If either one was selected as “agree” by participant, his/her response would be discarded. Internal reliability of the GPS in the current study was found acceptable (Cronbach’s α = 0.890). No significant difference was found between groups for GPS scores (t = -1.08, 95% CI: -4.296 1.283, p = .283; Jeffreys-Zellner-Siow Bayesian factor, BF, BF₁₀ = 0.455, error = 0.020 %).

To quantify one’s daily emotions, the Positive and Negative Affect Schedule (PANAS) developed by Watson et al. (1988) was adopted. This scale consists of two subscales (i.e., positive affect, PA and negative affect, NA), each containing 10 terms, where higher scores indicate stronger affective states (Terracciano et al., 2003). To control for potential confounding effects of daily emotions, both subscale scores were included as covariates of no interest in the statistical model. Additionally, this tool demonstrated good internal consistency for both sub-scales (α = 0.801 for PA, α = 0.793 for NA).

Experimental design and procedure

Nested cross-sectional longitudinal design

This study used a nested cross-sectional longitudinal design to investigate whether the multiple-session anodal HD-tDCS targeting the left DLPFC could reduce actual procrastination behavior and to probe how this effect manifests. To assess procrastination in daily life, we implemented a 15-day protocol alternating between Neuromodulation Days (Days 2, 4, 6, 8, 10, 12, 14) and Task Days (Days 1, 3, 5, 7, 9, 11, 13, 15). On the Neuromodulation days, the 20-min anodal HD-tDCS neuromodulation targeting the left DLPFC was performed for HD-tDCS active group at intervals of 2 days, while the sham-control group received sham HD-tDCS training. This HD-tDCS training was repeated for a total of seven sessions, and lasted 15 days (see Fig. 1). Crucially, to capture procrastination in ecologically valid contexts, prior to receiving either active or sham HD-tDCS (administered between 09:00-18:00), participants were instructed to specify a real-life task they were personally obligated to complete the following day, with a self-defined deadline strictly constrained to 18:00-24:00 to ensure ≥24 hours between stimulation offset and task deadline, thereby isolating offline after-effects. This task should meet the following three criteria: (a) it should be already assigned in the real-world settings; (b) deadline should be constrained to 18:00-24:00 (see above); (c) it should be more likely to induce procrastinate. By doing so, more than 300 real-life tasks were collected, spanning academic (e.g., “submit a statistics homework assignment”), occupational (e.g., “draft and email a project proposal”), administrative (e.g., “complete online application for Class C driver’s license”), self-improvement (e.g., “practice guitar for ≥ 30 minutes”), domestic (e.g., “do laundry”), and health-related (e.g., “running 2,000m for exercise”). Full task list has been tabulated in the Appendix 1. As primary outcomes, all the participants were required to report task-execution willingness (TEW) (Zhang & Feng, 2020; Zhang, Liu, et al., 2019), for a real-life task 24 hours post-neuromodulation. Thus, procrastination willingness was quantified as 100-TEW score (see underneath for details). Furthermore, we asked participants to report the actual task completion rate (CR) of the task at the deadline (e.g. participant A finished 90% homework at deadline and reported this situation to us at deadline). In this vein, the actual procrastination rate (PR) was quantified as 1-CR.

On the Task day, we developed a mobile app to implement experience sampling method (ESM) for tracking one’s real-time evaluation of task aversiveness and task outcome value (see Fig. 1). The task aversiveness describes how disagreeable one perceives performing a given real-life task to be, whereas outcome value refers to the subjective benefits of the task outcome brought about by completing the task before the deadline (Zhang & Feng, 2020). As theoretically conceptualized by the temporal decision model (TDM) of procrastination, the perceived task aversiveness is hyperbolically discounted when approaching deadline, showing sharply discounting when faring away from deadline but slowly discounting once nearing deadline (Zhang & Feng, 2020). Thus, considering this nonlinear dynamics inherent in this hyperbolic discounting, the five recording moments of ESM were selected per task a priori by using a log-spaced temporal sampling scheme (Myerson et al., 2001), with increasing sampling density toward the deadline, such as moments of 10:00 (earliest), 16:00, 18:00, 19:30, 20:00 (deadline). The five sampling points could meet statistical prerequisite in the hyperbolic model fitting, requiring ≥ 4 points (Green & Myerson, 2004). To do so, recording moments of tasks were individually tailored for each task per participant in this ESM procedure. To obviate the confounds of daily emotions in task aversiveness evaluation, we used the averaged scores of PANAS at 10:00 (noon) and 16:00 (afternoon) as anchoring points to quantify one’s daily emotions by using this ESM app. Before each session of HD-tDCS training, each participant was required to report a real-life task whose deadline is tomorrow. To obtain the long-term effect of HD-tDCS (i.e., the interval between HD-tDCS and task completion is at least 24 hours), the task deadline that participants reported was required to be between 18:00 - 24:00. Once a sampling time reached, this app would send a digital message to require participants to fill online form for data collection.

Quantification of covariates of interests

Outcome variables of this study were twofold: one is task-execution willingness and another is procrastination rate (PR). Task-execution willingness is used to evaluate one’s subjective inclination to avoid procrastination (Zhang & Feng, 2020). In this vein, we used a 100-point scale to require participants to report their task-execution willingness (0 for “I will definitely procrastinate this task” and 100 for “I will take action to complete this task immediately”). This metric was recorded 24 hours after neuromodulation to examine its long-term effects. PR is used to quantify the extent to which one task has been procrastinated, and was calculated as 1 - CR (task completion rate). Critically, at the precise deadline, the app prompted participants to (a) indicate task completion status (yes/no), and if incomplete, (b) report the percentage completed (1-99%), defined as the Task CR, while simultaneously uploading objective evidence (e.g., screenshots of submitted files, photos of physical outputs, system-generated logs, or app-exported records). If the task was actually completed before the deadline, the CR would be 100% and the PR would be calculated as 0% (1-CR). PR was recorded at the actual task deadline for each participant. We were also interested in re-investigating their actual procrastination by using PR 6 months after the last neuromodulation to test the long-term retention of this neuromodulation effect.

From what has been mentioned above, task aversiveness and outcome value were considered key factors to explain the effect of neuromodulation on reducing procrastination in the current study. To quantify one’s task aversiveness, participants were required to rate their feelings towards the task by using a 100-point visual analog scale (i.e., How do you feel in the current moment when you need to complete this task before deadline, with 0 for “extremely unpleasant”, 50 for “totally neutral” and 100 for “extremely pleasant”). Likewise, participants are also required to rate their outcome value by using a 100-point visual analog scale (i.e., How much do you desire to obtain incentive outcome of this task, with 0 for “extremely weak”, 50 for “medium” and 100 for “extremely strong”). As articulated temporal decision theoretical model above, the task aversiveness evoked by executing a task was temporally dynamic in a hyperbolic discounting pattern, with sharply discounting in faring away from deadline but slowly discounting in nearing deadline (Zhang & Feng, 2020). To quantitatively characterize the task aversiveness with consideration for its dynamics, the model-free area under the curve (AUC) was calculated. Specifically, based on the log-spaced temporal sampling rule, task aversiveness was measured by 100-point visual analog scale at the five sampling moments. Then, the task aversiveness discounting (A) was calculated as 1- (A(t) / A(earliest)), where t(earliest) was the earliest sampling point, serving as the reference for immediate execution. Subsequently, using the GraphPad Prism software (v9, 525), the AUC was computed as the trapezoidal integration between task aversiveness discounting and time across five data points, basing on the Myerson algorithm (Myerson et al., 2001). By doing so, a higher AUC reflects stronger temporal discounting of task aversiveness along with nearing deadline, which means that participants experience a faster decline in subjective aversiveness as execution is delayed, yielding lower effective aversiveness and reduced avoidance behavior. As for the task outcome value, it was theoretically posited as a relatively stable evaluation of the task (Zhang & Feng, 2020). Therefore, it was quantified by the self-reported 100-point visual analog scale after neuromodulation at least 12 hours later, to ensure no online effect.

HD-tDCS protocol

The HD-tDCS suit (stimulater and 4 × 1 multichannel stimulation adapter, MSA) that this study used was produced by the Soterix Medical Inc., and has been widely verified safe, effective and reliable for public (Villamar et al., 2013). Based on advanced properties of 4 × 1 MSA, the targeted areas for current flow can be constrained within 2.5 cm² (Villamar et al., 2013).

To position electrodes into targeted areas (left DLPFC), the 10-20 international system for EEG was initially used to mark potential nodes, and determined Cz as reference point. There is compelling evidence to claim that the left F3 could be used as target node for modulating the left DLPFC (Seibt et al., 2015; Tsukuda et al., 2025). In this vein, the central anodal electrode was determined onto F3, and four return electrodes surrounded central electrode at outside of 7.5 cm, including F5, AF3, FC3 and F1 (see Fig. 2F). Ramp-up and ramp-down durations were set to 30 seconds. To further locate the targeted areas, the high-definition neuronavigation system (ANT Neuro Inc., Welbergweg, Germany) was performed. Results indicated the accurate position that we pre-determined by showing a high overlap probability over the left DLPFC (MNI Coordinate: -51 40 18, 94.33 % overlapping probability) (see SM Method and Fig. 2F). In addition, the coordinates of this targeted area were retrieved from the Brede Database (http://neuro.imm.dtu.dk/services/), and showed highly pertinent functions related to DLPFC.

Before stimulation, participants were informed to clean the scalp to reduce resistance. Then, cotton swabs were used to separate hair until the scalp surface become visible. Subsequently, the electrically conductive gel (about 1.5 ml) was introduced into the plastic casing facilitating constraint of current flow. Next, the Ag/AgCI sintered ring electrodes were placed onto plastic casing and covered with a cap to lock them in the right positions. To reduce discomfort, the electrode cables were taped elsewhere. Further, the above processes would be re-adjusted if any electrode resistances were found larger than 1.5 units (Villamar et al., 2013). Once all the processes had been completed, the stimulator would be launched.

Participants in the HD-tDCS training group underwent constant electric current of 2.0 mA targeting the left DLPFC for 20 minutes. Results from the simulation of electric density showed a peak current of ~0.5mA/cm² at the central electrode and of ~0.125 mA/cm² at the four return electrodes, thereby indicating the safety and effectiveness. As for the sham-controlled group, the stimulator would deliver current flow with 2.0 mA during the first and last 30 seconds to elicit sense of electric stimulation for blinding of them. To obtain the pure offline effect, these measures for task-execution willingness, task aversiveness, and outcome value were conducted after stimulation at least 12 hours (Bikson et al., 2016).

Statistics

All the statistics were implemented by R (https://www.rstudio.com/) and R-dependent packages.

To clarify whether multiple-session HD-tDCS neuromodulation can reduce procrastination, the generalized mixed-effects linear model (GLMM) was constructed with full factorial design for subjective procrastination willingness (i.e., self-reported visual analog scores) and actual procrastination behavior (i.e., real-world task-completion rate before deadline). Here, sex, age and socioeconomic status (SES) were modeled as covariates of no interest. As the National Bureau of Statistics (China) issued (https://www.stats.gov.cn/sj/tjbz/gjtjbz/), on the basis of per capita annual household income, the SES was divided into seven hierarchical tiers from 1 (poor) to 7 (rich). To obviate subjective rating bias stemming from individual daily mood, we separately measured participants’ daily emotional fluctuation at 10:00 and 16:00 using a self-rating visual analog item (i.e., “How do feel for your mood today?”, 0 for “completely uncomfortable” and 100 for “definitely happy”). By doing so, the averaged score of those self-rating emotions at the two time points was modeled into the GLMM as covariate of no interest, yielding the final expression of “outcome ~ Group*Treatment_Day + Age + Gender + SES + Emotions + (1 + Treatment_Day | SubjectID)” in the statistical model”. This analysis was implemented using the “lme4” and “lmerTest” packages. Employing “emmeans” package, simple effects were also tested at baseline and post-last-intervention using Tukey-adjusted pairwise comparisons of estimated marginal means from the full GLMM, controlling for covariates and random-effects structure. To validate statistical robustness, instead of continuous outcomes for parametric tests, we also conducted a between-group comparison for the number of tasks that procrastination emerges by using the nonparametric x² test with φ correction or Fisher exact test. Regarding the 6-month follow-up investigation, this GLMM was also built to examine the long-term retention of neuromodulation on reducing actual procrastination.

To ascertain the neurocognitive mechanism of tDCS in reducing procrastination, the Quasi-Bayesian causal mediation analysis was used to model the association between the effects of tDCS, task aversiveness/outcome and decreased procrastination. To build upon this model, the tDCS treatments were inputted as independent variables, and the task aversiveness/outcomes were modeled as causal mediating variables by using the “Mediation” package (https://cran.r-project.org/web/packages/mediation/) (Imai et al., 2010). We estimated these pathway effects (i.e., averaged causal mediation effects, δ; averaged direct effects, ζ; total effects, ρ) by using Markov Chain Monte Carlo (MCMC) sampling. To improve the statistical reliability, the sequential ignorability assumption was tested by using sensitivity analysis. Details for the statistical principals and basis could be found elsewhere (Imai et al., 2010).

Results

Blinding

In both groups, almost all participants reported perceiving acceptable pain stemming from current stimulation, and believed they were receiving treatment, with 91.30% (21/23) for active neuromodulation group (NM) and with 86.95% (20/23) for sham control group (SC) (x² = 0.224, p = .636). All the participants were engaged in the identical experimental procedures excepting stimulation’s type (active vs sham). In addition, statistical models excluded Session 1 and Session 4 because participants reported additional unexpected events that uncontrollably disrupt task execution in both groups (see SI Result and Tab. S2).

Multiple-sessions HD-tDCS (ms-tDCS) can alleviate procrastination

To identify whether ms-tDCS targeting the left DLPFC can alleviate subjective procrastination willingness and actual procrastination behavior, a generalized linear mixed-effects model (GLMM) with Satterwhite algorithm was built, with task-execution willingness and actual procrastination rates (PR) as primary outcomes, respectively. For procrastination willingness, results showed a statistically significant interaction effect between multi-session neuromodulations and groups (β = -7.8, SE = 1.8, DF = 45.6, p < .001; Fig. 3A). In the post-hoc simple effect analysis, it demonstrated a significantly increased task-execution willingness (i.e., decreased procrastination willingness) after neuromodulation in the active neuromodulation group (NM-before: 35.65 ± 30.20, NM-after: 80.43 ± 19.92, t.ratio = 5.4, p < .0001, Tukey correction), but no such effects were identified in the sham control group (SC-before: 37.57 ± 26.46, SC-after: 47.35 ± 30.49, t.ratio = 0.3, p = .77, Tukey correction) (Fig. 3B-C). A linear uptrend for task-execution willingness was further observed across multiple sessions in the active NM group, indicating gradually increasing neuromodulation effects (Fig. 3D; p < .01, Mann-Kendall test). For actual procrastination behavior, changes to actual procrastination rates across all the sessions have been detailed in the Fig. 3E. Similarly, a statistically significant interaction effect was identified here (β = -7.4, SE = 2.4, DF = 46.6, p = .004), and the simple effect analysis further revealed decreased actual procrastination rates after ms-tDCS in the active neuromodulation group (NM-before: 43.26 ± 39.09, NM-after: 0.00 ± 0.00, t.ratio = 5.1, p < .0001, Tukey correction), but no such prominent changes found in the sham control group (SC-before: 46.47 ± 40.75, SC-after: 33.34 ± 37.82, t.ratio = 0.7, p = .48, Tukey correction) (Fig. 3F-G). Also, a significant downtrend for procrastination rates across all the sessions was identified in the active NM group (Fig. 3H; p < .01, Mann-Kendall test).

Figure 3

Furthermore, the nonparametric x² test of R × C contingency table was conducted for the count of procrastinated tasks, by treating this outcome (i.e., whether a participant actually procrastinates the task in real-world settings) as an ordinal variable. Results showed a significant reduction in group-averaged procrastination frequency in the active neuromodulation group after last-session HD-tDCS, but not in the sham control group (NM-before: 69.56% (16/23 participants), NM-after: 0.00% (0/23 participants); SC-before: 69.56% (16/23 participants), SC-after: 56.52% (13/23 participants), x²= 10.08, p < .001), partly indicating a high statistical robustness. To systematically test the statistical robustness, we reconstructed GLMMs by iteratively removing data from the last two sessions, which showed extraordinarily high effectiveness from neuromodulation. Results showed the significant group*neuromodulation_sessions interaction effects across all those nested models (removing session #6, #7 or both, all p < .05; see SI Results and Tab. S3-4). In brief, these findings provided empirical evidence to support that ms-tDCS neuromodulation targeting the left DLPFC can be an effective way to reduce both procrastination willingness and actual procrastination.

Ms-tDCS changes task aversiveness and task-outcome value

Both task aversiveness and task outcome value serve as key pathways determining whether one would procrastinate. To this end, we further utilized a generalized linear mixed-effects model to examine the effects of ms-tDCS on changes in task aversiveness and task outcome value. Task aversiveness changes across all the sessions are shown in the Fig. 4A and 4C. We demonstrated a statistically significant decrease in task aversiveness and an increase in task outcome value via ms-tDCS in the neuromodulation group (Task aversiveness: interaction effect, β = -0.12, SE = 0.04, DF = 46.7, p = .002; simple effect, NM-before_(AUC): 1.13 ± 0.53, NM-after_(AUC): 1.95 ± 0.85, t.ratio = 4.5, p < .001, Tukey correction; Outcome value: β = -6.8, SE = 1.74, DF = 46.2, p< .001; simple effect, NM-before: 35.86 ± 27.82, NM-after: 73.08 ± 23.33, t.ratio = 5.0, p < .001, Tukey correction; see Fig. 4B), but not in the sham control group (Task aversiveness: SC-before _(AUC): 1.07 ± 0.51, SC-after _(AUC): 1.28 ± 0.46, t.ratio = 1.3, p = .20, Tukey correction; Outcome value: SC-before: 34.00 ± 25.17, SC-after: 40.13 ± 28.94, t.ratio = 0.8, p = .41, Tukey correction; see Fig. 4D). In the neuromodulation (NM) group, task aversiveness steadily decreased with the cumulative number of stimulation sessions, while perceived task outcome value increased significantly (see Fig. 4E-F, p < .05, Mann-Kendall test). Thus, it provides causal evidence clarifying that neuromodulation to left DLPFC reduces task aversiveness and enhances task-outcome value meanwhile.

Figure 4

Increased task outcome value but not decreased task aversiveness predicts reduced procrastination

Given the dual neurocognitive pathways identified above—reduced task aversiveness and increased task-outcome value—we proposed that these changes, conceptually driven by enhanced self-control via ms-tDCS over left DLPFC, account for how neuromodulation reduces procrastination. To this end, we utilized a generalized linear model to regress decreased task aversiveness and increased task outcome value to changes in task-execution willingness. In this model, increased task outcome value (Δ _{Outcome value}) significantly predicted increased task-execution willingness (Δ _{task-execution willingness}) (x² = 15.95, p <.01, R² = .40; Δ _{Outcome value}:β= 0.61, S.E. = 0.12, p< .001, 95% CI: 0.37 - 0.86), whereas no significant effect was observed for predicting task-execution willingness through decreased task aversiveness (Δ _{task aversiveness}:β= 0.10, S.E. = 0.12, p= .41, 95% CI: -0.14 - 0.34). Likewise, for actual procrastination behavior in real-world settings, increased outcome value (Δ _{Outcome value}) was identified to be significantly predictive, whereas decreased task aversiveness showed null effects (see Tab. 2). Collectively, these findings provide causal evidence supporting notion that the outcome-value neurocognitive pathway accounts well for procrastination reduction, rather than a dual circuits incorporating task-aversiveness.

Table 2

Increased task outcome value plays causal role to explain why ms-tDCS reduces procrastination

To clarify the causal neurocognitive mechanism of procrastination, the Quasi-Bayesian causal mediation analysis was undertaken by using White’s heteroskedasticity-consistent estimator, with increased task outcome value as a mediated variable. Results demonstrated the significant causal mediated role of increased task outcome value in increasing the task-execution willingness (δ = 21.73, p < .01; ζ = 11.25, p = .07, ρ = 32.99, p < .01, simulation = 1,000; see Fig. 5A) and real-world procrastination (δ = 30.75, p < .01; ζ = 3.05, p = .52, ρ = 33.81, p < .01, simulation = 1,000; see Fig. 5B), as caused by ms-tDCS neuromodulation. To ensure the robustness and specificity of these findings, the sensitivity analysis was implemented by changing sampling parameters and outcome variables. By doing so, those findings were validated highly robust, as shown by replicated observations across bootstrapping sampling algorithms (see SI Results and Tab. S5-6). Moreover, the results of the control analysis further validated the specificity of these findings by showing a null causal mediated effect of this model to predict one’s task aversiveness (see SI Results and Tab. S7). In summary, these findings demonstrated a mechanistic pathway underlying procrastination: the self-control that was conceptualized to be governed by left DLPFC mitigate procrastination by plausibly increasing task-outcome value.

Figure 5

Long-term effects of ms-tDCS

We have also attempted to conduct a follow-up investigation to test the long-term retention of ms-tDCS in reducing actual procrastination. Almost all the participants had undergone follow-up except one in the neuromodulation group after last neuromodulation for 6 months (N_NM = 22, N_SC = 23). Thus, the GLMM was constructed, with the PR before first neuromodulation vs. PR after last neuromodulation for 6 months as covariates of interest. Results showed the statistically significant group*time interaction effects (β = 16.5, SE = 9.9, p = .049). Simple-effect model demonstrated a decrease in actual procrastination rates in the active neuromodulation group after last stimulation for 6 months compared to baseline (β = -22.05, SE = 10.0, p = .038, Tukey correction; NM-before: 40.68 ± 37.96, NM-after_6-months: 18.63 ± 29.80), and revealed null effects in the SC group (β= 1.26, SE = 9.78, p = .99, Tukey correction; SC-before: 46.47 ± 40.75, SC-after_6-months: 47.73 ± 39.18) (see Fig. 6).. Furthermore, using a nonparametric x² test to compare differences in the number of procrastinated tasks, we still found a statistically significant reduction in procrastination frequency in NM group after neuromodulation for 6 months compared to baseline (x² = 3.30, p = .035, NM-before: 68.19% (15/22), NM-after_6-months: 40.91% (9/22)), while no significant changes were observed in the SC group (x² = 0.11, p = .74, SC-before: 69.56% (16/23), SC-after_6-months: 73.91% (17/23)). Therefore, beyond short-term effects, the benefits of ms-tDCS neuromodulation to reduce procrastination pose the long-term retention.

Figure 6

Discussion

In the current study, by performing anodal ms-tDCS neuromodulation on the left DLPFC, both procrastination willingness and actual procrastination behavior were significantly decreased in real life. Additionally, a 6-month follow-up investigation revealed the long-term retention of such effects. Furthermore, this neuromodulation was found to decrease task aversiveness and increase outcome values; notably, only increased task-outcome value could predict decreased procrastination. On balance, our findings clarified the neurocognitive mechanism of procrastination by showing that self-control could increase task-outcome value so as to reduce procrastination. In addition, this study provided an effective way to reduce actual procrastination by using ms-tDCS neuromodulation.

One major contribution this study has made is to disentangle the neurocognitive mechanism of procrastination by demonstrating that self-control could increase task-outcome value so as to reduce procrastination. Neurobiological substrates of procrastination have been investigated in recent years, and have demonstrated the crucial roles of the left DLPFC in predicting procrastination (Chen & Feng, 2022; Chen et al., 2021; Chen et al., 2022; Hu et al., 2018; Liu & Feng, 2017; Zhang et al., 2017; Zhang et al., 2016). Not only the brain functional anomalies of the left DLPFC but also the neuroanatomical disruptions of self-control brain network constituted by the left DLPFC were found to be linked with more procrastination behaviors (Chen & Feng, 2022; Zhang et al., 2016). Notwithstanding this, it still remains unclear to claim their causal brain-behavior relationship - that is - no known evidence existed to clarity whether changes of the left DLPFC lead to procrastination or vice versa. The current study demonstrated the causal role of the left DLPFC in procrastination by showing that the neuromodulation of the left DLPFC indeed manipulated procrastination, and thus provided straightforward and powerful evidence to fill this gap.

It has long been acknowledged that the left DLPFC is associated with many aspects of self-control communities, such as patience to wait long-term gratification for a delay, inhibition of impulsiveness, and control of game addiction (Cohen & Lieberman, 2010; Lin & Feng, 2024). Furthermore, the increased activation of the left DLPFC has been observed during the exertion of self-control which modulates value signals (Hare et al., 2009; Harris et al., 2013). Meanwhile, procrastination has been argued to be the consequence of self-control failures and self-regulation failure for a long time (Ariely & Wertenbroch, 2002; Rebetez et al., 2018; Rozental & Carlbring, 2014). Supporting this, both the brain morphological disruptions in the DLPFC and anomalies in the functional coupling of the DLPFC-based self-control network were interpreted as the phenotype of reduced self-control, making individuals more prone to procrastinate tasks, as reported in existing literature (Xu et al., 2021; Yang et al., 2021). Moreover, it was worth noting that the increased activation of the left DLPFC was found to be involved in outcome value evaluation through self-control regulation (Chen et al., 2018; Zha et al., 2019). There was more straightforward evidence to substantiate the role of manipulating the DLPFC in changing one’s subjective value evaluation (Huang et al., 2017; Minati et al., 2012). On the other hand, the theoretical explanations and empirical evidence have increasingly converged into one line for claiming that the increased task outcome value would prompt more motivation to drive one to take action immediately and thus reduce procrastination (Zhang, Liu, et al., 2019). In this vein, this study advanced our understanding of the neurocognitive mechanism of procrastination by showing that the cortical excitability of the DLPFC produced by active neuromodulation could boost self-control to increase task outcome value so as to reduce procrastination behavior.

In addition, another contribution of the current study is to provide an effective way to reduce both procrastination willingness and actual procrastination in real-life tasks. As mentioned above, despite the fact that multifarious behavioral interventions and evidence have been massively studied for overcoming procrastination, they have shared a common aim - that is - reducing the intention-action gap (Miao et al., 2024; van Hooft et al., 2005). Eerde and Klingsieck (2018) put forward an insightful standpoint established by meta-analytic evidence: procrastination is characterized as an intention-action gap rather than an intention to postpone (van Eerde & Klingsieck, 2018). This notion has been partly supported by showing that cognitive behavior therapy (CBT) for goal-directed behaviors may outperform other interventions focusing on time management (Rozental et al., 2018). Moreover, the trans-theoretical model of procrastination has shown that the behavioral intervention may be effective in changing one’s motivation to overcome procrastination but not in actual behaviors (Grunschel & Schopenhauer, 2015). Thus, the ms-tDCS neuromodulation the current study performed has a remarkable advantage in reducing procrastination - that is, the intention-action gap was attenuated so as to overcome procrastination behavior effectively. Furthermore, both 2-day-interval long-term effects and the 6-month long-term retention of the effects of ms-tDCS on reducing actual procrastination have been revealed as well. Thus far, the trends in adopting tDCS neuromodulation techniques in many aspects of behavioral therapies have emerged, but concern for a long-lasting effect of single session stimulation has continued (Brunoni et al., 2013; Brunoni et al., 2012). To tackle this concern well, instead of single-session tDCS, the current study adopted multiple-session stimulation to implement neuromodulation on the left DLPFC, which facilitates long-term effects (Au et al., 2017; Tedesco Triccas et al., 2016). Existing neurobiological theories and empirical evidence have demonstrated that multiple-session tDCS stimulation could boost cumulative effects of consolidation for activity-dependent LTP (long-term potentiation), which is crucial to neurobehavioral learning, and thus produce robust long-term after-effects (Agboada et al., 2020; Au et al., 2017). Intriguingly, the activity-dependent LTP process produced by multiple-session consolidation was found to contribute to long-term cortical plasticity, especially in the DLPFC (Jannati et al., 2023; Siebner & Rothwell, 2003). Thus, it led us to presume that the 6-month long-term retention of the ms-tDCS effect on reducing procrastination might be attributed to long-term neuroplastic changes in the DLPFC. On balance, this study provided an effective way to help procrastinators overcome actual procrastination in real-life.

Limitations

While the use of a multi-session design and the long-term assessment can be considered a strength of the present study, it also has several limitations. Even though various tDCS effects have been demonstrated so far, they also tend to be difficult to replicate and sensitive to not yet fully understood context conditions and interindividual differences, which also applies to transcranial magnetic stimulation (TMS) (Valle et al., 2009). To overcome this shortcoming, it will be necessary to establish an individually tailored tDCS protocol to improve the sensitivity of corresponding interventions (Chew et al., 2015). Thus, future research could further improve the effects of tDCS on reducing procrastination by adopting more individualized tDCS protocols. Another limitation is the lack of real-time functional neuroimaging measures to better monitor the impact of our intervention. In the absence of such measures, we had to rely on behavioral indicators to assess the success of the tDCS training. In addition to technical limitations, given the apparently limited size of the sample (total N = 46), it warrants caution in generalizing these findings elsewhere, and necessitates further validations in a large-scale cohort. Also, considering the lack of medical screening for psychiatric conditions (e.g., ADHD or depression) in this sample, it remains unclear whether these training effects are domain-specific for procrastination. Moreover, this study did not collect data for assessing participants’ self-control at either baseline or post-neuromodulation, thereby limiting our ability to determine whether the effects on procrastination were uniquely attributable to neuromodulation-induced changes in self-control. In addition, despite instructing to report valid real-life tasks with high probabilities to procrastinate, we had not yet measured the task difficulty and consistency across sessions for each participant. Consequently, interpreting the effects of neuromodulation to mitigate procrastination as “unique contributions” should warrant cautions. Finally, given the absence of cathodal HD-tDCS stimulation as a contrast condition in causal inference, it warrants caution that the increased DLPFC excitability may not be the exclusively neural mechanism for procrastination.

Conclusions

In conclusion, this study potentially provides an effective way to reduce both procrastination willingness and actual procrastination behavior by using neuromodulation on the left DLPFC. Furthermore, such effects have been observed for 2-day-interval long-term after-effects, and were also found for 6-month long-term retention in part. More importantly, this study identified that the ms-tDCS neuromodulation could decrease task aversiveness and increase task outcome value, and further demonstrated that the increased task outcome value could predict decreased procrastination, a relationship conceptually driven by enhancing self-control. In this vein, the current study enriches our understanding of neurocognitive mechanism of procrastination by showing the prominent role of increased task outcome value in reducing procrastination. Also, it may provide an effective method for intervening in human procrastination.

Supplementary Information

SI Methods

Reconstructed preregistration statement

This study has been originally preregistered in the Open Science Framework (OSF, 10.17605/OSF.IO/Y3EDT) preregistration service on September, 2020. However, the creator’s OSF account was suspended and further disabled due to an automated system flag, rendering the original HTML preregistration page inaccessible, including to the authors. To ensure the accessibility and transparency in full adherence with open science best practices, we have reconstructed the preregistration content herein, This reconstructed statement is not post hoc, but reflects the exact hypotheses, sample size, and analysis plan as executed (Table. S1).

Randomised participants

To randomise participants into neuromodulation and sham-control group, the full randomised block design (FRBD) was conducted here. Furthermore, it could be beneficial from FRBD that the within-group variances can be minimized. In detail, the whole sample would be portioned into k blocks, which were manipulated for high homogeneity. For each block, the pairs of participants were then randomly designated to neuromodulation and sham-control group. Thus, adopting FRBD for randomised processes can reap huge fruits to keep within-group homogeneity for each group than do of that full randomised design (King & Eckersley, 2019). The randomised codes came from “www.random.org”, which produced random labels by using atmospheric.

Estimation for statistical power

To determine adequate statistical power beforehand, we capitalized on the G*Power software to estimate the minimum sample size obtaining the medium effect size (α err prob = 0.05, 1-β err prob = 0.80, f = 0.25) by building a repeated measures, within-between, and interaction effect ANOVA model (Faul et al., 2007). In this vein, the medium effect size can be reached once total sample size n = 18, noncentrality λ = 15.75, critical F = 2.1945, numerator Df = 6, and denominator Df = 96. In addition, owing to the repeated measures for each participant, the 2-dimensional plot, so-called Power Contours estimation, was used to visualize and calculate the acceptable sample size by corresponding experimental design (Baker et al., 2020). Thus, according to the design this study made, the Power Contours estimation was done up front and indicated the total sample size of this study can attain the adequate statistical power (see Fig. S1). More detail for how to estimate and plot this map can be found elsewhere (Baker et al., 2020).

How to determine one’s socioeconomic status (SES)

In line with Human Connectome Project (HCP) project proposed by National Institutes of Health (NIH), this study also acquired demographic information for each participant, including gender, age, nationality, educational level (years), family’s economic status (poor, modest, middle and affluent class), family structure (FS), and status of birth (SB). In terms of criteria endorsed by the Food and Agriculture Organization (FAO) of the United Nation (UN) and the financial statement published by National Bureau of Statistics (NBS) of China in 2019 (http://www.stats.gov.cn/tjsj/), the economic status of the family was rated as poor, modest, middle and affluent class according to family incomes, with < ¥ 45 thousands per year for poor class, ¥ 45-65 thousands per year for modest class, ¥ 65-105 thousands per year for middle class, and ≥ ¥ 105 for affluent class.

To determine homogeneity between the HD-tDCS group and sham-controlled group, non- / parametric and Jeffreys-Zellner-Siow Bayesian examinations were adopted to identify significant differences between demographic variable.

Semi-structural interview questionnaire

To ensure the high ecological validity, we developed a semi-structural interview questionnaire to test whether participants were eligible for the current study. There were major four profiles to investigate them, including evaluation of how they perceived pain for procrastination, whether their social functions were disrupted by procrastination, whether their attitudes are to change procrastination and whether they accepted our protocol. The outlines of this questionnaire has been provided as below:

1. Please details your identity and investigation proposal; 2. Do you perceive you have procrastination symptoms, and how severe it is do you think; 3. Are you ever assumed you have “procrastination disorder”; 4. Do you feel pain due to procrastination and how it impacts your daily life; 5. What types of procrastination are do you think in yourself; 6. Do you think you would suffer from procrastination all the life; 7. Do you want to get rid of procrastination; 8. Have you try to stop procrastination; 9. Do you know neuromodulation technique, such as tDCS or TMS; 10. Do you feel panic or worrisome for medical technique, especially in electric medical technique; 11. Do you feel panic or worrisome for electric current; 12. Do you willing to receive electric medical technique for getting rid of procrastination despite limited skin pains.

High-definition neuronavigation system

To ensure the accuracy of targeting location (left DLPFC), this neuronavigation system was used to estimate the location from skull. This system reported the MNI coordinate of what we predetermined for the first, and would re-adjust automatically twice to self-verify the estimation accuracy. Subsequently, the brain labels for these coordinates were reported by using AAL atlas and Broadman atlas, respectively. Results indicated that the F3 node overlapped the left DLPFC with 94.33% probability, which was cross-checked automatically.

Temporal difference-to-difference model

In the current study, in addition to the estimation for the model including all the repeated measures, we still aimed to do a random-sham pre-post tests for clarifying whether this HD-tDCS by using multiple sessions protocol was effective. In this vein, the difference coefficient (Δ) was computed as difference between post-test after last session and pre-test before first session, one-by-one for participants. In this vein, the pre-post within-participant differences upon outcome variables can be estimated as the HD-tDCS training effect.

SI Results

Unexpected event effects

In the Session 1, a significant unexpected life event effect occurred by this fact that almost all the participants in both group do not procrastinate (Neuromodulation group, NM, Procrastination rate (PR): 4.34 % (1/23), Sham control group, SC, PR: 8.69 % (2/23)). In the follow-up investigation, all the participants reported a strong expectation for this neuromodulation as they received such treatment for the first time. In the Session 4, the PR was observed to be increased dramatically in both groups (Neuromodulation group, NM, Procrastination rate (PR): 86.95 % (20/23), Sham control group, SC, PR: 86.95 % (20/23)). In the follow-up investigation, all the participants reported that taking action immediately to complete task was hampered significantly due to weekend effect (Ryan et al., 2010, Stone et al., 2012). In this vein, data for these sessions were excluded in the analysis. Here, the follow-up investigation required participants to report whether their performance for completing task was influenced by additional effects. If in this case, they should report what unexpected events occur here. The x² test was used to examine whether those reported unexpected events confounding task execution posed significantly differences between groups across all the sessions (see Table. S2).

Robustness check for interaction effects

To examine whether Group*Neuromodulation_Session interactions are statistically robust, we reanalyzed interaction effects by removing data from session #6, #7 and both, respectively in which those sessions showed extraordinary high effectiveness of neuromodulation (i.e., outliers). The findings derived from those nested models showed that all the interaction effects are all retained, indicating a high statistical robustness (Table. S3-4).

Results of linear probability panel model

Given the panel data pattern (7 longitudinal sessions × task-execution willingness), this study has drawn upon the linear probability panel model (PLM) fitting the task aversiveness and outcome value to task-execution willingness. Specifically, the Fisher’s Augmented Dickey-Fuller Test (ADF) tests were performed to examine whether these data are suitable for this model. Results indicated the stationary properties for all the variables (task aversiveness, DF = -5.78, p < .05; outcome value, DF = -5.83, p < .05; task-execution willingness, DF = -5.83, p < .01). Further, the findings derived from Breusch-Godfrey/Wooldridge test maintained the decision to reject the stability of pool model (x² = 22.75, p < .0001). Also, the Hausman test was done for determining what types of models would be accepted. Results indicated that the random effect model is unstability (x² = 12.92, p < .0015). Thus, the fixed effect model controlling both time and individual variants was adopted as final one.

Results of GLMM adjusted for baseline

To minimize false-positive risks, rather to take time and group as predictors meantime, we reanalyzed these data by remodeling post-neuromodulation procrastination as dependent and remodeling pre-neuromodulation procrastination, group and other covariates as predictors. Results showed the significantly predictive of pre-neuromodulation procrastination willingness (β = 24.48, SE = 6.24, p = .0018) and procrastination rate (PR, β = 30.66, SE = 8.48, p = .0015) to post-neuromodulation ones. Moreover, for those covariates of interests on cognitive mechanisms, these findings are validated as well, showing statistically significant predictions from pre-neuromodulation cognitive processes to post-neuromodulation ones (task aversiveness, β = 0.58, SE = 0.23, p= .018; outcome value, β= 22.86, SE = 5.11, p= .0005). This replicated effect has been observed in predicting post-neuromodulation PR by pre-neuromodulation one for 6 month (β = 34.79, SE = 11.1, p = .005). Taken together, using stringent statistical constrain to adjust baseline, we revealed the same findings compared to traditional GLMM.

Results of sensitivity analysis

To examine the robustness and specificity of this causal medication model, we conducted several sensitive analyses. Thus, the we attempted to re-do this mediation model by replacing the sampling method from bootstrap-based bias-corrected and accelerated (BCa) intervals to “bca” sampling at 5000 simulations. Results demonstrated the robustness of this model by showing same findings (see Table. S5-6).

Further, we inputted the age and gender as outcome variables into this model for testing whether this causal mediation model is specific to predict decreased procrastination. As hypothesized, no significant effects were found to predict task aversiveness by using this model, and thus supported the specificity of these findings (see Table. S7).

Figure S1

Table S1

Table S2

Table S3

Table S4

Table S5

Table S6

Table S7

Data availability

We report how we determined our sample size, all data exclusions (if any), all manipulations, and all measures in the study, and the study follows JARS (Appelbaum, et al., 2018). Data, analysis code, and research materials are available at Science Data Bank (ScienceDB, https://doi.org/10.57760/sciencedb.35140). Data were analyzed using R, version 4.4.1 (R Core Team, 2020) and the package ggplot, version 3.5.2, as well as MATLAB (2021, MathWork Inc.) and GraphPad Prisma.

Acknowledgements

We appreciate Zhibing Xiao (Beijing Normal University) for assistance with programming and coding; Chao Ran (Alibaba technical team) for assistance with developing experience sampling online platform; YanCheng Tang (Peking University), Dr. Peikai Li (Utrecht University) and Dr. Hu Chuan-Peng (Nanjing Normal University) for assistance with statistics.

Additional information

Funding

This study was supported by the National Natural Science Foundation of China (32300907; 32571253, 32271123), Key Projects for Technological Innovation and Application Development in Chongqing [CSTB2022TIAD-KPX0150], the National Key Research and Development Program of China [2022YFC2705201], and Innovation Research 2035 Pilot Plan of Southwest University [SWUPilotPlan006].

Funding

MOST | National Natural Science Foundation of China (NSFC) (32300907)

• Zhiyi Chen

MOST | National Key Research and Development Program of China (NKPs) (2022YFC2705201)

• Tingyong Feng