Prefrontal deficits in procrastinators: impaired impulse control or emotion-regulation difficulties

doi:10.21203/rs.3.rs-3222375/v1

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Prefrontal deficits in procrastinators: impaired impulse control or emotion-regulation difficulties

https://doi.org/10.21203/rs.3.rs-3222375/v1

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Prefrontal aberrations are robustly associated with high tendency to procrastinate, but cognitive and neural mechanisms underlying this link remain unknown. An impulse-control hypothesis predicts insufficient prefrontal regulation over reward-processing regions, leading failure to forgo immediate pleasure. While an emotion-regulation hypothesis predicts inadequate prefrontal regulation over emotion-processing systems, resulting in deficits to endure adversity. To investigate which hypothesis is more valid, the current study presented procrastinators with conflicts between pleasant distraction (photographs watching) and difficult-but-rewarding mission (dots counting) under fMRI scanning. The results favored the emotion-regulation hypothesis relative to the impulse-control hypothesis in three regards: (1) High procrastinators performed worse in the mission because of high task difficulty but not fun distraction. (2) Inadequate prefrontal regulation over emotional insular activities underlay failure in both persisting in the mission and resisting the distraction. (3) Trait procrastination correlated with regulatory prefrontal signals during persisting in the mission but not during resisting the distraction. These results indicate procrastinators suffer from emotion-regulation prefrontal deficits, and thus recommend emotional supports rather than criticism on them.

Biological sciences/Neuroscience

Biological sciences/Psychology

procrastination

prefrontal cortex

anterior insula

emotion-regulation

impulse to distraction

Prefrontal deficits would lead to high tendency to procrastinate [1], which further results in increased likelihood of unemployment, lower income, more distress and lower satisfaction across a wide range of domains [2]. Procrastination is form of self-control failure common in daily life, ranging from going to bed later than intended [3] to delaying treatment of existing health problems [4, 5]. As a survey indicated, more than forty percent of respondents procrastinated in important life-domains like health, career, education, romance and family despite experiencing adverse consequences [6]. Therefore, understanding roles of prefrontal cortex in procrastination could extend knowledge of prefrontal regulation to more general and ecological contexts.

High tendency to procrastinate has been found associated with prefrontal aberrations. A resting-state fMRI study reported high procrastinators showed increased spontaneous metabolism in ventromedial prefrontal cortex (vmPFC) but decreased metabolism in right dorsolateral prefrontal cortex (dlPFC) [7]. Another resting-state study found high trait procrastination associated with multiple functional couplings between prefrontal ROIs [8], such as increased lPFC-mPFC coupling and decreased vmPFC’s coupling with dorsal media prefrontal cortex (dmPFC). Prefrontal links with procrastination have also been observed using structural measures. High procrastinators exhibited increased gray matter volume in vmPFC but decreased volume in dlPFC [9]. Such a structural pattern has been replicated using cortex thickness with three large and independent samples [10]. Despite the robustness of this link, how does prefrontal cortex contribute to trait procrastination remains poorly understood.

Procrastinator’s prefrontal deficits might result from difficulties in either impulse control or emotion regulation. The impulse-control hypothesis underlines fun distraction as the cause to procrastination. That is, procrastination of important tasks like essay writing might simply result from impulse to entertainments at hand like socializing [11]. Supporting this, researchers reported that fun distractions indeed exaggerated procrastination [12, 13]. On the other hand, the emotion-regulation hypothesis emphasized deficits in coping with negative emotions. Unlike non-procrastinators who face the distress and focus on how to deal with the problems, procrastinators are instinctively eager to evade it [14]. As task delay reliefs negative emotions temporarily, procrastination becomes a short-sighted option to achieve procrastinators’ goal [15]. Supporting this account, individuals reporting difficulties in regulating negative affects procrastinate frequently [16], while individuals believing task delay changes no emotion never procrastinate [17].

Prefrontal regions participated in either impulse control or emotion regulation, which are two dispensable forms of self-control for success. Increased prefrontal signals during impulse control predicted better self-control outcomes such as low body mass index [18], and prefrontal activations during emotion regulation were correlated with greater appraisal success [19]. Impulse control and down-regulation of negative emotions shared common prefrontal regions, such as dlPFC and ventral lateral prefrontal cortex (vlPFC) (for meta-analysis see [20]). However, the two kinds of self-control rely on prefrontal regulation over different subcortical regions. During impulse control, prefrontal regulation over craving was mediated by decreased activity in reward-processing regions like ventral striatum [21]. While successful down-regulation of negative emotions was associated with hyperactive prefrontal regions but hypoactive subcortical regions like amygdala [22, 23]. For example, a group of alcohol users exhibited decreased reward-related activities in ventral striatum, vmPFC and orbital frontal cortex (OFC) when regulate their craving, but showed reduced amygdala signals when down-regulating negative emotions [24].

The impulse-control and emotion-regulation hypotheses predict prefrontal regulation over distinct subcortex during procrastinators’ self-control. If the impulse-control hypothesis is true, procrastinators would exhibit insufficient prefrontal control over reward-processing regions in face of fun distractions [25]. The reward-processing regions might involve orbital frontal cortex (OFC) and striatum as both regions are implicated in craving for various addictive substances and impulsive behavior [26, 27]. If the emotion-regulation hypothesis holds, inadequate prefrontal regulation over emotion-processing regions would present when procrastinators are avoiding a difficult task [28]. The emotion-processing regions might include amygdala and anterior insula, which both elicited converged activations across negative emotions including fear, anxiety, anger, sadness, and disgust [29, 30].

The present study developed a novel laboratory paradigm — mission/distraction task — to explore roles of prefrontal cortex in procrastination. In this task, participants can freely allocate limited time budget between a difficult but goal-relevant “mission” (counting dots) and a pleasant but goal-irrelevant “distraction” (watching photographs) (see Fig. 1). This paradigm requires regulation against temptation of the photographs and distress toward the dots counting with high difficulty. If the impulse-control hypothesis is true, high procrastinators would show inadequate prefrontal regulation over reward-processing brain regions. While if the emotion-regulation hypothesis is more valid, insufficient prefrontal regulation over emotion-processing brain regions would be observed. Of note, the two accounts may not necessarily underlie only one of the distraction and mission phases. High procrastinators have reported that they were playing video games to reduce stress rather than for fun [31]. Therefore, one of the hypothesized neural patterns might govern both phrases when the corresponding account is true.

Participants. Forty-four volunteers (30 females, M _age = 21.32, SD _age = 1.89) completed our mission/distraction task in a fMRI scanner and were paid with monetary rewards. All participants were right-handed, heterosexual, had normal or corrected-to-normal vision. None of them reported a history of psychiatric or neurological disorder. One participant was excluded from fMRI analysis, but not behavioral analysis, due to excessive head movement (> 2 mm or > 2°) during data acquisition. As an exploratory study, we adopted no statistic tests to predefine sample size but aimed at a final sample size within the standard range in the field of neuroscience. Data collection was approved by the Southwest University Institutional Review Board and written informed consent was provided by all participants.

Procedure. In the mission/distraction task, participants can freely allocate an around 45-minute time budget between effortful dots counting which brings extra money when correct (“mission”) and pleasant photographs watching which pays no wage (“distraction”). The dots counting consists of three difficulty levels which differ in their dots number. After being informed the difficulty, participants have an opportunity to appreciate photographs of good-looking celebrities before the mission starts. Several settings were made to frame the dots counting as an aversive but goal-directed task that shouldn’t be avoid: (1) The high difficulty level was designed so that every participant has low probability to be correct. (2) Participants were told “you can allocate the time budget at will but please complete as many dots counting as you can”; (3) We informed them that each time being correct in dots counting will add into their payment with a fixed amount of extra money regardless of difficulty level; (4) Participants were aware that being wrong at any one difficulty level, the dots counting task will be stuck in this level until correct. Under these four settings, to maximize one’s payment, participants need to spend less time in photographs and try their best in every dot-counting trial even when it is aversive (for manipulation check, see Supplementary Information).

Each trial indicated the difficulty level of the coming dots counting task by presenting its scatter diagram for 2 seconds and subsequently collected aversiveness rating toward it as a measure of negative emotions for manipulation check. Before the mission starts, participants have a maximum of 8 seconds for the photographs. These photographs were presented to our heterosexual participants, with female photographs presented to male participants and male photographs to female participants. Each photograph lasted for 2 seconds, and no one repeated. Participants can end the photograph phrase and start the mission by pressing a button after an unknown least photograph time (drawn from a 2 ~ 4 uniform distribution). Therefore, a higher frequency of button pressing before success indicates higher willingness to skip the photographs. Each dot-counting phase lasts for a maximum of 150 seconds, while participants can terminate it at any time by pressing a button and then input their answer with a virtual keypad. If participants were correct, another difficulty level will be the next; while if wrong, they will face the same difficulty but a new scatter diagram with altered dots number. In principle, only inputs exactly matching with the dots number were taken as “correct” (data analysis adopted this criteria). However, to avoid participants getting stuck at a certain difficulty too long, our task program would temporarily loosen its criteria as failed attempts for the same difficulty accumulated (for details, see Supplementary Information).

After the scanning, participants completed a pure procrastination scale (PPS) [32]. Besides, we also used a debriefing questionnaire to measure participants’ attitudes toward the photographs and dots counting (for details, see Supplement Information).

Materials. To roughly measure participants’ ability in dots counting before scanning, we asked them to complete nine trials of the dots counting task (3 trials for each difficulty level; 15 ± 4 dots for the low difficulty, 40 ± 4 dots for the medium and 90 ± 4 dots for the high). As individuals might differ in dot-counting ability, we set dots number for each participant so that they need similar time to be correct under the same difficulty level (see Figure S2). As a result, participants’ averaged dots number for the low, medium, and high difficulty ranges, respectively, from a minimum of [15, 40, 79] to a maximum of [39, 80, 130]. The specific dots number for a trial was within ± 4 dots from the mean corresponding to each difficulty level. To ensure attractiveness of the photographs, six additional heterosexual volunteers (3 males and 3 females) had rated photographs of their opposite gender and screened out those with low attractiveness, resulting in 332 female photographs and 314 male photographs.

Image Acquisition. Our imaging data was collected on a Siemens 3 T MRI system (Siemens Magnetom Trio TIM, Erlangen, Germany). Functional (T2*-weighted) images were acquired with an echoplanar BOLD-sensitive sequence with the following parameters: 64 × 64, 3 × 3-mm ² pixels, repetition time (TR) = 2.0 s, echo time (TE) = 30 ms, flip angle (FA) = 90°, 32 × 3-mm axial slices allowing whole brain coverage. At the start of the imaging session, a high-resolution structural volume was collected with the following sequence parameters: 1-mm ³ voxels, 250 × 250 × 176 slices, TR = 1.90 s, TE = 2.52 ms, FA = 9°.

Behavioral Analysis. At the trial level, we relied on mixed linear models to explore whether counting difficulty has an impact on individual’s behavioral patterns. Individual’s PPS score was involved as a moderator to test whether the impact of task difficulty differ between high and low procrastinators. In these mixed linear models, participants were involved as the random factor to control for their intraclass differences (i.e., random intercept models). For cases with heterogeneous variances, we altered the assumptions of the error term to allow unequal variances using the ‘lme’ function in R.

fMRI Analysis. Neuroimaging data was preprocessed and analyzed in SPM12 (http://www.fil.ion.ucl.ac.uk/spm/software/spm12/). Preprocessing included correction for time differences in slice acquisition, realignment, coregistration to the individual’s structural image, normalization to the Montreal Neurological Institute (MNI) space and spatial smoothing with a Gaussian kernel (8-mm, FWHM).

To identify neural signals related to photographs watching and dots counting, we implemented the first GLM (GLM1) with six conditions: Difficulty Cue, Aversion Rating, Photographs phrase (divided into skipped and not-skipped trials), Counting phrase, Counting’s end and Feedback (the last three conditions were divided into wrong and correct trials). Our primary focus is on the Photographs phrase and Counting’s end. The first two conditions were associated with a parametric modulator of dots number and rated aversiveness, respectively. The photographs phrase and counting phrase were modeled based on the duration of each phrase for each trial. The other four conditions were modeled based on the duration of zero for assuming instantaneous neural reactions to the event. GLM2 was identical to the first, except that photographs phrase and counting’s end were associated with a parametric modulator of the key frequency to skip and dots number, respectively. GLM3 was identical to GLM2 except the parametric modulator associated with counting’s end was replaced by counting error. A standard hemodynamic response function was used for the GLMs.

To reveal neural couplings in photographs phrase and counting’s end, we conducted four psychophysiological interaction (PPI) analyses [33]. The PPI1 and PPI2 investigated dlPFC couplings differences during photographs phrase and the counting’s end, respectively. The PPI1 used individual’s dlPFC sub-peak closest to the group peak from the skipped > not-skipped contrast and compared dlPFC’s couplings between skipped and not-skipped photograph trials. The PPI2 also adopted individual’s closest dlPFC sub-peaks from the wrong > correct contrast and compared dlPFC’s couplings between wrong and correct counting trials. The PPI3 and PP4 were identical to PPI1 and PPI2, respectively, except involving corresponding insula peaks as the seed.

For whole brain analysis, we used FDR-corrected q < 0.05 as the threshold. For the small volume correction, we used a FWE cluster-corrected threshold of P_FWE−SVM < 0.05 under an uncorrected p-value of 0.005. All ROIs were defined as spherical with a 6-mm radium.

Predefined ROIs. This study predefined two bilateral insular ROIs and two bilateral amygdala ROIs in search of brain regions processing negative emotions. The two insular ROIs came from an insular meta-analysis study [30], centering at the peaks of reported anterior-basal insula processing emotions (left insular ROI: x = -31, y = 24, z = -4; right insula ROI: x = 42, y = 15, z = -3; both with a 6-mm radium, MNI coordinates). The two amygdala ROIs came from a meta-analysis linking amygdala and emotions [29], and adopted the center of bilateral amygdala peaks (left amygdala ROI: x = -21, y = -5, z = -16; right amygdala ROI: x = 22, y = -4, z = -15; both with a 6-mm radium, MNI coordinates).

High procrastinators did not seek more fun distractions

After a cue informing difficulty of the next dots counting task, participants have a chance to enjoy photographs before the counting starts. In each trial, participants can press a button to skip the photographs after an unpredictable delay (2 ~ 4 seconds from a uniform distribution). Thus, higher frequency in pressing the button before a successful skip indicates higher willingness to resist the photographs. If impulse-control hypothesis is true, high procrastinators should show less willingness to resist the photographs and thus perform worse on dots counting. To examine this prediction, we tested an interaction between trait procrastination scores and the frequency in key pressing on counting error. However, a non-significant interaction provided no support (β = -0.01, t ₍₁₃₀₆₎ = །0.27, p > 0.25, see Fig. 2A). At the individual level, high procrastinators neither spent more time on photographs (r ₍₄₂₎ = 0.06, p > 0.25), nor reported higher proportion of favorable photographs (r ₍₄₂₎ = 0.27, p = 0.08; Spearman’s ρ ₍₄₂₎ = 0.19, p = 0.22). Of note, participants on average thought at least a proportion of photographs were favorable (mean favorable proportion = 33.43%, SD = 30.65%, significantly differing from zero: t ₍₄₃₎ = 7.23, p < 0.001), ruling out an alternative explanation that the photographs have no attractiveness. These results spoke against the impulse-control hypothesis.

High procrastinators made greater error under high difficulty

If emotion-regulation hypothesis is true, high procrastinators should have increased difficulty in coping with negative emotions, and thus make greater error as counting difficulty increased. By involving trait procrastination score as a moderator, this possibility was confirmed by a significant interaction between trait procrastination and counting difficulty (β = 0.08, t ₍₁₃₀₆₎ = 3.35, p < 0.001, see Fig. 2B). Essential same conclusion arose at the individual level: Higher PPS scores were significantly associated with lower accuracy in the dots counting under the high difficulty level (r ₍₄₂₎ = -0.26, p < 0.05, one-tailed, Spearman’s ρ ₍₄₂₎ = -0.30, p < 0.05, see Figure S1). Of note, this association was not significant under the medium (r ₍₄₂₎ = -0.16, p = 0.15, one-tailed) nor the low difficulty level (r ₍₄₂₎ = -0.02, p > 0.25, one-tailed). Debriefing questionnaires also revealed different attitudes toward the dots counting task between high and low procrastinators (see Table S2). Specifically, individuals with higher PPS tended to be more likely to think “the dots in great numbers were too annoying to count” (t ₍₄₂₎ = 1.81, p = 0.07) and less likely to respond on “I didn’t think anything, just counted dots with my whole attention” (t ₍₄₂₎ = -2.20, p = 0.03). These results confirmed the emotion-regulation hypothesis.

Resisting distraction: regulatory neural pattern during photographs watching

We planned a contrast between photographs been skipped and not been skipped because the former indicates more successful self-regulation than the latter. The skipped photographs were associated with increased signals in regions including left anterior insula and bilateral dlPFC (see Fig. 2C, FDR-q < 0.05, for details see Table S3). To test whether the dlPFC regions play a regulatory role, a parametric contrast was conducted to test their association with the key frequency to skip photographs. As expected, the signals within the bilateral activated dlPFC ROIs positively responded to increasing frequency to skip (both P_FWE−SVM < 0.05). Because the emotion-regulation hypothesis indicates that fun distraction might serve as a mood-repairing process, the activated anterior insula might respond to counting difficulty. To test this possibility, we examined whether signals within the activated insular ROI increased with the parametric regressor of dots number but found no support (P_FWE−SVM > 0.25).

To investigate the dlPFC’s couplings responsible for successful self-regulation, we conducted a PPI analysis with individual dlPFC sub-peaks closest to the group one as the seed. Compared to photographs not been skipped, the photographs been skipped induced increased dlPFC’s couplings with vmPFC, amPFC, dmPFC and bilateral hippocampal clusters extending to amygdala (FDR-q < 0.05, for details see Table S4). Of note, the photographs been skipped evoked decreased dlPFC’s coupling with bilateral anterior insula relative to the photographs not been skipped (see Fig. 2D). We again tested whether the anterior insula areas showing decreased couplings was related to negative emotions with the same parametric contrast. As the result revealed, there was two clusters within the left anterior insular ROI significantly responded to increasing dots number (P_FWE−SVM < 0.05) and one cluster within the right insular ROI responded at a marginally significant level (P_FWE−SVM = 0.054). These results supported the emotion-regulation prediction that participants could be repairing mood even during fun distractions.

Persisting in mission: regulatory neural pattern during dots counting

We planned a contrast at the counting’s end between wrong and correct dots counting trials because a wrong answer was more likely to result from self-regulatory failure against high distress. As expected, the wrong > correct contrast activated regions including bilateral anterior insula and dorsal lateral prefrontal cortex (dlPFC) (FDR-q < 0.01, see Fig. 3A; for details, see Table S5).

We tested the emotion-processing role of amygdala and anterior insula by checking whether their signals correspond to both increasing counting difficulty and greater error. To reveal brain regions processing negative emotions trial by trial, we separately involved dots number and counting error as a parametric regressor of neural signals at the end of dots counting. Among our pre-defined emotion-processing ROIs, only a left anterior insula ROI showed increased signals corresponding to both increasing dots number and counting error (both P_FWE−SVM < 0.05). We also tested the mediating role of signals from the activated anterior insula peaks between individuals’ mean dots number and median counting error. Among the bilateral activated insular peaks, signals from the right anterior insula exhibited a significant mediation effect (Bias Corrected 95% CI = [0.01, 0.17]; see Fig. 3B). These results confirmed our hypothesis of anterior insula as the emotion-processing region.

As the wrong trials were associated with higher difficulty, these dlPFC activations might respond to an increased need of self-regulation against negative emotion [34]. If this is true, a voxel-wise indirect effect analysis should reveal dlPFC as a region suppressing the insula’s impact on aggravating counting error. Using the toolbox of Mediation Effect Parametric Mapping [19], we searched suppressing regions within bilateral dlPFC masks, with a predictor of the insula signals which significantly mediated the effects of dots on error. These dlPFC masks were obtained from a meta-analysis in the neurosynth (https://neurosynth.org/) data set using a term of dlPFC and the association test. As expected, two dlPFC clusters, which overlapped with the activated dlPFC regions in the wrong > correct contrast, suppressed the effects of the insula on counting error (see Fig. 3C, P_FWE−SVM < 0.05). This result indicates dlPFC as a regulatory brain region against anterior insula processing negative emotions.

To reveal the dlPFC’s regulatory couplings at the end of dots counting, we conducted a PPI analysis using individual dlPFC sub-peaks closest to the group one from the wrong > correct contrast. The dlPFC was associated with increased couplings with ventral lateral PFC (vlPFC), middle frontal gyrus (MFG), ACC and MCC in the wrong trials than the correct trials (FDR-q < 0.05, for details, see Table S6). Of note, the dlPFC was associated with increased couplings with insula (see Fig. 3E), presenting an opposing pattern relative to successfully resisting photographs.

Together, these results revealed a dlPFC-insula regulatory process, with greater self-regulation failure featured by insufficient dlPFC inhibition against anterior insula activities.

Emotion-regulation couplings overlapped between the distraction and mission phrase

As an emotion-regulation process underlies both photographs watching and dots counting, regulatory neural couplings should be convergent between the two phrases. To test this convergence, we planned to reveal conjunct insular couplings between the two phrases because anterior insula responded to counting difficulty in both phrases. To this end, we conducted two separate PPIs using each phrase’s anterior insular peak as the seed. Combining the results in photographs watching and dots counting, we observed convergent insular couplings on PFC regions including dlPFC, vmPFC and dmPFC. For the photographs watching, failing to skip photographs was associated with increased insular couplings with dlPFC but decreased insular couplings with vmPFC and dmPFC (see Fig. 4A, FDR-q < 0.05; for detail, see Table S7). Likewise, such a coupling pattern presented for failure of persisting in dots counting as well (see Fig. 4B, FDR-q < 0.05; for detail, see Table S8). According to conjunction analysis, these were overlap for vmPFC and dmPFC of self-regulation between two phrases (see Fig. 4C). Despite no overlap for the dlPFC, the dlPFC coupling regions observed in the two phrases were also adjacent to each other.

Prefrontal regions related to trait procrastination

As procrastinators are associated with emotion-regulation deficits, trait procrastination might be correlated with prefrontal-insular couplings. To explore this possibility, we separately predicted the insular PPI couplings during photographs watching and dots counting with individuals’ PPS score. For the photographs watching, we found no region showing a trend even with a low threshold (p < 0.001, voxels > 5, uncorrected). For dots counting, a more anterior part of median prefrontal cortex presented as a potential biomarker for its trend of positive association with the PPS scores (amPFC, p < 0.001, voxels = 8, uncorrected, see Fig. 5A). To further test the robustness of this link, the amPFC was then selected as a ROI (x = -9, y = 60, z = 3, radium = 6 mm) to test its association with PPS scores across other conditions. We found that individuals with a higher PPS score showed less amPFC difference between the wrong trials and the correct trials (see Fig. 5B, r ₍₄₁₎ = -0.31, p < 0.05), less amPFC responses to increasing difficulty (see Fig. 5C, r ₍₄₁₎ = -0.45, p < 0.01), and less amPFC responses to increasing error (see Fig. 5D, r ₍₄₁₎ = -0.37, p < 0.05). These results remained significant or marginally significant when using Spearman’s correlation (all ps < 0.08), ruling out causes of potential outliers. In sum, these results indicate high procrastinators are featured by prefrontal-insular deficits in face of negative emotions rather than fun distractions.

The current study investigated prefrontal contributions to procrastination with a novel mission/distraction task during fMRI scanning. Our findings favor an emotion-regulation hypothesis, which suggests procrastinators evade negative emotions by delaying difficulties, over an impulse-control hypothesis, which proposes procrastinators seek fun distractions at cost of long-term goals. At the behavioral level, high procrastinators performed worse in the mission because of high difficulty but not fun distraction, compared with low procrastinators. At the neural level, both resisting the distractions and persisting in the mission involved an emotion-regulation neural pattern, characterized by regulatory PFC regions functioning against anterior insula processing negative emotions. At individual level, high procrastination traits were significantly associated with inefficiency of an amPFC region during emotion-regulation in the mission phrase. Together, these results provided evidence to the emotion-regulation hypothesis of procrastinators’ prefrontal deficits rather than the impulse-control hypothesis.

This study revealed anterior insula as an emotion-processing region and dlPFC as an emotion-regulation region. In line with the insular role, another study also found a predefined anterior insula ROI responded to increasing negative emotion toward real-life tasks [35]. Along with anterior insula, co-activations of dlPFC were repeatedly observed during emotion-regulation using different strategies [36]. Reappraisal resulted in increased dlPFC responses, decreased negative emotion experience, and decreased insular signals; while suppression produced increased dlPFC responses, decreased reported negative emotions, but increased insula responses [37]. Another emotion-regulation study suggested insular-prefrontal couplings as strategy-specific [38], reporting increased correlation between insula-dlPFC couplings and regulatory effects only when participants were using suppression but not reappraisal nor distraction strategies. Accordingly, whether the insula-dlPFC coupling differences in our study raised from different regulatory strategies is worthy to investigate for future studies.

During regulation in both the mission and distraction phases, vmPFC and dmPFC showed insular couplings patterns opposite to dlPFC’s. Similar dissociation of PFC regions has be observed in their correlations with trait procrastination. Zhang, Wang (7) reported positive correlation with spontaneous neural activity in vmPFC but negative correlation in lPFC. However, this dissociation may not necessarily suggest distinct functions. Do the vmPFC and dmPFC contribute to emotion regulation or otherwise processing? The answer depends on the content they processed, according to an appraisal-by-content model [39]. This model views the whole PFC responsible for appraisal, with different subregions accomplishing this by preferentially processing different types of content. The lPFC, including its dorsal parts, could process task rules and long-term goals, allowing use of expected rewards to motivate subsequent action [40]. The vmPFC might be responsible for evaluating internal thoughts or memories, enabling diverse forms of self-reflection [41]. In contrast to the vmPFC, the dmPFC was preferentially implicated in inferring other’s mental states rather than self-evaluation [42]. These PFC functions invite a possibility: the insular couplings with vmPFC and dmPFC might result from evaluation related to self and others. How people view themselves has great impacts on procrastination, with negative self-evaluation aggravates procrastination and positive one mitigates it [43]. On the other hand, higher procrastinators were more sensitive to how others view them, delaying more if fearing of being perceived as a loser [44].

This study revealed amPFC as a robust biomarker of trait procrastination during self-regulation. This robustness is evidenced by the amPFC’s association with trait procrastination across conditions. The amPFC, also known as rostral medial prefrontal cortex (rmPFC), was suggested as specialized in assigning positive or negative value to self-image [39]. High procrastinators are of low self-efficiency and vulnerable to negative feedbacks [43]. In the current study, the increased amPFC-insula coupling probably resulted from more negative self-evaluation, thus positively correlated with trait procrastination. Adding weights to this argument, a study observed co-activation of amPFC and anterior insula when participants were judging how appropriately negative adjectives described themselves under observation [45]. Of note, the amPFC may not directly generate negative self-images, but be implicated in reappraising or filtering inputs from insula [46]. For example, amPFC was found as assessing certainty and importance of personal traits [47]. Moreover, increased neural signals of amPFC predicted greater individual’s bias of updating self-images toward desirable social feedbacks over undesirable ones [48]. This suggests a possibility that the inefficient amPFC responses might reflect failures in blocking negative thoughts, thus negatively correlated with trait procrastination.

Last but not least, this current study has implications for intervention development. The impulse-control hypothesis and emotion-regulation hypothesis suggested distinct ways of treatment. The impulse-control hypothesis requires procrastinators to curb impulse to entertainment activities and would be critical of failure in this process. On the contrary, emotion-regulation hypothesis would prioritize dealing with distresses over criticizing mistakes. The current study provided justifications for adopting the latter. In line with our proposal, self-criticism predicted increased procrastination [49], while self-compassion, an accepting and non-judgmental mindset, was associated with less procrastination [50]. At the neural level, our results offer the amPFC as a new candidate for brain stimulation interventions [51]. Besides, with the mission/distraction task, identifying individualized biomarkers also become possible. Finally, our results suggested that focused counters who didn’t think anything but paid whole attention performed better despite reporting similar negative emotion before the dots counting. This suggests “just do it” as promising way to combat procrastination, and a process-focus mindset might do help in this way [52].

Together, those results suggested that procrastinators’ prefrontal deficits might arise from emotion-regulation deficits rather than impaired impulse control. Thus, emotional supports and ways to mitigate negative emotions should be prioritized to help procrastinators.

Author contributions

S.M.Z is responsible for conceptualization, methodology, investigation, writing, editing the original draft. T.Y.F is responsible for supervision, reviewing and editing.

Declaration of interest

The authors declare no competing interests.

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The authors have declared there is NO conflict of interest to disclose

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Prefrontal deficits in procrastinators: impaired impulse control or emotion-regulation difficulties

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Abstract

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Introduction

Materials and Methods

Results

High procrastinators did not seek more fun distractions

High procrastinators made greater error under high difficulty

Resisting distraction: regulatory neural pattern during photographs watching

Persisting in mission: regulatory neural pattern during dots counting

Emotion-regulation couplings overlapped between the distraction and mission phrase

Prefrontal regions related to trait procrastination

Discussion

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References

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