Every day we encounter various situations that require decision-making, from food, clothing, and housing to jobs and marriage, and yet people have different habitual decision-making patterns (Basu and Dixit, 2022) that lead to significant individual differences in their decision-making styles (Li et al., 2017). For example, certain individuals tend to make decisions expeditiously, while others may delay or avoid decision-making altogether, particularly in situations involving conflict or dilemma. This latter propensity is termed decisional procrastination (Ferrari et al., 1995; Ferrari, 2000). Decisional procrastination is known to produce many aspects of negative outcomes, such as stress (Thunholm, 2008), burnout (Michailidis and Banks, 2016), anxiety (Pittig et al., 2015), and sleep disturbance (Salo and Allwood, 2011), and is therefore considered a risk factor for mental health (Bavolar and Bacikova-Sleskova, 2018). Previous studies have mainly focused on cognitive factors of decisional procrastination, such as the effects of task characteristics and environmental conditions (Devine and Kozlowski, 1995; Rusou et al., 2013; Berens and Funke, 2020), but the neural underpinnings of this phenomenon remain largely unexplored.

In contrast, extensive research has explored the neural correlates of general procrastination using various modalities such as structural, resting-state, and task-functional MRI (fMRI) (Zhang et al., 2016, 2021). Notably, the parahippocampal gyrus (PHG) has emerged as a pivotal region in general procrastination (Zhang et al., 2016, 2017; Liu and Feng, 2018; Chen et al., 2019; Gao et al., 2021). Voxel-Based Morphometry (VBM) analyses indicate that the gray matter volume of the PHG serves as a neural substrate for procrastination (Hu et al., 2018; Liu and Feng, 2018; Chen et al., 2019), with larger volumes correlating with increased procrastinatory behavior. Resting-state studies demonstrate a positive correlation between spontaneous neural activity in the PHG, quantified through z-score amplitude of low-frequency fluctuations (zALFF) and regional homogeneity (ReHo), and trait procrastination. It is suggested that heightened PHG activity, linked to processing negative future events, may contribute to elevated procrastination levels (Zhang et al., 2016). Functional connectivity studies show a nuanced relationship between the PHG and procrastination, including increased functional connectivity between the putamen and PHG associated with higher procrastination levels (Gao et al., 2021). Task-fMRI results also indicate abnormal PHG activity in individuals with alcoholism, who exhibit higher procrastination tendencies compared to the general population (Westgate et al., 2017).

Considering that decisional procrastination represents a specific facet of procrastination within the decision-making context (Gambetti et al., 2008; Gambetti and Giusberti, 2019), our study explores the potential role of the PHG in decisional procrastination. However, it is worth noting that the PHG comprises distinct functional subdivisions. Research suggests that its posterior segment is involved in processing visual–spatial information, while its anterior part is associated with contextual memory associations (Baumann and Mattingley, 2016) and the processing of emotional memories (Smith et al., 2004; Sterpenich et al., 2006). Furthermore, posterior PHG functions include mental simulation and episodic future thinking (Bastin et al., 2013; Bellmund et al., 2016). Notably, differences between decisional and general procrastination have been observed in various domains, including personality traits (Morris and Fritz, 2015), memory (Tibbett and Ferrari, 2015), and self-defeating behavior (Ferrari, 1994; Orellana-Damacela et al., 2000; Hen and Goroshit, 2020).

Given these distinctions, our study uniquely subdivides the PHG to investigate its distinct relationships with decisional procrastination. We employed resting-state fMRI to assess spontaneous neural activity in the PHG among a substantial participant cohort (N = 264). We examined the association between the fractional amplitude of low-frequency fluctuations (fALFF), a well-established indicator of local spontaneous neural activity (Zou et al., 2008; Wang et al., 2021), and decisional procrastination levels, as measured using the avoidant decision-making style subscale of the General Decision-Making Scale (GDMS) (Scott and Bruce, 1995). Notably, fALFF, a sensitive measure reflecting spontaneous neural activity, has been linked to local brain glucose metabolism (Aiello et al., 2015; Nugent et al., 2015; Marchitelli et al., 2018; Jiao et al., 2019). It quantifies the ratio of low-frequency power (0.01–0.08 Hz) to the entire frequency range (0–0.25 Hz). Importantly, fALFF is less susceptible to motion artifacts compared to functional connectivity (Yan et al., 2013) and exhibits improved sensitivity and specificity in detecting spontaneous brain activities (Zou et al., 2008; Zuo et al., 2010).

Having established a correlation between the PHG and decisional procrastination, our study delves further into the role of self-esteem in this relationship. This inquiry is informed by existing literature highlighting the substantial impact of self-esteem on decision-making styles, as proposed in the conflict theory of decision making (Mann et al., 1997). Evidence also supports PHG activation during self-esteem-related tasks (Onoda et al., 2010; Premkumar, 2012; Frewen et al., 2013). The conflict theory of decision-making posits that individuals’ concerns about potential decision errors leading to reputation and self-esteem loss can induce stress, impairing their ability to make high-quality decisions, such as opting for decisional procrastination (Mann et al., 1997). Consequently, lower self-esteem may lead to greater stress, defensive behaviors (Larrick, 1993), and higher levels of decisional procrastination. Indeed, self-esteem has been shown to be a critical predictor of general procrastination, with lower self-esteem individuals exhibiting higher procrastination levels (Ferrari, 1991). As decisional procrastination represents a specific facet of procrastination within decision-making (Gambetti et al., 2008; Gambetti and Giusberti, 2019), our study proposes that self-esteem, as a significant personality variable influencing procrastination, should similarly predict decisional procrastination levels. In summary, we suggest that the PHG’s impact on decisional procrastination may be mediated by self-esteem, considering self-esteem’s role in decision-making styles and its shared neural basis with procrastination in the PHG.

Each participant’s decisional procrastination level was determined by their avoidant decision-making style score, with a higher score signifying a higher tendency to procrastinate or avoid decisions. The score’s kurtosis (−0.19) and skewness (0.05) suggest a normal distribution (Marcoulides and Hershberger, 2014) of decisional procrastination among participants. The mean was 17.33 and the SD was 4.41, indicating that the individual differences in decisional procrastination were large and thus suitable for exploring its neural correlates using the individual difference approach.

To validate the avoidant decision-making style as an accurate measure of decisional procrastination, we compared it against the General Procrastination Scale. A significant positive correlation was observed between decisional procrastination as measured by the avoidant decision-making style and general procrastination as measured by the GPS (r = 0.49, p < 0.001), which remained significant even after controlling for age, gender, and intelligence (r = 0.49, p < 0.001). Subsequently, we explored the relationship between the PHG and decisional procrastination.

In order to explore whether spontaneous activity in the PHG during resting state is associated with decisional procrastination, we examined the correlation between the mean fALFF values in the PHG and decisional procrastination levels. Our findings revealed a negative correlation (r = −0.11) between these two variables. However, this negative correlation did not reach statistical significance (p = 0.07). This outcome must be considered in the context that the PHG is a large brain area, comprising a total of 1767 voxels, with multiple subdivisions each performing different functions (Baumann and Mattingley, 2016) (refer to Methods, Figure 1A). Analyzing the PHG as a whole may potentially confound the effects of its distinct subregions on decisional procrastination, leading to biases in the results. Therefore, we examined the relationship for each of the 6 subdivisions separately. There was a significant inverse correlation between decisional procrastination levels and the mean fALFF values in the caudal area of PHG (cPHG, 312 voxels; MNI peak coordinates for left hemisphere x, y, z = −25, −25, −26; for right hemisphere x, y, z = 26, −23, −27) (r = −0.121, p = 0.049). This implies individuals with higher cPHG fALFF values were less prone to decisional procrastination. This correlation sustained even after controlling for age, gender, intelligence, and head motion (r = −0.126, p = 0.042). No significant correlations were detected in other PHG subdivisions (ps > 0.05) (Figure 1B).

Building upon the relationship between self-esteem, the PHG, and decisional procrastination, we examined if self-esteem mediates the correlation between the PHG and decisional procrastination. Echoing earlier findings (Mann et al., 1998; Thunholm, 2004; Deniz, 2006), a negative correlation was evident between self-esteem and decisional procrastination (r = −0.396, p < 0.001), indicating lower decisional procrastination in individuals with higher self-esteem. This correlation persisted when age, gender, and intelligence were controlled (r = −0.387, p < 0.001). Furthermore, there was a correlation between participants’ self-esteem levels and the fALFF magnitude in the cPHG (r = 0.13, p = 0.04), in line with previous research linking the PHG with self-esteem (Onoda et al., 2010; Premkumar, 2012; Frewen et al., 2013). Notably, after regressing for self-esteem variance, the correlation between the cPHG and decisional procrastination was not significant (r = −0.08, p = 0.18), suggesting that the relationship between the cPHG and decisional procrastination is reliant on self-esteem.

To substantiate our hypothesis, we performed a mediation analysis, with the path diagram illustrated in Figure 2. Bootstrap simulation (n = 5,000) revealed a significant indirect effect via self-esteem (95% percentile CI = [−12.06 to −0.47], p < 0.05). This indirect effect remained significant even after controlling for age, gender, intelligence, and head motion (95% percentile CI = [−11.64 to −0.62], p < 0.05, see Figure 2), suggesting that the cPHG affects decisional procrastination through self-esteem. It’s worth noting that the direct effect in the mediation model was not significant (95% percentile CI = [−22.56 to 4.14], p > 0.05), implying that the correlation between the cPHG and decisional procrastination (r = −0.12, p = 0.04) lost its significance after adjusting for self-esteem (r = −0.08, p = 0.18). Hence, self-esteem fully mediates the effect of the cPHG on decisional procrastination.

In this study, we utilized rs-fMRI to explore the neural underpinnings of decisional procrastination. We discovered that spontaneous neural activity within the caudal region of the PHG, as indicated by local fALFF, can predict decisional procrastination as measured by the avoidant decision-making style scale. This discovery broadens the PHG’s known role in general procrastination to the specific realm of decision-making. Moreover, our mediation analysis indicates that self-esteem serves as a bridge between the cPHG and decisional procrastination, suggesting that self-esteem could be a critical factor in how the PHG influences procrastination.

Our study is consistent with previous studies that the PHG is involved in procrastination (Zhang et al., 2016, 2017; Liu and Feng, 2018; Chen et al., 2019; Gao et al., 2021). Furthermore, our study extended the association between the PHG and general procrastination to procrastination in the domain of decision-making domain. This is important, because decision procrastination is considered different from general procrastination in many aspects. First, general procrastination is usually defined as a person’s voluntary postponement of an intended action despite the foreseeable negative consequences (Ferrari, 2010), whereas decisional procrastination indicates a strong tendency to fail to make timely decisions (Fabio, 2006) especially in stressful situations (Ferrari, 1994). That is, decisions are logically and psychologically preceded by action, and only when decisions have been made do individuals take action (Milgram and Tenne, 2000). Thus, decisional procrastination focuses on the early planning stages of the action process, such as information integration (Ferrari, 2000), whereas general procrastination is closer to action (Hen and Goroshit, 2020) and focuses on the behavioral component of procrastination. In addition, differences in decisional procrastination and general procrastination (Orellana-Damacela et al., 2000; Hen and Goroshit, 2020) are also found in personality traits (Morris and Fritz, 2015), memory (Tibbett and Ferrari, 2015), and the self-defeating behavior (Ferrari, 1994). Taken together, this distinction may explain that decisional procrastination was found to be associated with the caudal area of the PHG, whereas the neural correlates of general procrastination are identified in other subdivisions of the PHG (Zhang et al., 2016; Chen et al., 2019; Gao et al., 2021).

Unlike the anterior part of the PHG, the posterior part of the PHG that consists of the cPHG can be activated by episodic future thinking (Stawarczyk and D’Argembeau, 2015), which constructs a detailed, sensory-rich representation of future experiences and outcomes for decision-making based on the expectations of future events. Indeed, previous studies have found that the pessimistic and negative future thinking likely leads to decisional procrastination (Rebetez et al., 2016; Chen et al., 2019). Further studies have shown that individuals with lower self-esteem tend to have more negative future thinking and feelings of uncertainty about their abilities and future outcomes (Coruh and Vural, 2019; Ogasawara et al., 2020). Indeed, we found that self-esteem mediated the relationship between the cPHG and decisional procrastination. That is, the cPHG may be related to decision procrastination through the proxy of self-esteem.

Both the conflict theory (Mann et al., 1997) and the terror management theory (TMT) (Greenberg et al., 1986) consider self-esteem to be a central psychological factor influencing various human behaviors (Pyszczynski et al., 2004). And a large body of research has shown that self-esteem has a broad and profound effect on both procrastination and decision-making (Solomon and Rothblum, 1984; Ferrari, 1994; Beck et al., 2000; Ferrari, 2000; Deniz, 2006; Anthony et al., 2007; Farčić et al., 2020). Therefore, the mediating role of self-esteem in the relationship between the PHG and decisional procrastination may be enabled by its role in the domains of procrastination and decision-making. In the domain of procrastination, individuals with low self-esteem are more likely to procrastinate (Ferrari, 1991), possibly because they are concerned about their poor performance (Thompson, 2018). As a result, they avoid taking actions such as making decisions (Ferrari et al., 1995; Duru and Balkis, 2014) to avoid experiencing failure and to protect their self-worth. In the domain of decision-making, individuals with lower self-esteem perform worse in the decision-making, as they are more irrational (Mann et al., 1998; Thunholm, 2004; Deniz, 2006; Kumar, 2018), less confident in their decision-making abilities (Ogasawara et al., 2020) for good outcomes (Coruh and Vural, 2019). Unfortunately, the correlation analysis nature of this study cannot disentangle whether self-esteem mediates the relationship between the PHG and decisional procrastination in the domain of procrastination, or in the domain of decision-making, or both. Future research with causal designs may help to illustrate the mediating role of self-esteem in the link between the PHG and decisional procrastination.

While our study provides insightful observations regarding the relationship between the fALFF in the PHG and its 6 subdivisions with decisional procrastination, several limitations warrant consideration. The effect size observed in our research is relatively small. This could be attributed to our focused approach, which exclusively examined the PHG region. It is crucial to acknowledge that decisional procrastination, as a complex cognitive process, is likely influenced by a multitude of brain regions. Our study’s scope, limited to the PHG, may not capture the entire spectrum of neural correlates contributing to decisional procrastination. Moreover, we acknowledge the absence of multiple comparison corrections in our analysis of the relationship between fALFF in the PHG’s 6 subdivisions and decisional procrastination. Although we attempted to apply corrections using methods such as False Discovery Rate (FDR), Bonferroni, or Westfall-Young, our results did not withstand these adjustments. While such corrections are crucial for reducing false positives, they also carry the risk of introducing false negatives, potentially affecting the interpretation of our findings. It is important to contextualize our results in light of existing research, particularly studies focusing on the PHG and its relationship with procrastination (Zhang et al., 2016, 2017; Hu et al., 2018; Liu and Feng, 2018; Chen et al., 2019; Gao et al., 2021). Our findings, while limited, do align with these previous studies, suggesting a degree of consistency in the observed neural patterns related to decisional procrastination. In conclusion, while our study offers valuable insights into the neural basis of decisional procrastination, especially in relation to the PHG region, these limitations highlight the need for more comprehensive and multi-faceted research in this area. Future studies exploring a broader range of brain regions and incorporating robust statistical methodologies are essential to deepen our understanding of the neural mechanisms underpinning decisional procrastination.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving humans were approved by the Institutional Review Board of Beijing Normal University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

WL: Writing – original draft, Data curation, Formal analysis, Investigation, Project administration, Validation, Visualization. FY: Writing – original draft, Data curation, Formal analysis, Validation. TH: Writing – original draft, Resources, Software. XL: Writing – review & editing, Data curation, Formal analysis, Investigation, Project administration, Resources, Supervision, Validation, Visualization.