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. 2023 Sep 29;44(17):6090–6104. doi: 10.1002/hbm.26499

Prosocial decision‐making under time pressure: Behavioral and neural mechanisms

Zhengjie Liu 1, Hailing Zhao 1, Yashi Xu 1, Jie Liu 1,2,✉, Fang Cui 1,2,✉
PMCID: PMC10619401  PMID: 37771259

Abstract

The present study employed a novel paradigm and functional magnetic resonance imaging (fMRI) to uncover the specific regulatory mechanism of time pressure and empathy trait in prosocial decision‐making, compared to self‐decision making. Participants were instructed to decide whether to spend their own monetary interest to alleviate themselves (or another person) from unpleasant noise threats under high and low time pressures. On the behavioral level, results showed that high time pressure had a significant effect on reducing participants' willingness to spend money on relieving themselves from the noise, while there is a similar but not significant trend in prosocial decision‐making. On the neural level, for self‐concerned decision‐making, low time pressure activated the bilateral insula more strongly than high time pressure. For prosocial decision‐making, high time pressure suppressed activations in multiple brain regions related to empathy (temporal pole, middle temporal gyrus, and inferior frontal gyrus), valuation (medial orbitofrontal cortex), and emotion (putamen). The functional connectivity strength among these regions, especially the connectivity between the medial orbitofrontal cortex and putamen, significantly predicted the effect of time pressure on prosocial decision‐making at the behavioral level. Additionally, we discovered the activation of the medial orbitofrontal cortex partially mediated the effect of empathy trait scores on prosocial decision‐making. These findings suggest that (1) there are different neural underpinnings for the modulation of time pressure for self and prosocial decision‐making, and (2) the empathy trait plays a crucial role in the latter.

Keywords: empathy, functional magnetic resonance imaging, medial orbitofrontal cortex, prosocial decision‐making, time pressure

This study reveals that distinct neural mechanisms were underlying the effects of time pressure on self‐concerned and prosocial decision‐making. Our findings reveal that the empathy trait plays a crucial role in modulating prosocial decision‐making, specifically through the activation of the orbitofrontal prefrontal cortex.

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1. INTRODUCTION

Understanding the neural processes behind prosocial decision‐making is integral to building a cohesive society. In everyday life, prosocial decision‐making often occurs under time pressure. For instance, if you witness someone drowning and have limited time to act, the decisions you make under time pressure can greatly impact the outcome.

Research has shown that time pressure can enhance decision quality and increase the likelihood of following established behavioral patterns in self‐concerned decision‐making (Betsch et al., 1998; Musculus et al., 2018). Time pressure may also impact an individual's risk preference, with high time pressure leading to riskier decisions (Hu et al., 2015; Lin & Jia, 2023). Neuroscience research indicated that low time pressure situations involve greater brain activity in areas including the prefrontal and parietal cortex, the hippocampus, and the striatum when making self‐concerned decision‐making while high time pressure situations force individuals to disregard less informative cues and activate regions associated with value integration, such as the midbrain, striatum, thalamus, and cerebellum (Oh‐Descher et al., 2017). Additionally, limbic regions such as the striatum were activated during instant decision‐making, while prefrontal regions are associated with delayed decision‐making (McClure et al., 2004).

The impact of time pressure on other‐concerned decision‐making, particularly in prosocial scenarios, is a more complex issue. Several studies have reported that time pressure can encourage cooperative (Rand, 2016; Rand et al., 2012, 2014) and sharing (Ploetner et al., 2021) behaviors. A recent study conducted in the context of COVID‐19 indicated that people are more likely to exhibit prosocial intentions when they are under time pressure (Costa et al., 2022). Nevertheless, other studies have yielded conflicting results, showing either no influence (Bouwmeester et al., 2017; Passarelli & Buchanan, 2020) or a decrease (Gaertner, 2018) in prosocial decision‐making under time pressure. Furthermore, a study by Jarke‐Neuert and Lohse (2022) suggested that time pressure can increase self‐serving choices by strengthening the incidence of information avoidance.

According to the literature, two main perspectives exist on how time pressure impacts prosocial decision‐making. The first is the dual‐process model, which proposes two processing modes for decision‐making: one is fast and intuitive, while the other is slow and deliberative (Evans & Stanovich, 2013). This view suggests that time pressure alters the processing mode of prosocial decision‐making with intuitive thinking automatically giving way to deliberative thinking as the time of decision‐making increases (Rand et al., 2014). Some behavioral studies based on classical economic paradigms have shown that time pressure can facilitate cooperative decision‐making (Isler et al., 2018; Rand, 2016), which lends support to the dual‐process model. The second viewpoint suggests that time pressure modulates prosocial decision‐making by altering the priority of information processing. For instance, studies reported that time pressure can elicit early gaze biases toward one's own outcomes (Teoh et al., 2020) and enhance the framing effect in decision‐making by shifting visual attention towards reward prediction cues (Roberts et al., 2022). Moreover, the impact of time pressure on prosocial decision‐making can be influenced by dynamic and context‐sensitive information search (Teoh & Hutcherson, 2022). These results underscore the important role of attention systems in the relationship between time pressure and prosocial decision‐making and suggest that individual or experimentally induced social preferences determine the extent to which time pressure affects prosocial decision‐making (Chen & Krajbich, 2018; Krawczyk & Sylwestrzak, 2018; Teoh et al., 2020).

Overall, it is evident that other‐concerned (prosocial) decision‐making has produced more inconsistent results compared to self‐concerned decision‐making on this topic. Two possible reasons for this inconsistency could be (1) lack of consideration of key personality traits (e.g., empathy) that affect prosocial decision‐making; (2) lack of comparison with non‐social decision‐making (self‐concerned decision‐making), to make it difficult to reveal the specific impact of time pressure on other‐concerned decision making. Previous studies on prosocial decision‐making often focused on the differences between strangers and close others when comparing beneficiaries, with little attention paid to the differences when beneficiaries were themselves and others (Rhoads et al., 2021). An fMRI study has shown that people tend to automatically process (model‐free decision making) when making altruistic rather than self‐concerned decisions and observe the activation of subgenual anterior cingulate cortex (sgACC) (Lockwood et al., 2020), which indicated that there may be different neural mechanisms between self‐concerned decision‐making and other‐concerned decision‐making. Furthermore, previous studies have often used the cooperation or dictator game as paradigms for examining prosocial decision‐making (Rand, 2016; Rand et al., 2014; Teoh & Hutcherson, 2022). However, decisions to help others in the face of a threat may be considered a more ‘pure’ form of prosocial behavior. Additionally, in our daily lives, decisions to help are often made under time pressure.

The current study aimed to investigate the neural mechanism of prosocial decision‐making under time pressure and the role of empathy trait in this process. To achieve this, we employed fMRI in combination with a novel paradigm that required participants to make decisions about whether to expend personal interests to protect themselves or others from exposure to highly unpleasant noises. This is a fresh exploration of altruistic decision‐making, which was defined as one of the main prosocial decisions (the other two are equity and cooperation) (Rhoads et al., 2021). Based on prior research, we hypothesized that (1) high time pressure may reduce the willingness of participants to spend money to save themselves or others from noise compared to low time pressure due to increasing cognitive load (Yu et al., 2014). (2) The neural correlates associated with the effects of time pressure on self‐centered and prosocial decision‐making may involve different brain regions. Specifically, the areas involved in self‐centered decision‐making may process harm avoidance and negative emotion (such as the insula and amygdala; Cai et al., 2018; Markett et al., 2013). For prosocial decision‐making, since participants need to consider the feelings and beliefs of others during decision‐making, it is expected that time pressure may modulate empathy‐related regions (such as the medial cingulate and paracingulate gyrus, Bellucci et al., 2020; temporal pole, Herlin et al., 2021; and prefrontal cortex, Masten et al., 2011). Furthermore, a more complex valuation process is required for prosocial decision‐making compared to self‐centered decision‐making because the decision‐maker needs to integrate others' needs, self‐interest, and other social factors. Thus, it is predicted that time pressure may also modulate regions related to valuation (such as the striatum, Ho et al., 2012; and orbital media prefrontal cortex, Liu et al., 2020). (3) The effect of time pressure on prosocial decision‐making may be correlated with empathy trait scores, considering the critical role of empathy in prosocial behavior.

2. METHODS

2.1. Participants

A priori power analysis was conducted using G*Power 3.1.9.7 (Faul et al., 2007, 2009) suggested that 36 participants were required to reach a statistical power of 0.95 to detect a medium effect size of 0.25 (Cohen's f) at the standard 0.05 alpha error probability for a 2 × 2 repeated measures ANOVA. With the possibility of data rejection, 40 participants were recruited from Shenzhen University, meeting the inclusion criteria of being right‐handed with normal or corrected‐to‐normal vision and without a history of neurological disorders, brain injury, or developmental disabilities. Four participants were excluded for excessive head motion (who had excessive head movements >2 in rotation or >2 mm in translation during the scanning), leaving 36 participants in the final sample (17 males, age: 19.83 ± 1.90 years [mean ± standard deviation]). The study adhered to the ethical principles and guidelines outlined in the Declaration of Helsinki and received approval from the Medical Ethics Committee of Shenzhen University Medical School. All participants received an explanation of the experimental procedures and provided their informed consent prior to the commencement of the study. As compensation for their participation, each participant received approximately 100–110 RMB (~15–16 US dollars).

2.2. Experimental design

Prior to the commencement of the experiment, participants were duly notified that they would be carrying out the task with another participant of the same gender. However, unbeknownst to them, the other participants were actually confederates. Preceding the scanning procedure, both participants were exposed to 30 instances of noise clips, each lasting for 2 s and varying in loudness, in a random arrangement. Subsequently, they rated the unpleasantness of each clip on an 11‐point visual analog scale (VAS) which rated from 0 (not unpleasant at all) to 10 (extremely unpleasant) (Cui et al., 2022; Hu et al., 2017, 2021). The noise stimuli were delivered by AKG K271 MKII headphones and controlled by PsychoPy V2021.2.3 (Open Science Tools Ltd., Nottingham, UK). The intensity of the noise stimuli in later experiments will be adjusted based on the results of emotional ratings.

The experiment applied a 2 × 2 within‐subject design with decision type (self‐concerned vs. other‐concerned/prosocial) and time pressure (high vs. low). At the onset of each trial, participants were endowed with five tokens, which could be exchanged for additional money after the experiment. In the ‘self‐concerned’ conditions, participants decided whether to use the tokens to relieve themselves from a 5 s highly unpleasant noise (at least level 8 in the preceding noise test) while in the ‘other‐concerned’ conditions, they decided whether to use their tokens to relieve the other player from the noise. The second factor manipulated time pressure by allowing participants either 1.5 s (high time pressure) or 5 s (low time pressure) to reach a decision. The amount of tokens needed to eliminate the noise was randomly assigned between 1 and 5 in each trial, and participants simply had to decide whether to accept or reject the proposal of removing that amount of tokens to eliminate the noise threat. This decision had to be made either for themselves (in the self‐concerned conditions) or for the other player (in the other‐concerned conditions) (Figure 1a). Each trial is independent of each other and the five initial tokens in each round can only be used in the current round (if participants do not use them, they will not accumulate in the next round). Noise stimuli are not immediately fed back during the scanning, but are executed after all decisions have been made to avoid the impact of negative emotions caused by noise on subsequent decision‐making. Only 10% of the trials were randomly chosen to calculate the amount of tokens and the duration of stimulation that subjects need to receive, which was previously informed to the participants.

FIGURE 1.

FIGURE 1

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Experimental design. (a) Experimental setting. In the self‐concerned decision‐making, participants decide whether to spend money to help eliminate noise for themselves and in the other‐concerned (prosocial) decision‐making, participants decide whether to spend money to help others eliminate noise. Both decisions need to be completed under high or low time pressure. (b) The four condition cues (recipient: You for participant, Other for confederate; time: 5 s, the red bar would uniformly shorten in 5 s; 1.5 s, the red bar would uniformly shorten in 1.5 s). (c) Trial structure of the experiment. In each trial, the participant would see a proposal after a 0.5 s fixation. The proposal will inform the participants of the recipient of the noise and how many tokens they need to pay to eliminate noise stimulation. The red bar would disappear in 1.5 or 5 s, however, the proposal interface would persist in 5 s although the red bar disappeared in 1.5 s. In the practice stage, participants would see feedback after making a decision, telling them the consequences of this decision, which was not available in the formal experiment process.

Four experimental conditions were created, based on self‐ and other‐concerned decision‐making and high and low time pressures (Self_High time pressure [Self_HTP], Self_LTP, Other_HTP, Other_LTP, Figure 1a). In each trial, participants viewed a fixation point for 0.5 s and then decided within 5 or 1.5 s whether to accept the proposal. Regardless of whether participants were under high or low time pressure, the decision screen would remain visible after they made their decision, until the end of the 5th second. After each decision, a random inter‐trial interval (mean = 2 s, range 1–4 s) was given before the start of the next trial (Figure 1c). Each condition consisted of 45 trials, with 9 trials for each cost level (1–5). The total of 180 trials was organized into three blocks, each lasting 8 min, and presented to participants pseudo‐randomly. The experiment was programmed using PsychoPy V2021.2.3 (Open Science Tools Ltd., Nottingham, UK). Prior to scanning, participants completed a practice block containing 12 trials. In order to evaluate the role of the empathy trait, all participants completed the Cognitive and Affective Empathy Questionnaire (QCAE) (Reniers et al., 2011) subsequent to the scanning process.

2.3. Neuroimaging data acquisition and preprocessing

We used a Siemens Prisma 3.0T MRI machine for data acquisition. Functional volumes were acquired using multiple slice T2‐weighted echo planar imaging (EPI) sequences with the following parameters: repetition time = 1500 ms, echo time = 30 ms, flip angle = 75°, field of view = 192 × 192 mm2, 72 slices covering the entire brain, slice thickness = 2 mm, voxel size = 2 × 2 × 2 mm3. fMRI data were preprocessed in SPM12 (Wellcome Department of Imaging Neurosciences, University College London, UK, http://www.fil.ion.ucl.ac.uk/spm). Images underwent slice timing correction, and motion correction, were normalized to Montreal Neurological Institute (MNI) space with a spatial resolution of 2 × 2 × 2 mm3, and were smoothed with an isotropic Gaussian kernel of 6 mm and high‐pass filtered at a cutoff of 128 Hz.

2.4. Statistical analysis

2.4.1. Behavioral data analysis

A 2 × 2 repeated measures ANOVAs were used to investigate the impact of time pressure and decision type on Acceptance_rate, calculated as the number of accepted trials divided by the total number of trials responded to (excluding those without key‐pressing), and reaction time. Among all participants, the maximum number of non‐response was 8 (4.4%) and the mean number was 1.3 (0.7%). Pairwise comparisons were conducted if the effects were significant. To access how the modulation effect of time pressure on decision‐making was correlated with empathy trait, we calculated the difference of Acceptance_rate between low time pressure (LTP) and high time pressure (HTP) for ‘other‐concerned’ and ‘self‐concerned’ decisions, respectively (Acceptance_rate Other_LTP > Other_HTP and Acceptance_rate Self_LTP > Self_HTP ), and conduct Persons’ correlational analysis with empathy scores. Greenhouse–Geisser method was applied to ANOVA results.

2.4.2. fMRI data analysis

General linear model

The general linear model (GLM) was set up to explore the brain activity differences among conditions. Statistical parametric maps were generated on a voxel‐by‐voxel basis, utilizing a hemodynamic model to estimate the brain's response. Incorporating the six rigid body parameters into the GLM helped to eliminate motion‐related interference. We conducted a repeated‐measures ANOVA, within‐subjects, with two factors: decision type (other‐concerned and self‐concerned) and time pressure (high and low), to analyze the outcomes. Group‐level analysis was carried out using a one‐sample t‐test, using the entire brain volume as the area of interest. The threshold for significance was established as p < .001 (uncorrected), k > 20 at the voxel level, and p < .05 with false discovery rate (FDR) correction at the cluster level.

RVR (relevance vector regression) analysis

We defined the brain regions that exhibited positive activation in the contrasts of Other_LTP > Other_HTP and Self_LTP > Self_HTP as “regions of interests” (ROIs). The GLM analysis identified eight significant peaks for the other‐concerned network and five significant peaks for the self‐concerned network (corrected threshold at p < .05 using FDR correction; voxel‐level p < .001), which were used to create spherical ROIs with a radius of 6 mm centered on the peak of significant activity for each network (Table 2). Using the Psychophysiological Interaction (PPI) model (Friston et al., 1997), we estimated task‐dependent functional connectivity (FC) patterns for each pair of ROIs among brain regions that displayed activation covariate with each condition. Using RVR, we assessed the correlation between the FC patterns and behavioral results (Acceptance_rate Other_LTP > Other_HTP and Acceptance_rate Self_LTP > Self_HTP ), separately.

TABLE 2.

Regions of interests (ROIs) in the RVR model.

Brain region Hem. MNI coordinates
x y z
Other‐concerned network
SPG L −24 −56 56
MCG L −8 0 40
PUT L −30 18 4
MCG R 14 −20 44
orbIFG R 28 30 −18
TPsup R 44 20 −28
MTG R 50 6 −24
omPFC R 2 48 −10
Self‐concerned network
SPG L −24 −52 58
INS L −36 2 10
SPG R 22 −60 60
INS R 44 −8 8
MCG R 8 8 40
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Abbreviations: INS, insula; MCG, medial cingulate and paracingulate gyrus; MTG, middle temporal gyrus; omPFC, medial orbitofrontal cortex; orbIFG, orbital part of inferior frontal gyrus; PUT, putamen; TPsup, temporal pole: superior temporal gyrus; SPG, superior parietal gyrus.

Specifically, we calculated the whole‐brain task‐dependent FC maps with 8 and 5 ROIs as seed regions, respectively. We then extracted the parameter estimates of each FC map within each ROI to obtain FC matrixes that represent the FC strength between each pair of ROIs for each participant. For the other‐concerned and self‐concerned networks separately, an 8 by 8 FC matrix and a 5 by 5 FC matrix were calculated. We selected the RVR method to conduct the prediction model, as it showed high prediction performance in brain−behavior (cognition) mapping in a previous study (Cui & Gong, 2018). Leave‐one‐out‐cross‐validation (LOOCV) was used to calculate the prediction accuracy, which was defined as the Pearson correlation coefficient between the predicted and actual labels. The significance level was computed based on 1000 permutation tests. For each permutation test, the prediction labels were randomized, and the same RVR prediction process was carried out for the actual data. After 1000 permutations, a random distribution of accuracy was obtained and the P‐value was computed as P= (number of permutation tests < actual accuracy + 1)/(number of permutation tests + 1). If the P‐value of the models were significant, we chose the FCs with the top four absolute weights as the connection contributing most to the relationship between the brain network and the behavior data to draw the predictive model (Cui & Gong, 2018).

Correlational and mediation analysis

Correlational analyses were conducted to investigate the associations between the activation of specific brain regions, empathy traits, and behavioral data (Acceptance_rate Other_LTP > Other_HTP and Acceptance_rate Self_LTP > Self_HTP ). In the case of significant correlations between these factors, a mediation model was established using the PROCESS Procedure Version 4.0 for SPSS (IBM, Armonk, NY), developed by Preacher and Hayes (2008), to uncover the underlying neural mechanisms.

3. RESULTS

3.1. Behavioral results

The 2 × 2 repeated measures ANOVA indicated that the main effect of the time pressure on the acceptance rate was significant (F(1,35) = 6.28, p = .017,η p 2 = 0.15), participants were more willing to accept the proposal under low time pressure than they were under high time pressure (HTP: 0.40 ± 0.29; LTP: 0.44 ± 0.28). The main effect of decision type on the acceptance rate was not significant (F(1,35) = 0.45, p = .506, η p 2 = 0.01). The interaction of the time pressure and the decision type on the acceptance rate was significant (F(1,35) = 6.97, p = .012, η p 2 = 0.17). As shown in Figure 2a, there was a significant difference between high time pressure and low time pressure when the decision type is ‘self‐concerned’ (Self_HTP: 0.40 ± 0.32; Self_LTP: 0.46 ± 0.32; p = .002); however, this difference was close but not significant in the ‘other‐concerned’ condition (Other_HTP: 0.40 ± 0.29; Other_LTP: 0.42 ± 0.28; p = .190).

FIGURE 2.

FIGURE 2

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The behavioral results. (a) The interaction of time pressure and decision type on the acceptance rate. (b) The correlation scatter chart between empathy trait and acceptance rate in different decision types (calculated by the Other_LTP > Other_HTP and the Self_LTP > Self_HTP). *, p < .05; **, p < .01, Error bar means ±1 standard error.

The main effect of the time pressure on the reaction time was significant (F(1,35) = 26.65, p < .001, η p 2 = 0.43), participants made decisions more quickly under high time pressure than low time pressure (HTP: 0.74 ± 0.15; LTP: 1.12 ± 0.55). The main effect of decision type (F(1,35) = 1.70, p = .201, η p 2 = 0.05) and interaction of time pressure and decision type (F(1,35) = 0.02, p = .902, η p 2 < 0.001) on the reaction type were not significant.

Pearson's correlational analysis indicated that for the other‐concerned (prosocial) decision type, the difference in acceptance rates between high and low time pressure conditions (Acceptance_rate Other_LTP > Other_HTP ) was significantly correlated with the empathy trait, specifically the affective component (r = 0.423, p = .010). For the self‐concerned decision type, this correlation was not significant (r = 0.218, p = .201) (Figure 2b). The self‐reported cognitive empathy as measured by QCAE was not significantly related to the Acceptance_rate Other_LTP > Other_HTP (r = 0.05, p = .773) and Acceptance_rate Self_LTP > Self_HTP (r = 0.09, p = .588). However, the normal distribution test showed that the behavioral data were non‐normal (the kurtosis and skewness are greater than 1.96) so we conducted the Spearman's correlation analysis. These results showed that the Spearman's rho value between Acceptance_rate Other_LTP > Other_HTP and affective empathy was 0.52 (p = .001) and the Spearman's rho value between Acceptance_rate Self_LTP > Self_HTP and affective empathy was 0.04 (p = .822).

3.2. fMRI results

3.2.1. GLM

The interaction contrast (Other_LTP > Other_HTP) > (Self_LTP > Self_HTP) revealed significant activation (all brain region names and abbreviations referred to the ‘Anatomical Automatic Labeling’ [AAL; Tzourio‐Mazoyer et al., 2002] template) in the left orbital part of inferior frontal gyrus (orbIFG, MNI[−44, 24, −6], cluster size = 104), left fusiform (FUS, MNI[−38, −58, −18], cluster size = 145), right inferior temporal gyrus (ITG, MNI[44, −50, −24], cluster size = 212), left medial superior frontal gyrus (mSFG, MNI[2, 54, 32], cluster size = 126) and right superior frontal gyrus (SFG, MNI[20, 38, 48], cluster size = 104). The reversed contrast revealed no significant activation.

The contrast Self_LTP > Self_HTP revealed significant activation in the right superior parietal gyrus (SPG, MNI[22, −60, 60], cluster size = 735), left superior parietal gyrus (SPG, MNI[−24, 52, 58], cluster size = 301), right middle temporal gyrus (MTG, MNI[48, −62, 0], cluster size = 93), right insula (INS, MNI[44, −8, 8], cluster size = 603), right medial cingulate and paracingulate gyrus (MCG, MNI[8, 8, 40], cluster size = 239), left insula (INS, MNI[−36, 2, 10], cluster size = 79). The reversed contrasts revealed significant activation in the right opercular part of the inferior frontal gyrus (opeIFG, MNI[34, 8, 32], cluster size =261).

The contrast Other_LTP > Other_HTP revealed significant activation in the left superior parietal gyrus (SPG, MNI[−24, −56, 56], cluster size = 13,758), right inferior temporal gyrus (ITG, MNI[44, −66, −2], cluster size = 481), right medial cingulate and paracingulate gyrus (MCG, MNI[14, −20, 44], cluster size = 128), right orbital part of inferior frontal gyrus (orbIFG, MNI[28, 30, −18], cluster size = 124), left medial cingulate and paracingulate gyrus (MCG, MNI[−8, 0, 40], cluster size = 1140), left middle frontal gyrus (MFG, MNI[−34, 42, 28], cluster size = 143), right temporal pole: superior temporal gyrus (TP, MNI[44, 20, −28], cluster size = 130), left putamen (PUT, MNI[−30, −18, 4], cluster size = 140), right middle temporal gyrus (MTG, MNI[50, 6, −24], cluster size = 156), right middle frontal gyrus (MFG, MNI[26, 42, 32], cluster size = 140), right medial orbitofrontal cortex (omPFC, MNI[2, 48, −10], cluster size = 128), left orbital part of inferior frontal gyrus (orbIFG, MNI[−42, 32, −8], cluster size = 76). The reversed contrasts revealed significant activation in the right fusiform (FUS, MNI [28, −82, −10], cluster size = 128) (Table 1 and Figure 3). The detailed activation results of the main effect of time pressure and decision type can be found in Table S1.

TABLE 1.

Whole‐brain regression results for contrasts (all the results reported below were significant at p < .001, k > 20, uncorrected at the voxel level, and p < .05, FDR corrected at the cluster level).

Hem. Region MNI coordinates No. of voxels t‐Value
x y z
(Other_LTP > Other_HTP) > (Self_LTP > Self_HTP)
L Middle occipital gyrus −32 −92 −2 833 6.87
−22 −94 −2 5.30
−26 −84 12 3.89
L Orbital part of inferior frontal gyrus −44 24 −6 104 4.81
−38 30 −16 3.64
L Fusiform −38 −58 −18 145 4.70
−46 −66 −20 4.58
−36 −72 −14 4.42
L Cerebellum −16 −86 −36 97 4.17
−8 −78 −42 3.99
L Medial superior frontal gyrus 2 54 32 126 4.05
0 46 30 3.79
2 48 16 3.72
R Middle occipital gyrus 36 −88 2 378 6.08
26 −96 −2 5.28
30 −78 2 3.91
R Inferior temporal gyrus 44 −50 −24 212 4.58
42 −64 −16 4.52
50 −70 −8 4.44
R Superior frontal gyrus 20 38 48 104 4.02
18 30 44 3.96
Other_LTP > Other_HTP
L Superior parietal gyrus −24 −56 56 13,758 10.97
−26 −88 20 8.57
−46 −66 4 8.53
L Medial cingulate and paracingulate gyrus −8 0 40 1140 5.80
0 2 46 5.76
6 −8 48 5.43
L Pallidum −22 4 2 118 5.44
L Middle frontal gyrus −34 42 28 143 5.42
−24 34 24 4.92
−26 38 34 4.05
L Putamen −26 −14 10 140 5.21
−34 −14 −2 4.80
−30 −18 4 4.80
L Orbital part of inferior frontal gyrus −42 32 −8 76 4.10
−50 26 −8 3.98
R Precentral gyrus 58 6 18 3350 7.92
44 −10 48 6.58
52 −24 48 6.47
R Inferior temporal gyrus 44 −66 −2 481 7.51
48 −60 −6 6.30
38 −60 0 5.46
R Medial cingulate and paracingulate gyrus 14 −20 44 128 6.88
12 −28 54 4.41
R Orbital part of inferior frontal gyrus 28 30 −18 124 6.34
R Temporal pole: superior temporal gyrus 44 20 −28 130 5.33
38 18 −40 4.53
R Middle temporal gyrus 50 6 −24 156 5.19
R Cerebellum 26 −50 −42 73 4.84
R Middle frontal gyrus 26 42 32 140 4.76
R Medial orbitofrontal cortex 2 48 −10 128 4.52
8 46 −16 4.13
−12 46 −10 3.82
Other_LTP < Other_HTP
R Fusiform 28 −82 −10 128 4.69
36 −80 −10 3.94
18 −84 −6 3.60
R Lingual gyrus 4 −68 4 70 4.51
Self_LTP > Self_HTP
L Superior parietal gyrus −24 −52 58 301 6.08
−20 −70 64 5.04
L Middle occipital gyrus −40 −66 8 307 5.55
−44 −72 4 5.19
L Postcentral gyrus −60 4 16 285 5.19
−54 −8 −4 4.53
−52 6 2 4.32
L Lingual gyrus −22 −54 −6 127 4.81
−28 −58 −10 3.98
−28 −66 −2 3.75
L Insula −36 2 10 79 4.53
−36 −6 14 4.14
−30 10 8 3.94
L Superior occipital gyrus −20 −84 24 63 4.10
R Superior parietal gyrus 22 −60 60 735 7.76
16 −66 68 6.38
22 −70 64 5.65
R Precentral gyrus 44 −16 56 959 5.58
60 −6 46 5.56
42 −24 68 5.31
R Middle temporal gyrus 48 −62 0 93 5.43
R Insula 44 −8 8 603 5.42
62 12 18 5.34
56 4 14 4.96
R Medial cingulate and paracingulate gyrus 8 8 40 239 4.75
8 2 50 4.41
10 −4 70 4.16
Self_LTP < Self_HTP
L Inferior occipital gyrus −28 −92 −8 556 6.87
−36 −88 −8 6.35
−48 −66 −22 5.80
L Cerebellum −20 −78 −36 287 4.97
−28 −80 −38 4.05
−18 −82 −22 3.44
R Inferior occipital gyrus 36 −84 −8 3176 6.60
2 −70 −2 6.37
44 −54 −18 6.02
R Opercular part of inferior frontal gyrus 34 8 32 261 4.42
34 18 32 4.22
50 24 42 3.96
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FIGURE 3.

FIGURE 3

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The activation maps. (a,b) The whole brain activation of corresponding contrast. (c) The overlap of contrast Other_LTP > Other_HTP and contrast Self_LTP > Self_HTP. INS, insula; ITG, inferior temporal gyrus; MCG, medial cingulate and paracingulate gyrus; MTG, middle temporal gyrus; omPFC, medial orbitofrontal cortex; orbIFG, orbital part of inferior frontal gyrus; PUT, putamen; SPG, superior parietal gyrus; TP, temporal pole: superior temporal gyrus.

3.2.2. RVR model

The RVR model based on functional connectivity (FC) within the other‐concerned network significantly predicted the intensity of the effect of time pressure on prosocial decision‐making (Acceptance_rate Other_LTP > Other_HTP ) (r = 0.356, p = .024). The four predictors that had the greatest impact on the relationship between the brain network and prosocial behavior were: FC with the left medial cingulate and paracingulate gyrus seeded by the right orbital part of the inferior frontal gyrus (weight = 0.31); FC with the left putamen seeded by the left superior parietal gyrus (weight = 0.29); FC with the right middle temporal gyrus seeded by the left putamen (weight = −0.36); and FC with the right medial orbitofrontal cortex seeded by the left putamen (weight = −0.37). The detailed weights of the RVR model are presented in Table 3. However, the FC‐based RVR model of the self‐concerned network did not significantly predict the effect intensity of time pressure on self‐concerned decision‐making (Acceptance_rate Self_LTP > Self_HTP ) (r = −0.076, p = .401) (Figure 4 and Table 3).

TABLE 3.

Feature weights of functional connections in the other‐concerned (prosocial) network.

SPG.L MCG.R orbIFG.R MCG.L TP.R PUT.L MTG.R omPFC.R
SPG.L −0.10 0.19 −0.04 0.11 0.00 0.29 −0.08 0.00
MCG.R −0.10 −0.17 −0.03 −0.02 −0.16 −0.20 −0.07
orbIFG.R −0.10 0.31 0.25 0.03 −0.15 −0.14
MCG.L 0.25 0.18 −0.05 0.07 −0.18
TP.R −0.16 −0.19 0.13 0.08
PUT.L 0.07 −0.36 −0.37
MTG.R −0.12 −0.07
omPFC.R 0.07
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Note: The positive weight indicates this FC can positively predict prosocial behavior and the negative weight indicates this FC can negatively predict prosocial behavior.

FIGURE 4.

FIGURE 4

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The RVR model. The red line indicates an FC with a positive weight, while the blue line represents an FC with a negative weight. The depth of blue and red represents the absolute value of weight. MCG, medial cingulate and paracingulate gyrus; MTG, middle temporal gyrus; omPFC, medial orbitofrontal cortex; orbIFG, orbital part of inferior frontal gyrus; PUT, putamen; SPG, superior parietal gyrus; TP, temporal pole: superior temporal gyrus.

3.2.3. Brain activity covariate with the prosocial behavior

Correlational analysis showed that the activation of the right omPFC was significantly correlated with Acceptance_rate Other_LTP > Other_HTP (r = 0.402, p = .015; Figure 5a) and empathy trait (r = 0.405, p = .014; Figure 5b). A mediation model was built with the empathy trait as the independent variable, the brain activities of the right omPFC Other_LTP > Other_HTP as the mediating variable, and Acceptance_rate Other_LTP > Other_HTP was considered as the dependent variable. The mediation effect was estimated by using the bootstrap approach with 5000 resampling. The indirect effect of the empathy trait on the Acceptance_rate Other_LTP > Other_HTP through the activation of the right omPFC was significant (M = 0.0411 × 0.0476 = 0.0020, SE = 0.0014, 95% CI = [0.0001, 0.0055]). Because the direct effect of empathy trait on the Acceptance_rate Other_LTP > Other_HTP was still significant after introducing the mediator of the activation of the right omPFC (M = 0.0080, SE = 0.0032, 95% CI = [0.0015, 0.0145]), this model exhibited a partial mediation effect (Figure 5c). The results of PPI seeded by the omPFC [2, 48, −10] under the contrast of Other_LTP > Other_HTP showed activation in the left inferior parietal lobe (IPL, MNI[−52, −38, 48], cluster size = 261) and right middle temporal gyrus (MNI[48, −62, 2], cluster size = 245). See detailed information of activation in Table S2.

FIGURE 5.

FIGURE 5

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The correlation and mediation effect analysis. (a) The correlation between the activation of the right omPFC and the Acceptance_rate Other_LTP > Other_HTP . (b) The correlation between the activation of the right omPFC and the empathy trait. (c) The partial mediation effect of empathy trait on the Acceptance_rate Other_LTP > Other_HTP through the activation of the right omPFC. *, p < .05; **, p < .01.

4. DISCUSSION

The present study found that time pressure decreased the willingness to spend money to eliminate threats in self‐concerned decision‐making making while this effect was not significant in prosocial decisions. Notably, only the impact of time pressure on prosocial decision‐making was significantly correlated with empathy scores. At the neural level, we found that regions associated with harm avoidance (i.e., INS) were activated to a greater extent under low time pressure than under high time pressure in self‐concerned decision‐making. As for prosocial decision‐making, brain regions associated with empathy (TP, orbIFG, and MTG) and valuation (omPFC) were more active under low time pressure than high time pressure, and the functional connections (FCs) of these regions could effectively predict the effect of time pressure on prosocial behavior. Additionally, we found that the empathy trait could modulate prosocial behavior through the mediation of the activation of the omPFC, a brain region that may be related to the valuation process (Liu et al., 2020).

At the behavioral level, we observed that time pressure had a significant negative effect on self‐concerned decision‐making but only a similar but not significant trend on prosocial decision‐making. In both types of decision‐making, the activation of the SPG, associated with goal‐directed attentional orientation (Vossel et al., 2012), was greater under low time pressure than high. The activation of SPG reflects top‐down attentional modulations (Lin et al., 2022), indicating that time pressure affected attentional load for both types of decision‐making in a similar manner. However, the effect was insignificant for prosocial decision‐making, possibly due to interference from other factors such as financial incentives (Iotzov et al., 2022), social observation (Li et al., 2022), and individual differences (Kuss et al., 2015; Wei et al., 2016) in prosocial decision making compared to self‐concerned decision making.

There were significant differences in the brain regions sensitive to the level of time pressure between the two types of decision‐making. Particularly, bilateral INS was only more strongly activated in self‐concerned decision‐making under low time pressure, consistent with Zhang et al.'s (2019) study. Activation of INS and its functional connectivity with the anterior cingulate (ACC) have been found to be positively correlated with individual harm avoidance scores (Markett et al., 2013; Paulus et al., 2003). When individuals make decisions for themselves under low time pressure, activated INS may represent harm (noise) avoidance, which is inhibited under high time pressure.

In terms of other‐concerned (prosocial) decision‐making, brain activations in the MCG, TP, MTG, omPFC, orbIFG, and PUT were found to be stronger under low time pressure than high time pressure. Previous studies have associated the MCG, TP, and IFG with empathy and socio‐emotional processing (Bellucci et al., 2020; Flournoy et al., 2016; Herlin et al., 2021), with the MCG particularly important in empathy for pain (Lamm et al., 2011). Thus, the activation of these regions may indicate the participants' perception of others' pain (Luo et al., 2014) when anticipating that others will be subjected to noise (Luo et al., 2014). Additionally, the MTG has been linked to emotional regulation and social perception in previous research (Allison et al., 2000; Goldin et al., 2008), and it appears that participants needed to activate brain regions related to emotional regulation during prosocial decision‐making under low time pressure (Geckeler et al., 2022). Meanwhile, the omPFC region is known to represent value integration (Park et al., 2011), with its functional connectivity with mPFC significantly associated with moral and economic values (Liu et al., 2020), reflecting the participants' attempt to balance money and morality. Additionally, activation of reward‐related regions such as the putamen (Mattfeld et al., 2011; Mizuno et al., 2016) may indicate individuals experiencing anxiety and stress (Corr et al., 2021) when making prosocial decisions, as helping others might harm their own interests, and not helping might lead to moral pressure.

The results of the RVR model demonstrate that changes in functional connections (FCs) among brain regions that exhibit greater activation under low time pressure in the other‐concerned (prosocial) network can reliably predict the effect of time pressure on prosocial decision‐making at the behavioral level. Notably, increased FC between the PUT and the omPFC was found to predict a reduced susceptibility to time pressure during prosocial decision‐making. This suggests that the emotional value and sense of morality associated with helping others (which can be considered as a reward) alleviate the effect of time pressure on prosocial decision‐making (Lazar & Eisenberger, 2022; Whillans et al., 2016). Additionally, an increased FC between PUT and the MTG was associated with a lower susceptibility to time pressure during prosocial decision‐making. Both PUT and MTG regions are involved in emotional regulation and social–emotional regulation, respectively (Corr et al., 2021; Goldin et al., 2008; Schreuders et al., 2018), and their connectivity may represent a moderating effect of emotional regulation on the influence of time pressure on prosocial behavior (Davis et al., 2018). On the other hand, FC between the MCG and the orbIFG was found to positively predict the influence of time pressure on prosocial decision‐making. These regions are both connected to empathy (Lamm et al., 2011; Seehausen et al., 2016), and this connectivity (MCG−orbIFG) verifies that individuals with higher empathy scores are more susceptible to the effects of time pressure when making prosocial decisions. Similar effects were found in the FC between PUT and the SPG. Increased FC between these regions suggests that participants may tend to voluntarily direct their attention (Shomstein, 2012) towards reward‐related information, such as the number of tokens deducted, thereby reinforcing the effect of time pressure on prosocial decision‐making.

The current study also found that individuals' empathy traits play a moderating role in the influence of time pressure on prosocial decision‐making. At the behavioral level, the effect of time pressure on prosocial behavior was positively related to individual empathy traits, which is consistent with previous findings (Chen & Krajbich, 2018; Mischkowski & Gloeckner, 2016). In other words, individuals' empathy traits can dictate the extent to which they are affected by time pressure in the decision‐making process (Locander et al., 2020). Specifically, participants with higher affective empathy scores were more susceptible to the effects of time pressure when making prosocial decisions, as evidenced by the greater discrepancy in acceptance rates between low and high time pressure conditions among high empathy participants compared to those with lower empathy scores. Considering the behavioral data shows a non‐normal distribution, we used Spearman correlation to verify the results of Pearson correlation analysis, and both revealed significant results. Furthermore, activation of the omPFC was found to mediate the influence of empathy traits on prosocial tendency, suggesting that the empathic trait regulates value‐related brain regions, such as the prefrontal cortex, and affects prosocial behavior (Janowski et al., 2013). Here we must emphasize that this is an exploratory analysis and the correlation coefficients between the omPFC activation values and behavioral data (p = .105 after FDR), as well as empathy trait (p = .098 after FDR). This insignificance may be caused by the limited sample size. However, this result can provide a reference for future research. The results of PPI further demonstrate the relationship between the omPFC and empathy since a positive connection with empathy‐related regions such as IPL and MTG was revealed (Goldin et al., 2008; Li et al., 2021). It is worth noting that only affective empathy had a significant influence on the effect of time pressure on prosocial decision‐making in our study because the experimental scenario mainly induced empathy for negative emotions, such as pain and fear induced by noise threat, without involving cognitive processing components. Additionally, self‐reported cognitive empathy and affective empathy were not related, indicating that they are independent components of each other. This discovery provides a novel perspective on the neural mechanism through which empathy traits affect prosocial decision‐making.

In summary, the present study revealed the neural mechanisms through which time pressure affects prosocial decision‐making, leading to a better understanding and prediction of prosocial behavior under such constraints. Unlike self‐concerned decision‐making, time pressure produces modulation in regions associated with empathy, valuation, emotion, and reward in prosocial decision‐making. Furthermore, the trait of empathy can regulate the influence of time pressure on prosocial decision‐making. By examining both the behavioral and neurological perspectives, the current study has elucidated the effect of time pressure on prosocial behavior and confirmed the pivotal role of empathy in mediating this effect. When individuals face prosocial decisions under time pressure, brain regions responsible for empathy and valuation are suppressed, indicating a potential to enhance an individual's prosocial tendency in emergencies through targeted brain stimulation (Yuan et al., 2022; Zinchenko et al., 2021). The prosocial decision‐making network in the RVR model also provides a reference for understanding how functional connections between other‐concerned brain regions influence prosocial behavior. Future research could further validate and refine the model through the examination of big data or cross‐cultural studies.

AUTHOR CONTRIBUTIONS

Fang Cui and Zhengjie Liu conceived the experiment. Zhengjie Liu, Yashi Xu, and Hailing Zhao conducted the experiment; Zhengjie Liu, Jie Liu, and Fang Cui analyzed data; Zhengjie Liu and Fang Cui wrote the manuscript; Fang Cui and Jie Liu reviewed the manuscript.

CONFLICT OF INTEREST STATEMENT

The authors declare no competing interests in relation to the subject of this study.

Supporting information

Appendix S1: Supporting information

Click here for additional data file. (20.7KB, docx)

ACKNOWLEDGMENTS

This work was supported by the Science and Technology Innovation Commission of Shenzhen (JCYJ20210308103903001 to Fang Cui) and the National Natural Science Foundation of China (no. 32171013 to Fang Cui).

Liu, Z. , Zhao, H. , Xu, Y. , Liu, J. , & Cui, F. (2023). Prosocial decision‐making under time pressure: Behavioral and neural mechanisms. Human Brain Mapping, 44(17), 6090–6104. 10.1002/hbm.26499

Contributor Information

Jie Liu, Email: ljier06@gmail.com.

Fang Cui, Email: cuifang0826@gmail.com.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are openly available in Zhengjie Liu at https://www.scidb.cn/s/nmIBZr.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix S1: Supporting information

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Data Availability Statement

The data that support the findings of this study are openly available in Zhengjie Liu at https://www.scidb.cn/s/nmIBZr.

Articles from Human Brain Mapping are provided here courtesy of Wiley

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