Neural mechanisms underlying reward processing and social cognition: A replication study with a Japanese sample

Kenta Ohnishi; Michiyo Sugawara; Yoko Mano; Shinsuke Suzuki

doi:10.1371/journal.pone.0328424

Abstract

Neural functions underlying reward processing and social cognition play a critical role in everyday decision-making. Given that these processes may be shaped by cultural factors, it is essential to examine their cross-cultural generalizability. In this study, we used functional MRI to scan native Japanese speakers as they performed two well-established experimental paradigms: the Monetary Incentive Delay (MID) task for reward processing and the Theory of Mind (ToM) task for social cognition. We successfully replicated previous findings. Specifically, in the MID task, reward expectation and reward outcome were associated with neural activity in the ventral striatum and ventromedial prefrontal cortex. In the ToM task, social cognition was linked to activation in the temporoparietal junction. Notably, the posterior cingulate cortex was engaged in both tasks, suggesting its integrative role across cognitive domains. Together, these results replicate and extend earlier work, supporting the cross-cultural generalizability of the neural mechanisms underlying reward and social cognition, and further validating our fMRI protocol for future research.

Citation: Ohnishi K, Sugawara M, Mano Y, Suzuki S (2025) Neural mechanisms underlying reward processing and social cognition: A replication study with a Japanese sample. PLoS One 20(10): e0328424. https://doi.org/10.1371/journal.pone.0328424

Editor: Akitoshi Ogawa, Sapporo Gakuin University, JAPAN

Received: July 1, 2025; Accepted: October 1, 2025; Published: October 22, 2025

Copyright: © 2025 Ohnishi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Behavioral data and code are available at Open Science Framework (https://osf.io/ahv9m/). MRI data are available at OpenNeuro (https://openneuro.org/datasets/ds006401/versions/1.0.0).

Funding: This work was supported by the JSPS KAKENHI Grant Number 22K21357 (S.S.), 25H01173 (S.S.), and JST SPRING Japan Grant Number JPMJSP2174 (K.O.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

A new interdisciplinary field known as neuroeconomics, which integrates neuroscience and economics, emerged in the early 2000s [1,2]. A series of studies in this field has elucidated the neural mechanisms underlying key cognitive and emotional processes that have been extensively examined in social sciences, particularly focusing on themes such as reward processing and social cognition. These studies have implicated a brain network including the ventral striatum and ventromedial prefrontal cortex (vmPFC) in reward processing [3–5], and another network including the temporoparietal junction (TPJ) in social cognition [6–8].

Our economic decisions are guided by expected rewards. For instance, investment behavior relies on expectations about whether the value of an asset will rise or fall. The expectations can subsequently be revised in response to the outcomes of realized rewards. Accumulating evidence from human neuroimaging studies employing the Monetary Incentive Delay (MID) task has demonstrated that the ventral striatum conveys information about expected rewards, while the vmPFC encodes reward outcomes [9–11]. Consistent with these findings, meta-analyses have repeatedly identified these two regions as central components of the brain’s reward processing network [3,4]. The MID task is also widely used to investigate individual differences in neural reward sensitivity and their relationships with various mental disorders and personality traits [12,13].

Economic decisions often involve social contexts. Therefore, the ability to infer others’ intentions, beliefs, and mental states is crucial for making appropriate decisions. For example, in stock trading, it is important to consider what other investors believe about market conditions, while in business decision-making, anticipating competitors’ next actions is essential. Brain regions associated with such social inference—also referred to as social cognition—have been localized using the Theory of Mind (ToM) task [14,15]. A large body of research using this task has shown that social cognition recruits multiple brain regions, including the TPJ, dmPFC, temporal pole, and posterior cingulate cortex (PCC) [16]. The ToM task is also commonly used in psychiatric and developmental research to explore individual differences in the neural basis of social cognition [17,18].

While reward processing and social cognition both play pivotal roles in economic decision-making, less is known about whether there is a brain region commonly involved in both functions. There is still a lack of research in which both the ToM task and the MID task are administered to the same participants [19]. Examining both functions within the same participants may offer a more unified account of the neural architecture supporting economic decision-making.

In this study, we aimed to replicate previous findings from the MID and ToM tasks in a Japanese sample, using a newly installed MRI scanner at Hitotsubashi University in Japan. Replication is a critical step in advancing scientific knowledge, as it allows researchers to evaluate the generalizability of original findings across different settings and populations [20]. This is particularly important for constructs such as social cognition, which may vary depending on cultural and linguistic context [21,22]. Given that cultural factors shape both social cognition and valuation processes, it is essential to examine the replicability of the neural correlates of social cognition across diverse populations [23]. Furthermore, to date, no study has investigated neural activity during both the MID and ToM tasks within the same participants using a Japanese sample.

In our experiment, the same group of participants completed both the MID and ToM tasks while undergoing functional MRI scanning. In the MID task, we observed neural activity associated with reward expectation in the bilateral ventral striatum, and activity related to reward outcomes in the ventromedial prefrontal cortex (vmPFC). In the ToM task, we identified neural activation associated with social cognition in the temporoparietal junction (TPJ). Notably, we also found significant activation in the posterior cingulate cortex (PCC) in response to both reward outcomes and social cognition, suggesting a potential integrative role for this region in economic decision-making.

Methods

This study was approved by the Ethics Committee of Hitotsubashi University (ID: 2024C051).

Participants

Fourteen participants (thirteen males; age = 25.64 6.74 years, mean SD) completed both the Monetary Incentive Delay (MID) task and the Theory of Mind (ToM) task from April 30 to May 16, 2025. All participants were native Japanese speakers, and pre-screened to exclude individuals with a history of neurological or psychiatric illness. Informed written consent was obtained from all participants. They received monetary rewards based on their performance in the MID task (see below), in addition to a participation fee of 4,000 Japanese Yen (JPY).

Experimental tasks

Participants performed the MID and ToM tasks in the fixed order.

MID task. In this task [9], participants were required to press a button as quickly as possible when a target shape appeared (Fig 1a). Each trial began with an inter-trial interval (ITI) lasting 2–12 seconds, followed by a cue indicating the amount of monetary reward available upon a successful response (Cue phase, 1 second). After a short inter-stimulus interval (ISI) of 2–2.5 seconds, a square target shape was presented (Target phase, approximately 0.25 seconds). If the participant pressed the button within the time limit (i.e., while the target was on screen), the trial was considered successful, and the participant received the indicated reward. If the response was too late, no reward was given. The time limit (i.e., target presentation duration) was individually adjusted to maintain a success rate of approximately 0.66. The initial duration was set to 0.25 seconds and was adaptively updated based on performance: if the correct response rate up to the current trial exceeded 66%, the time limit was reduced by 0.025 seconds in the next trial; if it fell below 66%, the time limit was increased by 0.025 seconds. Finally, participants received feedback indicating the success or failure of their response (Outcome phase, 1 second).

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Fig 1. Experimental task and behavior.

(a) Illustration of the Monetary Incentive Delay (MID) task. On each trial, participants were instructed to press a button as quickly as possible when a target rectangle appeared. If the button was pressed within the time limit (i.e., while the target was on screen), the trial outcome was considered successful, and the participant received the monetary reward indicated in the Cue phase. ITI, inter-trial-interval; ISI, inter-stimulus-interval. (b) Illustration of the Theory of Mind (ToM) task. On each trial, participants read a sentence stimulus presented on a computer screen and responded by pressing a button to indicate whether the content was “True” or “False.” The sentence set included 10 descriptions about social situations (Social condition) and 10 about nonsocial situations (Nonsocial condition). (c) Success rate in the MID task (Mean SEM across participants). Success rates are plotted as a function of reward expectation (i.e., the monetary amount available upon a successful outcome). *p < 0.05. (d) Correct rate and reaction time in the ToM task (Mean SEM across participants). The data are shown separately for the Social and Nonsocial conditions. Left, correct rate. Right, reaction time. ***p < 0.001.

https://doi.org/10.1371/journal.pone.0328424.g001

Participants completed two runs of the task, each consisting of 30 trials. The reward amount varied across trials (0 JPY, 20 JPY, or 400 JPY), and the order of reward conditions was randomized across participants.

ToM task. In this task [14,15], participants read sentences presented on a computer screen and responded by pressing a button to indicate whether the content was “True” or “False” (Fig 1b). Each trial began with an inter-trial interval (ITI) of 12 seconds, followed by the presentation of a sentence stimulus (Text phase, 10 seconds). Participants were then prompted to judge the truthfulness of the statement (Question phase, 4 seconds). No feedback was provided regarding their responses. The sentence set consisted of 20 stimuli: 10 described social situations (Social condition: e.g., “The morning of high school dance Yuki placed her high heel shoes under her dress and then went shopping. That afternoon, her sister borrowed the shoes and later put them under Yuki’s bed. Yuki gets ready assuming her shoes are under the dress.”) and 10 described nonsocial situations (Nonsocial condition: e.g., “Part of the garden is supposed to be reserved for the roses; it’s labeled accordingly. Recently the garden has run wild, and dandelions have taken over the entire flower bed. According to the label, these flowers are roses.”). The sentences were presented in random order across participants. See the article [15] for the full list of sentence stimuli.

Reward payment

Participants received a participation fee of 4,000 JPY. In addition, they were awarded a performance-based bonus from the MID task. Specifically, at the end of the experiment, one trial was randomly selected, and the monetary reward from that trial was implemented as an additional payment.

Behavioral data analysis

MID task. To examine the effect of reward expectation on the success rate, we conducted a generalized linear mixed-effects model (GLMM) analysis:

where R denotes the amount of monetary reward presented during the Cue phase, and T represents the trial number (i.e., 1 for the first trial, 2 for the second trial, and so on). We included the trial number, T, to assess the effect of experience (learning).

ToM task. We conducted GLMM analyses to examine the effects of experimental condition on correct rate and reaction time. The models were specified as follows:

where C indicates the condition (1 for Social, 0 for Nonsocial), and T again represents the trial number.

fMRI data acquisition

We collected fMRI images using a 3T Siemens MAGNETOM Prisma scanner located at the HIAS Brain Research Center (Hitotsubashi University, Tokyo, Japan) with a 32-channel radio frequency coil. The BOLD signal was measured using a T2*-weighted gradient-echo echo-planar imaging (EPI) sequence with the following parameters: TR = 800 ms, TE = 34.4 ms, flip angle = 52°, voxel size = 2.4 × 2.4 × 2.4 mm, matrix size = 86 × 86, 60 slices aligned to the AC–PC plane, multiband acceleration factor = 6. Before the functional runs, high-resolution (0.9 mm³) anatomical images were acquired using a standard MPRAGE pulse sequence (TR = 2,300 ms, TE = 2.32 ms, FA = 8°).

fMRI data preprocessing

Functional MRI data were preprocessed using standard procedures implemented in SPM12. For each participant, images were realigned to the first volume to correct for head motion, spatially normalized to the MNI template, and temporally filtered using a high-pass filter with a cutoff of 128 seconds. Spatial smoothing was applied using an 8-mm full-width at half-maximum (FWHM) Gaussian kernel. In addition, geometric distortions in the fMRI images were corrected using the SynBOLD-DisCo method after the realignment [24].

fMRI data analysis

To identify the neural correlates of reward processing and social cognition, we performed standard General Linear Model (GLM) analyses.

GLM for the MID task. We defined a separate GLM for each participant. The design matrix included the following regressors: (1) a boxcar function representing the Cue phase, (2) a boxcar function for the Target phase, and (3) two boxcar functions for the Outcome phase, modeled separately for success and failure trials. In addition, a parametric modulator was included for the Cue phase boxcar function, encoding the amount of monetary reward that could be obtained upon a successful outcome. All regressors were convolved with a canonical hemodynamic response function (HRF). Six motion-correction parameters were included as regressors of no interest to account for head movement artifacts. Furthermore, we included one-hot indicator regressors for volumes exhibiting excessive framewise displacement (FD > 0.9 mm), thereby censoring motion outliers [25–27]. We defined two contrasts of interest: reward expectation (i.e., the parametric modulator during the Cue phase) and reward outcome (i.e., the contrast between success and failure trials during the Outcome phase). For each participant, contrast images were estimated at every voxel across the whole brain and then submitted to a random-effects group-level analysis.

GLM for the ToM task. The design matrix included two regressors: boxcar functions covering the Text and Question phases for the Social and Nonsocial conditions, respectively. Following previous studies [15], we modeled neural activity across the Text and Question phases as a single regressor for each condition. Six motion-correction parameters were included as regressors of no interest. The primary contrast of interest for social cognition was the comparison between the Social and Nonsocial conditions.

Whole-brain analysis. We set the significance threshold at p < 0.05, corrected for multiple comparisons at the cluster level across the whole brain, using a cluster-forming threshold of p < 0.001 (uncorrected) [28]. The unthresholded activation maps are available at NeuroVault (https://neurovault.org/collections/21164/).

ROI analysis. Regions of interest (ROIs) were defined based on association maps from Neurosynth (https://neurosynth.org/) [29]. We used the terms “reward” and “social” to identify peak voxels associated with reward processing and social cognition, respectively. Spherical ROIs (10 mm radius) were then centered on these peak coordinates (reward-related ROIs: ventromedial prefrontal cortex, [x, y, z] = [2.0, 58.0, −8.0]; right ventral striatum, [12.0, 10.0, −8.0], and left ventral striatum, [−12.0, 10.0, −8.0]; and social cognition-related ROIs: right temporoparietal junction, [50.0, −44.0, 10.0], left temporoparietal junction, [−56.0, −58.0, 20.0], right temporal pole, [52.0, 8.0, −32.0], left temporal pole, [−50.0, 16.0, −28.0], dorsomedial prefrontal cortex, [2.0, 56.0, 20.0], posterior cingulate cortex, [−2.0,-56.0,36.0]). Statistical significance was assessed at p < 0.05 (uncorrected), and results were also evaluated using Bonferroni correction for multiple comparisons across ROIs.

Results

Behavior

In the Monetary Incentive Delay (MID) task, participants successfully pressed the button within the time limit in approximately 64% of trials (success rate = 0.641 0.010, mean SEM across participants). Furthermore, the success rate was significantly modulated by reward expectation—that is, the amount of money obtained upon a successful outcome (see Fig 1c; effect of reward expectation: = 0.089 x 10⁻² 0.040 x 10⁻², t = 2.188, and p = 0.029). There was no significant improvement in the success rate over time (effect of trial number: = −0.066 x 10⁻² 0.416 x 10⁻², t = −0.160, and p = 0.873).

In the Theory of Mind (ToM) task, participants gave the correct answer in approximately 82% of trials (correct rate = 0.821 0.029). When comparing the Social and Nonsocial conditions, the correct rate was slightly higher in the Nonsocial condition (see Fig 1d, left; effect of condition: = −0.988 0.416, t = −2.373, and p = 0.018). Reaction time was also shorter in the Nonsocial than in the Social condition (see Fig 1d, right; effect of condition: = 0.336 0.066, t = 5.059, and p < 0.001). Neither the correct rate nor reaction time was significantly improved over time (effect of trial number on correct rate: = 0.055 0.039, t = 1.394, and p = 0.164; effect of trial number on reaction time: = 0.007 0.006, t = −1.451, and p = 0.148).

Neuroimaging: MID task

We first identified neural correlates of reward expectation (i.e., the parametric modulation by the amount of monetary reward presented at the Cue phase) and outcomes (i.e., the contrast between success and failure at the Outcome phase) in the MID task. Whole-brain analysis revealed that, at the time of cue presentation, reward expectation was encoded in a large cluster including the ventral striatum (Fig 2a, p < 0.05, whole-brain corrected at the cluster level; see S1 Table for other activated regions). The BOLD signal within this cluster was significantly correlated with the amount of money obtainable upon a successful outcome during the Cue phase. Further analysis on independently defined regions of interest (ROIs) demonstrated that the left and right ventral striatum, as well as the ventromedial prefrontal cortex (vmPFC), significantly represented reward expectation (Fig 2b: t = 4.869, p = 0.003 in the left ventral striatum; t = 5.102, p = 0.002 in the right ventral striatum; t = 2.564, p = 0.027 in the vmPFC). Notably, the BOLD signal in the ventral striatum varied in a graded manner according to the magnitude of reward expectation (S1a Fig).

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Fig 2. Neuroimaging results.

(a) Whole-brain analysis of neural correlates of reward expectation in the MID task. Statistical parametric map showing neural activity parametrically modulated by the amount of monetary reward presented during the Cue phase. The activation map is thresholded at p < 0.001 (uncorrected) for display purposes. (b) Independent ROI analysis of reward expectation in the MID task. Bar plots show the effect sizes of reward expectation (Mean ± SEM across participants) in independently defined regions of interest. L.vStr, left ventral striatum; R.vStr, right ventral striatum; and vmPFC, ventromedial prefrontal cortex. ***p < 0.001 and *p < 0.05. (c) Whole-brain analysis of neural correlates of reward outcome in the MID task. Statistical map showing neural activity associated with the contrast between success and failure during the Outcome phase. The activation map is thresholded at p < 0.001 (uncorrected) for display purposes. (d) Independent ROI analysis of reward outcome in the MID task. Bar plots show the effect sizes of reward outcome (Mean ± SEM across participants). (e) Whole-brain analysis of neural correlates of social cognition in the ToM task. Statistical map showing neural activity associated with the contrast between Social and Nonsocial conditions. The activation map is thresholded at p < 0.001 (uncorrected) for display purposes. (f) Independent ROI analysis of social cognition in the ToM task. Bar plots show effect sizes of social cognition (Mean ± SEM across participants). L.TPJ, left temporoparietal junction; R.TPJ, right temporoparietal junction; L.TP, left temporal pole; R.TP, right temporal pole; dmpFC, dorsomedial prefrontal cortex; and PCC, posterior cingulate cortex. **p < 0.01.

https://doi.org/10.1371/journal.pone.0328424.g002

Whole-brain analysis of reward outcomes revealed that the vmPFC showed greater neural activity for successful compared to unsuccessful trials during the Outcome phase (Fig 2c, p < 0.05, whole-brain corrected at the cluster level; see S1 Table for other activated regions). Independent ROI analyses further demonstrated that, in addition to the vmPFC, the right ventral striatum also represented reward outcomes (Fig 2d: t = 7.021, p < 0.001 in the vmPFC; t = 2.281, p = 0.040 in the right ventral striatum; t = 1.850, p = 0.087 in the left ventral striatum). See S1b Fig for separate visualizations of neural activation in success and failure trials.

Neuroimaging: ToM task

In the ToM task, whole-brain analysis revealed that neural activity in the right temporoparietal junction (TPJ) and temporal poles (TP) was associated with social cognition (Fig 2e, p < 0.05, whole-brain corrected at the cluster level; see S2 Table for other activated regions). BOLD signals in the TPJ and TP were significantly elevated in the Social condition compared to the Nonsocial condition. In addition to these areas, independent ROI analyses showed that the posterior cingulate cortex (PCC), but not the dorsomedial prefrontal cortex (dmPFC), exhibited differential activation between the Social and Nonsocial conditions (Fig 2f: t = 3.595, p = 0.003 in the left TPJ; t = 3.366, p = 0.005 in the right TPJ; t = 2.914, p = 0.012 in the left TP; t = 3.693, p = 0.003 in the right TP; t = 1.506, p = 0.156 in the dmPFC; t = 2.878, p = 0.013 in the PCC). See S1c Fig for neural responses in the Social and Nonsocial conditions displayed separately.

Neuroimaging: shared representation between the MID and ToM tasks

Next, we examined whether any brain regions are commonly involved in both reward processing and social cognition. To this end, we tested whether activity in the social cognition-related ROIs was modulated by reward expectation or outcome in the MID task, and conversely, whether activity in the reward-related ROIs was modulated by social cognition during the ToM task.

Several social cognition-related ROIs were found to be associated with reward processing (Fig 3a,3b). Neural activity in these ROIs was significantly modulated by the magnitude of monetary reward presented during the Cue phase (t = 2.504, p = 0.026 in the left TPJ; t = 2.665, p = 0.019 in the right TPJ; t = 2.249, p = 0.043 in the left TP; t = 2.078, p = 0.058 in the right TP; t = 2.450, p = 0.029 in the dmPFC; t = 2.110, p = 0.055 in the PCC) and/or by the contrast between success and failure during the Outcome phase of the MID task (t = 2.400, p = 0.032 in the left TPJ; t = 0.402, p = 0.695 in the right TPJ; t = 2.513, p = 0.026 in the left TP; t = 1.440, p = 0.173 in the right TP; t = 2.631, p = 0.021 in the dmPFC; t = 3.685, p = 0.003 in the PCC). However, after applying Bonferroni correction for multiple comparisons across ROIs, only the modulation of the posterior cingulate cortex (PCC) by reward outcomes remained significant, suggesting that the PCC may play a pivotal role in both reward processing and social cognition.

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Fig 3. Shared representation between MID and ToM tasks.

(a) Neural correlates of reward expectation during the MID task on the social cognition-related ROIs. Bar plots show the effect sizes of reward expectation (i.e., the parametric modulation by the amount of monetary reward presented during the Cue phase) (mean ± SEM across participants). L.TPJ, left temporoparietal junction; R.TPJ, right temporoparietal junction; L.TP, left temporal pole; R.TP, right temporal pole; dmpFC, dorsomedial prefrontal cortex; and PCC, posterior cingulate cortex. *p < 0.05. (b) Neural correlates of reward outcome during the MID task on the social cognition-related ROIs. Bar plots show the effect sizes of reward outcome (i.e., the contrast between success and failure during the Outcome phase) (mean ± SEM across participants). **p < 0.01. (c) Neural correlates of social cognition during the ToM task on the reward-related ROIs. Bar plots show the effect sizes of social cognition (i.e., the contrast between Social and Nonsocial conditions) (mean ± SEM across participants).

https://doi.org/10.1371/journal.pone.0328424.g003

In contrast, none of the reward-related ROIs showed significant modulation by social cognition (Fig 3c). Specifically, there were no significant differences in neural activity between the Social and Nonsocial conditions of the ToM task within these regions (t = −0.535, p = 0.602 in the left ventral striatum; t = −0.099, p = 0.922 in the right ventral striatum; t = 1.286, p = 0.221 in the vmPFC).

Neuroimaging: robustness check

In this study, ROIs were defined as 10-mm-radius spheres centered on peak coordinates identified with Neurosynth [29]. During preprocessing, we applied spatial smoothing using an 8-mm full-width at half-maximum (FWHM) Gaussian kernel. As these settings may be suboptimal for small loci such as the ventral striatum, we conducted robustness checks using smaller ROIs (5-mm-radius spheres) and a lighter smoothing kernel (FWHM = 4 mm). The additional analyses yielded results consistent with the originals: the ventral striatum and vmPFC were associated with reward processing, the TPJ and TP with social cognition, and the PCC with both reward-outcome processing and social cognition (S2 Fig).

Discussion

This study aimed to replicate previous findings on the neural correlates of reward processing and social cognition in a Japanese sample. To this end, we conducted a functional MRI (fMRI) experiment using the Monetary Incentive Delay (MID) task [9] and the Theory of Mind (ToM) task [14,15] with participants who were native Japanese speakers.

While the MID and ToM tasks are widely used around the world to investigate neural activity related to reward processing and social cognition, prior research has also reported cultural differences in the underlying neural mechanisms [21,23]. For example, one study demonstrated that, during the MID task, European American and Chinese participants exhibited differential neural responses to social rewards in the ventral striatum (nucleus accumbens) [30]. Furthermore, in a false-belief task—a common Theory of Mind paradigm—differences in inferior frontal gyrus activation were observed between American English-speaking monolinguals and Japanese-English bilinguals [31].

In the MID task, we observed that reward expectation and reward outcome were associated with neural activity in the ventral striatum and ventromedial prefrontal cortex (vmPFC), consistent with previous findings [9–11]. In the ToM task, social cognition was associated with activation in the temporoparietal junction (TPJ) and temporal pole (TP), aligning with prior studies [16]. In contrast, we did not observe significant activation in the dorsomedial prefrontal cortex (dmPFC) associated with social cognition. Overall, these results broadly replicate and extend earlier findings—with the exception of dmPFC activation in the ToM task—and support the cross-cultural generalizability of the neural mechanisms underlying reward processing and social cognition.

Moreover, we found that the posterior cingulate cortex (PCC) was activated in response to both reward processing and social cognition, suggesting a potential integrative role for this region in economic decision-making. This finding aligns with recent theories proposing the PCC as part of a domain-general network for internally guided cognition, serving as a neural bridge between reward evaluation and social inference. More specifically, we conjecture that the PCC is involved in computing the values of available options for decision making by integrating multiple information sources, including expected rewards, inferences about others’ mental states, and social norms. This view is consistent with the PCC’s anatomical connectivity [32–35]: it is interconnected with reward-related regions (e.g., vmPFC and ventral striatum) and with regions implicated in social cognition (e.g., TPJ, TP, and posterior superior temporal sulcus). Furthermore, the PCC has been implicated in self-referential processing [36], reward-seeking decision-making [37], and mentalizing [38], and is recognized as a core component of the default mode network [39]. Together, these findings highlight the PCC as a potential hub integrating reward-related and social cognitive processes.

This study has several limitations. First, the sample size (= 14 participants) is smaller than is typical by current standards, which reduces statistical power and can increase the likelihood of false negatives as well as the possibility of false positives [40]. Accordingly, the results should be interpreted with caution. Second, the fMRI data were acquired using multiband EPI with a relatively high acceleration factor (MBF = 6). This may complicate direct comparisons with prior work, as multiband acquisition has been reported to attenuate the detection of reward-anticipation responses in subcortical regions [41].

In conclusion, this study supports the generalizability of previously reported neural and behavioral correlates of reward processing and social cognition by successfully replicating these findings in a Japanese sample. The successful replication also affirms the validity of our fMRI protocols for use in future neuroeconomics research.

Supporting information

S1 Fig. Supplementary analysis on the neuroimaging data I.

https://doi.org/10.1371/journal.pone.0328424.s001

(PDF)

S2 Fig. Supplementary analysis on the neuroimaging data II.

https://doi.org/10.1371/journal.pone.0328424.s002

(PDF)

S1 Table. Brain areas exhibiting significant changes in the BOLD signal associated with reward processing in the Monetary Incentive Delay task.

https://doi.org/10.1371/journal.pone.0328424.s003

(PDF)

S2 Table. Brain areas exhibiting significant changes in the BOLD signal associated with social cognition in the Theory of Mind task.

https://doi.org/10.1371/journal.pone.0328424.s004

(PDF)

References

1. Fehr E, Camerer CF. Social neuroeconomics: the neural circuitry of social preferences. Trends Cogn Sci. 2007;11(10):419–27. pmid:17913566
- View Article
- PubMed/NCBI
- Google Scholar
2. Glimcher PW. Neuroeconomics: Decision Making and the Brain. Academic Press. 2013.
3. Bartra O, McGuire JT, Kable JW. The valuation system: a coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value. Neuroimage. 2013;76:412–27. pmid:23507394
- View Article
- PubMed/NCBI
- Google Scholar
4. Clithero JA, Rangel A. Informatic parcellation of the network involved in the computation of subjective value. Soc Cogn Affect Neurosci. 2014;9(9):1289–302. pmid:23887811
- View Article
- PubMed/NCBI
- Google Scholar
5. Suzuki S. Constructing value signals for food rewards: determinants and the integration. Current Opinion in Behavioral Sciences. 2022;46:101178.
- View Article
- Google Scholar
6. Frith CD, Frith U. Mechanisms of social cognition. Annu Rev Psychol. 2012;63:287–313. pmid:21838544
- View Article
- PubMed/NCBI
- Google Scholar
7. Van Overwalle F. Social cognition and the brain: a meta-analysis. Hum Brain Mapp. 2009;30(3):829–58. pmid:18381770
- View Article
- PubMed/NCBI
- Google Scholar
8. Suzuki S, O’Doherty JP. Breaking human social decision making into multiple components and then putting them together again. Cortex. 2020;127:221–30. pmid:32224320
- View Article
- PubMed/NCBI
- Google Scholar
9. Knutson B, Westdorp A, Kaiser E, Hommer D. FMRI visualization of brain activity during a monetary incentive delay task. Neuroimage. 2000;12(1):20–7. pmid:10875899
- View Article
- PubMed/NCBI
- Google Scholar
10. Oldham S, Murawski C, Fornito A, Youssef G, Yücel M, Lorenzetti V. The anticipation and outcome phases of reward and loss processing: A neuroimaging meta-analysis of the monetary incentive delay task. Hum Brain Mapp. 2018;39(8):3398–418. pmid:29696725
- View Article
- PubMed/NCBI
- Google Scholar
11. Jauhar S, Fortea L, Solanes A, Albajes-Eizagirre A, McKenna PJ, Radua J. Brain activations associated with anticipation and delivery of monetary reward: A systematic review and meta-analysis of fMRI studies. PLoS One. 2021;16(8):e0255292. pmid:34351957
- View Article
- PubMed/NCBI
- Google Scholar
12. Ng TH, Alloy LB, Smith DV. Meta-analysis of reward processing in major depressive disorder reveals distinct abnormalities within the reward circuit. Transl Psychiatry. 2019;9(1):293. pmid:31712555
- View Article
- PubMed/NCBI
- Google Scholar
13. Matyjek M, Bayer M, Dziobek I. Reward responsiveness in autism and autistic traits - Evidence from neuronal, autonomic, and behavioural levels. Neuroimage Clin. 2023;38:103442. pmid:37285795
- View Article
- PubMed/NCBI
- Google Scholar
14. Dodell-Feder D, Koster-Hale J, Bedny M, Saxe R. fMRI item analysis in a theory of mind task. Neuroimage. 2011;55(2):705–12. pmid:21182967
- View Article
- PubMed/NCBI
- Google Scholar
15. Ogawa A, Yokoyama R, Kameda T. Development of a Japanese version of a theory-of-mind functional localizer for functional magnetic resonance imaging. Shinrigaku Kenkyu. 2017;88: 366–75.
- View Article
- Google Scholar
16. Schurz M, Radua J, Aichhorn M, Richlan F, Perner J. Fractionating theory of mind: a meta-analysis of functional brain imaging studies. Neurosci Biobehav Rev. 2014;42:9–34. pmid:24486722
- View Article
- PubMed/NCBI
- Google Scholar
17. Dodell-Feder D, DeLisi LE, Hooker CI. Neural disruption to theory of mind predicts daily social functioning in individuals at familial high-risk for schizophrenia. Soc Cogn Affect Neurosci. 2014;9(12):1914–25. pmid:24396009
- View Article
- PubMed/NCBI
- Google Scholar
18. Mahy CEV, Moses LJ, Pfeifer JH. How and where: theory-of-mind in the brain. Dev Cogn Neurosci. 2014;9:68–81. pmid:24552989
- View Article
- PubMed/NCBI
- Google Scholar
19. Erk S, Mohnke S, Ripke S, Lett TA, Veer IM, Wackerhagen C, et al. Functional neuroimaging effects of recently discovered genetic risk loci for schizophrenia and polygenic risk profile in five RDoC subdomains. Transl Psychiatry. 2017;7(1):e997. pmid:28072415
- View Article
- PubMed/NCBI
- Google Scholar
20. Munafò MR, Nosek BA, Bishop DVM, Button KS, Chambers CD, du Sert NP, et al. A manifesto for reproducible science. Nat Hum Behav. 2017;1(1):0021. pmid:33954258
- View Article
- PubMed/NCBI
- Google Scholar
21. Kitayama S, Uskul AK. Culture, mind, and the brain: current evidence and future directions. Annu Rev Psychol. 2011;62:419–49. pmid:21126182
- View Article
- PubMed/NCBI
- Google Scholar
22. Park DC, Huang C-M. Culture Wires the Brain: A Cognitive Neuroscience Perspective. Perspect Psychol Sci. 2010;5(4):391–400. pmid:22866061
- View Article
- PubMed/NCBI
- Google Scholar
23. Han S, Northoff G. Culture-sensitive neural substrates of human cognition: a transcultural neuroimaging approach. Nat Rev Neurosci. 2008;9(8):646–54. pmid:18641669
- View Article
- PubMed/NCBI
- Google Scholar
24. Yu T, Cai LY, Torrisi S, Vu AT, Morgan VL, Goodale SE, et al. Distortion correction of functional MRI without reverse phase encoding scans or field maps. Magn Reson Imaging. 2023;103:18–27. pmid:37400042
- View Article
- PubMed/NCBI
- Google Scholar
25. Siegel JS, Power JD, Dubis JW, Vogel AC, Church JA, Schlaggar BL, et al. Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points. Hum Brain Mapp. 2014;35(5):1981–96. pmid:23861343
- View Article
- PubMed/NCBI
- Google Scholar
26. Power JD, Mitra A, Laumann TO, Snyder AZ, Schlaggar BL, Petersen SE. Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage. 2014;84:320–41. pmid:23994314
- View Article
- PubMed/NCBI
- Google Scholar
27. Power JD, Schlaggar BL, Petersen SE. Recent progress and outstanding issues in motion correction in resting state fMRI. Neuroimage. 2015;105:536–51. pmid:25462692
- View Article
- PubMed/NCBI
- Google Scholar
28. Eklund A, Nichols TE, Knutsson H. Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. Proc Natl Acad Sci U S A. 2016;113(28):7900–5. pmid:27357684
- View Article
- PubMed/NCBI
- Google Scholar
29. Yarkoni T, Poldrack RA, Nichols TE, Van Essen DC, Wager TD. Large-scale automated synthesis of human functional neuroimaging data. Nat Methods. 2011;8(8):665–70. pmid:21706013
- View Article
- PubMed/NCBI
- Google Scholar
30. Blevins E, Ko M, Park B, Qu Y, Knutson B, Tsai JL. Cultural variation in neural responses to social but not monetary reward outcomes. Soc Cogn Affect Neurosci. 2023;18.
- View Article
- Google Scholar
31. Kobayashi C, Glover GH, Temple E. Cultural and linguistic influence on neural bases of “Theory of Mind”: an fMRI study with Japanese bilinguals. Brain Lang. 2006;98(2):210–20. pmid:16753205
- View Article
- PubMed/NCBI
- Google Scholar
32. Leech R, Sharp DJ. The role of the posterior cingulate cortex in cognition and disease. Brain. 2014;137(Pt 1):12–32. pmid:23869106
- View Article
- PubMed/NCBI
- Google Scholar
33. Kunishio K, Haber SN. Primate cingulostriatal projection: limbic striatal versus sensorimotor striatal input. J Comp Neurol. 1994;350(3):337–56. pmid:7533796
- View Article
- PubMed/NCBI
- Google Scholar
34. Parvizi J, Van Hoesen GW, Buckwalter J, Damasio A. Neural connections of the posteromedial cortex in the macaque. Proc Natl Acad Sci U S A. 2006;103(5):1563–8. pmid:16432221
- View Article
- PubMed/NCBI
- Google Scholar
35. Bubb EJ, Metzler-Baddeley C, Aggleton JP. The cingulum bundle: Anatomy, function, and dysfunction. Neurosci Biobehav Rev. 2018;92:104–27. pmid:29753752
- View Article
- PubMed/NCBI
- Google Scholar
36. Northoff G, Heinzel A, de Greck M, Bermpohl F, Dobrowolny H, Panksepp J. Self-referential processing in our brain--a meta-analysis of imaging studies on the self. Neuroimage. 2006;31(1):440–57. pmid:16466680
- View Article
- PubMed/NCBI
- Google Scholar
37. Pearson JM, Heilbronner SR, Barack DL, Hayden BY, Platt ML. Posterior cingulate cortex: adapting behavior to a changing world. Trends Cogn Sci. 2011;15(4):143–51. pmid:21420893
- View Article
- PubMed/NCBI
- Google Scholar
38. Li W, Mai X, Liu C. The default mode network and social understanding of others: what do brain connectivity studies tell us. Front Hum Neurosci. 2014;8:74. pmid:24605094
- View Article
- PubMed/NCBI
- Google Scholar
39. Menon V. 20 years of the default mode network: A review and synthesis. Neuron. 2023;111(16):2469–87. pmid:37167968
- View Article
- PubMed/NCBI
- Google Scholar
40. Button KS, Ioannidis JPA, Mokrysz C, Nosek BA, Flint J, Robinson ESJ, et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci. 2013;14(5):365–76. pmid:23571845
- View Article
- PubMed/NCBI
- Google Scholar
41. Srirangarajan T, Mortazavi L, Bortolini T, Moll J, Knutson B. Multi-band FMRI compromises detection of mesolimbic reward responses. Neuroimage. 2021;244:118617. pmid:34600102
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Fehr E, Camerer CF. Social neuroeconomics: the neural circuitry of social preferences. Trends Cogn Sci. 2007;11(10):419–27. pmid:17913566
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Glimcher PW. Neuroeconomics: Decision Making and the Brain. Academic Press. 2013.

[ref3] 3. Bartra O, McGuire JT, Kable JW. The valuation system: a coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value. Neuroimage. 2013;76:412–27. pmid:23507394
View Article
PubMed/NCBI
Google Scholar

[7] View Article

[8] PubMed/NCBI

[9] Google Scholar

[ref4] 4. Clithero JA, Rangel A. Informatic parcellation of the network involved in the computation of subjective value. Soc Cogn Affect Neurosci. 2014;9(9):1289–302. pmid:23887811
View Article
PubMed/NCBI
Google Scholar

[11] View Article

[12] PubMed/NCBI

[13] Google Scholar

[ref5] 5. Suzuki S. Constructing value signals for food rewards: determinants and the integration. Current Opinion in Behavioral Sciences. 2022;46:101178.
View Article
Google Scholar

[15] View Article

[16] Google Scholar

[ref6] 6. Frith CD, Frith U. Mechanisms of social cognition. Annu Rev Psychol. 2012;63:287–313. pmid:21838544
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref7] 7. Van Overwalle F. Social cognition and the brain: a meta-analysis. Hum Brain Mapp. 2009;30(3):829–58. pmid:18381770
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref8] 8. Suzuki S, O’Doherty JP. Breaking human social decision making into multiple components and then putting them together again. Cortex. 2020;127:221–30. pmid:32224320
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref9] 9. Knutson B, Westdorp A, Kaiser E, Hommer D. FMRI visualization of brain activity during a monetary incentive delay task. Neuroimage. 2000;12(1):20–7. pmid:10875899
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref10] 10. Oldham S, Murawski C, Fornito A, Youssef G, Yücel M, Lorenzetti V. The anticipation and outcome phases of reward and loss processing: A neuroimaging meta-analysis of the monetary incentive delay task. Hum Brain Mapp. 2018;39(8):3398–418. pmid:29696725
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref11] 11. Jauhar S, Fortea L, Solanes A, Albajes-Eizagirre A, McKenna PJ, Radua J. Brain activations associated with anticipation and delivery of monetary reward: A systematic review and meta-analysis of fMRI studies. PLoS One. 2021;16(8):e0255292. pmid:34351957
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref12] 12. Ng TH, Alloy LB, Smith DV. Meta-analysis of reward processing in major depressive disorder reveals distinct abnormalities within the reward circuit. Transl Psychiatry. 2019;9(1):293. pmid:31712555
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref13] 13. Matyjek M, Bayer M, Dziobek I. Reward responsiveness in autism and autistic traits - Evidence from neuronal, autonomic, and behavioural levels. Neuroimage Clin. 2023;38:103442. pmid:37285795
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref14] 14. Dodell-Feder D, Koster-Hale J, Bedny M, Saxe R. fMRI item analysis in a theory of mind task. Neuroimage. 2011;55(2):705–12. pmid:21182967
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref15] 15. Ogawa A, Yokoyama R, Kameda T. Development of a Japanese version of a theory-of-mind functional localizer for functional magnetic resonance imaging. Shinrigaku Kenkyu. 2017;88: 366–75.
View Article
Google Scholar

[54] View Article

[55] Google Scholar

[ref16] 16. Schurz M, Radua J, Aichhorn M, Richlan F, Perner J. Fractionating theory of mind: a meta-analysis of functional brain imaging studies. Neurosci Biobehav Rev. 2014;42:9–34. pmid:24486722
View Article
PubMed/NCBI
Google Scholar

[57] View Article

[58] PubMed/NCBI

[59] Google Scholar

[ref17] 17. Dodell-Feder D, DeLisi LE, Hooker CI. Neural disruption to theory of mind predicts daily social functioning in individuals at familial high-risk for schizophrenia. Soc Cogn Affect Neurosci. 2014;9(12):1914–25. pmid:24396009
View Article
PubMed/NCBI
Google Scholar

[61] View Article

[62] PubMed/NCBI

[63] Google Scholar

[ref18] 18. Mahy CEV, Moses LJ, Pfeifer JH. How and where: theory-of-mind in the brain. Dev Cogn Neurosci. 2014;9:68–81. pmid:24552989
View Article
PubMed/NCBI
Google Scholar

[65] View Article

[66] PubMed/NCBI

[67] Google Scholar

[ref19] 19. Erk S, Mohnke S, Ripke S, Lett TA, Veer IM, Wackerhagen C, et al. Functional neuroimaging effects of recently discovered genetic risk loci for schizophrenia and polygenic risk profile in five RDoC subdomains. Transl Psychiatry. 2017;7(1):e997. pmid:28072415
View Article
PubMed/NCBI
Google Scholar

[69] View Article

[70] PubMed/NCBI

[71] Google Scholar

[ref20] 20. Munafò MR, Nosek BA, Bishop DVM, Button KS, Chambers CD, du Sert NP, et al. A manifesto for reproducible science. Nat Hum Behav. 2017;1(1):0021. pmid:33954258
View Article
PubMed/NCBI
Google Scholar

[73] View Article

[74] PubMed/NCBI

[75] Google Scholar

[ref21] 21. Kitayama S, Uskul AK. Culture, mind, and the brain: current evidence and future directions. Annu Rev Psychol. 2011;62:419–49. pmid:21126182
View Article
PubMed/NCBI
Google Scholar

[77] View Article

[78] PubMed/NCBI

[79] Google Scholar

[ref22] 22. Park DC, Huang C-M. Culture Wires the Brain: A Cognitive Neuroscience Perspective. Perspect Psychol Sci. 2010;5(4):391–400. pmid:22866061
View Article
PubMed/NCBI
Google Scholar

[81] View Article

[82] PubMed/NCBI

[83] Google Scholar

[ref23] 23. Han S, Northoff G. Culture-sensitive neural substrates of human cognition: a transcultural neuroimaging approach. Nat Rev Neurosci. 2008;9(8):646–54. pmid:18641669
View Article
PubMed/NCBI
Google Scholar

[85] View Article

[86] PubMed/NCBI

[87] Google Scholar

[ref24] 24. Yu T, Cai LY, Torrisi S, Vu AT, Morgan VL, Goodale SE, et al. Distortion correction of functional MRI without reverse phase encoding scans or field maps. Magn Reson Imaging. 2023;103:18–27. pmid:37400042
View Article
PubMed/NCBI
Google Scholar

[89] View Article

[90] PubMed/NCBI

[91] Google Scholar

[ref25] 25. Siegel JS, Power JD, Dubis JW, Vogel AC, Church JA, Schlaggar BL, et al. Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points. Hum Brain Mapp. 2014;35(5):1981–96. pmid:23861343
View Article
PubMed/NCBI
Google Scholar

[93] View Article

[94] PubMed/NCBI

[95] Google Scholar

[ref26] 26. Power JD, Mitra A, Laumann TO, Snyder AZ, Schlaggar BL, Petersen SE. Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage. 2014;84:320–41. pmid:23994314
View Article
PubMed/NCBI
Google Scholar

[97] View Article

[98] PubMed/NCBI

[99] Google Scholar

[ref27] 27. Power JD, Schlaggar BL, Petersen SE. Recent progress and outstanding issues in motion correction in resting state fMRI. Neuroimage. 2015;105:536–51. pmid:25462692
View Article
PubMed/NCBI
Google Scholar

[101] View Article

[102] PubMed/NCBI

[103] Google Scholar

[ref28] 28. Eklund A, Nichols TE, Knutsson H. Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. Proc Natl Acad Sci U S A. 2016;113(28):7900–5. pmid:27357684
View Article
PubMed/NCBI
Google Scholar

[105] View Article

[106] PubMed/NCBI

[107] Google Scholar

[ref29] 29. Yarkoni T, Poldrack RA, Nichols TE, Van Essen DC, Wager TD. Large-scale automated synthesis of human functional neuroimaging data. Nat Methods. 2011;8(8):665–70. pmid:21706013
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref30] 30. Blevins E, Ko M, Park B, Qu Y, Knutson B, Tsai JL. Cultural variation in neural responses to social but not monetary reward outcomes. Soc Cogn Affect Neurosci. 2023;18.
View Article
Google Scholar

[113] View Article

[114] Google Scholar

[ref31] 31. Kobayashi C, Glover GH, Temple E. Cultural and linguistic influence on neural bases of “Theory of Mind”: an fMRI study with Japanese bilinguals. Brain Lang. 2006;98(2):210–20. pmid:16753205
View Article
PubMed/NCBI
Google Scholar

[116] View Article

[117] PubMed/NCBI

[118] Google Scholar

[ref32] 32. Leech R, Sharp DJ. The role of the posterior cingulate cortex in cognition and disease. Brain. 2014;137(Pt 1):12–32. pmid:23869106
View Article
PubMed/NCBI
Google Scholar

[120] View Article

[121] PubMed/NCBI

[122] Google Scholar

[ref33] 33. Kunishio K, Haber SN. Primate cingulostriatal projection: limbic striatal versus sensorimotor striatal input. J Comp Neurol. 1994;350(3):337–56. pmid:7533796
View Article
PubMed/NCBI
Google Scholar

[124] View Article

[125] PubMed/NCBI

[126] Google Scholar

[ref34] 34. Parvizi J, Van Hoesen GW, Buckwalter J, Damasio A. Neural connections of the posteromedial cortex in the macaque. Proc Natl Acad Sci U S A. 2006;103(5):1563–8. pmid:16432221
View Article
PubMed/NCBI
Google Scholar

[128] View Article

[129] PubMed/NCBI

[130] Google Scholar

[ref35] 35. Bubb EJ, Metzler-Baddeley C, Aggleton JP. The cingulum bundle: Anatomy, function, and dysfunction. Neurosci Biobehav Rev. 2018;92:104–27. pmid:29753752
View Article
PubMed/NCBI
Google Scholar

[132] View Article

[133] PubMed/NCBI

[134] Google Scholar

[ref36] 36. Northoff G, Heinzel A, de Greck M, Bermpohl F, Dobrowolny H, Panksepp J. Self-referential processing in our brain--a meta-analysis of imaging studies on the self. Neuroimage. 2006;31(1):440–57. pmid:16466680
View Article
PubMed/NCBI
Google Scholar

[136] View Article

[137] PubMed/NCBI

[138] Google Scholar

[ref37] 37. Pearson JM, Heilbronner SR, Barack DL, Hayden BY, Platt ML. Posterior cingulate cortex: adapting behavior to a changing world. Trends Cogn Sci. 2011;15(4):143–51. pmid:21420893
View Article
PubMed/NCBI
Google Scholar

[140] View Article

[141] PubMed/NCBI

[142] Google Scholar

[ref38] 38. Li W, Mai X, Liu C. The default mode network and social understanding of others: what do brain connectivity studies tell us. Front Hum Neurosci. 2014;8:74. pmid:24605094
View Article
PubMed/NCBI
Google Scholar

[144] View Article

[145] PubMed/NCBI

[146] Google Scholar

[ref39] 39. Menon V. 20 years of the default mode network: A review and synthesis. Neuron. 2023;111(16):2469–87. pmid:37167968
View Article
PubMed/NCBI
Google Scholar

[148] View Article

[149] PubMed/NCBI

[150] Google Scholar

[ref40] 40. Button KS, Ioannidis JPA, Mokrysz C, Nosek BA, Flint J, Robinson ESJ, et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci. 2013;14(5):365–76. pmid:23571845
View Article
PubMed/NCBI
Google Scholar

[152] View Article

[153] PubMed/NCBI

[154] Google Scholar

[ref41] 41. Srirangarajan T, Mortazavi L, Bortolini T, Moll J, Knutson B. Multi-band FMRI compromises detection of mesolimbic reward responses. Neuroimage. 2021;244:118617. pmid:34600102
View Article
PubMed/NCBI
Google Scholar

[156] View Article

[157] PubMed/NCBI

[158] Google Scholar

Figures

Abstract

Introduction

Methods

Participants

Experimental tasks

Reward payment

Behavioral data analysis

fMRI data acquisition

fMRI data preprocessing

fMRI data analysis

Results

Behavior

Neuroimaging: MID task

Neuroimaging: ToM task

Neuroimaging: shared representation between the MID and ToM tasks

Neuroimaging: robustness check

Discussion

Supporting information

S1 Fig. Supplementary analysis on the neuroimaging data I.

S2 Fig. Supplementary analysis on the neuroimaging data II.

S1 Table. Brain areas exhibiting significant changes in the BOLD signal associated with reward processing in the Monetary Incentive Delay task.

S2 Table. Brain areas exhibiting significant changes in the BOLD signal associated with social cognition in the Theory of Mind task.

References