Motivation as a Lens for Understanding Information-seeking Behaviors.

Most prior research characterizes information-seeking behaviors as serving utilitarian purposes, such as whether the obtained information can help solve practical problems. However, information-seeking behaviors are sensitive to different contexts (i.e., threat vs. curiosity), despite having equivalent utility. Furthermore, these search behaviors can be modulated by individuals' life history and personality traits. Yet the emphasis on utilitarian utility has precluded the development of a unified model, which explains when and how individuals actively seek information. To account for this variability and flexibility, we propose a unified information-seeking framework that examines information-seeking through the lens of motivation. This unified model accounts for integration across individuals' internal goal states and the salient features of the environment to influence information-seeking behavior. We propose that information-seeking is determined by motivation for information, invigorated either by instrumental utility or hedonic utility, wherein one's personal or environmental context moderates this relationship. Furthermore, we speculate that the final common denominator in guiding information-seeking is the engagement of different neuromodulatory circuits centered on dopaminergic and noradrenergic tone. Our framework provides a unified framework for information-seeking behaviors and generates several testable predictions for future studies.

Tensorial independent component analysis reveals social and reward networks associated with major depressive disorder.

Major depressive disorder (MDD) has been associated with changes in functional brain connectivity. Yet, typical analyses of functional connectivity, such as spatial independent components analysis (ICA) for resting-state data, often ignore sources of between-subject variability, which may be crucial for identifying functional connectivity patterns associated with MDD. Typically, methods like spatial ICA will identify a single component to represent a network like the default mode network (DMN), even if groups within the data show differential DMN coactivation. To address this gap, this project applies a tensorial extension of ICA (tensorial ICA)-which explicitly incorporates between-subject variability-to identify functionally connected networks using functional MRI data from the Human Connectome Project (HCP). Data from the HCP included individuals with a diagnosis of MDD, a family history of MDD, and healthy controls performing a gambling and social cognition task. Based on evidence associating MDD with blunted neural activation to rewards and social stimuli, we predicted that tensorial ICA would identify networks associated with reduced spatiotemporal coherence and blunted social and reward-based network activity in MDD. Across both tasks, tensorial ICA identified three networks showing decreased coherence in MDD. All three networks included ventromedial prefrontal cortex, striatum, and cerebellum and showed different activation across the conditions of their respective tasks. However, MDD was only associated with differences in task-based activation in one network from the social task. Additionally, these results suggest that tensorial ICA could be a valuable tool for understanding clinical differences in relation to network activation and connectivity.

Stress, trauma, and perception of eating behavior changes during the first weeks of the COVID-19 outbreak.

OBJECTIVE: The COVID-19 pandemic is one of the largest collective stressors in recent history. Consistent with prior research, this stress has led to impactful eating behavior change. While prior life traumas also impact eating behavior, it is unclear whether the current stress experienced during COVID-19, and prior life traumas (overall, socially relevant, and nonsocially relevant), interact to influence eating behavior changes. Moreover, it is unclear whether current stress and prior traumas impact how eating behavior changes are perceived (i.e., in magnitude, valence, or both) by the individuals experiencing the changes. Therefore, this study sought to examine both the relationship between current stress and perception of eating behavior changes, as well as the moderating impact of prior life traumas on this relationship. METHODS: Between March and April 2020, participants completed a subjective, self-report online assessment of current stress, prior life traumas, pandemic-related changes in eating behaviors, and the perceived impact of eating behavior changes. RESULTS: Higher current stress was associated with larger, more negative perceptions of eating behavior changes. This relationship was moderated by prior life traumas. Specifically, the association between current stress and perceived negative impact of eating behavior change was potentiated among those with more prior socially relevant (but not nonsocially relevant) traumas. DISCUSSION: These results suggest eating behavior changes occurred early in the pandemic and were uniquely impacted by the cumulative effect of present stress and socially relevant prior life traumas. PUBLIC SIGNIFICANCE: Changes in eating behaviors and pathology have been prevalent during COVID-19. We examined how stress and prior life traumas interacted during the first weeks of COVID-19 to influence perceptions of eating behavior change. As altered perception of eating behaviors is a notable feature of eating pathology, these results will help inform development of intervention targets for those at risk for developing disordered eating during future- and post-pandemic recovery.

Decision uncertainty during hypothesis testing enhances memory accuracy for incidental information.

Humans actively seek information to reduce uncertainty, providing insight on how our decisions causally affect the world. While we know that episodic memories can help support future goal-oriented behaviors, little is known about how hypothesis testing during exploration influences episodic memory. To investigate this question, we designed a hypothesis testing paradigm, in which participants figured out rules to unlock treasure chests. Using this paradigm, we characterized how hypothesis testing during exploration influenced memory for the contents of the treasure chests. We found that there was an inverted U-shaped relationship between decision uncertainty and memory, such that memory was best when decision uncertainty was moderate. An exploratory analysis also showed that surprising outcomes lead to lower memory confidence independent of accuracy. These findings support a model in which moderate decision uncertainty during hypothesis testing enhances incidental information encoding.

Reward enhances connectivity between the ventral striatum and the default mode network.

The default mode network (DMN) has been theorized to participate in a range of social, cognitive, and affective functions. Yet, previous accounts do not consider how the DMN contributes to other brain regions depending on psychological context, thus rendering our understanding of DMN function incomplete. We addressed this gap by applying a novel network-based psychophysiological interaction (nPPI) analysis to the reward task within the Human Connectome Project. We first focused on the task-evoked responses of the DMN and other networks involving the prefrontal cortex, including the executive control network (salience network) and the left and right frontoparietal networks. Consistent with a host of prior studies, the DMN exhibited a relative decrease in activation during the task, while the other networks exhibited a relative increase during the task. Next, we used nPPI analyses to assess whether these networks exhibit task-dependent changes in connectivity with other brain regions. Strikingly, we found that the experience of reward enhances task-dependent connectivity between the DMN and the ventral striatum, an effect that was specific to the DMN. Surprisingly, the strength of DMN-VS connectivity was correlated with personality characteristics relating to openness. Taken together, these results advance models of DMN by demonstrating how it contributes to other brain systems during task performance and how those contributions relate to individual differences.

Age-related differences in ventral striatal and default mode network function during reciprocated trust.

Social relationships change across the lifespan as social networks narrow and motivational priorities shift to the present. Interestingly, aging is also associated with changes in executive function, including decision-making abilities, but it remains unclear how age-related changes in both domains interact to impact financial decisions involving other people. To study this problem, we recruited 50 human participants (Nyounger = 26, ages 18-34; Nolder = 24, ages 63-80) to play an economic trust game as the investor with three partners (friend, stranger, and computer) who played the role of investee. Investors underwent functional magnetic resonance imaging (fMRI) during the trust game while investees were seated outside of the scanner. Building on our previous work with younger adults showing both enhanced striatal responses and altered default-mode network (DMN) connectivity as a function of social closeness during reciprocated trust, we predicted that these relations would exhibit age-related differences. We found that striatal responses to reciprocated trust from friends relative to strangers and computers were blunted in older adults relative to younger adults, thus supporting our primary pre-registered hypothesis regarding social closeness. We also found that older adults exhibited enhanced DMN connectivity with the temporoparietal junction (TPJ) during reciprocated trust from friends compared to computers while younger adults exhibited the opposite pattern. Taken together, these results advance our understanding of age-related differences in sensitivity to social closeness in the context of trusting others.

Family history of depression is associated with alterations in task-dependent connectivity between the cerebellum and ventromedial prefrontal cortex.

BACKGROUND: A family history of major depressive disorder (MDD) increases the likelihood of a future depressive episode, which itself poses a significant risk for disruptions in reward processing and social cognition. However, it is unclear whether a family history of MDD is associated with alterations in the neural circuitry underlying reward processing and social cognition. METHODS: We subdivided 279 participants from the Human Connectome Project into three groups: 71 with a lifetime history of MDD, 103 with a family history (FH) of MDD, and 105 healthy controls (HCs). We then evaluated task-based functional magnetic resonance imaging data on a social cognition and a reward processing task and found a region of the ventromedial prefrontal cortex (vmPFC) that responded to both tasks, independent of the group. To investigate whether the vmPFC shows alterations in functional connectivity between groups, we conducted psychophysiological interaction analyses using the vmPFC as a seed region. RESULTS: We found that FH (relative to HC) was associated with increased sadness scores, and MDD (relative to both FH and HC) was associated with increased sadness and MDD symptoms. Additionally, the FH group had increased vmPFC functional connectivity within the nucleus accumbens, left dorsolateral PFC, and subregions of the cerebellum relative to HC during the social cognition task. CONCLUSIONS: These findings suggest that aberrant neural mechanisms among those with a familial risk of MDD may underlie vulnerability to altered social cognition.

Reason's Enemy Is Not Emotion: Engagement of Cognitive Control Networks Explains Biases in Gain/Loss Framing.

In the classic gain/loss framing effect, describing a gamble as a potential gain or loss biases people to make risk-averse or risk-seeking decisions, respectively. The canonical explanation for this effect is that frames differentially modulate emotional processes, which in turn leads to irrational choice behavior. Here, we evaluate the source of framing biases by integrating functional magnetic resonance imaging data from 143 human participants performing a gain/loss framing task with meta-analytic data from >8000 neuroimaging studies. We found that activation during choices consistent with the framing effect were most correlated with activation associated with the resting or default brain, while activation during choices inconsistent with the framing effect was most correlated with the task-engaged brain. Our findings argue against the common interpretation of gain/loss framing as a competition between emotion and control. Instead, our study indicates that this effect results from differential cognitive engagement across decision frames.SIGNIFICANCE STATEMENT The biases frequently exhibited by human decision makers have often been attributed to the presence of emotion. Using a large fMRI sample and analysis of whole-brain networks defined with the meta-analytic tool Neurosynth, we find that neural activity during frame-biased decisions was more significantly associated with default behaviors (and the absence of executive control) than with emotion. These findings point to a role for neuroscience in shaping long-standing psychological theories in decision science.

Meta-analysis of psychophysiological interactions: Revisiting cluster-level thresholding and sample sizes.

Within the neuroimaging community, coordinate based meta-analyses (CBMAs) are essential for aggregating findings across studies and testing whether those studies report similar anatomical locations. This approach has been predominantly applied to studies that focus on whether activation of a brain region is associated with a given psychological process. In a recent paper, we used CBMA to examine a distinct set of studies-that is, those focusing on whether connectivity between brain regions is modulated by a given psychological process (Smith et al. [2016]: Hum Brain Mapp 37:2904-2917). Specifically, we reviewed 284 studies examining brain connectivity with psychophysiological interactions (PPI). Our meta-analytic results indicated that PPI yields connectivity patterns that are consistent across studies and that can be specific for a given psychological process and seed region. After publication of our findings, we learned that the analysis software we used to conduct our CBMAs (GingerALE v2.3.3) contained an implementation error that led to results that were more liberal than intended. Here, we comment on the impact of this implementation error on the results of our paper, new recommendations for sample sizes in CBMAs, and the importance of communication between software users and developers. We show that our key claims are supported in a reanalysis and that our results are robust to new guidelines on sample sizes. Hum Brain Mapp 38:588-591, 2017. © 2016 Wiley Periodicals, Inc.

Using fMRI to study reward processing in humans: past, present, and future.

Functional magnetic resonance imaging (fMRI) is a noninvasive tool used to probe cognitive and affective processes. Although fMRI provides indirect measures of neural activity, the advent of fMRI has allowed for1) the corroboration of significant animal findings in the human brain, and2) the expansion of models to include more common human attributes that inform behavior. In this review, we briefly consider the neural basis of the blood oxygenation level dependent signal to set up a discussion of how fMRI studies have applied it in examining cognitive models in humans and the promise of using fMRI to advance such models. Specifically, we illustrate the contribution that fMRI has made to the study of reward processing, focusing on the role of the striatum in encoding reward-related learning signals that drive anticipatory and consummatory behaviors. For instance, we discuss how fMRI can be used to link neural signals (e.g., striatal responses to rewards) to individual differences in behavior and traits. While this functional segregation approach has been constructive to our understanding of reward-related functions, many fMRI studies have also benefitted from a functional integration approach that takes into account how interconnected regions (e.g., corticostriatal circuits) contribute to reward processing. We contend that future work using fMRI will profit from using a multimodal approach, such as combining fMRI with noninvasive brain stimulation tools (e.g., transcranial electrical stimulation), that can identify causal mechanisms underlying reward processing. Consequently, advancements in implementing fMRI will promise new translational opportunities to inform our understanding of psychopathologies.

Toward a cumulative science of functional integration: A meta-analysis of psychophysiological interactions.

Much of the work in cognitive neuroscience is shifting from a focus on single brain regions to a focus on the connectivity between multiple brain regions. These inter-regional connectivity patterns contribute to a wide range of behaviors and are studied with models of functional integration. The rapid expansion of the literature on functional integration offers an opportunity to scrutinize the consistency and specificity of one of the most popular approaches for quantifying connectivity: psychophysiological interaction (PPI) analysis. We performed coordinate-based meta-analyses on 284 PPI studies, which allowed us to test (a) whether those studies consistently converge on similar target regions and (b) whether the identified target regions are specific to the chosen seed region and psychological context. Our analyses revealed two key results. First, we found that different types of PPI studies-e.g., those using seeds such as amygdala and dorsolateral prefrontal cortex (DLPFC) and contexts such as emotion and cognitive control, respectively-each consistently converge on similar target regions, thus supporting the reliability of PPI as a tool for studying functional integration. Second, we also found target regions that were specific to the chosen seed region and psychological context, indicating distinct patterns of brain connectivity. For example, the DLPFC seed reliably contributed to a posterior cingulate cortex target during cognitive control but contributed to an amygdala target in other contexts. Our results point to the robustness of PPI while highlighting common and distinct patterns of functional integration, potentially advancing models of brain connectivity. Hum Brain Mapp 37:2904-2917, 2016. © 2016 Wiley Periodicals, Inc.

Decoding the anatomical network of spatial attention.

The study of stroke patients with modern lesion-symptom analysis techniques has yielded valuable insights into the representation of spatial attention in the human brain. Here we introduce an approach--multivariate pattern analysis--that no longer assumes independent contributions of brain regions but rather quantifies the joint contribution of multiple brain regions in determining behavior. In a large sample of stroke patients, we found patterns of damage more predictive of spatial neglect than the best-performing single voxel. In addition, modeling multiple brain regions--those that are frequently damaged and, importantly, spared--provided more predictive information than modeling single regions. Interestingly, we also found that the superior temporal gyrus demonstrated a consistent ability to improve classifier performance when added to other regions, implying uniquely predictive information. In sharp contrast, classifier performance for both the angular gyrus and insular cortex was reliably enhanced by the addition of other brain regions, suggesting these regions lack independent predictive information for spatial neglect. Our findings highlight the utility of multivariate pattern analysis in lesion mapping, furnishing neuroscience with a modern approach for using lesion data to study human brain function.

Precuneus is a functional core of the default-mode network.

Efforts to understand the functional architecture of the brain have consistently identified multiple overlapping large-scale neural networks that are observable across multiple states. Despite the ubiquity of these networks, it remains unclear how regions within these large-scale neural networks interact to orchestrate behavior. Here, we collected functional magnetic resonance imaging data from 188 human subjects who engaged in three cognitive tasks and a resting-state scan. Using multiple tasks and a large sample allowed us to use split-sample validations to test for replication of results. We parceled the task-rest pairs into functional networks using a probabilistic spatial independent components analysis. We examined changes in connectivity between task and rest states using dual-regression analysis, which quantifies voxelwise connectivity estimates for each network of interest while controlling for the influence of signals arising from other networks and artifacts. Our analyses revealed systematic state-dependent functional connectivity in one brain region: the precuneus. Specifically, task performance led to increased connectivity (compared to rest) between the precuneus and the left frontoparietal network (lFPN), whereas rest increased connectivity between the precuneus and the default-mode network (DMN). The absolute magnitude of this effect was greater for DMN, suggesting a heightened specialization for resting-state cognition. All results replicated within the two independent samples. Our results indicate that the precuneus plays a core role not only in DMN, but also more broadly through its engagement under a variety of processing states.

Functional connectivity with distinct neural networks tracks fluctuations in gain/loss framing susceptibility.

Multiple large-scale neural networks orchestrate a wide range of cognitive processes. For example, interoceptive processes related to self-referential thinking have been linked to the default-mode network (DMN); whereas exteroceptive processes related to cognitive control have been linked to the executive-control network (ECN). Although the DMN and ECN have been postulated to exert opposing effects on cognition, it remains unclear how connectivity with these spatially overlapping networks contribute to fluctuations in behavior. While previous work has suggested the medial-prefrontal cortex (MPFC) is involved in behavioral change following feedback, these observations could be linked to interoceptive processes tied to DMN or exteroceptive processes tied to ECN because MPFC is positioned in both networks. To address this problem, we employed independent component analysis combined with dual-regression functional connectivity analysis. Participants made a series of financial decisions framed as monetary gains or losses. In some sessions, participants received feedback from a peer observing their choices; in other sessions, feedback was not provided. Following feedback, framing susceptibility-indexed as the increase in gambling behavior in loss frames compared to gain frames-was heightened in some participants and diminished in others. We examined whether these individual differences were linked to differences in connectivity by contrasting sessions containing feedback against those that did not contain feedback. We found two key results. As framing susceptibility increased, the MPFC increased connectivity with DMN; in contrast, temporal-parietal junction decreased connectivity with the ECN. Our results highlight how functional connectivity patterns with distinct neural networks contribute to idiosyncratic behavioral changes.

Resting state networks distinguish human ventral tegmental area from substantia nigra.

Dopaminergic networks modulate neural processing across a spectrum of function from perception to learning to action. Multiple organizational schemes based on anatomy and function have been proposed for dopaminergic nuclei in the midbrain. One schema originating in rodent models delineated ventral tegmental area (VTA), implicated in complex behaviors like addiction, from more lateral substantia nigra (SN), preferentially implicated in movement. However, because anatomy and function in rodent midbrain differs from the primate midbrain in important ways, the utility of this distinction for human neuroscience has been questioned. We asked whether functional definition of networks within the human dopaminergic midbrain would recapitulate this traditional anatomical topology. We first developed a method for reliably defining SN and VTA in humans at conventional MRI resolution. Hand-drawn VTA and SN regions-of-interest (ROIs) were constructed for 50 participants, using individually-localized anatomical landmarks and signal intensity. Individual segmentation was used in seed-based functional connectivity analysis of resting-state functional MRI data; results of this analysis recapitulated traditional anatomical targets of the VTA versus SN. Next, we constructed a probabilistic atlas of the VTA, SN, and the dopaminergic midbrain region (comprised of SN plus VTA) from individual hand-drawn ROIs. The combined probabilistic (SN plus VTA) ROI was then used for connectivity-based dual-regression analysis in two independent resting-state datasets (n = 69 and n = 79). Results of the connectivity-based, dual-regression functional segmentation recapitulated results of the anatomical segmentation, validating the utility of this probabilistic atlas for future research.

Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches.

A central challenge for neuroscience lies in relating inter-individual variability to the functional properties of specific brain regions. Yet, considerable variability exists in the connectivity patterns between different brain areas, potentially producing reliable group differences. Using sex differences as a motivating example, we examined two separate resting-state datasets comprising a total of 188 human participants. Both datasets were decomposed into resting-state networks (RSNs) using a probabilistic spatial independent component analysis (ICA). We estimated voxel-wise functional connectivity with these networks using a dual-regression analysis, which characterizes the participant-level spatiotemporal dynamics of each network while controlling for (via multiple regression) the influence of other networks and sources of variability. We found that males and females exhibit distinct patterns of connectivity with multiple RSNs, including both visual and auditory networks and the right frontal-parietal network. These results replicated across both datasets and were not explained by differences in head motion, data quality, brain volume, cortisol levels, or testosterone levels. Importantly, we also demonstrate that dual-regression functional connectivity is better at detecting inter-individual variability than traditional seed-based functional connectivity approaches. Our findings characterize robust-yet frequently ignored-neural differences between males and females, pointing to the necessity of controlling for sex in neuroscience studies of individual differences. Moreover, our results highlight the importance of employing network-based models to study variability in functional connectivity.

Meta-Analysis Reveals That Explore-Exploit Decisions are Dissociable by Activation in the Dorsal Lateral Prefrontal Cortex and the Anterior Cingulate Cortex.

Explore-exploit research has challenges in generalizability due to a limited theoretical basis of exploration and exploitation. Neuroimaging can help identify whether explore-exploit decisions use an opponent processing system to address this issue. Thus, we conducted a coordinate-based meta-analysis (N=23 studies) where we found activation in the dorsal lateral prefrontal cortex and anterior cingulate cortex during exploration versus exploitation, providing some evidence for opponent processing. However, the conjunction of explore-exploit decisions was associated with activation in the dorsal anterior cingulate cortex, dorsal medial prefrontal cortex, and anterior insula, suggesting that these brain regions do not engage in opponent processing. Further, exploratory analyses revealed heterogeneity in brain responses between task types during exploration and exploitation respectively. Coupled with results suggesting that activation in exploration and exploitation decisions is generally more similar than it is different suggests there remain significant challenges toward characterizing explore-exploit decision making. Nonetheless, dlPFC and ACC activation differentiate explore and exploit decisions and identifying these responses can help in targeted interventions aimed at manipulating these decisions.

An fMRI dataset of social and nonsocial reward processing in young adults.

Trait reward sensitivity, risk for developing substance use, and mood disorders have each been linked with altered striatal responses to reward. Moreover, striatal response to reward is sensitive to social context, such as the presence of a peer, and drugs are often sought out and consumed in social contexts or as a result of social experiences. Thus, mood disorder symptoms, striatal responses to social context and social reward may play a role in substance use. To investigate this possibility, this dataset was collected as part of a National Institute on Drug Abuse (NIDA) grant titled "Aberrant Reward Sensitivity: Mechanisms Underlying Substance Use" (R03-DA046733). The overarching goal was to characterize the associations between neural responses to social and nonsocial rewards, trait reward sensitivity, substance use, and mood disorder symptoms. After obtaining questionnaire data quantifying reward sensitivity, substance use, and other psychosocial characteristics, young adults (N=59; 14 male, 45 female; mean age: 20.89 years ± 1.75 years) completed four fMRI tasks testing different features of social and reward processing. These included: 1) a strategic reward-based decision-making task with Ultimatum and Dictator Game conditions; 2) a task where participants shared rewards or losses with peers, strangers, or non-human partners; 3) a task in which participants received well-matched social and monetary rewards and punishment; and 4) a monetary incentive delay (MID) task in which participants tried to obtain or avoid rewards and losses of different magnitude. This dataset includes sociodemographic questionnaire data, anatomical, task-based fMRI, and corresponding behavioral task-based data. We outline several opportunities for extension and reuse, including exploration of individual differences, cross-task comparisons, and representational similarity analyses.

An fMRI Dataset on Social Reward Processing and Decision Making in Younger and Older Adults.

Behavioural and neuroimaging research has shown that older adults are less sensitive to financial losses compared to younger adults. Yet relatively less is known about age-related differences in social decisions and social reward processing. As part of a pilot study, we collected behavioural and functional magnetic resonance imaging (fMRI) data from 50 participants (Younger: N = 26, ages 18-34 years; Older: N = 24, ages 63-80 years) who completed three tasks in the scanner: an economic trust game as the investor with three partners (computer, stranger, friend) as the investee; a card-guessing task with monetary gains and losses shared with three partners (computer, stranger, friend); and an ultimatum game as responder to three anonymous proposers (computer, age-similar adults, age-dissimilar adults). We also collected B0 field maps and high-resolution structural images (T1-weighted and T2-weighted images). These data could be reused to answer questions about moment-to-moment variability in fMRI signal, representational similarity between tasks, and brain structure.

Trait Reward Sensitivity Modulates Connectivity with the Temporoparietal Junction and Anterior Insula during Strategic Decision Making.

Many decisions happen in social contexts such as negotiations, yet little is understood about how people balance fairness versus selfishness. Past investigations found that activation in brain areas involved in executive function and reward processing was associated with people offering less with no threat of rejection from their partner, compared to offering more when there was a threat of rejection. However, it remains unclear how trait reward sensitivity may modulate activation and connectivity patterns in these situations. To address this gap, we used task-based fMRI to examine the relation between reward sensitivity and the neural correlates of bargaining choices. Participants (N = 54) completed the Sensitivity to Punishment (SP)/Sensitivity to Reward (SR) Questionnaire and the Behavioral Inhibition System/Behavioral Activation System scales. Participants performed the Ultimatum and Dictator Games as proposers and exhibited strategic decisions by being fair when there was a threat of rejection, but being selfish when there was not a threat of rejection. We found that strategic decisions evoked activation in the Inferior Frontal Gyrus (IFG) and the Anterior Insula (AI). Next, we found elevated IFG connectivity with the Temporoparietal junction (TPJ) during strategic decisions. Finally, we explored whether trait reward sensitivity modulated brain responses while making strategic decisions. We found that people who scored lower in reward sensitivity made less strategic choices when they exhibited higher AI-Angular Gyrus connectivity. Taken together, our results demonstrate how trait reward sensitivity modulates neural responses to strategic decisions, potentially underscoring the importance of this factor within social and decision neuroscience.