The medial prefrontal cortex (mPFC) has long been recognized as the key component of the neurocircuitry involved in various social as well as non-social behaviors, however, little is known regarding ...the organizing principle of distinctive subregions in the mPFC that integrates a wide range of mPFC functions. The present study proposes a hierarchical model of mPFC functionality, where three functionally dissociable subregions, namely, the ventromedial prefrontal cortex (vmPFC), rostromedial prefrontal cortex (rmPFC), and dorsomedial prefrontal cortex (dmPFC), are differentially involved in computing values of decision-making. According to this model, the mPFC subregions interact with each other in such a way that more dorsal regions utilize additional external sensory information from environment to predict and prevent conflicts occurring in more ventral regions tuned to internal bodily signals, thereby exerting the hierarchically organized allostatic regulatory control over homeostatic reflexes. This model also emphasizes the role of the thalamic reticular nucleus (TRN) in arbitrating the transitions between different thalamo-cortical loops, detecting conflicts between competing options for decision-making, and in shifting flexibly between decision modes. The hierarchical architecture of the mPFC working in conjunction with the TRN may play a key role in adjusting the internal (bodily) needs to suit the constraints of external (environmental) variables better, thus effectively addressing the stability-plasticity dilemma.
: In model‐based functional magnetic resonance imaging (fMRI), signals derived from a computational model for a specific cognitive process are correlated against fMRI data from subjects performing a ...relevant task to determine brain regions showing a response profile consistent with that model. A key advantage of this technique over more conventional neuroimaging approaches is that model‐based fMRI can provide insights into how a particular cognitive process is implemented in a specific brain area as opposed to merely identifying where a particular process is located. This review will briefly summarize the approach of model‐based fMRI, with reference to the field of reward learning and decision making, where computational models have been used to probe the neural mechanisms underlying learning of reward associations, modifying action choice to obtain reward, as well as in encoding expected value signals that reflect the structure of a decision problem. Finally, some of the limitations of this approach will be discussed.
How do people protect themselves in response to negative social feedback from others? How does such a self-protective system develop and affect social decisions? Here, using a novel reciprocal ...artwork evaluation task, we demonstrate that youths show self-protective bias based on current negative social evaluation, whereas into early adulthood, individuals show self-protective bias based on accumulated evidence of negative social evaluation. While the ventromedial prefrontal cortex (VMPFC) mediates self-defensive behavior based on both current and accumulated feedback, the rostromedial prefrontal cortex (RMPFC) exclusively mediates self-defensive behavior based on longer feedback history. Further analysis using a reinforcement learning model suggests that RMPFC extending into VMPFC, together with posterior parietal cortex (PPC), contribute to age-related increases in self-protection bias with deep feedback integration by computing the discrepancy between current feedback and previously estimated value of self-protection. These findings indicate that the development of RMPFC function is critical for sophisticated self-protective decisions.
Although much is known about the neural substrates of reward, the question of whether expectation of different types of reinforcers engage distinct or overlapping brain circuitry has not been ...addressed definitively. In the present study, human subjects, while being scanned with functional magnetic resonance imaging, performed a simple reward-based action selection task to obtain different magnitudes of either monetary outcomes (winning or losing money) or juice outcomes (pleasant apple juice or an unpleasant salt flavor). At the group level, we found partially overlapping value-related activity within ventromedial prefrontal cortex (vmPFC) during anticipation of juice and money reward outcomes. Analogous results were found in the right anterior insula, except that this region showed negative correlations as a function of increasing expected reward. These results indicate that vmPFC and anterior insula contain overlapping representations of anticipatory value, consistent with the existence of a common currency for the value of expected outcomes in these regions.
Avoidance learning poses a challenge for reinforcement-based theories of instrumental conditioning, because once an aversive outcome is successfully avoided an individual may no longer experience ...extrinsic reinforcement for their behavior. One possible account for this is to propose that avoiding an aversive outcome is in itself a reward, and thus avoidance behavior is positively reinforced on each trial when the aversive outcome is successfully avoided. In the present study we aimed to test this possibility by determining whether avoidance of an aversive outcome recruits the same neural circuitry as that elicited by a reward itself. We scanned 16 human participants with functional MRI while they performed an instrumental choice task, in which on each trial they chose from one of two actions in order to either win money or else avoid losing money. Neural activity in a region previously implicated in encoding stimulus reward value, the medial orbitofrontal cortex, was found to increase, not only following receipt of reward, but also following successful avoidance of an aversive outcome. This neural signal may itself act as an intrinsic reward, thereby serving to reinforce actions during instrumental avoidance.
How do we incorporate contextual information to infer others’ emotional state? Here we employed a naturalistic context-dependent facial expression estimation task where participants estimated ...pleasantness levels of others’ ambiguous expression faces when sniffing different contextual cues (e.g., urine, fish, water, and rose). Based on their pleasantness rating data, we placed participants on a context-dependency continuum and mapped the individual variability in the context-dependency onto the neural representation using a representational similarity analysis. We found that the individual variability in the context-dependency of facial expression estimation correlated with the activity level of the pregenual anterior cingulate cortex (pgACC) and the amygdala and was also decoded by the neural representation of the ventral anterior insula (vAI). A dynamic causal modeling revealed that those with higher context-dependency exhibited a greater degree of the modulation from vAI to the pgACC. These findings provide novel insights into the neural circuitry associated with the individual variability in context-dependent facial expression estimation and the first empirical evidence for individual variability in the predictive accounts of affective states.
Despite the importance of valuing another personâs welfare for prosocial behavior, currently we have only a limited understanding of how these values are represented in the brain and, more ...importantly, how they give rise to individual variability in prosociality. In the present study, participants underwent functional magnetic resonance imaging while performing a prosocial learning task in which they could choose to benefit themselves and/or another person. Choice behavior indicated that participants valued the welfare of another person, although less so than they valued their own welfare. Neural data revealed a spatial gradient in activity within the medial prefrontal cortex (MPFC), such that ventral parts predominantly represented self-regarding values and dorsal parts predominantly represented other-regarding values. Importantly, compared with selfish individuals, prosocial individuals showed a more gradual transition from self-regarding to other-regarding value signals in the MPFC and stronger MPFCâstriatum coupling when they made choices for another person rather than for themselves. The present study provides evidence of neural markers reflecting individual differences in human prosociality.
Significance How do selfish and prosocial brains function differently with regard to valuing the welfare of others? The present study addresses this question by combining neuroimaging, computational modeling, and an instrumental conditioning paradigm. Contrary to the conventional notion of the dorsal medial prefrontal cortex (MPFC) implicated in mentalization, we found that it was selfish individuals who showed greater spatial segregation between ventral and dorsal MPFC, which encoded self- and other-regarding values, respectively. Prosocial individuals, on the other hand, were characterized by overlapping selfâother representation in the ventral MPFC and by stronger functional coupling between MPFC and striatum while representing and updating the value of other-regarding choices. These findings provide rigorous scientific evidence of neural markers reflecting individual differences in human prosociality.
In this functional neuroimaging study, we investigated neural activations during the process of learning to gain monetary rewards and to avoid monetary loss, and how these activations are modulated ...by individual differences in reward and punishment sensitivity. Healthy young volunteers performed a reinforcement learning task where they chose one of two fractal stimuli associated with monetary gain (reward trials) or avoidance of monetary loss (avoidance trials). Trait sensitivity to reward and punishment was assessed using the behavioral inhibition/activation scales (BIS/BAS). Functional neuroimaging results showed activation of the striatum during the anticipation and reception periods of reward trials. During avoidance trials, activation of the dorsal striatum and prefrontal regions was found. As expected, individual differences in reward sensitivity were positively associated with activation in the left and right ventral striatum during reward reception. Individual differences in sensitivity to punishment were negatively associated with activation in the left dorsal striatum during avoidance anticipation and also with activation in the right lateral orbitofrontal cortex during receiving monetary loss. These results suggest that learning to attain reward and learning to avoid loss are dependent on separable sets of neural regions whose activity is modulated by trait sensitivity to reward or punishment.
Ingroup favoritism and fairness are two potentially competing motives guiding intergroup behaviors in human. Here, we investigate if and how limited resources can modulate the way these two motives ...affect individuals' decisions in intergroup situation. In the present study, participants (N = 58) were asked to accept or reject three types of resource allocation proposals generated by a computer: the ingroup advantageous condition, outgroup advantageous condition, and neutral condition. In general, participants were more willing to accept the proposals in the ingroup advantageous condition than the outgroup advantageous or the neutral conditions, and also in the moderate inequality than the extreme inequality condition. This may indicate that people sought a careful balance between ingroup favoritism and fairness, although we also found marked individual differences in their preferences for ingroup favoritism or fairness. Importantly, as predicted, participants were more likely to show ingroup favoritism only when limited resources affect the well-being of ingroup members. The present study provides novel insights into the situational and personality factors affecting human intergroup behaviors, shedding light on motives underlying intergroup conflicts prevalent in human societies.
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