Integration of individual and social information for decision-making in groups of different sizes.

When making judgments in a group, individuals often revise their initial beliefs about the best judgment to make given what others believe. Despite the ubiquity of this phenomenon, we know little about how the brain updates beliefs when integrating personal judgments (individual information) with those of others (social information). Here, we investigated the neurocomputational mechanisms of how we adapt our judgments to those made by groups of different sizes, in the context of jury decisions for a criminal. By testing different theoretical models, we showed that a social Bayesian inference model captured changes in judgments better than 2 other models. Our results showed that participants updated their beliefs by appropriately weighting individual and social sources of information according to their respective credibility. When investigating 2 fundamental computations of Bayesian inference, belief updates and credibility estimates of social information, we found that the dorsal anterior cingulate cortex (dACC) computed the level of belief updates, while the bilateral frontopolar cortex (FPC) was more engaged in individuals who assigned a greater credibility to the judgments of a larger group. Moreover, increased functional connectivity between these 2 brain regions reflected a greater influence of group size on the relative credibility of social information. These results provide a mechanistic understanding of the computational roles of the FPC-dACC network in steering judgment adaptation to a group's opinion. Taken together, these findings provide a computational account of how the human brain integrates individual and social information for decision-making in groups.

Wait and you shall see: sexual delay discounting in hypersexual Parkinson's disease.

Patients with Parkinson's disease may develop impulse control disorders under dopaminergic treatments. Impulse control disorders include a wide spectrum of behaviours, such as hypersexuality, pathological gambling or compulsive shopping. Yet, the neural systems engaged in specific impulse control disorders remain poorly characterized. Here, using model-based functional MRI, we aimed to determine the brain systems involved during delay-discounting of erotic rewards in hypersexual patients with Parkinson's disease (PD+HS), patients with Parkinson's disease without hypersexuality (PD - HS) and controls. Patients with Parkinson's disease were evaluated ON and OFF levodopa (counterbalanced). Participants had to decide between two options: (i) wait for 1.5 s to briefly view an erotic image; or (ii) wait longer to see the erotic image for a longer period of time. At the time of decision-making, we investigated which brain regions were engaged with the subjective valuation of the delayed erotic reward. At the time of the rewarded outcome, we searched for the brain regions responding more robustly after waiting longer to view the erotic image. PD+HS patients showed reduced discounting of erotic delayed rewards, compared to both patients with Parkinson's disease and controls, suggesting that they accepted waiting longer to view erotic images for a longer period of time. Thus, when using erotic stimuli that motivate PD+HS, these patients were less impulsive for the immediate reward. At the brain system level, this effect was paralleled by the fact that PD+HS, as compared to controls and PD - HS, showed a negative correlation between subjective value of the delayed reward and activity of medial prefrontal cortex and ventral striatum. Consistent with the incentive salience hypothesis combining learned cue-reward associations with current relevant physiological state, dopaminergic treatment in PD+HS boosted excessive 'wanting' of rewards and heightened activity in the anterior medial prefrontal cortex and the posterior cingulate cortex, as reflected by higher correlation with subjective value of the option associated to the delayed reward when ON medication as compared to the OFF medication state. At the time of outcome, the anterior medial prefrontal/rostral anterior cingulate cortex showed an interaction between group (PD+HS versus PD - HS) and medication (ON versus OFF), suggesting that dopaminergic treatment boosted activity of this brain region in PD+HS when viewing erotic images after waiting for longer periods of time. Our findings point to reduced delay discounting of erotic rewards in PD+HS, both at the behavioural and brain system levels, and abnormal reinforcing effect of levodopa when PD+HS patients are confronted with erotic stimuli.10.1093/brain/awy298_video1awy298media15983845074001.

Goal-oriented representations in the human hippocampus during planning and navigation.

Recent work in cognitive and systems neuroscience has suggested that the hippocampus might support planning, imagination, and navigation by forming cognitive maps that capture the abstract structure of physical spaces, tasks, and situations. Navigation involves disambiguating similar contexts, and the planning and execution of a sequence of decisions to reach a goal. Here, we examine hippocampal activity patterns in humans during a goal-directed navigation task to investigate how contextual and goal information are incorporated in the construction and execution of navigational plans. During planning, hippocampal pattern similarity is enhanced across routes that share a context and a goal. During navigation, we observe prospective activation in the hippocampus that reflects the retrieval of pattern information related to a key-decision point. These results suggest that, rather than simply representing overlapping associations or state transitions, hippocampal activity patterns are shaped by context and goals.

Neural mechanisms of credit assignment for inferred relationships in a structured world.

Animals abstract compact representations of a task's structure, which supports accelerated learning and flexible behavior. Whether and how such abstracted representations may be used to assign credit for inferred, but unobserved, relationships in structured environments are unknown. We develop a hierarchical reversal-learning task and Bayesian learning model to assess the computational and neural mechanisms underlying how humans infer specific choice-outcome associations via structured knowledge. We find that the medial prefrontal cortex (mPFC) efficiently represents hierarchically related choice-outcome associations governed by the same latent cause, using a generalized code to assign credit for both experienced and inferred outcomes. Furthermore, the mPFC and lateral orbitofrontal cortex track the current "position" within a latent association space that generalizes over stimuli. Collectively, these findings demonstrate the importance of both tracking the current position in an abstracted task space and efficient, generalizable representations in the prefrontal cortex for supporting flexible learning and inference in structured environments.

A Neural Network Model of Continual Learning with Cognitive Control.

Neural networks struggle in continual learning settings from catastrophic forgetting: when trials are blocked, new learning can overwrite the learning from previous blocks. Humans learn effectively in these settings, in some cases even showing an advantage of blocking, suggesting the brain contains mechanisms to overcome this problem. Here, we build on previous work and show that neural networks equipped with a mechanism for cognitive control do not exhibit catastrophic forgetting when trials are blocked. We further show an advantage of blocking over interleaving when there is a bias for active maintenance in the control signal, implying a tradeoff between maintenance and the strength of control. Analyses of map-like representations learned by the networks provided additional insights into these mechanisms. Our work highlights the potential of cognitive control to aid continual learning in neural networks, and offers an explanation for the advantage of blocking that has been observed in humans.

The orbital frontal cortex, task structure, and inference.

The orbital frontal cortex (OFC) has long been linked to goal-directed, flexible behaviors. Recent evidence suggests the OFC plays key roles in representing the abstracted structure of task spaces, and using this representation for flexible inferences during both learning and choice. Here, we review convergent evidence from studies in animal models and humans in support of this view. We begin by considering early accounts of OFC function, then discuss how more recent evidence supports theories that have re-cast OFC's function as representing the structure of a task or environment for flexible inference. Finally, we turn to neural recording studies that provide insights into the underlying representations and computations the OFC may implement in coordination with other brain areas. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Inferences on a multidimensional social hierarchy use a grid-like code.

Generalizing experiences to guide decision-making in novel situations is a hallmark of flexible behavior. Cognitive maps of an environment or task can theoretically afford such flexibility, but direct evidence has proven elusive. In this study, we found that discretely sampled abstract relationships between entities in an unseen two-dimensional social hierarchy are reconstructed into a unitary two-dimensional cognitive map in the hippocampus and entorhinal cortex. We further show that humans use a grid-like code in entorhinal cortex and medial prefrontal cortex for inferred direct trajectories between entities in the reconstructed abstract space during discrete decisions. These grid-like representations in the entorhinal cortex are associated with decision value computations in the medial prefrontal cortex and temporoparietal junction. Collectively, these findings show that grid-like representations are used by the human brain to infer novel solutions, even in abstract and discrete problems, and suggest a general mechanism underpinning flexible decision-making and generalization.

Protocol for building a cognitive map of structural knowledge in humans by integrating abstract relationships from separate experiences.

Humans are adept at learning the latent structure of the relationship between abstract concepts and can build a cognitive map from limited experiences. However, examining internal representations of the cognitive map is challenging because they are unobservable and differ across individuals. Here, we introduce a behavioral training protocol designed for human participants to implicitly build a map of two-dimensional social hierarchies while making a series of binary choices and analytic tools for measuring the internal representation of this structural knowledge. For complete details on the use and execution of this protocol, please refer to Park et al. (2020a, 2020b).

Complementary Structure-Learning Neural Networks for Relational Reasoning.

The neural mechanisms supporting flexible relational inferences, especially in novel situations, are a major focus of current research. In the complementary learning systems framework, pattern separation in the hippocampus allows rapid learning in novel environments, while slower learning in neocortex accumulates small weight changes to extract systematic structure from well-learned environments. In this work, we adapt this framework to a task from a recent fMRI experiment where novel transitive inferences must be made according to implicit relational structure. We show that computational models capturing the basic cognitive properties of these two systems can explain relational transitive inferences in both familiar and novel environments, and reproduce key phenomena observed in the fMRI experiment.

Map Making: Constructing, Combining, and Inferring on Abstract Cognitive Maps.

Cognitive maps enable efficient inferences from limited experience that can guide novel decisions. We tested whether the hippocampus (HC), entorhinal cortex (EC), and ventromedial prefrontal cortex (vmPFC)/medial orbitofrontal cortex (mOFC) organize abstract and discrete relational information into a cognitive map to guide novel inferences. Subjects learned the status of people in two unseen 2D social hierarchies, with each dimension learned on a separate day. Although one dimension was behaviorally relevant, multivariate activity patterns in HC, EC, and vmPFC/mOFC were linearly related to the Euclidean distance between people in the mentally reconstructed 2D space. Hubs created unique comparisons between the hierarchies, enabling inferences between novel pairs. We found that both behavior and neural activity in EC and vmPFC/mOFC reflected the Euclidean distance to the retrieved hub, which was reinstated in HC. These findings reveal how abstract and discrete relational structures are represented, are combined, and enable novel inferences in the human brain.

Neural computations underlying strategic social decision-making in groups.

When making decisions in groups, the outcome of one's decision often depends on the decisions of others, and there is a tradeoff between short-term incentives for an individual and long-term incentives for the groups. Yet, little is known about the neurocomputational mechanisms at play when weighing different utilities during repeated social interactions. Here, using model-based fMRI and Public-good-games, we find that the ventromedial prefrontal cortex encodes immediate expected rewards as individual utility while the lateral frontopolar cortex encodes group utility (i.e., pending rewards of alternative strategies beneficial for the group). When it is required to change one's strategy, these brain regions exhibited changes in functional interactions with brain regions engaged in switching strategies. Moreover, the anterior cingulate cortex and the temporoparietal junction updated beliefs about the decision of others during interactions. Together, our findings provide a neurocomputational account of how the brain dynamically computes effective strategies to make adaptive collective decisions.

Modeling other minds: Bayesian inference explains human choices in group decision-making.

To make decisions in a social context, humans have to predict the behavior of others, an ability that is thought to rely on having a model of other minds known as "theory of mind." Such a model becomes especially complex when the number of people one simultaneously interacts with is large and actions are anonymous. Here, we present results from a group decision-making task known as the volunteer's dilemma and demonstrate that a Bayesian model based on partially observable Markov decision processes outperforms existing models in quantitatively predicting human behavior and outcomes of group interactions. Our results suggest that in decision-making tasks involving large groups with anonymous members, humans use Bayesian inference to model the "mind of the group," making predictions of others' decisions while also simulating the effects of their own actions on the group's dynamics in the future.

Reappraising abstract paintings after exposure to background information.

Can knowledge help viewers when they appreciate an artwork? Experts' judgments of the aesthetic value of a painting often differ from the estimates of naïve viewers, and this phenomenon is especially pronounced in the aesthetic judgment of abstract paintings. We compared the changes in aesthetic judgments of naïve viewers while they were progressively exposed to five pieces of background information. The participants were asked to report their aesthetic judgments of a given painting after each piece of information was presented. We found that commentaries by the artist and a critic significantly increased the subjective aesthetic ratings. Does knowledge enable experts to attend to the visual features in a painting and to link it to the evaluative conventions, thus potentially causing different aesthetic judgments? To investigate whether a specific pattern of attention is essential for the knowledge-based appreciation, we tracked the eye movements of subjects while viewing a painting with a commentary by the artist and with a commentary by a critic. We observed that critics' commentaries directed the viewers' attention to the visual components that were highly relevant to the presented commentary. However, attention to specific features of a painting was not necessary for increasing the subjective aesthetic judgment when the artists' commentary was presented. Our results suggest that at least two different cognitive mechanisms may be involved in knowledge- guided aesthetic judgments while viewers reappraise a painting.

TV programs that denounce unfair advantage impact women's sensitivity to defection in the public goods game.

We explore the neural underpinnings of gender differences in cooperation and their modulation by intensive media watching. We compared cooperative decisions and electroencephalograph data between genders from who participated in repeated rounds of the public goods game (PGG) and investigated within groups changes that occurred after watching a TV program known as "investigative reporting" that denounces unfair advantages taken by free-riders against the public. Women tended to be more cooperative than men during early rounds of PGG, mostly because they react differently to the defection of others; women also had greater ß and γ band activity in regions estimated to be associated with social cognition. These gender differences disappeared after participants watched the TV programs: women were more likely to choose free-riding in response to the defection of others that elicits significant increases in γ band activities that were estimated to be right insula. Greater activity in social cognition leads women to make decisions considering the motives of others, while men tend to make a decision by complying with the social norm. Watching the investigative TV reports produced a greater negative emotion to the defection and led women, in a similar manner as men, to opt for a "tit-for-tat" strategy.