The social Bayesian brain: does mentalizing make a difference when we learn?

Neural correlates of static and dynamic social decision-making in real-time sibling interactions

In traditional game theory tasks, social decision-making is centered on the prediction of the intentions (i.e., mentalizing) of strangers or manipulated responses. In contrast, real-life scenarios often involve familiar individuals in dynamic environments. Further research is needed to explore neural correlates of social decision-making with changes in the available information and environmental settings. This study collected fMRI hyperscanning data (N = 100, 46 same-sex pairs were analyzed) to investigate sibling pairs engaging in an iterated Chicken Game task within a competitive context, including two decision-making phases. In the static phase, participants chose between turning (cooperate) and continuing (defect) in a fixed time window. Participants could estimate the probability of different events based on their priors (previous outcomes and representation of other's intentions) and report their decision plan. The dynamic phase mirrored real-world interactions in which information is continuously changing (replicated within a virtual environment). Individuals had to simultaneously update their beliefs, monitor the actions of the other, and adjust their decisions. Our findings revealed substantial choice consistency between the two phases and evidence for shared neural correlates in mentalizing-related brain regions, including the prefrontal cortex, temporoparietal junction (TPJ), and precuneus. Specific neural correlates were associated with each phase; increased activation of areas associated with action planning and outcome evaluation were found in the static compared with the dynamic phase. Using the opposite contrast, dynamic decision-making showed higher activation in regions related to predicting and monitoring other's actions, including the anterior cingulate cortex and insula. Cooperation (turning), compared with defection (continuing), showed increased activation in mentalizing-related regions only in the static phase, while defection, relative to cooperation, exhibited higher activation in areas associated with conflict monitoring and risk processing in the dynamic phase. Men were less cooperative and had greater TPJ activation. Sibling competitive relationship did not predict competitive behavior but showed a tendency to predict brain activity during dynamic decision-making. Only individual brain activation results are included here, and no interbrain analyses are reported. These neural correlates emphasize the significance of considering varying levels of information available and environmental settings when delving into the intricacies of mentalizing during social decision-making among familiar individuals.

Repeated rock, paper, scissors play reveals limits in adaptive sequential behavior

How do people adapt to others in adversarial settings? Prior work has shown that people often violate rational models of adversarial decision-making in repeated interactions. In particular, in mixed strategy equilibrium (MSE) games, where optimal action selection entails choosing moves randomly, people often do not play randomly, but instead try to outwit their opponents. However, little is known about the adaptive reasoning that underlies these deviations from random behavior. Here, we examine strategic decision-making across repeated rounds of rock, paper, scissors, a well-known MSE game. In experiment 1, participants were paired with bot opponents that exhibited distinct stable move patterns, allowing us to identify the bounds of the complexity of opponent behavior that people can detect and adapt to. In experiment 2, bot opponents instead exploited stable patterns in the human participants' moves, providing a symmetrical bound on the complexity of patterns people can revise in their own behavior. Across both experiments, people exhibited a robust and flexible attention to transition patterns from one move to the next, exploiting these patterns in opponents and modifying them strategically in their own moves. However, their adaptive reasoning showed strong limitations with respect to more sophisticated patterns. Together, results provide a precise and consistent account of the surprisingly limited scope of people's adaptive decision-making in this setting.

Neurocomputational mechanisms involved in adaptation to fluctuating intentions of others

Humans frequently interact with agents whose intentions can fluctuate between competition and cooperation over time. It is unclear how the brain adapts to fluctuating intentions of others when the nature of the interactions (to cooperate or compete) is not explicitly and truthfully signaled. Here, we use model-based fMRI and a task in which participants thought they were playing with another player. In fact, they played with an algorithm that alternated without signaling between cooperative and competitive strategies. We show that a neurocomputational mechanism with arbitration between competitive and cooperative experts outperforms other learning models in predicting choice behavior. At the brain level, the fMRI results show that the ventral striatum and ventromedial prefrontal cortex track the difference of reliability between these experts. When attributing competitive intentions, we find increased coupling between these regions and a network that distinguishes prediction errors related to competition and cooperation. These findings provide a neurocomputational account of how the brain arbitrates dynamically between cooperative and competitive intentions when making adaptive social decisions.

Autonomous behaviour and the limits of human volition

Humans and some other animals can autonomously generate action choices that contribute to solving complex problems. However, experimental investigations of the cognitive bases of human autonomy are challenging, because experimental paradigms typically constrain behaviour using controlled contexts, and elicit behaviour by external triggers. In contrast, autonomy and freedom imply unconstrained behaviour initiated by endogenous triggers. Here we propose a new theoretical construct of adaptive autonomy, meaning the capacity to make behavioural choices that are free from constraints of both immediate external triggers and of routine response patterns, but nevertheless show appropriate coordination with the environment. Participants (N = 152) played a competitive game in which they had to choose the right time to act, in the face of an opponent who punished (in separate blocks) either choice biases (such as always responding early), sequential patterns of action timing across trials (such as early, late, early, late…), or predictable action-outcome dependence (such as win-stay, lose-shift). Adaptive autonomy was quantified as the ability to maintain performance when each of these influences on action selection was punished. We found that participants could become free from habitual choices regarding when to act and could also become free from sequential action patterns. However, they were not able to free themselves from influences of action-outcome dependence, even when these resulted in poor performance. These results point to a new concept of autonomous behaviour as flexible adaptation of voluntary action choices in a way that avoids stereotypy. In a sequential analysis, we also demonstrated that participants increased their reliance on belief learning in which they attempt to understand the competitor's beliefs and intentions, when transition bias and reinforcement bias were punished. Taken together, our study points to a cognitive mechanism of adaptive autonomy in which competitive interactions with other agents could promote both social cognition and volition in the form of non-stereotyped action choices.

People can use the placement of objects to infer communicative goals

Beyond words and gestures, people have a remarkable capacity to communicate indirectly through everyday objects: A hat on a chair can mean it is occupied, rope hanging across an entrance can mean we should not cross, and objects placed in a closed box can imply they are not ours to take. How do people generate and interpret the communicative meaning of objects? We hypothesized that this capacity is supported by social goal inference, where observers recover what social goal explains an object being placed in a particular location. To test this idea, we study a category of common ad-hoc communicative objects where a small cost is used to signal avoidance. Using computational modeling, we first show that goal inference from indirect physical evidence can give rise to the ability to use object placement to communicate. We then show that people from the U.S. and the Tsimane'-a farming-foraging group native to the Bolivian Amazon-can infer the communicative meaning of object placement in the absence of a pre-existing convention, and that people's inferences are quantitatively predicted by our model. Finally, we show evidence that people can store and retrieve this meaning for use in subsequent encounters, revealing a potential mechanism for how ad-hoc communicative objects become quickly conventionalized. Our model helps shed light on how humans use their ability to interpret other people's behavior to embed social meaning into the physical world.

Autistic adults benefit from and enjoy learning via social interaction as much as neurotypical adults do

BACKGROUND

Autistic people show poor processing of social signals (i.e. about the social world). But how do they learn via social interaction?

METHODS

68 neurotypical adults and 60 autistic adults learned about obscure items (e.g. exotic animals) over Zoom (i) in a live video-call with the teacher, (ii) from a recorded learner-teacher interaction video and (iii) from a recorded teacher-alone video. Data were analysed via analysis of variance and multi-level regression models.

RESULTS

Live teaching provided the most optimal learning condition, with no difference between groups. Enjoyment was the strongest predictor of learning: both groups enjoyed the live interaction significantly more than other condition and reported similar anxiety levels across conditions.

LIMITATIONS

Some of the autistic participants were self-diagnosed-however, further analysis where these participants were excluded showed the same results. Recruiting participants over online platforms may have introduced bias in our sample. Future work should investigate learning in social contexts via diverse sources (e.g. schools).

CONCLUSIONS

These findings advocate for a distinction between learning about the social versus learning via the social: cognitive models of autism should be revisited to consider social interaction not just as a puzzle to decode but rather a medium through which people, including neuro-diverse groups, learn about the world around them. Trial registration Part of this work has been pre-registered before data collection https://doi.org/10.17605/OSF.IO/5PGA3.

A (Dis-)information Theory of Revealed and Unrevealed Preferences: Emerging Deception and Skepticism via Theory of Mind

In complex situations involving communication, agents might attempt to mask their intentions, exploiting Shannon's theory of information as a theory of misinformation. Here, we introduce and analyze a simple multiagent reinforcement learning task where a buyer sends signals to a seller via its actions, and in which both agents are endowed with a recursive theory of mind. We show that this theory of mind, coupled with pure reward-maximization, gives rise to agents that selectively distort messages and become skeptical towards one another. Using information theory to analyze these interactions, we show how savvy buyers reduce mutual information between their preferences and actions, and how suspicious sellers learn to reinterpret or discard buyers' signals in a strategic manner.

Introducing tomsup: Theory of mind simulations using Python

Theory of mind (ToM) is considered crucial for understanding social-cognitive abilities and impairments. However, verbal theories of the mechanisms underlying ToM are often criticized as under-specified and mutually incompatible. This leads to measures of ToM being unreliable, to the extent that even canonical experimental tasks do not require representation of others' mental states. There have been attempts at making computational models of ToM, but these are not easily available for broad research application. In order to help meet these challenges, we here introduce the Python package tomsup: Theory of mind simulations using Python. The package provides a computational eco-system for investigating and comparing computational models of hypothesized ToM mechanisms and for using them as experimental stimuli. The package notably includes an easy-to-use implementation of the variational recursive Bayesian k-ToM model developed by (Devaine, Hollard, & Daunizeau, 2014b) and of simpler non-recursive decision models, for comparison. We provide a series of tutorials on how to: (i) simulate agents relying on the k-ToM model and on a range of simpler types of mechanisms; (ii) employ those agents to generate online experimental stimuli; (iii) analyze the data generated in such experimental setup, and (iv) specify new custom ToM and heuristic cognitive models.

Formalising social representation to explain psychiatric symptoms

Recent work in social cognition has moved beyond a focus on how people process social rewards to examine how healthy people represent other agents and how this is altered in psychiatric disorders. However, formal modelling of social representation has not kept pace with these changes, impeding our understanding of how core aspects of social cognition function, and fail, in psychopathology. Here, we suggest that belief-based computational models provide a basis for an integrated sociocognitive approach to psychiatry, with the potential to address important but unexamined pathologies of social representation, such as maladaptive schemas and illusory social agents.

Proselfs depend more on model-based than model-free learning in a non-social probabilistic state-transition task

Humans form complex societies in which we routinely engage in social decision-making regarding the allocation of resources among ourselves and others. One dimension that characterizes social decision-making in particular is whether to prioritize self-interest or respect for others-proself or prosocial. What causes this individual difference in social value orientation? Recent developments in the social dual-process theory argue that social decision-making is characterized by its underlying domain-general learning systems: the model-free and model-based systems. In line with this "learning" approach, we propose and experimentally test the hypothesis that differences in social preferences stem from which learning system is dominant in an individual. Here, we used a non-social state transition task that allowed us to assess the balance between model-free/model-based learning and investigate its relation to the social value orientations. The results showed that proselfs depended more on model-based learning, whereas prosocials depended more on model-free learning. Reward amount and reaction time analyses showed that proselfs learned the task structure earlier in the session than prosocials, reflecting their difference in model-based/model-free learning dependence. These findings support the learning hypothesis on what makes differences in social preferences and have implications for understanding the mechanisms of prosocial behavior.

Neural implementation of computational mechanisms underlying the continuous trade-off between cooperation and competition

Social interactions evolve continuously. Sometimes we cooperate, sometimes we compete, while at other times we strategically position ourselves somewhere in between to account for the ever-changing social contexts around us. Research on social interactions often focuses on a binary dichotomy between competition and cooperation, ignoring people's evolving shifts along a continuum. Here, we develop an economic game - the Space Dilemma - where two players change their degree of cooperativeness over time in cooperative and competitive contexts. Using computational modelling we show how social contexts bias choices and characterise how inferences about others' intentions modulate cooperativeness. Consistent with the modelling predictions, brain regions previously linked to social cognition, including the temporo-parietal junction, dorso-medial prefrontal cortex and the anterior cingulate gyrus, encode social prediction errors and context-dependent signals, correlating with shifts along a cooperation-competition continuum. These results provide a comprehensive account of the computational and neural mechanisms underlying the continuous trade-off between cooperation and competition.

Dissecting functional contributions of the social brain to strategic behavior

Social interactions routinely lead to neural activity in a "social brain network" comprising, among other regions, the temporoparietal junction (TPJ) and the dorsomedial prefrontal cortex (dmPFC). But what is the function of these areas? Are they specialized for behavior in social contexts or do they implement computations required for dealing with any reactive process, even non-living entities? Here, we use fMRI and a game paradigm separating the need for these two aspects of cognition. We find that most social-brain areas respond to both social and non-social reactivity rather than just to human opponents. However, the TPJ shows a dissociation from the dmPFC: its activity and connectivity primarily reflect context-dependent outcome processing and reactivity detection, while dmPFC engagement is linked to implementation of a behavioral strategy. Our results characterize an overarching computational property of the social brain but also suggest specialized roles for subregions of this network.

Neural correlates of recursive thinking during interpersonal strategic interactions

To navigate the complex social world, individuals need to represent others' mental states to think strategically and predict their next move. Strategic mentalizing can be classified into different levels of theory of mind according to its order of mental state attribution of other people's beliefs, desires, intentions, and so forth. For example, reasoning people's beliefs about simple world facts is the first-order attribution while going further to reason people's beliefs about the minds of others is the second-order attribution. The neural substrates that support such high-order recursive reasoning in strategic interpersonal interactions are still unclear. Here, using a sequential-move interactional game together with functional magnetic resonance imaging (fMRI), we showed that recursive reasoning engaged the frontal-subcortical regions. At the stimulus stage, the ventral striatum was more activated in high-order reasoning as compared with low-order reasoning. At the decision stage, high-order reasoning activated the medial prefrontal cortex (mPFC) and other mentalizing regions. Moreover, functional connectivity between the dorsomedial prefrontal cortex (dmPFC) and the insula/hippocampus was positively correlated with individual differences in high-order social reasoning. This work delineates the neural correlates of high-order recursive thinking in strategic games and highlights the key role of the interplay between mPFC and subcortical regions in advanced social decision-making.

Enhancing models of social and strategic decision making with process tracing and neural data

Every decision we take is accompanied by a characteristic pattern of response delay, gaze position, pupil dilation, and neural activity. Nevertheless, many models of social decision making neglect the corresponding process tracing data and focus exclusively on the final choice outcome. Here, we argue that this is a mistake, as the use of process data can help to build better models of human behavior, create better experiments, and improve policy interventions. Specifically, such data allow us to unlock the "black box" of the decision process and evaluate the mechanisms underlying our social choices. Using these data, we can directly validate latent model variables, arbitrate between competing personal motives, and capture information processing strategies. These benefits are especially valuable in social science, where models must predict multi-faceted decisions that are taken in varying contexts and are based on many different types of information. This article is categorized under: Economics > Interactive Decision-Making Neuroscience > Cognition Psychology > Reasoning and Decision Making.

Reward and fictive prediction error signals in ventral striatum: asymmetry between factual and counterfactual processing

Reward prediction error, the difference between the expected and obtained reward, is known to act as a reinforcement learning neural signal. In the current study, we propose a model fitting approach that combines behavioral and neural data to fit computational models of reinforcement learning. Briefly, we penalized subject-specific fitted parameters that moved away too far from the group median, except when that deviation led to an improvement in the model's fit to neural responses. By means of a probabilistic monetary learning task and fMRI, we compared our approach with standard model fitting methods. Q-learning outperformed actor-critic at both behavioral and neural level, although the inclusion of neuroimaging data into model fitting improved the fit of actor-critic models. We observed both action-value and state-value prediction error signals in the striatum, while standard model fitting approaches failed to capture state-value signals. Finally, left ventral striatum correlated with reward prediction error while right ventral striatum with fictive prediction error, suggesting a functional hemispheric asymmetry regarding prediction-error driven learning.

Sensitivity to Social Agency in Autistic Adults

The presence of other people, whether real or implied, can have a profound impact on our behaviour. However, it is argued that autistic individuals show decreased interest in social phenomena, which leads to an absence of these effects. In this study, the agency of a cue was manipulated such that the cue was either described as representing a computer program or the eye movements of another participant. Both neurotypical and autistic participants demonstrated a social facilitation effect and were significantly more accurate on a prediction task when they believed the cue represented another participant. This demonstrates that whilst autistic adults may show difficulties in interpreting social behaviour this does not necessarily arise from a lack of sensitivity to social agency.

Neural arbitration between social and individual learning systems

Decision making requires integrating knowledge gathered from personal experiences with advice from others. The neural underpinnings of the process of arbitrating between information sources has not been fully elucidated. In this study, we formalized arbitration as the relative precision of predictions, afforded by each learning system, using hierarchical Bayesian modeling. In a probabilistic learning task, participants predicted the outcome of a lottery using recommendations from a more informed advisor and/or self-sampled outcomes. Decision confidence, as measured by the number of points participants wagered on their predictions, varied with our definition of arbitration as a ratio of precisions. Functional neuroimaging demonstrated that arbitration signals were independent of decision confidence and involved modality-specific brain regions. Arbitrating in favor of self-gathered information activated the dorsolateral prefrontal cortex and the midbrain, whereas arbitrating in favor of social information engaged the ventromedial prefrontal cortex and the amygdala. These findings indicate that relative precision captures arbitration between social and individual learning systems at both behavioral and neural levels.

Is There a 'Social' Brain? Implementations and Algorithms

A fundamental question in psychology and neuroscience is the extent to which cognitive and neural processes are specialised for social behaviour, or are shared with other 'non-social' cognitive, perceptual, and motor faculties. Here we apply the influential framework of Marr (1982) across research in humans, monkeys, and rodents to propose that information processing can be understood as 'social' or 'non-social' at different levels. We argue that processes can be socially specialised at the implementational and/or the algorithmic level, and that changing the goal of social behaviour can also change social specificity. This framework could provide important new insights into the nature of social behaviour across species, facilitate greater integration, and inspire novel theoretical and empirical approaches.

A Neuro-computational Account of Arbitration between Choice Imitation and Goal Emulation during Human Observational Learning

When individuals learn from observing the behavior of others, they deploy at least two distinct strategies. Choice imitation involves repeating other agents' previous actions, whereas emulation proceeds from inferring their goals and intentions. Despite the prevalence of observational learning in humans and other social animals, a fundamental question remains unaddressed: how does the brain decide which strategy to use in a given situation? In two fMRI studies (the second a pre-registered replication of the first), we identify a neuro-computational mechanism underlying arbitration between choice imitation and goal emulation. Computational modeling, combined with a behavioral task that dissociated the two strategies, revealed that control over behavior was adaptively and dynamically weighted toward the most reliable strategy. Emulation reliability, the model's arbitration signal, was represented in the ventrolateral prefrontal cortex, temporoparietal junction, and rostral cingulate cortex. Our replicated findings illuminate the computations by which the brain decides to imitate or emulate others.

Theory of mind and decision science: Towards a typology of tasks and computational models

The ability to form a Theory of Mind (ToM), i.e., to theorize about others' mental states to explain and predict behavior in relation to attributed intentional states, constitutes a hallmark of human cognition. These abilities are multi-faceted and include a variety of different cognitive sub-functions. Here, we focus on decision processes in social contexts and review a number of experimental and computational modeling approaches in this field. We provide an overview of experimental accounts and formal computational models with respect to two dimensions: interactivity and uncertainty. Thereby, we aim at capturing the nuances of ToM functions in the context of social decision processes. We suggest there to be an increase in ToM engagement and multiplexing as social cognitive decision-making tasks become more interactive and uncertain. We propose that representing others as intentional and goal directed agents who perform consequential actions is elicited only at the edges of these two dimensions. Further, we argue that computational models of valuation and beliefs follow these dimensions to best allow researchers to effectively model sophisticated ToM-processes. Finally, we relate this typology to neuroimaging findings in neurotypical (NT) humans, studies of persons with autism spectrum (AS), and studies of nonhuman primates.

Mentalizing during social InterAction: A four component model

Mentalizing, conventionally defined as the process in which we infer the inner thoughts and intentions of others, is a fundamental component of human social cognition. Yet its role, and the nuanced layers involved, in real world social interaction are rarely discussed. To account for this lack of theory, we propose the interactive mentalizing theory (IMT) -to emphasize the role of metacognition in different mentalizing components. We discuss the connection between mentalizing, metacognition, and social interaction in the context of four elements of mentalizing: (i) Metacognition-inference of our own thought processes and social cognitions and which is central to all other components of mentalizing including: (ii) first-order mentalizing-inferring the thoughts and intentions of an agent's mind; (iii) personal second-order mentalizing-inference of other's mentalizing of one's own mind; (iv) Collective mentalizing: which takes at least two forms (a) vicarious mentalizing: adopting another's mentalizing of an agent (i.e., what we think others think of an agent) and (b) co-mentalizing: mentalizing about an agent in conjunction with others' mentalizing of that agent (i.e., conforming to others beliefs about another agent's internal states). The weights of these four elements is determined by metacognitive insight and confidence in one's own or another's mentalizing ability, yielding a dynamic interaction between these circuits. To advance our knowledge on mentalizing during live social interaction, we identify how these subprocesses can be organized by different target agents and facilitated by combining computational modeling and interactive brain approaches.

Social behavioural adaptation in Autism

Autism is still diagnosed on the basis of subjective assessments of elusive notions such as interpersonal contact and social reciprocity. We propose to decompose reciprocal social interactions in their basic computational constituents. Specifically, we test the assumption that autistic individuals disregard information regarding the stakes of social interactions when adapting to others. We compared 24 adult autistic participants to 24 neurotypical (NT) participants engaging in a repeated dyadic competitive game against artificial agents with calibrated reciprocal adaptation capabilities. Critically, participants were framed to believe either that they were competing against somebody else or that they were playing a gambling game. Only the NT participants did alter their adaptation strategy when they held information regarding others' competitive incentives, in which case they outperformed the AS group. Computational analyses of trial-by-trial choice sequences show that the behavioural repertoire of autistic people exhibits subnormal flexibility and mentalizing sophistication, especially when information regarding opponents’ incentives was available. These two computational phenotypes yield 79% diagnosis classification accuracy and explain 62% of the severity of social symptoms in autistic participants. Such computational decomposition of the autistic social phenotype may prove relevant for drawing novel diagnostic boundaries and guiding individualized clinical interventions in autism.

Autism or AS is mostly characterized by impairments in a very specific yet intricate skill set, namely: social intelligence. In this work, we focus on "social reciprocity", i.e. the continuous adaptation of one's behaviour that both moulds and appropriately responds to others' behaviour. Our working hypothesis is that social reciprocity deficits in people with AS derive from a basic inability to tune one's adaptation strategy to contextual knowledge about the stakes of social interactions (e.g., others' cooperative or competitive incentives). We ask participants to engage in simple interactive games with AI agents that are endowed with calibrated reciprocal adaptation capabilities. Critically, participants are framed to believe either that they are competing against somebody else (social framing) or that they are playing a gambling game (non-social framing). Only in the social condition do participants know about the (competitive) incentives of their opponents. Computational analyses of action sequences in the games show that, contrary to healthy controls, people with AS do not change their strategy according to whether they hold information regarding their opponents' incentives or not. In addition, these analyses yield 79% diagnosis out-of-sample classification accuracy (AS versus controls) and predict 62% of the severity of social symptoms in people with AS. This demonstrates the feasibility of AI-based quantitative assessments of social cognition and its deficits.

Fictional narrative as a variational Bayesian method for estimating social dispositions in large groups

Modelling intentions in large groups is cognitively costly. Not alone must first order beliefs be tracked ('what does A think about X?'), but also beliefs about beliefs ('what does A think about B's belief concerning X?'). Thus linear increases in group size impose non-linear increases in cognitive processing resources. At the same time, however, large groups offer coordination advantages relative to smaller groups due to specialisation and increased productive capacity. How might these competing demands be reconciled? We propose that fictional narrative can be understood as a cultural tool for dealing with large groups. Specifically, we argue that prototypical action roles that are removed from real-world interactions function as interpretive priors in a form of variational Bayesian inference, such that they allow estimations can be made of unknown social motives. We offer support for this claim in two ways. Firstly, by evaluating the existing literature on narrative cognition and showing where it anticipates a variational model; and secondly, by simulation, where we show that an agent-based model naturally converges on a set of social categories that resemble narrative across a wide range of starting points.

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.

Asymmetric valuation of gains and losses in effort-based decision making

Our decisions are often swayed by a desire to avoid losses over a desire to acquire gains. While loss aversion has been confirmed for decisions about money or commodities, it is unclear how individuals generally value gains relative to losses in effort-based decisions. For example, do individuals avoid greater work more than they seek out less work? We examined this question in the context of physical effort, using an arm-reaching task in which decreased effort was framed as a gain and increased effort was framed as a loss. Subjects performed reaching movements against different levels of resistance that increased or decreased the effort demands of the reaches. They then chose to accept or reject various lotteries, each with a possibility of performing less effortful reaches and a possibility of performing more effortful reaches, compared to the certain outcome of performing reaches against a fixed reference level of effort. Subjects avoided higher effort conditions more than they sought lower effort conditions, demonstrating asymmetric valuation of gains and losses. Using prospect theory, we explored various model formulations to determine subject-specific valuation of effort in these mixed gambles. A nonlinear model of effort valuation demonstrating increasing sensitivity to absolute effort best described the effort lottery choices. In contrast to the loss-aversion observed in financial decisions, there was no evidence of loss aversion in effort-based decisions. Rather, we observed moderate relief-seeking behavior. This model confirms that gains and losses are valued asymmetrically. This is due to the combined effects of increasing sensitivity to absolute effort and moderate relief-seeking, leading to a net effect of greater avoidance of higher effort. Asymmetric valuation was magnified on a later day of testing. In contrast, subjects were loss-averse in a comparable financial task. We suggest that consideration of nonlinear effort valuation can inform future studies of sensorimotor control and exercise motivation.

Resolving uncertainty in a social world

Consider the range of social behaviours we engage in every day. In each case, there are a multitude of unknowns, reflecting the many sources of uncertainty inherent to social inference. We describe how uncertainty manifests in social environments (the thoughts and intentions of others are largely hidden, making it difficult to predict a person's behaviour) and why people are motivated to reduce the aversive feelings generated by uncertainty. We propose a three-part model whereby social uncertainty is initially reduced through automatic modes of inference (such as impression formation) before more control-demanding modes of inference (such as perspective-taking) are deployed to narrow one's predictions even more. Finally, social uncertainty is attenuated further through learning processes that update these predictions based on new information. Our framework integrates research across fields to offer an account of the mechanisms motivating social cognition and action, laying the groundwork for future experiments that can illuminate the impact of uncertainty on social cognition.

The application of computational models to social neuroscience: promises and pitfalls

Interactions with conspecifics are key to any social species. In order to navigate this social world, it is crucial for individuals to learn from and about others. From learning new skills by observing parents perform them to making complex collective decisions, understanding the mechanisms underlying social cognitive processes has been of considerable interest to psychologists and neuroscientists. Here, we review studies that have used computational modelling techniques, combined with neuroimaging, to shed light on how people learn and make decisions in social contexts. As opposed to standard social neuroscience methods, the computational approach allows one to directly examine where in the brain particular computations, as estimated by models of behavior, are implemented. Findings suggest that people use several strategies to learn from others: vicarious reward learning, where one learns from observing the reward outcomes of another agent; action imitation, which relies on encoding a prediction error between the expected and actual actions of the other agent; and social inference, where one learns by inferring the goals and intentions of others. These computations are implemented in distinct neural networks, which may be recruited adaptively depending on task demands, the environment and other social factors.

Ventral anterior cingulate cortex and social decision-making

Studies in the field of social neuroscience have recently made use of computational models of decision-making to provide new insights into how we learn about the self and others during social interactions. Importantly, these studies have increasingly drawn attention to brain areas outside of classical cortical "social brain" regions that may be critical for social processing. In particular, two portions of the ventral anterior cingulate cortex (vACC), subgenual anterior cingulate cortex and perigenual anterior cingulate cortex, have been linked to social and self learning signals, respectively. Here we discuss the emerging parallels between these studies. Uncovering the function of vACC during social interactions could provide important new avenues to understand social decision-making in health and disease.

A Probabilistic Model of Social Working Memory for Information Retrieval in Social Interactions

Social working memory (SWM) plays an important role in navigating social interactions. Inspired by studies in psychology, neuroscience, cognitive science, and machine learning, we propose a probabilistic model of SWM to mimic human social intelligence for personal information retrieval (IR) in social interactions. First, we establish a semantic hierarchy as social long-term memory to encode personal information. Next, we propose a semantic Bayesian network as the SWM, which integrates the cognitive functions of accessibility and self-regulation. One subgraphical model implements the accessibility function to learn the social consensus about IR-based on social information concept, clustering, social context, and similarity between persons. Beyond accessibility, one more layer is added to simulate the function of self-regulation to perform the personal adaptation to the consensus based on human personality. Two learning algorithms are proposed to train the probabilistic SWM model on a raw dataset of high uncertainty and incompleteness. One is an efficient learning algorithm of Newton's method, and the other is a genetic algorithm. Systematic evaluations show that the proposed SWM model is able to learn human social intelligence effectively and outperforms the baseline Bayesian cognitive model. Toward real-world applications, we implement our model on Google Glass as a wearable assistant for social interaction.

Hierarchical prediction errors in midbrain and septum during social learning

Social learning is fundamental to human interactions, yet its computational and physiological mechanisms are not well understood. One prominent open question concerns the role of neuromodulatory transmitters. We combined fMRI, computational modelling and genetics to address this question in two separate samples (N = 35, N = 47). Participants played a game requiring inference on an adviser’s intentions whose motivation to help or mislead changed over time. Our analyses suggest that hierarchically structured belief updates about current advice validity and the adviser’s trustworthiness, respectively, depend on different neuromodulatory systems. Low-level prediction errors (PEs) about advice accuracy not only activated regions known to support ‘theory of mind’, but also the dopaminergic midbrain. Furthermore, PE responses in ventral striatum were influenced by the Met/Val polymorphism of the Catechol-O-Methyltransferase (COMT) gene. By contrast, high-level PEs (‘expected uncertainty’) about the adviser’s fidelity activated the cholinergic septum. These findings, replicated in both samples, have important implications: They suggest that social learning rests on hierarchically related PEs encoded by midbrain and septum activity, respectively, in the same manner as other forms of learning under volatility. Furthermore, these hierarchical PEs may be broadcast by dopaminergic and cholinergic projections to induce plasticity specifically in cortical areas known to represent beliefs about others.

Data quality over data quantity in computational cognitive neuroscience

We analyzed factors that may hamper the advancement of computational cognitive neuroscience (CCN). These factors include a particular statistical mindset, which paves the way for the dominance of statistical power theory and a preoccupation with statistical replicability in the behavioral and neural sciences. Exclusive statistical concerns about sampling error occur at the cost of an inadequate representation of the problem of measurement error. We contrasted the manipulation of data quantity (sampling error, by varying the number of subjects) against the manipulation of data quality (measurement error, by varying the number of data per subject) in a simulated Bayesian model identifiability study. The results were clear-cut in showing that - across all levels of signal-to-noise ratios - varying the number of subjects was completely inconsequential, whereas the number of data per subject exerted massive effects on model identifiability. These results emphasize data quality over data quantity, and they call for the integration of statistics and measurement theory.

Stepwise training supports strategic second-order theory of mind in turn-taking games

People model other people’s mental states in order to understand and predict their behavior. Sometimes they model what others think about them as well: “He thinks that I intend to stop.” Such second-order theory of mind is needed to navigate some social situations, for example, to make optimal decisions in turn-taking games. Adults sometimes find this very difficult. Sometimes they make decisions that do not fit their predictions about the other player. However, the main bottleneck for decision makers is to take a second-order perspective required to make a correct opponent model. We report a methodical investigation into supporting factors that help adults do better. We presented subjects with two-player, three-turn games in which optimal decisions required second-order theory of mind (Hedden and Zhang, 2002). We applied three “scaffolds” that, theoretically, should facilitate second-order perspective-taking: 1) stepwise training, from simple one-person games to games requiring second-order theory of mind; 2) prompting subjects to predict the opponent’s next decision before making their own decision; and 3) a realistic visual task representation. The performance of subjects in the eight resulting combinations shows that stepwise training, but not the other two scaffolds, improves subjects’ second-order opponent models and thereby their own decisions.

Reading wild minds: A computational assay of Theory of Mind sophistication across seven primate species

Theory of Mind (ToM), i.e. the ability to understand others' mental states, endows humans with highly adaptive social skills such as teaching or deceiving. Candidate evolutionary explanations have been proposed for the unique sophistication of human ToM among primates. For example, the Machiavellian intelligence hypothesis states that the increasing complexity of social networks may have induced a demand for sophisticated ToM. This type of scenario ignores neurocognitive constraints that may eventually be crucial limiting factors for ToM evolution. In contradistinction, the cognitive scaffolding hypothesis asserts that a species' opportunity to develop sophisticated ToM is mostly determined by its general cognitive capacity (on which ToM is scaffolded). However, the actual relationships between ToM sophistication and either brain volume (a proxy for general cognitive capacity) or social group size (a proxy for social network complexity) are unclear. Here, we let 39 individuals sampled from seven non-human primate species (lemurs, macaques, mangabeys, orangutans, gorillas and chimpanzees) engage in simple dyadic games against artificial ToM players (via a familiar human caregiver). Using computational analyses of primates' choice sequences, we found that the probability of exhibiting a ToM-compatible learning style is mainly driven by species' brain volume (rather than by social group size). Moreover, primates' social cognitive sophistication culminates in a precursor form of ToM, which still falls short of human fully-developed ToM abilities.

The contribution of Theory of Mind (ToM), i.e. the ability to understand others' mental states, to the cognitive toolkit of non-human animal species (including primates), is fiercely disputed. We contribute to this debate by (i) proposing a computational definition of ToM sophistication that is amenable to behavioural testing in non-human primates (which we had previously validated in humans), and (ii) performing a balanced comparison of seven primates species (from lemurs to monkeys to great apes). In turn, our study provides an unprecedented computational insight into the evolutionary roots of human social intelligence. In particular, we provide empirical evidence against the common-sense idea that sophisticated ToM evolved mostly as an "on-demand" response to social challenges posed by big herds. Rather, the evolution of sophisticated ToM seems to be mainly determined by neurobiological limiting factors such as the species' "cognitive reservoir". En passant, we identify an evolutionary gap between great apes and humans, in terms of the sophistication of their respective ToM skills.

Computational Psychosomatics and Computational Psychiatry: Toward a Joint Framework for Differential Diagnosis

This article outlines how a core concept from theories of homeostasis and cybernetics, the inference-control loop, may be used to guide differential diagnosis in computational psychiatry and computational psychosomatics. In particular, we discuss 1) how conceptualizing perception and action as inference-control loops yields a joint computational perspective on brain-world and brain-body interactions and 2) how the concrete formulation of this loop as a hierarchical Bayesian model points to key computational quantities that inform a taxonomy of potential disease mechanisms. We consider the utility of this perspective for differential diagnosis in concrete clinical applications.

A causal account of the brain network computations underlying strategic social behavior

During competitive interactions, humans have to estimate the impact of their own actions on their opponent's strategy. Here we provide evidence that neural computations in the right temporoparietal junction (rTPJ) and interconnected structures are causally involved in this process. By combining inhibitory continuous theta-burst transcranial magnetic stimulation with model-based functional MRI, we show that disrupting neural excitability in the rTPJ reduces behavioral and neural indices of mentalizing-related computations, as well as functional connectivity of the rTPJ with ventral and dorsal parts of the medial prefrontal cortex. These results provide a causal demonstration that neural computations instantiated in the rTPJ are neurobiological prerequisites for the ability to integrate opponent beliefs into strategic choice, through system-level interaction within the valuation and mentalizing networks.

Learning about and from others' prudence, impatience or laziness: The computational bases of attitude alignment

Peoples' subjective attitude towards costs such as, e.g., risk, delay or effort are key determinants of inter-individual differences in goal-directed behaviour. Thus, the ability to learn about others' prudent, impatient or lazy attitudes is likely to be critical for social interactions. Conversely, how adaptive such attitudes are in a given environment is highly uncertain. Thus, the brain may be tuned to garner information about how such costs ought to be arbitrated. In particular, observing others' attitude may change one's uncertain belief about how to best behave in related difficult decision contexts. In turn, learning from others' attitudes is determined by one's ability to learn about others' attitudes. We first derive, from basic optimality principles, the computational properties of such a learning mechanism. In particular, we predict two apparent cognitive biases that would arise when individuals are learning about others’ attitudes: (i) people should overestimate the degree to which they resemble others (false-consensus bias), and (ii) they should align their own attitudes with others’ (social influence bias). We show how these two biases non-trivially interact with each other. We then validate these predictions experimentally by profiling people's attitudes both before and after guessing a series of cost-benefit arbitrages performed by calibrated artificial agents (which are impersonating human individuals).

What do people learn from observing others' attitudes, such as "prudence", "impatience" or "laziness"? Rather than viewing these attitudes as examples of highly subjective personality traits, we assume that they derive from uncertain (and mostly implicit) beliefs about how to best weigh risks, delays and efforts in ensuing cost-benefit trade-offs. In this view, it is adaptive to update one's belief after having observed others' attitude, which provides valuable information regarding how to best behave in related difficult decision contexts. This is the starting point of our computational model of attitude alignment, which we derive from first optimality principles as well as from recent neuroscientific findings. Critical here is the impact of one's ability to learn about others' covert mental states or attitudes, which is known as "mentalizing" or "Theory of Mind". In particular, this model makes two (otherwise unrelated) predictions that conform to known but puzzling cognitive biases of social cognition in humans, namely: "false consensus" and "social influence". It also shows how attitude alignment may eventually follow from the interaction between these two biases. Using state-of-the-art behavioural and computational methods, we provide experimental evidence that confirm these predictions. Finally, we discuss the relevance and implications of this work, both from a neuroscientific and economic perspective.

Mind the Noise When Identifying Computational Models of Cognition from Brain Activity

The aim of this study was to analyze how measurement error affects the validity of modeling studies in computational neuroscience. A synthetic validity test was created using simulated P300 event-related potentials as an example. The model space comprised four computational models of single-trial P300 amplitude fluctuations which differed in terms of complexity and dependency. The single-trial fluctuation of simulated P300 amplitudes was computed on the basis of one of the models, at various levels of measurement error and at various numbers of data points. Bayesian model selection was performed based on exceedance probabilities. At very low numbers of data points, the least complex model generally outperformed the data-generating model. Invalid model identification also occurred at low levels of data quality and under low numbers of data points if the winning model's predictors were closely correlated with the predictors from the data-generating model. Given sufficient data quality and numbers of data points, the data-generating model could be correctly identified, even against models which were very similar to the data-generating model. Thus, a number of variables affects the validity of computational modeling studies, and data quality and numbers of data points are among the main factors relevant to the issue. Further, the nature of the model space (i.e., model complexity, model dependency) should not be neglected. This study provided quantitative results which show the importance of ensuring the validity of computational modeling via adequately prepared studies. The accomplishment of synthetic validity tests is recommended for future applications. Beyond that, we propose to render the demonstration of sufficient validity via adequate simulations mandatory to computational modeling studies.