Neural correlates of depth of strategic reasoning in medial prefrontal cortex

An Active-Inference Approach to Second-Person Neuroscience

Social neuroscience has often been criticized for approaching the investigation of the neural processes that enable social interaction and cognition from a passive, detached, third-person perspective, without involving any real-time social interaction. With the emergence of second-person neuroscience, investigators have uncovered the unique complexity of neural-activation patterns in actual, real-time interaction. Social cognition that occurs during social interaction is fundamentally different from that unfolding during social observation. However, it remains unclear how the neural correlates of social interaction are to be interpreted. Here, we leverage the active-inference framework to shed light on the mechanisms at play during social interaction in second-person neuroscience studies. Specifically, we show how counterfactually rich mutual predictions, real-time bodily adaptation, and policy selection explain activation in components of the default mode, salience, and frontoparietal networks of the brain, as well as in the basal ganglia. We further argue that these processes constitute the crucial neural processes that underwrite bona fide social interaction. By placing the experimental approach of second-person neuroscience on the theoretical foundation of the active-inference framework, we inform the field of social neuroscience about the mechanisms of real-life interactions. We thereby contribute to the theoretical foundations of empirical second-person neuroscience.

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.

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.

An outside individual option increases optimism and facilitates collaboration when groups form flexibly

Voluntary participation is a central yet understudied aspect of collaboration. Here, we model collaboration as people's voluntary choices between joining an uncertain public goods provisioning in groups and pursuing a less profitable but certain individual option. First, we find that voluntariness in collaboration increases the likelihood of group success via two pathways, both contributing to form more optimistic groups: pessimistic defectors are filtered out from groups, and some individuals update their beliefs to become cooperative. Second, we reconcile these findings with existing literature that highlights the detrimental effects of an individual option. We argue that the impact of an outside individual option on collaboration depends on the "externality" of loners - the influence that those leaving the group still exert on group endeavors. Theoretically and experimentally, we show that if collaboration allows for flexible group formation, the negative externality of loners remains limited, and the presence of an individual option robustly aids collaborative success.

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.

The Neuroscience of Human and Artificial Intelligence Presence

Two decades of social neuroscience and neuroeconomics research illustrate the brain mechanisms that are engaged when people consider human beings, often in comparison to considering artificial intelligence (AI) as a nonhuman control. AI as an experimental control preserves agency and facilitates social interactions but lacks a human presence, providing insight into brain mechanisms that are engaged by human presence and the presence of AI. Here, I review this literature to determine how the brain instantiates human and AI presence across social perception and decision-making paradigms commonly used to realize a social context. People behave toward humans differently than they do toward AI. Moreover, brain regions more engaged by humans compared to AI extend beyond the social cognition brain network to all parts of the brain, and the brain sometimes is engaged more by AI than by humans. Finally, I discuss gaps in the literature, limitations in current neuroscience approaches, and how an understanding of the brain correlates of human and AI presence can inform social science in the wild.

Q-learning model of insight problem solving and the effects of learning traits on creativity

Despite the fact that insight is a crucial component of creative thought, the means by which it is cultivated remain unknown. The effects of learning traits on insight, specifically, has not been the subject of investigation in pertinent research. This study quantitatively examines the effects of individual differences in learning traits estimated using a Q-learning model within the reinforcement learning framework and evaluates their effects on insight problem solving in two tasks, the 8-coin and 9-dot problems, which fall under the umbrella term "spatial insight problems." Although the learning characteristics of the two problems were different, the results showed that there was a transfer of learning between them. In particular, performance on the insight tasks improved with increasing experience. Moreover, loss-taking, as opposed to loss aversion, had a significant effect on performance in both tasks, depending on the amount of experience one had. It is hypothesized that loss acceptance facilitates analogical transfer between the two tasks and improves performance. In addition, this is one of the few studies that attempted to analyze insight problems using a computational approach. This approach allows the identification of the underlying learning parameters for insight problem solving.

A calculus of probability or belief? Neural underpinnings of social decision-making in a card game

For decades, the prefrontal cortex (PFC) has been the focus of social neuroscience research, specifically regarding its role in competitive social decision-making. However, the distinct contributions of PFC subregions when making strategic decisions involving multiple types of information (social, non-social, and mixed information) remain unclear. This study investigates decision-making strategies (pure probability calculation vs. mentalizing) and their neural representations using functional near-infrared spectroscopy (fNIRS) data collected during a two-person card game. We observed individual differences in information processing strategy, indicating that some participants relied more on probability than others. Overall, the use of pure probability decreased over time in favor of other types of information (e.g., mixed information), with this effect being more pronounced within-round trials than across rounds. In the brain, (1) the lateral PFC activates when decisions are driven by probability calculations; (2) the right lateral PFC responds to trial difficulty; and (3) the anterior medial PFC is engaged when decision-making involves mentalizing. Furthermore, neural synchrony, which reflects the real-time interplay between individuals' cognitive processes, did not consistently contribute to correct decisions and fluctuated throughout the experiment, suggesting a hierarchical mentalizing mechanism at work.

Teachers recruit mentalizing regions to represent learners' beliefs

Teaching enables humans to impart vast stores of culturally specific knowledge and skills. However, little is known about the neural computations that guide teachers' decisions about what information to communicate. Participants (N = 28) played the role of teachers while being scanned using fMRI; their task was to select examples that would teach learners how to answer abstract multiple-choice questions. Participants' examples were best described by a model that selects evidence that maximizes the learner's belief in the correct answer. Consistent with this idea, participants' predictions about how well learners would do closely tracked the performance of an independent sample of learners (N = 140) who were tested on the examples they had provided. In addition, regions that play specialized roles in processing social information, namely the bilateral temporoparietal junction and middle and dorsal medial prefrontal cortex, tracked learners' posterior belief in the correct answer. Our results shed light on the computational and neural architectures that support our extraordinary abilities as teachers.

Neural bases of loss aversion when choosing for oneself versus known or unknown others

Despite the ubiquitous interdependence between one's own decisions and others' welfare, and the controversial evidence on the behavioral effect of choosing for others, the neural bases of making decisions for another versus oneself remain unexplored. We investigated whether loss aversion (LA; the tendency to avoid losses over approaching equivalent gains) is modulated by (i) choosing for oneself, other individuals, or both; (ii) knowing or not knowing the other recipients; or (iii) an interaction between these factors. We used fMRI to assess the brain activations associated with choosing whether to accept or reject mixed gambles, either for oneself, for another player, or both, in 2 groups of 28 participants who had or had not briefly interacted with the other players before scanning. Participants displayed higher LA for choices involving their payoff compared with those affecting only the payoff of other, known, players. This "social" modulation of decision-making was found to engage the dorsomedial prefrontal cortex and its inhibitory connectivity to the middle cingulate cortex. This pattern might underpin decision-making for known others via self-other distinction processes associated with dorsomedial prefrontal areas, with this in turn promoting the inhibition of socially oriented responses through the downregulation of the midcingulate node of the empathy network.

The role of personality beliefs and “small talk” in strategic behaviour

Humans are predisposed to forming “first impressions” about the people we encounter including impressions about their personality traits. While the relationship between personality and strategic decision-making has been widely explored, we examine the role of personality impressions in predicting strategic behaviour and devising behavioural responses. In a laboratory setting, after only 4-minutes of “small talk”, subjects developed a sense of the personality of their partners, particularly extraversion, which consequently changed their behaviour in future interactions. Subjects cooperated more in public goods games when they believed their partner to be extraverted and found it more difficult to out-guess opponents they perceived as similar to themselves in a level-k reasoning task, having engaged in conversation with them. We trace how language can generate these effects using text analysis, showing that talking more makes individuals appear extraverted and pro-social which in turn engenders pro-social behaviour in others.

Learning under social versus nonsocial uncertainty: A meta-analytic approach

Much of the uncertainty that clouds our understanding of the world springs from the covert values and intentions held by other people. Thus, it is plausible that specialized mechanisms that compute learning signals under uncertainty of exclusively social origin operate in the brain. To test this hypothesis, we scoured academic databases for neuroimaging studies involving learning under uncertainty, and performed a meta‐analysis of brain activation maps that compared learning in the face of social versus nonsocial uncertainty. Although most of the brain activations associated with learning error signals were shared between social and nonsocial conditions, we found some evidence for functional segregation of error signals of exclusively social origin during learning in limited regions of ventrolateral prefrontal cortex and insula. This suggests that most behavioral adaptations to navigate social environments are reused from frontal and subcortical areas processing generic value representation and learning, but that a specialized circuitry might have evolved in prefrontal regions to deal with social context representation and strategic action.

Social Interaction With an Anonymous Opponent Requires Increased Involvement of the Theory of Mind Neural System: An fMRI Study

An anonymous interaction might facilitate provoking behavior and modify the engagement of theory of mind (TOM) brain mechanisms. However, the effect of anonymity when processing unfair behavior of an opponent remains largely unknown. The current functional magnetic resonance imaging (fMRI) study applied the Taylor aggression paradigm, introducing an anonymous opponent to this task. Thirty-nine healthy right-handed subjects were included in the statistical analysis (13 males/26 females, mean age 24.5 ± 3.6 years). A player winning the reaction-time game could subtract money from the opponent during the task. Participants behaved similarly to both introduced and anonymous opponents. However, when an anonymous opponent (when compared to the introduced opponent) subtracted money, the right inferior frontal gyrus (IFG) demonstrated an increased BOLD signal and increased functional connectivity with the left IFG. Further, increased functional connectivity between the right IFG, the right temporal parietal junction and precuneus was observed during the perception of high provocation (subtracting a large amount of money) from the anonymous compared to the introduced opponent. We speculate that the neural changes may underlie different inferences about the opponents’ mental states. The idea that this reorganization of the TOM network reflects the attempt to understand the opponent by “completing” socially relevant details requires further investigation.

Adaptive Rationality in Strategic Interaction: Do Emotions Regulate Thinking About Others?

Forming beliefs or expectations about others’ behavior is fundamental to strategy as it codetermines the outcomes of interactions in and across organizations. In the game-theoretic conception of rationality, agents reason iteratively about each other to form expectations about behavior. According to prior scholarship, actual strategists fall short of this ideal, and attempts to understand the underlying cognitive processes of forming expectations about others are in their infancy. We propose that emotions help regulate iterative reasoning, that is, their tendency to not only reflect on what others think, but also on what others think about their thinking. Drawing on a controlled experiment, we find that a negative emotion (fear) deepens the tendency to engage in iterative reasoning compared with a positive emotion (amusement). Moreover, neutral emotions yield even deeper levels of iterative reasoning. We tentatively interpret these early findings and speculate about the broader link of emotions and expectations in the context of strategic management. Extending the view of emotional regulation as a capability, emotions may be building blocks of rational heuristics for strategic interaction and enable interactive decision making when strategists have little experience with the environment.

Mine or Ours? Neural Basis of the Exploitation of Common-Pool Resources

Why do people often exhaust unregulated common (shared) natural resources but manage to preserve similar private resources? To answer this question, in this study we combine a neurobiological, economic and cognitive modeling approach. Using functional magnetic resonance imaging on 50 participants, we show that a sharp decrease of common and private resources is associated with deactivation of the ventral striatum, a brain region involved in the valuation of outcomes. Across individuals, when facing a common resource, ventral striatal activity is anticorrelated with resource preservation (less harvesting), whereas with private resources the opposite pattern is observed. This indicates that neural value signals distinctly modulate behavior in response to the depletion of common vs private resources. Computational modeling suggested that overharvesting of common resources was facilitated by the modulatory effect of social comparison on value signals. These results provide an explanation of people’s tendency to over-exploit unregulated common natural resources.

Adaptive Empathy: A Model for Learning Empathic Responses in Response to Feedback

Empathy is usually deployed in social interactions. Nevertheless, common measures and examinations of empathy study this construct in isolation from the person in distress. In this article we seek to extend the field of examination to include both empathizer and target to determine whether and how empathic responses are affected by feedback and learned through interaction. Building on computational approaches in feedback-based adaptations (e.g., no feedback, model-free and model-based learning), we propose a framework for understanding how empathic responses are learned on the basis of feedback. In this framework, adaptive empathy, defined as the ability to adapt one's empathic responses, is a central aspect of empathic skills and can provide a new dimension to the evaluation and investigation of empathy. By extending existing neural models of empathy, we suggest that adaptive empathy may be mediated by interactions between the neural circuits associated with valuation, shared distress, observation-execution, and mentalizing. Finally, we propose that adaptive empathy should be considered a prominent facet of empathic capabilities with the potential to explain empathic behavior in health and psychopathology.

The computational challenge of social learning

The complex reward structure of the social world and the uncertainty endemic to social contexts poses a challenge for modeling. For example, during social interactions, the actions of one person influence the internal states of another. These social dependencies make it difficult to formalize social learning problems in a mathematically tractable way. While it is tempting to dispense with these complexities, they are a defining feature of social life. Because the structure of social interactions challenges the simplifying assumptions often made in models, they make an ideal testbed for computational models of cognition. By adopting a framework that embeds existing social knowledge into the model, we can go beyond explaining behaviors in laboratory tasks to explaining those observed in the wild.

Timing of social feedback shapes observational learning in strategic interaction

Many types of social interaction require the ability to anticipate others' behavior, which is commonly referred to as strategic sophistication. In this context, observational learning can represent a decisive tool for behavioral adaptation. However, little is known on whether and when individuals learn from observation in interactive settings. In the current study, 321 participants played one-shot interactive games and, at a given time along the experiment, they could observe the choices of an overtly efficient player. This social feedback could be provided before or after the participant's choice in each game. Results reveal that players with a sufficient level of strategic skills increased their level of sophistication only when the social feedback was provided after their choices, whereas they relied on blind imitation when they received feedback before their decision. Conversely, less sophisticated players did not increase their level of sophistication, regardless of the type of social feedback. Our findings disclose the interplay between endogenous and exogenous factors modulating observational learning in strategic interaction.

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.

Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach

Although it is considered that two heads are better than one, related studies argued that groups rarely outperform their best members. This study examined not only whether two heads are better than one but also whether three heads are better than two or one in the context of two-armed bandit problems where learning plays an instrumental role in achieving high performance. This research revealed that a U-shaped correlation exists between performance and group size. The performance was highest for either individuals or triads, but the lowest for dyads. Moreover, this study estimated learning properties and determined that high inverse temperature (exploitation) accounted for high performance. In particular, it was shown that group effects regarding the inverse temperatures in dyads did not generate higher values to surpass the averages of their two group members. In contrast, triads gave rise to higher values of the inverse temperatures than their averages of their individual group members. These results were consistent with our proposed hypothesis that learning coherence is likely to emerge in individuals and triads, but not in dyads, which in turn leads to higher performance. This hypothesis is based on the classical argument by Simmel stating that while dyads are likely to involve more emotion and generate greater variability, triads are the smallest structure which tends to constrain emotions, reduce individuality, and generate behavioral convergences or uniformity because of the ''two against one" social pressures. As a result, three heads or one head were better than two in our study.

Reflectivity relates differently to pro sociality in naïve and strategic subjects

Is pro sociality a natural impulse or the result of a self-controlled behavior? We investigate this issue in a lab in the field experiment with participants from the general adult population in Italy. We find two key results: first, that there is a positive relationship between pro sociality and strategic reasoning. Second, that reflectivity relates to lower pro sociality but only among strategic subjects, indicating that the intuitive view of pro sociality is valid only among strategic individuals. Non-strategic individuals are instead intuitively selfish. We surmise that these results emerge due to a common cognitive root between strategizing and pro sociality, namely empathy.

Bad machines corrupt good morals

As machines powered by artificial intelligence (AI) influence humans' behaviour in ways that are both like and unlike the ways humans influence each other, worry emerges about the corrupting power of AI agents. To estimate the empirical validity of these fears, we review the available evidence from behavioural science, human-computer interaction and AI research. We propose four main social roles through which both humans and machines can influence ethical behaviour. These are: role model, advisor, partner and delegate. When AI agents become influencers (role models or advisors), their corrupting power may not exceed the corrupting power of humans (yet). However, AI agents acting as enablers of unethical behaviour (partners or delegates) have many characteristics that may let people reap unethical benefits while feeling good about themselves, a potentially perilous interaction. On the basis of these insights, we outline a research agenda to gain behavioural insights for better AI oversight.

Dorsolateral and dorsomedial prefrontal cortex track distinct properties of dynamic social behavior

Understanding how humans make competitive decisions in complex environments is a key goal of decision neuroscience. Typical experimental paradigms constrain behavioral complexity (e.g. choices in discrete-play games), and thus, the underlying neural mechanisms of dynamic social interactions remain incompletely understood. Here, we collected fMRI data while humans played a competitive real-time video game against both human and computer opponents, and then, we used Bayesian non-parametric methods to link behavior to neural mechanisms. Two key cognitive processes characterized behavior in our task: (i) the coupling of one’s actions to another’s actions (i.e. opponent sensitivity) and (ii) the advantageous timing of a given strategic action. We found that the dorsolateral prefrontal cortex displayed selective activation when the subject’s actions were highly sensitive to the opponent’s actions, whereas activation in the dorsomedial prefrontal cortex increased proportionally to the advantageous timing of actions to defeat one’s opponent. Moreover, the temporoparietal junction tracked both of these behavioral quantities as well as opponent social identity, indicating a more general role in monitoring other social agents. These results suggest that brain regions that are frequently implicated in social cognition and value-based decision-making also contribute to the strategic tracking of the value of social actions in dynamic, multi-agent contexts.

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.

The Role of the Medial Prefrontal Cortex in Moderating Neural Representations of Self and Other in Primates

As a frontal node in the primate social brain, the medial prefrontal cortex (MPFC) plays a critical role in coordinating one's own behavior with respect to that of others. Current literature demonstrates that single neurons in the MPFC encode behavior-related variables such as intentions, actions, and rewards, specifically for self and other, and that the MPFC comes into play when reflecting upon oneself and others. The social moderator account of MPFC function can explain maladaptive social cognition in people with autism spectrum disorder, which tips the balance in favor of self-centered perspectives rather than taking into consideration the perspective of others. Several strands of evidence suggest a hypothesis that the MPFC represents different other mental models, depending on the context at hand, to better predict others' emotions and behaviors. This hypothesis also accounts for aberrant MPFC activity in autistic individuals while they are mentalizing others.

Reading between the lines: Listener's vmPFC simulates speaker cooperative choices in communication games

Humans have a remarkable ability to understand what is and is not being said by conversational partners. It has been hypothesized that listeners decode the intended meaning of a communicative signal by assuming speakers speak cooperatively, rationally simulating the speaker's choice process and inverting it to recover the speaker's most probable meaning. We investigated whether and how rational simulations of speakers are represented in the listener's brain, by combining referential communication games with functional neuroimaging. We show that listeners' ventromedial prefrontal cortex encodes the probabilistic inference of what a cooperative speaker should say given a communicative goal and context, even when such inferences are irrelevant for reference resolution. The listener's striatum encodes the amount of update on intended meaning, consistent with inverting a simulated mental model. These findings suggest a neural generative mechanism, subserved by the frontal-striatal circuits, that underlies our ability to understand communicative and, more generally, social actions.

Mood and Risk-Taking as Momentum for Creativity

This study examined the effects of mood and risk-taking on divergent and convergent thinking using a Q-learning computation model. The results revealed that while mood was not significantly related to divergent or convergent thinking (as creative thinking types), risk-taking exerted positive effects on divergent thinking in the face of negative rewards. The results were consistent with the representational change theory in insight problem solving. Although this theory accounts directly for insight, the underlying idea of going beyond current contexts and implicit constrains could be applied to creative thinking as well. The results indeed accounted for the relevance of this theory to divergent thinking. The current study is one of the first empirical studies simultaneously examining the role of mood and risk-taking in creativity. In particular, no related studies exist that took a computational approach to estimate the relevant parameters in the framework of dynamic optimization. Our Q learning model enables to distinguish and identify the different roles of mood and risk-taking in updating Q values and making decisions.

The neural underpinnings of allocentric thinking in a novel signaling task

The ability to adopt the perspectives of others is fundamental to effective communication in social interactions. However, the neural correlates of allocentric thinking in communicative signaling remain unclear. We adapted a novel signaling task in which the signaler was given the target word and must choose a one-word signal to help the receiver guess the target. Behavioral results suggest that speakers can use allocentric thinking to choose signals that are salient from the perspective of the receiver rather than their own point of view. At the neural level, functional magnetic resonance imaging (fMRI) data reveal that the medial prefrontal cortex (mPFC), ventral striatum, and temporal-parietal junction are more activated when signalers engage in allocentric than egocentric thinking. Moreover, functional connectivity between the mPFC and ventral striatum predicted individuals' perspective-taking ability during successful communication. These findings reveal that neural representations in the mPFC-striatum network support perspective-taking in complex social decision making, providing a new perspective on how the brain arbitrates between allocentric thinking and egocentric thinking in communication and social coordination.

Chimpanzee histology and functional brain imaging show that the paracingulate sulcus is not human-specific

The paracingulate sulcus -PCGS- has been considered for a long time to be specific to the human brain. Its presence/absence has been discussed in relation to interindividual variability of personality traits and cognitive abilities. Recently, a putative PCGS has been observed in chimpanzee brains. To demonstrate that this newly discovered sulcus is the homologue of the PCGS in the human brain, we analyzed cytoarchitectonic and resting-state functional magnetic resonance imaging data in chimpanzee brains which did or did not display a PCGS. The results show that the organization of the mid-cingulate cortex of the chimpanzee brain is comparable to that of the human brain, both cytoarchitectonically and in terms of functional connectivity with the lateral frontal cortex. These results demonstrate that the PCGS is not human-specific but is a shared feature of the primate brain since at least the last common ancestor to humans and great apes ~6 mya.

The paracingulate sulcus (PCGS) is a brain structure long thought to be specific to humans, and variation in this structure has been linked to personality traits and cognitive abilities. In this study, Céline Amiez and Jérôme Sallet et al. analyze brain imaging data from humans and chimpanzees to demonstrate that the PCGS is in fact present in our closest relative and its functional connectivity in chimpanzees is comparable to that in humans.

Interpersonal brain synchronization under bluffing in strategic games

People commonly use bluffing as a strategy to manipulate other people's beliefs about them for gain. Although bluffing is an important part of successful strategic thinking, the inter-brain mechanisms underlying bluffing remain unclear. Here, we employed a functional near-infrared spectroscopy hyperscanning technique to simultaneously record the brain activity in the right temporal-parietal junction in 32 pairs of participants when they played a bluffing game against each other or with computer opponents separately. We also manipulated the penalty for bluffing (high vs low). Under the condition of high relative to low penalty, results showed a higher bluffing rate and a higher calling rate in human-to-human as compared to human-to-computer pairing. At the neural level, high relative to low penalty condition increased the interpersonal brain synchronization (IBS) in the right angular gyrus (rAG) during human-to-human as compared to human-to-computer interaction. Importantly, bluffing relative to non-bluffing, under the high penalty and human-to-human condition, resulted in an increase in response time and enhanced IBS in the rAG. Participants who bluffed more frequently also elicited stronger IBS. Our findings support the view that regions associated with mentalizing become synchronized during bluffing games, especially under the high penalty and human-to-human condition.

Functional magnetic resonance imaging multivoxel pattern analysis reveals neuronal substrates for collaboration and competition with myopic and predictive strategic reasoning

Competition and collaboration are strategies that can be used to optimize the outcomes of social interactions. Research into the neuronal substrates underlying these aspects of social behavior has been limited due to the difficulty in distinguishing complex activation via univariate analysis. Therefore, we employed multivoxel pattern analysis of functional magnetic resonance imaging to reveal the neuronal activations underlying competitive and collaborative processes when the collaborator/opponent used myopic/predictive reasoning. Twenty‐four healthy subjects participated in 2 × 2 matrix‐based sequential‐move games. Searchlight‐based multivoxel patterns were used as input for a support vector machine using nested cross‐validation to distinguish game conditions, and identified voxels were validated via the regression of the behavioral data with bootstrapping. The left anterior insula (accuracy = 78.5%) was associated with competition, and middle frontal gyrus (75.1%) was associated with predictive reasoning. The inferior/superior parietal lobules (84.8%) and middle frontal gyrus (84.7%) were associated with competition, particularly in trials with a predictive opponent. The visual/motor areas were related to response time as a proxy for visual attention and task difficulty. Our results suggest that multivoxel patterns better represent the neuronal substrates underlying the social cognition of collaboration and competition intermixed with myopic and predictive reasoning than do univariate features.

Experimental Games and Social Decision Making

Experimental games model situations in which the future outcomes of individuals and groups depend on their own choices and on those of other (groups of) individuals. Games are a powerful tool to identify the neural and psychological mechanisms underlying interpersonal and group cooperation and coordination. Here we discuss recent developments in how experimental games are used and adapted, with an increased focus on repeated interactions, partner control through sanctioning, and partner (de)selection for future interactions. Important advances have been made in uncovering the neurobiological underpinnings of key factors involved in cooperation and coordination, including social preferences, cooperative beliefs, (emotion) signaling, and, in particular, reputations and (in)direct reciprocity. Emerging trends at the cross-sections of psychology, economics, and the neurosciences include an increased focus on group heterogeneities, intergroup polarization and conflict, cross-cultural differences in cooperation and norm enforcement, and neurocomputational modeling of the formation and updating of social preferences and beliefs.

Strategic thinking and behavior during a pandemic

This paper introduces a novel theoretical model and measure of strategic thinking in social decision making. The model distinguishes four strategic orientations: egocentric (thinking about how one’s actions shape one’s outcomes), impact (thinking about how one’s actions shapes others’ outcomes), dependency (thinking about how others’ actions shape one’s outcomes), and altercentric (thinking about how others’ actions shape their outcomes). Applying this model to explain social behavior in the context of the COVID-19 pandemic, an exploratory study finds that the more people think about how their actions shape others’ outcomes, the more likely they are to: (a) comply with social distancing restrictions designed to curb the spread of the virus, and (b) donate money they received in the study to charitable organizations. These findings advance understanding of the multifaceted nature of strategic thinking and highlight the usefulness of the Strategic Thinking Scale for explaining social behavior.

Effort shapes social cognition and behaviour: A neuro-cognitive framework

Theoretical accounts typically posit that variability in social behaviour is a function of capacity limits. We argue that many social behaviours are goal-directed and effortful, and thus variability is not just a function of capacity, but also motivation. Leveraging recent work examining the cognitive, computational and neural basis of effort processing, we put forward a framework for motivated social cognition. We argue that social cognition is demanding, people avoid its effort costs, and a core-circuit of brain areas that guides effort-based decisions in non-social situations may similarly evaluate whether social behaviours are worth the effort. Thus, effort sensitivity dissociates capacity limits from social motivation, and may be a driver of individual differences and pathological impairments in social cognition.

The effects of risk-taking, exploitation, and exploration on creativity

The purpose of this paper was to investigate the effects of risk-taking, exploitation, and exploration on creativity by taking a model-based computational approach to both divergent and convergent thinking as primary ingredients of creativity. We adopted a reinforcement learning framework of Q learning to provide a simple, rigorous account of behavior in the decision-making process and examined the determinants of divergent and convergent thinking. Our findings revealed that risk-taking has positive effects on divergent thinking, but not related to convergent thinking. In particular, divergent thinkers with a high learning capacity were more likely to engage in risk-taking when facing losses than when facing gains. This risk-taking behavior not only contributes to the rapid achievement of learning convergence, but is also associated with high performance in divergent thinking tasks. Conversely, both exploitation and exploration had no significant effects on creativity once these risk attitudes were considered. Moreover, while convergent thinking relied on personality characteristics, it was not associated with risk-taking, exploitation, or exploration.

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.

Dissociable roles of left and right temporoparietal junction in strategic competitive interaction

Although many studies have shown that the temporoparietal junction (TPJ) is involved in inferring others' beliefs, neural correlates of 'second-order' inferences (inferring another's inference about one's own belief) are still elusive. Here we report a functional magnetic resonance imaging experiment to examine the involvement of TPJ for second-order inferences. Participants played an economic game with three types of opponents: a human opponent outside the scanner, an artificial agent that followed a fixed probabilistic strategy according to a game-theoretic solution (FIX) and an artificial agent that adjusted its choices through a machine-learning algorithm (LRN). Participants' choice behaviors against the human opponent and LRN were similar but remarkably different from those against FIX. The activation of the left TPJ (LTPJ) was correlated with choice behavior against the human opponent and LRN but not against FIX. The overall activity pattern of the LTPJ for the human opponent was also similar to that for LRN but not for FIX. In contrast, the right TPJ (RTPJ) showed higher activation for the human opponent than FIX and LRN. These results suggest that, while the RTPJ is associated with the perception of human agency, the LTPJ is involved in second-order inferences in strategic decision making.

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.

Sleep Spindle-dependent Functional Connectivity Correlates with Cognitive Abilities

EEG studies have shown that interindividual differences in the electrophysiological properties of sleep spindles (e.g., density, amplitude, duration) are highly correlated with trait-like "reasoning" abilities (i.e., "fluid intelligence"; problem-solving skills; the ability to employ logic or identify complex patterns), but not interindividual differences in STM or "verbal" intellectual abilities. Previous simultaneous EEG-fMRI studies revealed brain activations time-locked to spindles. Our group has recently demonstrated that the extent of activation in a subset of these regions was related to interindividual differences in reasoning intellectual abilities, specifically. However, spindles reflect communication between spatially distant and functionally distinct brain areas. The functional communication among brain regions related to spindles and their relationship to reasoning abilities have yet to be investigated. Using simultaneous EEG-fMRI sleep recordings and psychophysiological interaction analysis, we identified spindle-related functional communication among brain regions in the thalamo-cortical-BG system, the salience network, and the default mode network. Furthermore, the extent of the functional connectivity of the cortical-striatal circuitry and the thalamo-cortical circuitry was specifically related to reasoning abilities but was unrelated to STM or verbal abilities, thus suggesting that individuals with higher fluid intelligence have stronger functional coupling among these brain areas during spontaneous spindle events. This may serve as a first step in further understanding the function of sleep spindles and the brain network functional communication, which support the capacity for fluid intelligence.

Action in auctions: neural and computational mechanisms of bidding behaviour

Competition for resources is a fundamental characteristic of evolution. Auctions have been widely used to model competition of individuals for resources, and bidding behaviour plays a major role in social competition. Yet, how humans learn to bid efficiently remains an open question. We used model‐based neuroimaging to investigate the neural mechanisms of bidding behaviour under different types of competition. Twenty‐seven subjects (nine male) played a prototypical bidding game: a double action, with three “market” types, which differed in the number of competitors. We compared different computational learning models of bidding: directional learning models (DL), where the model bid is “nudged” depending on whether it was accepted or rejected, along with standard reinforcement learning models (RL). We found that DL fit the behaviour best and resulted in higher payoffs. We found the binary learning signal associated with DL to be represented by neural activity in the striatum distinctly posterior to a weaker reward prediction error signal. We posited that DL is an efficient heuristic for valuation when the action (bid) space is continuous. Indeed, we found that the posterior parietal cortex represents the continuous action space of the task, and the frontopolar prefrontal cortex distinguishes among conditions of social competition. Based on our findings, we proposed a conceptual model that accounts for a sequence of processes that are required to perform successful and flexible bidding under different types of competition.

Anodal tDCS Over the Right Temporoparietal Junction Lowers Overbidding in Contests

Overbidding, which means bidding over the Nash equilibrium, is commonly observed in competitive social interactions, such as a contest or auction. Recent neuroscience studies show that the right temporoparietal junction (rTPJ) is related to overbidding and associated with inferring the intentions of others during competitive interactions. The present study investigates the neural underpinnings of overbidding and how the rTPJ impacts bidding behavior by using tDCS to modulate the activation of the rTPJ. Participants completed a two-person proportional prize contest, in which overbidding was frequently observed and each participant's share of the prize was equal to the individual's expenditure divided by the aggregated expenditure. We observed a significant tDCS effect, i.e., participants' average expenditure and overbidding rate were significantly reduced in the anodal stimulation compared with the cathodal and sham stimulation. Possible explanations include that enhanced activity in the rTPJ via the anodal stimulation increased the accuracy of a participant's inference of the strategies of others, or a participant's concern for others, and thus helped the participant bid optimally. Our findings provide evidence supporting that the activation of the rTPJ in contests affects overbidding and bidding strategy, and further confirm that the rTPJ is involved in the inference of mental states in a competition context.

Understanding individual differences in theory of mind via representation of minds, not mental states

The human ability to make inferences about the minds of conspecifics is remarkable. The majority of work in this area focuses on mental state representation ('theory of mind'), but has had limited success in explaining individual differences in this ability, and is characterized by the lack of a theoretical framework that can account for the effect of variability in the population of minds to which individuals are exposed. We draw analogies between faces and minds as complex social stimuli, and suggest that theoretical and empirical progress on understanding the mechanisms underlying mind representation can be achieved by adopting a 'Mind-space' framework; that minds, like faces, are represented within a multidimensional psychological space. This Mind-space framework can accommodate the representation of whole cognitive systems, and may help to explain individual differences in the consistency and accuracy with which the mental states of others are inferred. Mind-space may also have relevance for understanding human development, intergroup relations, and the atypical social cognition seen in several clinical conditions.

Computing Social Value Conversion in the Human Brain

Social signals play powerful roles in shaping self-oriented reward valuation and decision making. These signals activate social and valuation/decision areas, but the core computation for their integration into the self-oriented decision machinery remains unclear. Here, we study how a fundamental social signal, social value (others' reward value), is converted into self-oriented decision making in the human brain. Using behavioral analysis, modeling, and neuroimaging, we show three-stage processing of social value conversion from the offer to the effective value and then to the final decision value. First, a value of others' bonus on offer, called offered value, was encoded uniquely in the right temporoparietal junction (rTPJ) and also in the left dorsolateral prefrontal cortex (ldlPFC), which is commonly activated by offered self-bonus value. The effective value, an intermediate value representing the effective influence of the offer on the decision, was represented in the right anterior insula (rAI), and the final decision value was encoded in the medial prefrontal cortex (mPFC). Second, using psychophysiological interaction and dynamic causal modeling analyses, we demonstrated three-stage feedforward processing from the rTPJ and ldPFC to the rAI and then from rAI to the mPFC. Further, we showed that these characteristics of social conversion underlie distinct sociobehavioral phenotypes. We demonstrate that the variability in the conversion underlies the difference between prosocial and selfish subjects, as seen from the differential strength of the rAI and ldlPFC coupling to the mPFC responses, respectively. Together, these findings identified fundamental neural computation processes for social value conversion underlying complex social decision making behaviors.SIGNIFICANCE STATEMENT In daily life, we make decisions based on self-interest, but also in consideration for others' status. These social influences modulate valuation and decision signals in the brain, suggesting a fundamental process called value conversion that translates social information into self-referenced decisions. However, little is known about the conversion process and its underlying brain mechanisms. We investigated value conversion using human fMRI with computational modeling and found three essential stages in a progressive brain circuit from social to empathic and decision areas. Interestingly, the brain mechanism of conversion differed between prosocial and individualistic subjects. These findings reveal how the brain processes and merges social information into the elemental flow of self-interested decision making.

Brain Activation Time-Locked to Sleep Spindles Associated With Human Cognitive Abilities

Simultaneous electroencephalography and functional magnetic resonance imaging (EEG–fMRI) studies have revealed brain activations time-locked to spindles. Yet, the functional significance of these spindle-related brain activations is not understood. EEG studies have shown that inter-individual differences in the electrophysiological characteristics of spindles (e.g., density, amplitude, duration) are highly correlated with “Reasoning” abilities (i.e., “fluid intelligence”; problem solving skills, the ability to employ logic, identify complex patterns), but not short-term memory (STM) or verbal abilities. Spindle-dependent reactivation of brain areas recruited during new learning suggests night-to-night variations reflect offline memory processing. However, the functional significance of stable, trait-like inter-individual differences in brain activations recruited during spindle events is unknown. Using EEG–fMRI sleep recordings, we found that a subset of brain activations time-locked to spindles were specifically related to Reasoning abilities but were unrelated to STM or verbal abilities. Thus, suggesting that individuals with higher fluid intelligence have greater activation of brain regions recruited during spontaneous spindle events. This may serve as a first step to further understand the function of sleep spindles and the brain activity which supports the capacity for Reasoning.

Assessing Actual Strategic Behavior to Construct a Measure of Strategic Ability

Strategic interactions have been studied extensively in the area of judgment and decision-making. However, so far no specific measure of a decision-maker's ability to be successful in strategic interactions has been proposed and tested. Our contribution is the development of a measure of strategic ability that borrows from both game theory and psychology. Such measure is aimed at providing an estimation of the likelihood of success in many social activities that involve strategic interaction among multiple decision-makers. To construct a reliable measure of strategic ability, that we propose to call “Strategic Quotient” (SQ), we designed a test where each item is a game and where, therefore, the individual obtained score depends on the distribution of choices of other decision-makers taking the test. The test is designed to provide information on the abilities related to two dimensions, mentalization and rationality, that we argue are crucial to strategic success, with each dimension being characterized by two main factors. Principal component analysis on preliminary data shows that indeed four factors (two for rationality, two for mentalization) account for strategic success in most of the strategically simpler games of the test. Moreover, two more strategically sophisticated games are inserted in the test and are used to investigate if and to what extent the four factors obtained by simpler games can predict strategic success in more sophisticated strategic interactions. Overall, the collected empirical evidence points to the possibility of building a SQ measure using only simple games designed to capture information about the four identified factors.