Planning with Theory of Mind

Simplifying social learning

Social learning is complex, but people often seem to navigate social environments with ease. This ability creates a puzzle for traditional accounts of reinforcement learning (RL) that assume people negotiate a tradeoff between easy-but-simple behavior (model-free learning) and complex-but-difficult behavior (e.g., model-based learning). We offer a theoretical framework for resolving this puzzle: although social environments are complex, people have social expertise that helps them behave flexibly with low cognitive cost. Specifically, by using familiar concepts instead of focusing on novel details, people can turn hard learning problems into simpler ones. This ability highlights social learning as a prototype for studying cognitive simplicity in the face of environmental complexity and identifies a role for conceptual knowledge in everyday reward learning.

Optimizing competence in the service of collaboration

In order to efficiently divide labor with others, it is important to understand what our collaborators can do (i.e., their competence). However, competence is not static-people get better at particular jobs the more often they perform them. This plasticity of competence creates a challenge for collaboration: For example, is it better to assign tasks to whoever is most competent now, or to the person who can be trained most efficiently "on-the-job"? We conducted four experiments (N=396) that examine how people make decisions about whom to train (Experiments 1 and 3) and whom to recruit (Experiments 2 and 4) to a collaborative task, based on the simulated collaborators' starting expertise, the training opportunities available, and the goal of the task. We found that participants' decisions were best captured by a planning model that attempts to maximize the returns from collaboration while minimizing the costs of hiring and training individual collaborators. This planning model outperformed alternative models that based these decisions on the agents' current competence, or on how much agents stood to improve in a single training step, without considering whether this training would enable agents to succeed at the task in the long run. Our findings suggest that people do not recruit and train collaborators based solely on their current competence, nor solely on the opportunities for their collaborators to improve. Instead, people use an intuitive theory of competence to balance the costs of hiring and training others against the benefits to the collaboration.

The Emerging Science of Interacting Minds

For over a century, psychology has focused on uncovering mental processes of a single individual. However, humans rarely navigate the world in isolation. The most important determinants of successful development, mental health, and our individual traits and preferences arise from interacting with other individuals. Social interaction underpins who we are, how we think, and how we behave. Here we discuss the key methodological challenges that have limited progress in establishing a robust science of how minds interact and the new tools that are beginning to overcome these challenges. A deep understanding of the human mind requires studying the context within which it originates and exists: social interaction.

Political reinforcement learners

Politics can seem home to the most calculating and yet least rational elements of humanity. How might we systematically characterize this spectrum of political cognition? Here, we propose reinforcement learning (RL) as a unified framework to dissect the political mind. RL describes how agents algorithmically navigate complex and uncertain domains like politics. Through this computational lens, we outline three routes to political differences, stemming from variability in agents' conceptions of a problem, the cognitive operations applied to solve the problem, or the backdrop of information available from the environment. A computational vantage on maladies of the political mind offers enhanced precision in assessing their causes, consequences, and cures.

The Human Affectome

Over the last decades, theoretical perspectives in the interdisciplinary field of the affective sciences have proliferated rather than converged due to differing assumptions about what human affective phenomena are and how they work. These metaphysical and mechanistic assumptions, shaped by academic context and values, have dictated affective constructs and operationalizations. However, an assumption about the purpose of affective phenomena can guide us to a common set of metaphysical and mechanistic assumptions. In this capstone paper, we home in on a nested teleological principle for human affective phenomena in order to synthesize metaphysical and mechanistic assumptions. Under this framework, human affective phenomena can collectively be considered algorithms that either adjust based on the human comfort zone (affective concerns) or monitor those adaptive processes (affective features). This teleologically-grounded framework offers a principled agenda and launchpad for both organizing existing perspectives and generating new ones. Ultimately, we hope the Human Affectome brings us a step closer to not only an integrated understanding of human affective phenomena, but an integrated field for affective research.

Neural representations of situations and mental states are composed of sums of representations of the actions they afford

Human behavior depends on both internal and external factors. Internally, people's mental states motivate and govern their behavior. Externally, one's situation constrains which actions are appropriate or possible. To predict others' behavior, one must understand the influences of mental states and situations on actions. On this basis, we hypothesize that people represent situations and states in terms of associated actions. To test this, we use functional neuroimaging to estimate neural activity patterns associated with situations, mental states, and actions. We compute sums of the action patterns, weighted by how often each action occurs in each situation and state. We find that these summed action patterns reconstructed the corresponding situation and state patterns. These results suggest that neural representations of situations and mental states are composed of sums of their action affordances. Summed action representations thus offer a biological mechanism by which people can predict actions given internal and external factors.

Computational Social Psychology

Social psychologists attempt to explain how we interact by appealing to basic principles of how we think. To make good on this ambition, they are increasingly relying on an interconnected set of formal tools that model inference, attribution, value-guided decision making, and multi-agent interactions. By reviewing progress in each of these areas and highlighting the connections between them, we can better appreciate the structure of social thought and behavior, while also coming to understand when, why, and how formal tools can be useful for social psychologists.

Storytelling as Inverse Inverse Planning

Great storytelling takes us on a journey the way ordinary reality rarely does. But what exactly do we mean by this "journey?" Recently, literary theorist Karin Kukkonen proposed that storytelling is "probability design:" the art of giving an audience pieces of information bit by bit, to craft the journey of their changing beliefs about the fictional world. A good "probability design" choreographs a delicate dance of certainty and surprise in the reader's mind as the story unfolds from beginning to end. In this paper, we computationally model this conception of storytelling. Building on the classic Bayesian inverse planning model of human social cognition, we treat storytelling as inverse inverse planning: the task of choosing actions to manipulate an inverse planner's inferences, and therefore a human audience's beliefs. First, we use an inverse inverse planner to depict social and physical situations, and present behavioral studies indicating that inverse inverse planning produces more expressive behavior than ordinary "naïve planning." Then, through a series of examples, we demonstrate how inverse inverse planning captures many storytelling elements from first principles: character, narrative arcs, plot twists, irony, flashbacks, and deus ex machina are all naturally encoded in the flexible language of probability design.

Social Understanding beyond the Familiar: Disparity in Visual Abilities Does Not Impede Empathy and Theory of Mind

Feeling with our conspecifics and understanding their sentiments and intentions is a crucial part of our lives. What is the basis for these forms of social understanding? If individuals ground their understanding of others' thoughts and feelings in their own perceptual and factual experiences, it could present a challenge to empathize and mentalize with those whose reality of life is significantly different. This preregistered study compared two groups of participants who differed in a central perceptual feature, their visual abilities (visually impaired vs. unimpaired; total N = 56), concerning their social understanding of others who were themselves either visually impaired or unimpaired. Employing an adjusted version of the EmpaToM task, participants heard short, autobiographic narrations by visually impaired or unimpaired individuals, and we assessed their empathic responding and mentalizing performance. Our findings did not reveal heightened empathy and mentalizing proclivities when the narrator's visual abilities aligned with those of the participant. However, in some circumstances, cognitive understanding of others' narrations benefitted from familiarity with the situation. Overall, our findings suggest that social understanding does not mainly rely on perceptual familiarity with concrete situations but is likely grounded in sharing emotions and experiences on a more fundamental level.

Towards a comprehensive approach to mentalization-based treatment for children with autism: integrating attachment, neurosciences, and mentalizing

Autism spectrum disorder (ASD) is diagnosed based on socio-communicative difficulties, which are believed to result from deficits in mentalizing, mainly evidenced by alterations in recognizing and responding to the mental states of others. In recent years, efforts have been made to develop mentalization-based treatment (MBT) models for this population. These models focus on enhancing individuals' ability to understand and reflect on their own mental states, as well as those of others. However, MBT approaches for people with ASD are limited by their existing theoretical background, which lacks a strong foundation grounded in neuroscience-based evidence properly integrated with attachment, and mentalizing. These are crucial aspects for understanding psychological processes in autism, and as such, they play a pivotal role in shaping the development of tailored and effective therapeutic strategies for this specific population. In this paper we review evidence related to the neurobiological, interpersonal, and psychological dimensions of autism and their implications for mentalizing processes. We also review previous mentalization-based frameworks on the psychosis continuum to provide a comprehensive understanding of attachment, neurobiology, and mentalization domains in therapeutic approaches for autism. After presenting a synthesis of the literature, we offer a set of clinical strategies for the work with children with autism. Finally, we provide recommendations to advance the field towards more robust models that can serve as a basis for evidence-based therapeutic strategies.

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.

Biology, Society, or Choice: How Do Non-Experts Interpret Explanations of Behaviour?

Explanations for human behaviour can be framed in many different ways, from the social-structural context to the individual motivation down to the neurobiological implementation. We know comparatively little about how people interpret these explanatory framings, and what they infer when one kind of explanation rather than another is made salient. In four experiments, UK general-population volunteers read vignettes describing the same behaviour, but providing explanations framed in different ways. In Study 1, we found that participants grouped explanations into 'biological', 'psychological' and 'sociocultural' clusters. Explanations with different framings were often seen as incompatible with one another, especially when one belonged to the 'biological' cluster and the other did not. In Study 2, we found that exposure to a particular explanatory framing triggered inferences beyond the information given. Specifically, psychological explanations led participants to assume the behaviour was malleable, and biological framings led them to assume it was not. In Studies 3A and 3B, we found that the choice of explanatory framing can affect people's assumptions about effective interventions. For example, presenting a biological explanation increased people's conviction that interventions like drugs would be effective, and decreased their conviction that psychological or socio-political interventions would be effective. These results illuminate the intuitive psychology of explanations, and also potential pitfalls in scientific communication. Framing an explanation in a particular way will often generate inferences in the audience-about what other factors are not causally important, how easy it is to change the behaviour, and what kinds of remedies are worth considering-that the communicator may not have anticipated and might not intend.

Emotion prediction as computation over a generative theory of mind

From sparse descriptions of events, observers can make systematic and nuanced predictions of what emotions the people involved will experience. We propose a formal model of emotion prediction in the context of a public high-stakes social dilemma. This model uses inverse planning to infer a person's beliefs and preferences, including social preferences for equity and for maintaining a good reputation. The model then combines these inferred mental contents with the event to compute 'appraisals': whether the situation conformed to the expectations and fulfilled the preferences. We learn functions mapping computed appraisals to emotion labels, allowing the model to match human observers' quantitative predictions of 20 emotions, including joy, relief, guilt and envy. Model comparison indicates that inferred monetary preferences are not sufficient to explain observers' emotion predictions; inferred social preferences are factored into predictions for nearly every emotion. Human observers and the model both use minimal individualizing information to adjust predictions of how different people will respond to the same event. Thus, our framework integrates inverse planning, event appraisals and emotion concepts in a single computational model to reverse-engineer people's intuitive theory of emotions. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.

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.

Verbal Communication with the Patient Is Not Enough: The Six Languages of the Sick

Evidence shows that verbal communication is just one of the ways patients indicate their wishes. For a sufficiently careful communication, we should also grasp other five unusual though evident languages: (a) body language, (b) the way patients manage their environment, (c) unconscious language, (d) lab-evidenced language, and (e) the way they master technology. So, we have six languages that should be intertwined to understand the real language of the sick. Grasping these languages helps health professionals frame the patient’s mood, their level of suffering or mental growth, and understand what words alone cannot express. Words cannot express completely what a patient senses: for subjection, shyness, because some patients are still non-verbal or because verbal communication is just a useful way of freezing concept but has not the same fluidity and liberty of the other above-described languages. It is mandatory for caregivers to wonder how many of these languages they are actually decrypting during an interview with the patient. On the other hand, caregivers unconsciously communicate much through two unexpected languages: the architectural language and the language of medical procedures. The way they welcome or obstruct the patient, their hesitations across a treatment, or in showing a serene collegiality are forms of subtle communication. A paradigmatic scenario where all these languages should be implemented is the “informed consent” process, which should be turned into a “shared therapeutic pathway”, summing up all the communicative modes illustrated in the text.