What people learn from punishment: A cognitive model.

Authorities, from parents of toddlers to leaders of formal institutions, use punishment to communicate disapproval and enforce social norms. Ideally, from whether and how severely a transgression is punished, targets and observers infer shared social norms. Yet in light of every punitive choice, observers also evaluate the motives and legitimacy of the authority.

Dissociable cortical regions represent things and stuff in the human brain.

Extensive prior work has identified regions of the human brain associated with visual perception of objects (lateral occipital complex [LOC]) and their physical properties and interactions ("frontoparietal physics network" [FPN]). However, this work has nearly exclusively tested the response of these regions to rigid objects. Deformable or nonsolid substances, or "stuff," including liquids such as water or honey and granular materials such as sand or snow, are of similar importance in everyday life but have different physical properties and invite different actions.

Evolving general cooperation with a Bayesian theory of mind.

Theories of the evolution of cooperation through reciprocity explain how unrelated self-interested individuals can accomplish more together than they can on their own. The most prominent theories of reciprocity, such as tit-for-tat or win-stay-lose-shift, are inflexible automata that lack a theory of mind-the human ability to infer the hidden mental states in others' minds. Here, we develop a model of reciprocity with a theory of mind, the Bayesian Reciprocator.

Decoding predicted future states from the brain's "physics engine".

Successful engagement with the physical world requires the ability to predict future events and plan interventions to alter that future. Growing evidence implicates a set of regions in the human parietal and frontal lobes [also known as the "physics network" (PN)] in such intuitive physical inferences. However, the central tenet of this hypothesis, that PN runs forward simulations to predict future states, remains untested.

Compositional Physical Reasoning of Objects and Events From Videos.

Understanding and reasoning about objects' physical properties in the natural world is a fundamental challenge in artificial intelligence. While some properties like colors and shapes can be directly observed, others, such as mass and electric charge, are hidden from the objects' visual appearance. This paper addresses the unique challenge of inferring these hidden physical properties from objects' motion and interactions and predicting corresponding dynamics based on the inferred physical properties.

Neural representation of action symbols in primate frontal cortex.

At the core of intelligence is proficiency in solving new problems, including those that differ dramatically from problems seen before. Problem-solving, in turn, depends on goal-directed generation of novel thoughts and behaviors, which has been proposed to rely on internal representations of discrete units, or symbols, and processes that can recombine them into a large set of possible composite representations. Although this view has been influential in formulating cognitive-level explanations of behavior, definitive evidence for a neuronal substrate of symbols has remained elusive.

Approximating Human-Level 3D Visual Inferences With Deep Neural Networks.

Humans make rich inferences about the geometry of the visual world. While deep neural networks (DNNs) achieve human-level performance on some psychophysical tasks (e.g.

Resource-Rational Virtual Bargaining for Moral Judgment: Toward a Probabilistic Cognitive Model.

Recent theoretical work has argued that moral psychology can be understood through the lens of "resource rational contractualism." The view posits that the best way of making a decision that affects other people is to get everyone together to negotiate under idealized conditions. The outcome of that negotiation is an arrangement (or "contract") that would lead to mutual benefit.

Electrophysiology Reveals That Intuitive Physics Guides Visual Tracking and Working Memory.

Starting in early infancy, our perception and predictions are rooted in strong expectations about the behavior of everyday objects. These intuitive physics expectations have been demonstrated in numerous behavioral experiments, showing that even pre-verbal infants are surprised when something impossible happens (e.g.

Resource-rational contractualism: A triple theory of moral cognition.

It is widely agreed upon that morality guides people with conflicting interests towards agreements of mutual benefit. We therefore might expect numerous proposals for organizing human moral cognition around the logic of bargaining, negotiation, and agreement. Yet, while "contractualist" ideas play an important role in moral philosophy, they are starkly underrepresented in the field of moral psychology.

Building machines that learn and think with people.

What do we want from machine intelligence? We envision machines that are not just tools for thought but partners in thought: reasonable, insightful, knowledgeable, reliable and trustworthy systems that think with us. Current artificial intelligence systems satisfy some of these criteria, some of the time. In this Perspective, we show how the science of collaborative cognition can be put to work to engineer systems that really can be called 'thought partners', systems built to meet our expectations and complement our limitations.

Probabilistic programming versus meta-learning as models of cognition.

We summarize the recent progress made by probabilistic programming as a unifying formalism for the probabilistic, symbolic, and data-driven aspects of human cognition. We highlight differences with meta-learning in flexibility, statistical assumptions and inferences about cogniton. We suggest that the meta-learning approach could be further strengthened by considering Connectionist Bayesian approaches, rather than exclusively one or the other.

Symbolic metaprogram search improves learning efficiency and explains rule learning in humans.

Throughout their lives, humans seem to learn a variety of rules for things like applying category labels, following procedures, and explaining causal relationships. These rules are often algorithmically rich but are nonetheless acquired with minimal data and computation. Symbolic models based on program learning successfully explain rule-learning in many domains, but performance degrades quickly as program complexity increases.

When rules are over-ruled: Virtual bargaining as a contractualist method of moral judgment.

Rules help guide our behavior-particularly in complex social contexts. But rules sometimes give us the "wrong" answer. How do we know when it is okay to break the rules? In this paper, we argue that we sometimes use contractualist (agreement-based) mechanisms to determine when a rule can be broken.

Using games to understand the mind.

Board, card or video games have been played by virtually every individual in the world. Games are popular because they are intuitive and fun. These distinctive qualities of games also make them ideal for studying the mind.

Evaluating language models for mathematics through interactions.

There is much excitement about the opportunity to harness the power of large language models (LLMs) when building problem-solving assistants. However, the standard methodology of evaluating LLMs relies on static pairs of inputs and outputs; this is insufficient for making an informed decision about which LLMs are best to use in an interactive setting, and how that varies by setting. Static assessment therefore limits how we understand language model capabilities.

In praise of folly: flexible goals and human cognition.

Humans often pursue idiosyncratic goals that appear remote from functional ends, including information gain. We suggest that this is valuable because goals (even prima facie foolish or unachievable ones) contain structured information that scaffolds thinking and planning. By evaluating hypotheses and plans with respect to their goals, humans can discover new ideas that go beyond prior knowledge and observable evidence.

Dissociating language and thought in large language models.

Large language models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their linguistic and cognitive capabilities remain split. Here, we evaluate LLMs using a distinction between formal linguistic competence (knowledge of linguistic rules and patterns) and functional linguistic competence (understanding and using language in the world). We ground this distinction in human neuroscience, which has shown that formal and functional competence rely on different neural mechanisms.

Lifelong learning of cognitive styles for physical problem-solving: The effect of embodied experience.

'Embodied cognition' suggests that our bodily experiences broadly shape our cognitive capabilities. We study how embodied experience affects the abstract physical problem-solving styles people use in a virtual task where embodiment does not affect action capabilities. We compare how groups with different embodied experience - 25 children and 35 adults with congenital limb differences versus 45 children and 40 adults born with two hands - perform this task, and find that while there is no difference in overall competence, the groups use different cognitive styles to find solutions.

Perception of 3D shape integrates intuitive physics and analysis-by-synthesis.

Many surface cues support three-dimensional shape perception, but humans can sometimes still see shape when these features are missing-such as when an object is covered with a draped cloth. Here we propose a framework for three-dimensional shape perception that explains perception in both typical and atypical cases as analysis-by-synthesis, or inference in a generative model of image formation. The model integrates intuitive physics to explain how shape can be inferred from the deformations it causes to other objects, as in cloth draping.

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.

Flexible social inference facilitates targeted social learning when rewards are not observable.

Groups coordinate more effectively when individuals are able to learn from others' successes. But acquiring such knowledge is not always easy, especially in real-world environments where success is hidden from public view. We suggest that social inference capacities may help bridge this gap, allowing individuals to update their beliefs about others' underlying knowledge and success from observable trajectories of behaviour.

Empowerment contributes to exploration behaviour in a creative video game.

Studies of human exploration frequently cast people as serendipitously stumbling upon good options. Yet these studies may not capture the richness of exploration strategies that people exhibit in more complex environments. Here we study behaviour in a large dataset of 29,493 players of the richly structured online game 'Little Alchemy 2'.

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.

DreamCoder: growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning.

Expert problem-solving is driven by powerful languages for thinking about problems and their solutions. Acquiring expertise means learning these languages-systems of concepts, alongside the skills to use them. We present DreamCoder, a system that learns to solve problems by writing programs.