People reward others based on their willingness to exert effort.

Individual contributors to a collaborative task are often rewarded for going above and beyond-salespeople earn commissions, athletes earn performance bonuses, and companies award special parking spots to their employee of the month. How do we decide when to reward collaborators, and are these decisions closely aligned with how responsible they were for the outcome of a collaboration? In Experiments 1a and 1b ( ), we tested how participants give bonuses, using stimuli and an experiment design that has previously been used to elicit responsibility judgments (Xiang et al., 2023a).

Learning from and about scientists: Consensus messaging shapes perceptions of climate change and climate scientists.

Despite overwhelming scientific consensus on the existence of human-caused climate change, public opinion among Americans remains split. Directly informing people of scientific consensus is among the most prominent strategies for climate communication, yet the reasons for its effectiveness and its limitations are not fully understood. Here, we propose that consensus messaging provides information not only about the existence of climate change but also traits of climate scientists themselves.

Blending simulation and abstraction for physical reasoning.

How are people able to understand everyday physical events with such ease? One hypothesis suggests people use an approximate probabilistic simulation of the world. A contrasting hypothesis is that people use a collection of abstractions or features. While it has been noted that the two hypotheses explain complementary aspects of physical reasoning, there has yet to be a model of how these two modes of reasoning can be used together.

Modeling human activity comprehension at human scale: prediction, segmentation, and categorization.

Humans form sequences of -representations of the current situation-to predict how activity will unfold. Multiple mechanisms have been proposed for how the cognitive system determines when to segment the stream of behavior and switch from one active event model to another. Here, we constructed a computational model that learns knowledge about event classes (event schemas), by combining recurrent neural networks for short-term dynamics with Bayesian inference over event classes for event-to-event transitions.

Policy complexity suppresses dopamine responses.

Limits on information processing capacity impose limits on task performance. We show that animals achieve performance on a perceptual decision task that is near-optimal given their capacity limits, as measured by policy complexity (the mutual information between states and actions). This behavioral profile could be achieved by reinforcement learning with a penalty on high complexity policies, realized through modulation of dopaminergic learning signals.

Scaling models of visual working memory to natural images.

Over the last few decades, psychologists have developed precise quantitative models of human recall performance in visual working memory (VWM) tasks. However, these models are tailored to a particular class of artificial stimulus displays and simple feature reports from participants (e.g.

Blocked training facilitates learning of multiple schemas.

We all possess a mental library of schemas that specify how different types of events unfold. How are these schemas acquired? A key challenge is that learning a new schema can catastrophically interfere with old knowledge. One solution to this dilemma is to use interleaved training to learn a single representation that accommodates all schemas.

Habituation as optimal filtering.

Habituation, the reduction of responding to repetitive stimuli, is often conceptualized as a kind of attentional filter, amplifying salient signals at the expense of non-salient signals. No prior account has explicitly formalized filtering principles that can explain the major characteristics of habituation. In this paper, a simple probabilistic model is developed which permits analysis of the optimal filtering problem.

Explaining dopamine through prediction errors and beyond.

The most influential account of phasic dopamine holds that it reports reward prediction errors (RPEs). The RPE-based interpretation of dopamine signaling is, in its original form, probably too simple and fails to explain all the properties of phasic dopamine observed in behaving animals. This Perspective helps to resolve some of the conflicting interpretations of dopamine that currently exist in the literature.

A Hierarchical Bayesian Model of Adaptive Teaching.

How do teachers learn about what learners already know? How do learners aid teachers by providing them with information about their background knowledge and what they find confusing? We formalize this collaborative reasoning process using a hierarchical Bayesian model of pedagogy. We then evaluate this model in two online behavioral experiments (N = 312 adults). In Experiment 1, we show that teachers select examples that account for learners' background knowledge, and adjust their examples based on learners' feedback.

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.

Resource-rational psychopathology.

Psychopathology is vast and diverse. Across distinct disease states, individuals exhibit symptoms that appear counter to the standard view of rationality (expected utility maximization). We argue that some aspects of psychopathology can be described as resource-rational, reflecting a rational trade-off between reward and cognitive resources.

Human decision making balances reward maximization and policy compression.

Policy compression is a computational framework that describes how capacity-limited agents trade reward for simpler action policies to reduce cognitive cost. In this study, we present behavioral evidence that humans prefer simpler policies, as predicted by a capacity-limited reinforcement learning model. Across a set of tasks, we find that people exploit structure in the relationships between states, actions, and rewards to "compress" their policies.

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).

The role of prospective contingency in the control of behavior and dopamine signals during associative learning.

Associative learning depends on contingency, the degree to which a stimulus predicts an outcome. Despite its importance, the neural mechanisms linking contingency to behavior remain elusive. Here we examined the dopamine activity in the ventral striatum - a signal implicated in associative learning - in a Pavlovian contingency degradation task in mice.

What have we learned about artificial intelligence from studying the brain?

Neuroscience and artificial intelligence (AI) share a long, intertwined history. It has been argued that discoveries in neuroscience were (and continue to be) instrumental in driving the development of new AI technology. Scrutinizing these historical claims yields a more nuanced story, where AI researchers were loosely inspired by the brain, but ideas flowed mostly in the other direction.

Dynamic computational phenotyping of human cognition.

Computational phenotyping has emerged as a powerful tool for characterizing individual variability across a variety of cognitive domains. An individual's computational phenotype is defined as a set of mechanistically interpretable parameters obtained from fitting computational models to behavioural data. However, the interpretation of these parameters hinges critically on their psychometric properties, which are rarely studied.

Nucleus accumbens dopamine release reflects Bayesian inference during instrumental learning.

Dopamine release in the nucleus accumbens has been hypothesized to signal reward prediction error, the difference between observed and predicted reward, suggesting a biological implementation for reinforcement learning. Rigorous tests of this hypothesis require assumptions about how the brain maps sensory signals to reward predictions, yet this mapping is still poorly understood. In particular, the mapping is non-trivial when sensory signals provide ambiguous information about the hidden state of the environment.

Actual and counterfactual effort contribute to responsibility attributions in collaborative tasks.

How do people judge responsibility in collaborative tasks? Past work has proposed a number of metrics that people may use to attribute blame and credit to others, such as effort, competence, and force. Some theories consider only the actual effort or force (individuals are more responsible if they put forth more effort or force), whereas others consider counterfactuals (individuals are more responsible if some alternative behavior on their or their collaborator's part could have altered the outcome). Across four experiments (N=717), we found that participants' judgments are best described by a model that considers both actual and counterfactual effort.

Emergence of belief-like representations through reinforcement learning.

To behave adaptively, animals must learn to predict future reward, or value. To do this, animals are thought to learn reward predictions using reinforcement learning. However, in contrast to classical models, animals must learn to estimate value using only incomplete state information.

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'.