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.

A qualitative study on the influence of COVID-19 on smoking behaviors through changing social and physical contexts.

Globally, COVID-19 has been a major societal stressor and disrupted social and physical environments for many. Elucidating mechanisms through which societal disruptions influence smoking behavior has implications for future tobacco control efforts. Qualitative interviews were conducted among 38 adults who smoked combustible cigarettes in 2020 and 2021.

The neural architecture of theory-based reinforcement learning.

Humans learn internal models of the world that support planning and generalization in complex environments. Yet it remains unclear how such internal models are represented and learned in the brain. We approach this question using theory-based reinforcement learning, a strong form of model-based reinforcement learning in which the model is a kind of intuitive theory.

Inductive biases in theory-based reinforcement learning.

Understanding the inductive biases that allow humans to learn in complex environments has been an important goal of cognitive science. Yet, while we have discovered much about human biases in specific learning domains, much of this research has focused on simple tasks that lack the complexity of the real world. In contrast, video games involving agents and objects embedded in richly structured systems provide an experimentally tractable proxy for real-world complexity.

What Is the Model in Model-Based Planning?

Flexibility is one of the hallmarks of human problem-solving. In everyday life, people adapt to changes in common tasks with little to no additional training. Much of the existing work on flexibility in human problem-solving has focused on how people adapt to tasks in new domains by drawing on solutions from previously learned domains.

Discovering social groups via latent structure learning.

Humans form social coalitions in every society on earth, yet we know very little about how social group boundaries are learned and represented. We derive predictions from a computational model of latent structure learning to move beyond explicit category labels and mere similarity as the sole inputs to social group representations. Four experiments examine (a) how evidence for group boundaries is accumulated in a consequential social context (i.

Learning the Structure of Social Influence.

We routinely observe others' choices and use them to guide our own. Whose choices influence us more, and why? Prior work has focused on the effect of perceived similarity between two individuals (self and others), such as the degree of overlap in past choices or explicitly recognizable group affiliations. In the real world, however, any dyadic relationship is part of a more complex social structure involving multiple social groups that are not directly observable.