Latent goal models for dynamic strategic interaction.

ABSTRACT: Understanding the principles by which agents interact with both complex environments and each other is a key goal of decision neuroscience. However, most previous studies have used experimental paradigms in which choices are discrete (and few), play is static, and optimal solutions are known. Yet in natural environments, interactions between agents typically involve continuous action spaces, ongoing dynamics, and no known optimal solution. Here, we seek to bridge this divide by using a "penalty shot" task in which pairs of monkeys competed against each other in a competitive, real-time video game. We modeled monkeys' strategies as driven by stochastically evolving goals, onscreen positions that served as set points for a control model that produced observed joystick movements. We fit this

SUBMITTER: Iqbal SN 

PROVIDER: S-EPMC6472832 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature