Kavli Affiliate: C. Daniel Salzman
| Authors: David L Barack, Akram Bakkour, Daphna Shohamy and C Daniel Salzman
| Summary:
Abstract In the real world, making sequences of decisions to achieve goals often depends upon the ability to learn aspects of the environment that are not directly perceptible. Learning these so-called latent features requires seeking information about them, a process distinct from learning about reinforcement contingencies. Prior efforts to study latent feature learning often use single decisions, use few features, and fail to distinguish between reinforcement-seeking and information-seeking. To overcome this, we designed a task in which humans and monkeys made a series of choices to search for shapes hidden on a grid. Reward and information outcomes from uncovering parts of shapes were not perfectly correlated and their effects could be disentangled. Members of both species adeptly learned the shapes. Both species preferred to select informative tiles earlier in trials than rewarding ones, searching a part of the grid until their outcomes dropped below the average information outcome–a pattern consistent with foraging behavior. In addition, how quickly humans learned the shapes was predicted by how well their choice sequences matched the foraging pattern. This adaptive search for information may underlie the ability in humans and monkeys to learn latent features to support goal-directed behavior in the long run. Competing Interest Statement The authors have declared no competing interest.