Abstract
Much animal learning is slow, with cumulative changes in behavior driven by reward prediction errors. When the abstract structure of a problem is known, however, both animals and formal learning models can rapidly attach new items to their roles within this structure, sometimes in a single trial. Frontal cortex is likely to play a key role in this process. To examine information seeking and use in a known problem structure, we trained monkeys in an explore/exploit task, requiring the animal first to test objects for their association with reward, then, once rewarded objects were found, to reselect them on further trials for further rewards. Many cells in the frontal cortex showed an explore/exploit preference aligned with one-shot learning in the monkeys' behavior: the population switched from an explore state to an exploit state after a single trial of learning but partially maintained the explore state if an error indicated that learning had failed. Binary switch from explore to exploit was not explained by continuous changes linked to expectancy or prediction error. Explore/exploit preferences were independent for two stages of the trial: object selection and receipt of feedback. Within an established task structure, frontal activity may control the separate processes of explore and exploit, switching in one trial between the two.SIGNIFICANCE STATEMENT Much animal learning is slow, with cumulative changes in behavior driven by reward prediction errors. When the abstract structure a problem is known, however, both animals and formal learning models can rapidly attach new items to their roles within this structure. To address transitions in neural activity during one-shot learning, we trained monkeys in an explore/exploit task using familiar objects and a highly familiar task structure. When learning was rapid, many frontal neurons showed a binary, one-shot switch between explore and exploit. Within an established task structure, frontal activity may control the separate operations of exploring alternative objects to establish their current role, then exploiting this knowledge for further reward.