Feature-based learning improves adaptability without compromising precision.

ABSTRACT: Learning from reward feedback is essential for survival but can become extremely challenging with myriad choice options. Here, we propose that learning reward values of individual features can provide a heuristic for estimating reward values of choice options in dynamic, multi-dimensional environments. We hypothesize that this feature-based learning occurs not just because it can reduce dimensionality, but more importantly because it can increase adaptability without compromising precision of learning. We experimentally test this hypothesis and find that in dynamic environments, human subjects adopt feature-based learning even when this approach does not reduce dimensionality. Even in static, low-dimensional environments, subjects initially adopt feature-based learning and gradually switch

SUBMITTER: Farashahi S 

PROVIDER: S-EPMC5700946 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature