RT Journal Article
SR Electronic
T1 Your favorite color makes learning more adaptable and precise
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 097741
DO 10.1101/097741
A1 Shiva Farashahi
A1 Katherine Rowe
A1 Zohra Aslami
A1 Daeyeol Lee
A1 Alireza Soltani
YR 2017
UL http://biorxiv.org/content/early/2017/01/03/097741.abstract
AB Learning from reward feedback is essential for survival but can become extremely challenging when choice options have multiple features and feature values (curse of dimensionality). Here, we propose a general framework for learning reward values in dynamic multi-dimensional environments via encoding and updating the average value of individual features. We predicted that this feature-based learning occurs not just because it can reduce dimensionality, but more importantly because it can increase adaptability without compromising precision. We experimentally tested this novel prediction and found that in dynamic environments, human subjects adopted feature-based learning even when this approach does not reduce dimensionality. Even in static low-dimensional environment, subjects initially tended to adopt feature-based learning and switched to learning individual option values only when feature values could not accurately predict all objects values. Moreover, behaviors of two alternative network models demonstrated that hierarchical decision-making and learning could account for our experimental results and thus provides a plausible mechanism for model adoption during learning in dynamic environments. Our results constrain neural mechanisms underlying learning in dynamic multi-dimensional environments, and highlight the importance of neurons encoding the value of individual features in this learning.