Department of Psychological and Brain Sciences
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 to learning reward values of individual options, depending on how accurately objects’ values can be predicted by combining feature values. Our computational models reproduce these results and highlight the importance of neurons coding feature values for parallel learning of values for features and objects.
Farashahi S, Rowe K, Aslami Z, Lee D, Soltani A. Feature-based learning improves adaptability without compromising precision. Nat Commun. 2017 Nov 24;8(1):1768. doi: 10.1038/s41467-017-01874-w. PMID: 29170381; PMCID: PMC5700946.
Dartmouth Digital Commons Citation
Farashahi, Shiva; Rowe, Katherine; Aslami, Zohra; Lee, Daeyeol; and Soltani, Alireza, "Feature-Based Learning Improves Adaptability without Compromising Precision" (2017). Dartmouth Scholarship. 2821.