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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, multidimensional
environments. We hypothesized 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 tested this hypothesis 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 environments, subjects
initially adopted feature-based learning and gradually switched to learning reward values of
individual options, depending on how accurately objects’ values can be predicted by combining
feature values. Our computational models reproduced these results and highlight the importance
of neurons coding feature values for parallel learning of values for features and objects.