Contributions of attention to learning in multidimensional reward environments

Abstract

Real-world choice options have many features or attributes, whereas the reward outcome from those options only depends on a few features/attributes. Identifying and attending to such informative features while ignoring irrelevant information can speed up learning and improve decision making. Most previous studies on reward learning and decision making, however, use tasks in which only one feature predicts reward outcome. Therefore, it is unclear how we learn and make decisions in multi-dimensional environments where multiple features and conjunctions of features are predictive of reward, and more importantly, how selective attention contributes to these processes. Here, we examined human behavior during a three-dimensional learning task in which reward outcomes for different stimuli could be predicted based on a combination of an informative feature and the conjunction of the other two features, the informative conjunction. Using multiple approaches, we found that choice behavior and estimated reward probabilities were best described by a model that learned the predictive values of both the informative feature and the informative conjunction. Moreover, attention was controlled by the difference in these values in a cooperative manner such that attention depended on the integrated feature and conjunction values, and the resulting attention weights modulated the learning of both. Finally, attention modulated learning by increasing the learning rate on attended features and conjunctions but had little effect on decision making. Together, our results suggest that when learning in high-dimensional environments, humans direct their attention not only to selectively process reward-predictive attributes, but also to find parsimonious representations of the reward contingencies to achieve more efficient learning.