Contributions of attention to learning in multi-dimensional 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. It is currently unclear how humans learn and make decisions when multiple features and conjunctions of features are predictive of reward, and moreover, 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 conjunction. Using multiple approaches, we found that choice behavior and reward probabilities estimated by participants 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 learning by increasing the learning rate on attended features and conjunctions. However, there was little effect of attention on decision making. These results suggest that 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.