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 or attributes. It has been shown that humans learn and combine feature-based with more complex conjunction-based learning to tackle challenges of learning in naturalistic reward environments. However, it remains unclear how different learning strategies interact to determine what features or conjunctions should be attended to and control choice behavior, and how subsequent attentional modulations influence future learning and choice. To address these questions, we examined the behavior of male and female human participants 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 both choice behavior and reward probabilities estimated by participants were most accurately described by attention-modulated models that learned the predictive values of both the informative feature and the informative conjunction. Specifically, in the reinforcement learning model that best fit choice data, attention was controlled by the difference in the integrated feature and conjunction values. The resulting attention weights modulated learning by increasing the learning rate on attended features and conjunctions. Critically, modulating decision making by attention weights did not improve the fit of data, providing little evidence for direct attentional effects on choice. These results suggest that in multidimensional environments, humans direct their attention not only to selectively process reward-predictive attributes, but also to find parsimonious representations of the reward contingencies for more efficient learning.Significance Statement From trying exotic recipes to befriending new social groups, outcomes of real-life actions depend on many factors, but how do we learn the predictive values of those factors based on feedback we receive? It has been shown that humans simplify this problem by focusing on individual features that are most predictive of the outcomes but can extend their learning strategy to include combinations of features when necessary. Here, we examined interaction between attention and learning in a multidimensional reward environment that requires learning about individual features and their conjunctions. Using multiple approaches, we found that learning about features and conjunctions control attention in a cooperative manner and that the ensuing attentional modulations mainly affects future learning and not decision making.