Abstract
Measurements of response time (RT) have long been used to infer neural processes underlying various cognitive functions such as working memory, attention, and decision making. However, it is currently unknown if RT is also informative about various stages of value-based choice, particularly how reward values are constructed. To investigate these questions, we analyzed the pattern of RT during a set of multi-dimensional learning and decision-making tasks that can prompt subjects to adopt different learning strategies. In our experiments, subjects could use reward feedback to directly learn reward values associated with possible choice options (object-based learning). Alternatively, they could learn reward values of options’ features (e.g. color, shape) and combine these values to estimate reward values for individual options (feature-based learning). We found that RT was slower when the difference between subjects’ estimates of reward probabilities for the two alternative objects on a given trial was smaller. Moreover, RT was overall faster when the preceding trial was rewarded or when the previously selected object was present. These effects, however, were mediated by an interaction between these factors such that subjects were faster when the previously selected object was present rather than absent but only after unrewarded trials. Finally, RT reflected the learning strategy (i.e. object-based or feature-based approach) adopted by the subject on a trial-by-trial basis, indicating an overall faster construction of reward value and/or value comparison during object-based learning. Altogether, these results demonstrate that the pattern of RT can be informative about how reward values are learned and constructed during complex value-based learning and decision making.
Highlights
Investigations of response time (RT) have long been the focus of human psychophysics studies in order to distinguish between alternative mechanisms underlying mental processes [1,2,3]
To examine how RT depends on the construction and comparison of reward values, we used various reinforcement learning models to determine whether object-based or feature-based learning was adopted by individual subjects in a given experiment
We investigated the pattern of RT during a set of a multi-dimensional learning and decisionmaking tasks in order to provide insights into neural mechanisms underlying the construction and comparison of reward values
Summary
Investigations of response time (RT) have long been the focus of human psychophysics studies in order to distinguish between alternative mechanisms underlying mental processes [1,2,3]. Humans must use information about an object, such as its features (i.e. color, taste, texture), to determine whether it is rewarding. This is because one cannot learn the reward values for a large number of options that exist in the real world. Rather than directly estimating the reward value of individual options based on reward feedback (object-based learning), subjects estimate average reward values of features and combine these values to estimate reward values for individual options (feature-based learning) These learning strategies require different representations of reward values and updates based on reward feedback and involve distinct methods for construction or comparison of reward values to make a decision
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