Are People Successful at Learning Sequential Decisions on a Perceptual Matching Task?

Abstract

Sequential decision-making tasks are commonplace in our everyday lives. We report the results of an experiment in which human subjects were trained to perform a perceptual matching task, an instance of a sequential decision-making task. We use two benchmarks to evaluate the quality of subjects' learning. One benchmark is based on optimal performance as defined by a dynamic programming procedure. The other is based on an adaptive computational agent that uses a reinforcement learning method known as Q-learning to learn to perform the task. Our analyses suggest that subjects learned to perform the perceptual matching task in a near-optimal manner at the end of training. Subjects were able to achieve near-optimal performance because they learned, at least partially, the causal structure underlying the task. Subjects' learning curves were broadly consistent with those of model-based reinforcement-learning agents that built and used internal models of how their actions influenced the external environment. We hypothesize that, in general, people will achieve near-optimal performances on sequential decision-making tasks when they can detect the effects of their actions on the environment, and when they can represent and reason about these effects using an internal mental model.


Back to Table of Contents