The Impact of Perceptual Aliasing on Exploration and Learning in a Dynamic Decision Making Task


Perceptual aliasing arises in situations where multiple, distinct states of the world give rise to the same percept. In this study, we examine how the degree of perceptual aliasing in a task impacts the ability of human agents to learn reward-maximizing decision strategies. Previous work has shown that the presence of perceptual cues that help signal distinct states of the environment can improve the ability of learners to adopt an optimal decision strategy in sequential decision making tasks (Gureckis & Love, 2009). In our experiments, we parametrically manipulated the degree of perceptual aliasing afforded by certain perceptual cues in a similar task. Our empirical results and simulations show how the ability of the learner improves as relevant states in the world uniquely map to differentiated percepts. The results provide further support for the model of sequential decision making proposed by Gureckis & Love (2009) and highlight the important role that state representations may have on behavior in dynamic decision making and learning tasks.

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