Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
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Humans and animals seek appropriate solutions to novel problems through trial-and-error (TE) actions and observation of their outcomes. Once an individual has obtained the knowledge (rule) to solve a problem, knowledge-based (KB) actions may be applied in a stereotypical manner. Solutions can thus be based on TE or KB actions. To characterize this learning process at the behavioral level, we developed a new cognitive task for a laboratory monkey (Macaca fuscata) to perform. In this task, a search array consisting of six elements of different colors was presented, one of which was the behaviorally relevant target. The target color was changed unpredictably with no instruction or signal, requiring the monkey to use a TE search strategy to find the target color. We found that once the monkey identified the relevant color by chance after a color change, correct performance increased in a step-like manner and at the same time, other response properties (reaction time and color-choice tendency) also changed discontinuously. These step-like alternations in behavioral performance may be attributed to the subject's switching between TE and KB search strategies in the two phases. The present study has therefore provided behavioral evidence for the timing and manner of switching between search strategies during the process of updating knowledge.