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Heuristics in exploration: Distributional information is selectively used for activelearning
Abstract
Everyday decision-making is filled with choices about what to act on, with outcomes playing a critical role in learn-ing. Information gain is oft cited as a valuable approach to maximize potential learning, but its computation is costly. It entailsevaluating the probability of multiple outcomes given any possible action, and then considering the degree of belief-change overall possibilities. Given the computational complexity of this evaluation, it becomes important to ask whether learners employcues to information gain; are there heuristics that drive choice in active learning? Our experiments ask participants to choosebetween two options (varying in distributional characteristics) in either a “learning-condition” or “collecting-condition”. Ourresults suggest that adults are sensitive to cues (e.g. variance) that tend to correlate with information gain. These cues areonly favored in learning-goal contexts, suggesting that certain distributional qualities are not always appealing, but rather areselectively-employed heuristics towards information gain.
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