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Alex Filipowicz, Britt Anderson, James Danckert; The Influence of Task-Irrelevant Spatial Regularities on Statistical Learning. Journal of Vision 2013;13(9):143. doi: https://doi.org/10.1167/13.9.143.
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© ARVO (1962-2015); The Authors (2016-present)
Research into statistical and sequence learning has demonstrated that we are sensitive to the statistical properties of events and can learn to approximate the probability of their occurrences. Research has also demonstrated that the spatial properties of an event can influence our perception of its non-spatial features, even if the spatial features are not relevant to the task itself. The goal of the current research was to test whether task-irrelevant spatial features could influence our ability to learn the regularities associated with non-spatial events. Using a computerized version of the children’s game ‘rock’-‘paper’-‘scissors’ (RPS), undergraduates were instructed in two separate experiments to win as often as possible against a computer that played varying RPS strategies. For each strategy, the computer’s plays were either presented with spatial regularity (i.e., ‘rock’ would always appear on the left, ‘paper’ in the middle, and ‘scissors’ on the right) or without spatial regularity (i.e., the items were equally likely to appear in any of the three screen locations). Results showed that, although irrelevant to the task itself, spatial regularities had a moderate influence when learning to play against easy strategies (Experiment 1 and 2a), and a more pronounced influence when learning to play against harder strategies (Experiment 2b). When exposed to harder strategies, we also found that, in addition to improving learning, participants were also able to detect switches in the computer’s strategies more readily when spatial regularities were evident. Our results suggest that task-irrelevant spatial features can improve statistical learning, especially when the regularities of task relevant non-spatial features are difficult to learn.
Meeting abstract presented at VSS 2013
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