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• W2794372913 abstract "The visual environment is filled with complex, multi-dimensional objects that vary in their value to an observer's current goals. When faced with multi-dimensional stimuli, humans may rely on biases to learn to select those objects that are most valuable to the task at hand. Here, we show that decision making in a complex task is guided by the sparsity bias: the focusing of attention on a subset of available features. Participants completed a gambling task in which they selected complex stimuli that varied randomly along three dimensions: shape, color, and texture. Each dimension comprised three features (e.g., color: red, green, yellow). Only one dimension was relevant in each block (e.g., color), and a randomly-chosen value ranking determined outcome probabilities (e.g., green > yellow > red). Participants were faster to respond to infrequent probe stimuli that appeared unexpectedly within stimuli that possessed a more valuable feature than to probes appearing within stimuli possessing a less valuable feature. Event-related brain potentials recorded during the task provided a neurophysiological explanation for sparsity as a learning-dependent increase in optimal attentional performance (as measured by the N2pc component of the human event-related potential) and a concomitant learning-dependent decrease in prediction errors (as measured by the feedback-elicited reward positivity). Together, our results suggest that the sparsity bias guides human reinforcement learning in complex environments." @default.
• W2794372913 created "2018-03-29" @default.
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• W2794372913 date "2018-05-01" @default.
• W2794372913 modified "2023-09-26" @default.
• W2794372913 title "Learning what matters: A neural explanation for the sparsity bias" @default.
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• W2794372913 doi "https://doi.org/10.1016/j.ijpsycho.2018.03.006" @default.
• W2794372913 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29551656" @default.
• W2794372913 hasPublicationYear "2018" @default.
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