Predicting Learned Inattention from Attentional Selectivity and Optimization

AbstractAlthough selective attention is useful in many situations, it also has costs. In addition to ignoring information that may become useful later, it can have long term costs, such as learned inattention – difficulty in learning from formerly irrelevant sources of information in novel situations. In the current study we tracked participants’ gaze while they completed a category learning task designed to elicit learned inattention. During learning an unannounced shift occurred such that information that was most relevant became irrelevant, whereas formerly irrelevant information became relevant. We assessed looking patterns during initial learning to understand how different aspects of attention allocation contribute to learned inattention. Our results indicate that learned inattention depends on both the overall level of selectivity (measured as entropy of proportion of looking to each feature) and the extent to which participants optimized attention (becoming more selective over time).

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