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Predicting Learned Inattention from Attentional Selectivity and Optimization

Abstract

Although selective attention is useful in many situations, it alsohas costs. In addition to ignoring information that may becomeuseful later, it can have long term costs, such as learnedinattention – difficulty in learning from formerly irrelevantsources of information in novel situations. In the current study wetracked participants’ gaze while they completed a categorylearning task designed to elicit learned inattention. Duringlearning an unannounced shift occurred such that information thatwas most relevant became irrelevant, whereas formerly irrelevantinformation became relevant. We assessed looking patternsduring initial learning to understand how different aspects ofattention allocation contribute to learned inattention. Our resultsindicate that learned inattention depends on both the overall levelof selectivity (measured as entropy of proportion of looking toeach feature) and the extent to which participants optimizedattention (becoming more selective over time).

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