Cost of Attention as an Indicator of Category Learning


Category learning often involves selective attention to category relevant information, which may result in learned inattention to category irrelevant information. This learned inattention is a cost of selective attention. In the current research, the cost of attention was used as an indicator of category learning. Participants were given a category learning task, and the amount of supervision given to them was manipulated. Along with behavioral data, recorded eye movements during the task showed signature patterns of learning via a cost of attention. In addition, a simple neural network (perceptron) was able to use these eye-tracking data to predict success in learning. Thus, the observed attentional pattern – the cost of selective attention – was proposed as an indicator of category learning.

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