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Anna Wright, Katherine Moen, Melissa Beck; Apple of my eye: Incidental learning of change probability biases visual attention to food categories. Journal of Vision 2018;18(10):1299. doi: https://doi.org/10.1167/18.10.1299.
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Participants can learn to bias attention towards items in a scene that have a high probability of change (Beck et al., 2017). The current study investigated this effect using categories of stimuli that have shared features. We used unprocessed and processed foods because processed foods tend to have angular edges, whereas unprocessed foods tend to be curvilinear. Participants detected identity changes in arrays of three processed and three unprocessed foods. In the control condition, processed and unprocessed foods changed equally often. In the unprocessed probable condition, unprocessed foods changed on 80% of the trials, and in the processed probable condition, processed foods changed on 80% of the trials. In both the control condition and processed probable conditions, change detection performance was higher for processed foods than for unprocessed foods. However, in the unprocessed probable condition, change detection performance was equal for the two food types. Eye tracking data demonstrated that this was due to an attentional bias for processed foods that was overcome when unprocessed foods were more likely to change. In both the control and the processed probable conditions, the first stimulus participants fixated was more often a processed item as opposed to an unprocessed item, and more time overall was spent fixating processed items. In the unprocessed probable condition, first fixations were equally likely to fall on processed and unprocessed items, and total time spent fixating was the same for both food types. These data show that existing attention biases toward a category in which the stimuli share features (e.g., processed foods) can be modified by making another category with a different shared feature more task relevant.
Meeting abstract presented at VSS 2018
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