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Sha Li, Roger Remington, Yuhong Jiang; Learning-induced changes in attentional priority map are task-specific. Journal of Vision 2018;18(10):1182. doi: 10.1167/18.10.1182.
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© ARVO (1962-2015); The Authors (2016-present)
Extensive behavioral and neuroscientific evidence supports the concept of an "attentional priority map," in which locations receiving higher weights are preferentially attended or prioritized. The weighting of locations is sensitive to prior search history and reward, suggesting that this map can adapt to one's previous visual experience. Here we investigated the specificity of priority learning across tasks. Using stimuli that mimic X-ray images, we employed a detection task that required participants to find a heavily camouflaged target among 1/f2 noise. In a separate discrimination task, the noise opacity was reduced, but the target appeared among visually similar distractors. The two tasks were associated with similar response time and accuracy, and both entailed a sequence of eye movements. To modify the attentional priority map, the target, when present, was more often located in one quadrant than the others during a training phase. This manipulation of location probability successfully induced a change in the priority map: participants more quickly found the target in the high-probability quadrant than the low-probability quadrants. To investigate the persistence and transfer of the learned spatial priority, we also measured response during a testing phase, in which the target appeared in all quadrants equally often. When the same task was used in both training and testing, the learned priority persisted in the testing phase for over 100 trials. But when the task changed across training and testing, the learned priority immediately ceased in the testing phase. Eye tracking showed fewer, but longer, fixations in the detection task than in the discrimination task. These results suggest that changes in the attentional priority map are task-specific, even when the tasks are performed in the same general space. These findings have implications for attention training in applied settings, such as cancer detection.
Meeting abstract presented at VSS 2018
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