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Statistical learning of distractor locations in visual search
Statistical learning of distractor locations in visual search
Observers can learn the locations where salient distractors appear frequently to reduce potential interference. The effect that observers appear to learn the spatial distribution of salient but task-irrelevant distractors in the visual environment to reduce the interference caused by such distractors was referred to as ‘statistical learning of distractor locations’. Emerging studies agreed that the observed reduction of distractor interference is largely attributable to better suppression of distractor in frequent locations, however, concerning how this spatial distractor suppression is implemented within the functional architecture of search guidance, and how the learned suppression is processed in the brain to reduce the interference, remains debated and poorly understood. The line of studies of the current dissertation, therefore, explores the cognitive and neural mechanism underlying spatial distractor suppression based on statistical learning. Using psychophysical experiments, the first two studies (Chapter 1 and Chapter 2) examined a series of potentially ‘confounding’ factors that might lead to different theoretical conclusions concerning the locus of learned spatial distractor suppression in different studies. We found that irrespective of whether a salient singleton distractor is more likely to occur at multiple locations within a subregion of the display (e.g., Sauter et al., 2018) or at a specific location (e.g., Wang and Theeuwes, 2018a), observers are both likely to adopt the priority-map-based suppression strategy and dimension-based suppression strategy to reduce interference of distractor in the frequent region. Notably, a critical factor determining which strategy is adopted is whether the distractor and non-distractor items swap colors randomly across trials: without color swapping, observers are more likely to acquire a dimension-based suppression; with color swapping, they prefer to develop a priority-map-based suppression in the first beginning, and potentially shift to dimension-based suppression later with the course of learning. The third study (Chapter 2.3) employed functional magnetic resonance imaging techniques with the distractor-location learning paradigm with two types of distractors defined in either the same- or different- visual dimension relative to the target. We disclosed that statistical learning of distractor locations involves (acquired) suppression down to the level of the early visual cortex to reduce distractor interference, potential different neural mechanisms of spatial distractor suppression between distractors defined in a different and the same dimension to the target. With different-dimension (color) distractors, higher-level, dimension-specific filtering mechanisms plays a role to reduce the interference; with same-dimension distractors, interference reduction relies on cutting down lower-level sensory signals.
attentional capture, distractor suppression, search guidance, statistical (distractor location) learning
Zhang, Bei
2021
English
Universitätsbibliothek der Ludwig-Maximilians-Universität München
Zhang, Bei (2021): Statistical learning of distractor locations in visual search. Dissertation, LMU München: Faculty of Psychology and Educational Sciences
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Abstract

Observers can learn the locations where salient distractors appear frequently to reduce potential interference. The effect that observers appear to learn the spatial distribution of salient but task-irrelevant distractors in the visual environment to reduce the interference caused by such distractors was referred to as ‘statistical learning of distractor locations’. Emerging studies agreed that the observed reduction of distractor interference is largely attributable to better suppression of distractor in frequent locations, however, concerning how this spatial distractor suppression is implemented within the functional architecture of search guidance, and how the learned suppression is processed in the brain to reduce the interference, remains debated and poorly understood. The line of studies of the current dissertation, therefore, explores the cognitive and neural mechanism underlying spatial distractor suppression based on statistical learning. Using psychophysical experiments, the first two studies (Chapter 1 and Chapter 2) examined a series of potentially ‘confounding’ factors that might lead to different theoretical conclusions concerning the locus of learned spatial distractor suppression in different studies. We found that irrespective of whether a salient singleton distractor is more likely to occur at multiple locations within a subregion of the display (e.g., Sauter et al., 2018) or at a specific location (e.g., Wang and Theeuwes, 2018a), observers are both likely to adopt the priority-map-based suppression strategy and dimension-based suppression strategy to reduce interference of distractor in the frequent region. Notably, a critical factor determining which strategy is adopted is whether the distractor and non-distractor items swap colors randomly across trials: without color swapping, observers are more likely to acquire a dimension-based suppression; with color swapping, they prefer to develop a priority-map-based suppression in the first beginning, and potentially shift to dimension-based suppression later with the course of learning. The third study (Chapter 2.3) employed functional magnetic resonance imaging techniques with the distractor-location learning paradigm with two types of distractors defined in either the same- or different- visual dimension relative to the target. We disclosed that statistical learning of distractor locations involves (acquired) suppression down to the level of the early visual cortex to reduce distractor interference, potential different neural mechanisms of spatial distractor suppression between distractors defined in a different and the same dimension to the target. With different-dimension (color) distractors, higher-level, dimension-specific filtering mechanisms plays a role to reduce the interference; with same-dimension distractors, interference reduction relies on cutting down lower-level sensory signals.