Do New Objects Capture Attention?

The visual system relies on several heuristics to direct attention to important locations and objects. One of these mechanisms directs attention to sudden changes in the environment. Although a substantial body of research suggests that this capture of attention occurs only for the abrupt appearance of a new perceptual object, more recent evidence shows that some luminance-based transients (e.g., motion and looming) and some types of brightness change also capture attention. These findings show that new objects are not necessary for attention capture. The present study tested whether they are even sufficient. That is, does a new object attract attention because the visual system is sensitive to new objects or because it is sensitive to the transients that new objects create? In two experiments using a visual search task, new objects did not capture attention unless they created a strong local luminance transient.

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Visual search for object categories is predicted by the representational architecture of high-level visual cortex

Journal of Neurophysiology ◽

10.1152/jn.00569.2016 ◽

2017 ◽

Vol 117 (1) ◽

pp. 388-402 ◽

Cited By ~ 14

Author(s):

Michael A. Cohen ◽

George A. Alvarez ◽

Ken Nakayama ◽

Talia Konkle

Keyword(s):

Visual Cortex ◽

Visual Search ◽

Visual System ◽

Visual Processing ◽

Search Task ◽

Visual Search Task ◽

Visual Object ◽

Neural Responses ◽

Object Categories ◽

High Level

Visual search is a ubiquitous visual behavior, and efficient search is essential for survival. Different cognitive models have explained the speed and accuracy of search based either on the dynamics of attention or on similarity of item representations. Here, we examined the extent to which performance on a visual search task can be predicted from the stable representational architecture of the visual system, independent of attentional dynamics. Participants performed a visual search task with 28 conditions reflecting different pairs of categories (e.g., searching for a face among cars, body among hammers, etc.). The time it took participants to find the target item varied as a function of category combination. In a separate group of participants, we measured the neural responses to these object categories when items were presented in isolation. Using representational similarity analysis, we then examined whether the similarity of neural responses across different subdivisions of the visual system had the requisite structure needed to predict visual search performance. Overall, we found strong brain/behavior correlations across most of the higher-level visual system, including both the ventral and dorsal pathways when considering both macroscale sectors as well as smaller mesoscale regions. These results suggest that visual search for real-world object categories is well predicted by the stable, task-independent architecture of the visual system. NEW & NOTEWORTHY Here, we ask which neural regions have neural response patterns that correlate with behavioral performance in a visual processing task. We found that the representational structure across all of high-level visual cortex has the requisite structure to predict behavior. Furthermore, when directly comparing different neural regions, we found that they all had highly similar category-level representational structures. These results point to a ubiquitous and uniform representational structure in high-level visual cortex underlying visual object processing.

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The persistence of value-driven attention capture is task dependent

10.31234/osf.io/967k8 ◽

2020 ◽

Cited By ~ 1

Author(s):

Anne Milner ◽

Mary MacLean ◽

Barry Giesbrecht

Keyword(s):

Visual Search ◽

Search Task ◽

Visual Search Task ◽

Attention Capture ◽

The Other ◽

Visual Features ◽

Capture Effect ◽

Bias Attention ◽

Task Irrelevant

Visual features previously associated with reward can automatically capture attention even when task-irrelevant, a phenomenon known as value-driven attention capture (VDAC, Anderson et al., 2011b). VDAC persists without reinforcement, unlike other forms of learning where removing reinforcement typically leads to extinction (Pavlov, 1927). This study examined the conditions under which VDAC could be extinguished. In four experiments, factors known to affect attention were manipulated to examine their impact on VDAC and its extinction. All experiments included learning and test phases. During learning, participants completed a visual search task during which one of two target colors was associated with a reward, and the other with no reward. During test, one week later, participants completed another visual search task during which the reward association was not reinforced. The task during test had twice as many exposures to the rewarded feature than during learning to ensure a sufficient number of exposures to observe extinction. When a rewarded feature remained task-relevant (Exp. 1), the capture effect was reduced, but extinction was not complete. When a rewarded feature was made task-irrelevant (Exp. 2) there was no evidence of extinction. When the frequency of exposure to the task-irrelevant rewarded feature was reduced (Exp. 4), VDAC also persisted. A physically salient target (Exp. 3) resulted in the fastest rate of VDAC extinction. These findings demonstrate that the extinction of VDAC depends on various factors that affect priority for attention, especially those that bias attention away from reward-associated features.

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