Competition for Limited Visual Capacity Follows Feedforward Processing of a Distractor.

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Bibliographic Details
Title: Competition for Limited Visual Capacity Follows Feedforward Processing of a Distractor.
Authors: Müller, Matthias M.1 (AUTHOR) m.mueller@uni-leipzig.de, Keil, Andreas1,2 (AUTHOR), De Echegaray, Javier1 (AUTHOR)
Source: Journal of Cognitive Neuroscience. Jun2026, Vol. 38 Issue 6, p1123-1140. 18p.
Subjects: Visual evoked potentials, Resource allocation, Emotional conditioning, Categorization (Linguistics), Attention, Stimulus & response (Psychology), Attention control
Abstract: The present study tested central predictions of our conceptual framework "distraction under competition" (DUC), including the extent to which semantic processing of emotional cues triggers competitive interactions among multiple stimuli. In situations in which stimuli compete for attentional processing resources, DUC proposes a time-delayed, biphasic process: An early feedforward gain elicited by the emotional distractor that needs to cross a certain threshold to trigger subsequent competitive interactions that results in the withdrawal of resources from a concurrent task stimulus. Competition was implemented by presenting naturalistic images in the background of a taxing foreground task. One emotional image was embedded in a stream of neutral images to trigger semantic categorization. The image stream and foreground task were frequency-tagged, thereby eliciting distinct steady-state visual evoked potentials (SSVEPs), allowing us to analyze the respective amplitude time-courses that provide temporal dynamics of the shifting of attentional resources in competitive interactions. We replicated a significant enhancement of SSVEP amplitudes for emotional pictures that was greater for pleasant compared with unpleasant pictures, commencing at about 180 msec. The SSVEP amplitude driven by the foreground task was reduced from about 300 msec after the onset of a pleasant image only. Results support the biphasic time-delayed nature of resource allocation and suggest that the initial feedforward gain evoked by salient distractors may trigger subsequent competitive interactions. Formal modeling analyses showed a better fit of a biphasic process as proposed by DUC compared with a standard model based on divisive normalization. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:The present study tested central predictions of our conceptual framework "distraction under competition" (DUC), including the extent to which semantic processing of emotional cues triggers competitive interactions among multiple stimuli. In situations in which stimuli compete for attentional processing resources, DUC proposes a time-delayed, biphasic process: An early feedforward gain elicited by the emotional distractor that needs to cross a certain threshold to trigger subsequent competitive interactions that results in the withdrawal of resources from a concurrent task stimulus. Competition was implemented by presenting naturalistic images in the background of a taxing foreground task. One emotional image was embedded in a stream of neutral images to trigger semantic categorization. The image stream and foreground task were frequency-tagged, thereby eliciting distinct steady-state visual evoked potentials (SSVEPs), allowing us to analyze the respective amplitude time-courses that provide temporal dynamics of the shifting of attentional resources in competitive interactions. We replicated a significant enhancement of SSVEP amplitudes for emotional pictures that was greater for pleasant compared with unpleasant pictures, commencing at about 180 msec. The SSVEP amplitude driven by the foreground task was reduced from about 300 msec after the onset of a pleasant image only. Results support the biphasic time-delayed nature of resource allocation and suggest that the initial feedforward gain evoked by salient distractors may trigger subsequent competitive interactions. Formal modeling analyses showed a better fit of a biphasic process as proposed by DUC compared with a standard model based on divisive normalization. [ABSTRACT FROM AUTHOR]
ISSN:0898929X
DOI:10.1162/JOCN.a.2431