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Abstract It has been debated whether salient distractors in visual search can be proactively suppressed to completely prevent attentional capture, as the occurrence of proactive suppression implies that the initial shift of attention is not entirely driven by physical salience. While the presence of a Pd component in the EEG (associated with suppression) without a preceding N2pc component (associated with selection) has been used as evidence for proactive suppression, the link between these ERPs and the underlying mechanisms is not always clear. This is exemplified in two recent articles that observed the same waveform pattern, where an early Pd-like component flipped to a N2pc-like component, but provided vastly different interpretations (Drisdelle, B. L., & Eimer, E. PD components and distractor inhibition in visual search: New evidence for the signal suppression hypothesis. Psychophysiology, 58, e13898, 2021; Kerzel, D., & Burra, N. Capture by context elements, not attentional suppression of distractors, explains the PD with small search displays. Journal of Cognitive Neuroscience, 32, 1170–1183, 2020). Using RAGNAROC (Wyble et al., Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition. Psychological Review, 127, 1163–1198, 2020), a computational model of reflexive attention, we successfully simulated this ERP pattern with minimal changes to its existing architecture, providing a parsimonious and mechanistic explanation for this flip in the EEG that is unique from both of the previous interpretations. Our account supports the occurrence of proactive suppression and demonstrates the benefits of incorporating computational modeling into theory building.
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Same-different conceptualization: a machine vision perspective
The goal of this review is to bring together material from cognitive psychology with recent machine vision studies to identify plausible neural mechanisms for visual same-different discrimination and relational understanding. We highlight how developments in the study of artificial neural networks provide computational evidence implicating attention and working memory in the ascertaining of visual relations, including same- different relations. We review some recent attempts to incorporate these mechanisms into flexible models of visual reasoning. Particular attention is given to recent models jointly trained on visual and linguistic information. These recent systems are promising, but they still fall short of the biological standard in several ways, which we outline in a final section.
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- PAR ID:
- 10205786
- Date Published:
- Journal Name:
- Current opinion in behavioral sciences
- Volume:
- 37
- ISSN:
- 2352-1546
- Page Range / eLocation ID:
- 47-55
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.cobeha.2020.08.008
