September 2024
Volume 24, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2024
The Architecture of Object-Based Attention
Author Affiliations
  • Patrick Cavanagh
    Glendon College, York University
  • Gideon P. Caplovitz
    University of Nevada, Reno
  • Taissa K. Lytchenko
    University of Nevada, Reno
  • Marvin R. Maechler
    Dartmouth College
  • Peter U. Tse
    Dartmouth College
  • David R. Sheinberg
    Brown University
Journal of Vision September 2024, Vol.24, 222. doi:https://doi.org/10.1167/jov.24.10.222
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      Patrick Cavanagh, Gideon P. Caplovitz, Taissa K. Lytchenko, Marvin R. Maechler, Peter U. Tse, David R. Sheinberg; The Architecture of Object-Based Attention. Journal of Vision 2024;24(10):222. https://doi.org/10.1167/jov.24.10.222.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Evidence for the existence of object-based attention raises several important questions: what are objects, how does attention access them, and what anatomical regions are involved? What are the “objects” that attention can access? Several studies have shown that items in visual search tasks are only loose collections of features prior to the arrival of attention. Nevertheless, findings from a wide variety of paradigms, including unconscious priming and cuing, have overturned this view. Instead, the targets of object-based attention appear to be fully developed object representations that have reached the level of identity prior to the arrival of attention. Where do the downward projections of object-based attention originate? Current research indicates that the control of object-based attention must come from ventral visual areas specialized in object analysis that project downward to early visual areas. If so, how can feedback from object areas accurately target the object’s early locations and features when the object areas have only crude location information? Critically, recent work on autoencoders has made this plausible as they are capable of recovering the locations and features of the target objects from the high level, low dimensional codes in the object areas. I will outline the architecture of object-based attention, the novel predictions it brings, and discuss how it works in parallel with other attention pathways.

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