July 2013
Volume 13, Issue 9
Vision Sciences Society Annual Meeting Abstract  |   July 2013
Evolutionary dynamics of visual memory
Author Affiliations
  • Jordan W. Suchow
    Department of Psychology, Harvard University
  • Benjamin Allen
    Department of Mathematics, Emmanuel College\nProgram for Evolutionary Dynamics, Harvard University
  • Martin A. Nowak
    Program for Evolutionary Dynamics, Harvard University
  • George A. Alvarez
    Department of Psychology, Harvard University
Journal of Vision July 2013, Vol.13, 20. doi:https://doi.org/10.1167/13.9.20
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      Jordan W. Suchow, Benjamin Allen, Martin A. Nowak, George A. Alvarez; Evolutionary dynamics of visual memory. Journal of Vision 2013;13(9):20. https://doi.org/10.1167/13.9.20.

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

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Visual memory enables a viewer to hold in mind the details of objects, textures, faces, and scenes. After initial exposure to an image, however, visual memories rapidly degrade because they are transferred from iconic memory, a high-capacity sensory buffer, to working memory, a low-capacity maintenance system whose flexibility affords a workspace for thought. Here, we extend the classic depiction of the dynamics of visual memory to account for competitive interactions between memories, fluid reallocation of mental resources, and mutual interference. The proposed model is equivalent to a generalization of the Moran process of evolution in finite populations. When applied to the mental commodity that provides a substrate for memories, the process helps to explain the time course of the capacity, quality, variability, and reliability of visual memory. The process also provides an explanation for why, in the absence of distraction or misattribution, the limiting behavior of visual memory is neither full retention nor complete loss, but the stability of a lone memory, having reached fixation in the mind. Structured substrates, e.g. gridded visuotopic maps like those found in visual areas in the brain, are shown to preserve memories better than substrates without explicit structure. Evolutionary models provide quantitative insights into the mechanisms of memory maintenance.

Meeting abstract presented at VSS 2013


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