September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Memory load modulates the dynamics of visual working memory.
Author Affiliations
  • Matthew Panichello
    Princeton Neuroscience Institute, Princeton University
  • Brian DePasquale
    Princeton Neuroscience Institute, Princeton University
  • Jonathan Pillow
    Princeton Neuroscience Institute, Princeton UniversityDepartment of Psychology, Princeton University
  • Timothy Buschman
    Princeton Neuroscience Institute, Princeton UniversityDepartment of Psychology, Princeton University
Journal of Vision September 2018, Vol.18, 189. doi:10.1167/18.10.189
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      Matthew Panichello, Brian DePasquale, Jonathan Pillow, Timothy Buschman; Memory load modulates the dynamics of visual working memory.. Journal of Vision 2018;18(10):189. doi: 10.1167/18.10.189.

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

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Abstract

Working memory is imperfect and dynamic – memories becomes less accurate with time. We combine psychophysical methods and computational modeling to precisely describe the dynamic forces governing working memory. We show that the accumulation of error in working memory over time is not random but reflects underlying attractor dynamics. Furthermore, these dynamics are modulated by memory load and they are modified by experience: memories drift towards common stimuli. To identify these attractor dynamics, we trained both monkeys and humans to perform a continuous-report working memory task. In brief, subjects were asked to remember 1 to 3 colored squares. After a variable memory delay, subjects had to report the color of a cued stimulus on a continuous color wheel. Dynamic models fit to the behavioral responses of both monkeys and humans showed random diffusion dominated the dynamics at low memory load but strong attractors dominated dynamics at high memory load. In other words, memory dynamics are load-dependent: attractor depth increased with the number of items in working memory. These results provide a mechanistic explanation for why increasing the number of items in memory impairs memory accuracy and provide a bridge between descriptive models of memory reports and neural network models of working memory.

Meeting abstract presented at VSS 2018

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