August 2012
Volume 12, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2012
Variability in encoding precision accounts for the limitations of visual short-term memory
Author Affiliations
  • Wei Ji Ma
    Department of Neuroscience, Baylor College of Medicine
Journal of Vision August 2012, Vol.12, 1102. doi:10.1167/12.9.1102
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Wei Ji Ma; Variability in encoding precision accounts for the limitations of visual short-term memory. Journal of Vision 2012;12(9):1102. doi: 10.1167/12.9.1102.

      Download citation file:


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

      ×
  • Supplements
Abstract

The question whether visual short-term memory (VSTM) consists of a fixed number of discrete chunks or of a continuous resource has been intensely debated. Recent work suggests that only the former model can explain human performance, in particular the observed increase in guessing with set size. However, continuous-resource models have not taken into account that the amount of resource per item may vary across items and trials, for example due to attentional fluctuations. We developed a new model that incorporates this variability. In this model, the encoding precision of a stimulus decreases on average inversely proportionally to set size but is variable around this average. We tested this variable-precision model against leading previous models in three experimental paradigms: delayed estimation (recall), change detection, and change localization. We performed each experiment for both orientation and color. In the change detection and change localization experiments, we varied not only set size, but also the magnitude of change and stimulus reliability. Across all six experiments, the variable-precision model provided excellent fits to the data, accounted for the increase in guessing, and outperformed the other models in a formal comparison. Furthermore, we found that the variable-precision model can readily be implemented using an existing neural architecture, namely the divisive normalization model of attention (Reynolds and Heeger). We modified this model to incorporate attentional fluctuations and stochasticity in spike generation. With these modifications, the neural network can quantitatively reproduce subjects’ behavior in delayed estimation, despite not containing any limit on the number of items kept in memory. The neural model is consistent with extant physiological findings. Together, these results provide strong converging evidence – both behavioral and neural – that VSTM resource might not be discrete and fixed, but continuous and variable. The variable-precision model might provide a unifying framework of attention and working memory.

Meeting abstract presented at VSS 2012

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×