June 2006
Volume 6, Issue 6
Free
Vision Sciences Society Annual Meeting Abstract  |   June 2006
A computational model for the distribution of spatial attention
Author Affiliations
  • Arvin Hsu
    University of California, Irvine
  • Ian Scofield
    University of California, Irvine
  • George Sperling
    University of California, Irvine
Journal of Vision June 2006, Vol.6, 507. doi:10.1167/6.6.507
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to Subscribers Only
      Sign In or Create an Account ×
    • Get Citation

      Arvin Hsu, Ian Scofield, George Sperling; A computational model for the distribution of spatial attention. Journal of Vision 2006;6(6):507. doi: 10.1167/6.6.507.

      Download citation file:


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

      ×
  • Supplements
Abstract

We describe a model for estimating the spatial distribution of visual attention for detecting a target embedded in arbitrarily shaped to-be-attended areas within a field of distracters and non-attended false targets. Expanded from the linear systems model of Gobbel, Tseng, and Sperling (Vision Research, 2004, 44: 1273–1296), the model comprises a spatial frequency modulation transfer function (MTF), an acuity strength map, local compensation for crowding, and a signal-detection decision mechanism. Computation sequence: The pattern of to-be-attended areas undergoes a continuous Fourier transform in order to create an attentional map in the spatial frequency domain that is multiplied by the MTF (attenuating middle and high spatial frequencies), and then inverse Fourier transformed, returning the map to retinal coordinates. The resulting strength map is multiplied by an acuity function, a linear spatial bias function, and a crowding function based on the number of nearest neighbors. This embellished strength map multiplies the input stimuli which consists of a 12×12 array of targets and distractors. Gaussian noise is then added to all locations; the highest valued location is designated as the target. Bootstrap methods are used to derive maximum likelihood estimates (MLE) of model parameters from a set of regular simple stimuli, and then applied to complex 8-block and 16-block patterns of to-be-attended areas (and to the inverse attention patterns). The model rendered parameter-free predictions of search performance in the 72/144 attended locations of the two 8-block and two 16-block patterns; observed data were then evaluated for closeness of MLE fit to model predictions.

Hsu, A. Scofield, I. Sperling, G. (2006). A computational model for the distribution of spatial attention [Abstract]. Journal of Vision, 6(6):507, 507a, http://journalofvision.org/6/6/507/, doi:10.1167/6.6.507. [CrossRef]
Footnotes
 This work was supported by the US Air Force Office of Scientific Research, Life Sciences, under grant no. FA9550-04-1-0225.
×
×

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.

×