August 2009
Volume 9, Issue 8
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2009
Response-triggered covariance analysis of letter features
Author Affiliations
  • Susana Chung
    UC Berkeley
  • Bosco Tjan
    University of Southern California
Journal of Vision August 2009, Vol.9, 1000. doi:10.1167/9.8.1000
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      Susana Chung, Bosco Tjan; Response-triggered covariance analysis of letter features. Journal of Vision 2009;9(8):1000. doi: 10.1167/9.8.1000.

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

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Abstract

Abnormal feature integration has been suggested as a cause of letter crowding. Nandy and Tjan (2007) reported that the number of letter features utilized by human observers is similar for identifying crowded and non-crowded letters, but there are fewer valid features and more invalid features for crowded letters. Using a set of 26 lowercase letters constructed of Gaussian patches that could be individually turned on or off, last year we reported that the patch locations within a given letter that correlated with an observer's response are largely invariant between the crowded and non-crowded conditions. To ascertain that the result was not an artefact of the assumption that each patch location was independent, and to identify higher-order features used by human observers, this year we adapted a covariance-based reverse correlation technique to examine if the amount of first- and second-order features (formed by a conjunction of patches) utilized for identifying crowded and non-crowded letters remains similar. We considered only the false-alarm trials and only at the target-letter location. We used the principal component analysis to partially prewhiten the distribution of the stimuli that were presented, before computing the mean stimulus (first-order classification image) and the covariance matrix, for each of the 26 letter responses. From the covariance matrix, we computed the second-order classification image for each letter response in the form of a correlogram. The RMS of the pixel values in each classification image was used to quantify the amount of features present. The amount of first- and second-order letter features at the target location was significantly lower (p [[lt]]0.05) for crowded than for non-crowded letters. Considering that the way we perturbed the stimulus did not include any spurious letter features, our finding is consistent with previous report that crowding leads to a decrease in the number of valid features.

Chung, S. Tjan, B. (2009). Response-triggered covariance analysis of letter features [Abstract]. Journal of Vision, 9(8):1000, 1000a, http://journalofvision.org/9/8/1000/, doi:10.1167/9.8.1000. [CrossRef]
Footnotes
 Supported by NIH research grants R01-EY012810 (SC) and R01-EY017707 (BT).
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