September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
Perceptual learning of spatial frequency identification through learned reweighting.
Author Affiliations
  • Barbara Dosher
    Cognitive Sciences, University of California, Irvine, CA
  • Jiajuan Liu
    Cognitive Sciences, University of California, Irvine, CA
  • Zhong-Lin Lu
    Psychology, The Ohio State University
Journal of Vision August 2017, Vol.17, 490. doi:
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      Barbara Dosher, Jiajuan Liu, Zhong-Lin Lu; Perceptual learning of spatial frequency identification through learned reweighting.. Journal of Vision 2017;17(10):490.

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

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The detection and discrimination of patterns are thought to reflect the selectivity and distribution of spatial frequency (and orientation) channels in early vision (Campbell et al., 1970). Spatial frequency discrimination (frequency differences) has historically been examined in two-alternative tasks in which a test stimulus is of either higher or lower spatial frequency than a standard. Perceptual learning has been documented in many tasks (Fine & Jacobs, 2002), and in some tasks the learning was specific to spatial frequency. However, perceptual learning of spatial frequency judgments themselves has not been extensively studied, although perceptual learning of the discrimination of contrast or phase differences in one component of a compound stimulus has been studied (e.g., Fiorentini & Berardi, 1981). The current study examines perceptual learning in identification of eight stimuli that differ in spatial frequency (8AFC), in half-octave intervals with 8 center frequencies from 0.48 to 5.41 cpd, embedded in external noise, and presented briefly at one of two pre-cued peripheral locations. Contrast thresholds were measured at 54% correct using adaptive methods in 8 sessions of 800 trials each. Some observers learned in this challenging task, showing improved accuracy in the confusion matrices and/or improved contrast thresholds over the course of learning. Other observers showed little learning. Qualitative properties of the performance are related to predictions of an elaboration of the Integrated Reweighting Theory (IRT, Dosher et al., 2013) for nAFC tasks using multiple decision units and a max rule for decision.

Meeting abstract presented at VSS 2017


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