August 2023
Volume 23, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2023
Channel-specific perceptual learning of texture detection
Author Affiliations
  • Zahra Hussain
    University of Plymouth
  • Melissa Allouche
    American University of Beirut
Journal of Vision August 2023, Vol.23, 5371. doi:
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      Zahra Hussain, Melissa Allouche; Channel-specific perceptual learning of texture detection. Journal of Vision 2023;23(9):5371.

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

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When detecting bandlimited patterns in noise, do observers learn information within particular spatial frequency bands, or do they learn a more general strategy for detection? We examined this issue by having separate groups of observers detect either low (2-4 cycles per image; cpi) or high (8-16 cpi) spatial frequency textures in Gaussian noise on two consecutive days. On both days, observers performed a yes-no detection task in which the stimulus (a texture randomly selected from a set of five), was present on half the trials. Stimuli were presented in two external noise levels (blocked), and stimulus contrast was varied using the method of constant stimuli. On day 2, half the observers in each group transferred to textures from the unpracticed spectral band. d’ and contrast thresholds were measured. Larger improvements were obtained in the same-band groups than in the transfer groups, and consistent with other findings, there was more transfer of learning from high to low spatial frequencies than the reverse. Hence, perceptual learning was partially and asymmetrically channel specific. Perhaps more interestingly, initial performance predicted specificity of learning: High performers (defined by a median split), showed channel-specific improvements that produced the maximum performance on day 2 for both spatial frequency bands (same-band groups). Low performers showed generalization of learning across spatial frequencies, albeit only to the performance level on day 1 of high performers. Therefore, initial performance differentiated the strategies used for detection, with diagnostic spectral content learned only at high performance levels.


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