Poster Session II: Spillover effects of color discrimination training on color category boundaries and color appearance.


Journal

Journal of vision
ISSN: 1534-7362
Titre abrégé: J Vis
Pays: United States
ID NLM: 101147197

Informations de publication

Date de publication:
01 Dec 2023
Historique:
medline: 18 12 2023
pubmed: 18 12 2023
entrez: 18 12 2023
Statut: ppublish

Résumé

Perceptual learning refers to the increase in perceptual sensitivity that results from several days of training on a perceptual task. Although perceptual learning has been shown to be effective in a variety of perceptual tasks, few studies have examined perceptual learning in color perception. In this study, we investigated how color discrimination training at a base color affected various aspects of color perception for entire hues. The training consisted of five days of S color discrimination (300 trials/day) at either the negative or positive L-M base color, depending on the observer groups. Before and after the training, three types of color perception tests (color difference, unique hue, and color category boundary) were conducted for colors with various hues to examine the changes in color perception due to the training. The results showed that the color discrimination thresholds in the training decreased as expected with repeated trials. Interestingly, the training also affected the performance of the three types of tests; the perceived color difference around the training color tended to increase, and some of the unique hues and the color category boundaries shifted significantly toward the training color. These results suggest that only a few days of color discrimination training can spill over to the entire color space and induce distortion of the perceptual color space.

Identifiants

pubmed: 38109595
pii: 2793151
doi: 10.1167/jov.23.15.53
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

53

Auteurs

Suzuha Horiuchi (S)

Tokyo Institute of Technology.

Takehiro Nagai (T)

Tokyo Institute of Technology.

Classifications MeSH