Acquisition of colour categories through learning

Differences between hue and lightness

Bibliographic Data

ID21306269
AuthorsJasna Martinovic (0000-0001-9141-2577, University of Edinburgh, corresponding author)
Year2024
Volume242
Pages105657
Publication date2024-01-01
Open AccessYes
TypeARTICLE
VenueCognition (JOURNAL)
Journal identifiersISSN: 0010-0277 • E-ISSN: 1873-7838
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.cognition.2023.105657
PMID37980878
OpenAlexW4388753429
LanguageEN
References cited62

Colour categories are acquired through learning, but the nature of this process is not fully understood. Some category distinctions are defined by hue (e.g. red/purple) but other by lightness (red/pink). The aim of this study was to investigate if the acquisition of key information for making accurate cross-boundary discriminations poses different challenges for hue-defined as opposed to lightness-defined boundaries. To answer this question, hue- and lightness-learners were trained on a novel category boundary within the GREEN region of colour space. After training, hue- and lightness-learners as well as untrained controls performed delayed same-different discrimination for lightness and hue pairs. In addition to discrimination data, errors during learning and category-labelling strategies were examined. Errors during learning distributed non-uniformly and in accordance with the Bezold-Brücke effect, which accounts for darker colours at the green-blue boundary appearing greener and lighter colours appearing bluer. Only hue-learners showed discrimination improvements due to category boundary acquisition. Thus, acquisition is more efficient for hue-category compared to lightness-category boundaries. Almost all learners reported using category-labelling strategies, with hue-learners almost exclusively using 'green'/'blue' and lightness learners using a wider range of labels, most often 'light'/'dark'. Thus, labels play an important role in colour category learning and such labelling does not conform to everyday naming: here, the label 'blue' is used for exemplars that would normally be named 'green'. In conclusion, labelling serves the purpose of highlighting key information that differentiates exemplars across the category boundary, and basic colour terms may be particularly effective in facilitating such attentional guidance

Categorization · Cognitive psychology · Hue · Lightness · Artificial Intelligence · Categorization, perception, and language · Color perception and design · Communication · Computer Science · Mathematics · Multisensory perception and integration · Psychology

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