title: Supervised and unsupervised learning of multidimensional acoustic categories.
abstract: Learning to recognize the contrasts of a language-specific phonemic repertoire can be viewed as forming categories in a multidimensional psychophysical space. Research on the learning of distributionally defined visual categories has shown that categories defined over 1 dimension are easy to learn and that learning multidimensional categories is more difficult but tractable under specific task conditions. In 2 experiments, adult participants learned either a unidimensional or a multidimensional category distinction with or without supervision (feedback) during learning. The unidimensional distinctions were readily learned and supervision proved beneficial, especially in maintaining category learning beyond the learning phase. Learning the multidimensional category distinction proved to be much more difficult and supervision was not nearly as beneficial as with unidimensionally defined categories. Maintaining a learned multidimensional category distinction was only possible when the distributional information that identified the categories remained present throughout the testing phase. We conclude that listeners are sensitive to both trial-by-trial feedback and the distributional information in the stimuli. Even given limited exposure, listeners learned to use 2 relevant dimensions, albeit with considerable difficulty.
authors.affiliation: University of Geneva, Geneva Emotion Research Group.
authors.forename: Martijn
authors.initials: M
authors.lastname: Goudbeek
authors.affiliation: University of Pennsylvania, Department of Psychology.
authors.forename: Daniel
authors.initials: D
authors.lastname: Swingley
authors.affiliation: Max Planck Institute for Psycholinguistics.
authors.forename: Roel
authors.initials: R
authors.lastname: Smits
mesh_terms: D000161:Acoustic Stimulation; D001307:Auditory Perception / Q000502:physiology*; D001309:Auditory Threshold / Q000502:physiology; D004193:Discrimination Learning / Q000502:physiology*; D030141:Feedback, Psychological / Q000502:physiology*; D006801:Humans; D016015:Logistic Models; D010898:Pitch Perception / Q000502:physiology; D011337:Probability Learning; D017603:Signal Detection, Psychological; D013016:Sound; D013997:Time Factors