The effect of newly trained verbal and nonverbal labels for the cues in probabilistic category learning
Learning in a well-established paradigm of probabilistic category learning, the weather prediction task, has been assumed to be mediated by a variety of strategies reflecting explicit learning processes, such as hypothesis testing, when it is administered to young healthy participants. Higher categorization accuracy has been observed in the task when explicit processes are facilitated. We hypothesized that furnishing verbal labels for the cues would boost the formation, testing, and application of verbal rules, leading to higher categorization accuracy. We manipulated the availability of cue names by training separate groups of participants for three consecutive days to associate hard-to-name artificial auditory cues to pseudowords or to hard-to-name ideograms, or to associate stimulus intensity with colors; a fourth group remained unexposed to the cues. Verbal labels, cue individuation, and exposure to the stimulus set each had an additive effect on categorization performance in a subsequent 200-trial session of the weather prediction task using these auditory cues. This study suggests that cue nameability, when controlled for cue individuation and cue familiarity, has an effect on hypothesis-testing processes underlying category learning.
KeywordsCategorization Training Memory Verbal labels
We thank George Gyftodimos for suggesting the idea of new names for the auditory cues, Eleni Vlahou for help with DMDX programming and preprocessing of the data, Lori Holt and Sung-joo Lim for providing the frequency-modulated tones, Martijn Meeter for providing the order of the trials in the WPT and for comments on an earlier draft, Catherine Myers for help with the literature, Maarten Van Casteren for adapting the MIX program to facilitate advanced trial randomization, Jonathan C. Forster for technical advice on DMDX remote-testing mode, and all of the ling-r-lang-L mailing list subscribers (especially Florian Jaeger) for advice on the statistical analyses. We also thank Argiro Vatakis and all of the members of the Language and Learning Lab at the University of Athens for help with recruiting participants.
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