Abstract
Classic studies of human categorization learning provided evidence that high-variability training in the prototype-distortion paradigm enhances subsequent generalization to novel test patterns from the learned categories. More recent work suggests, however, that when the number of training trials is equated across low-variability and high-variability training conditions, it is low-variability training that yields better generalization performance. Whereas the recent studies used cartoon-animal stimuli varying along binary-valued dimensions, in the present work we return to the use of prototype-distorted dot-pattern stimuli that had been used in the original classic studies. In accord with the recent findings, we observe that high-variability training does not enhance generalization in the dot-pattern prototype-distortion paradigm when the total number of training trials is equated across the conditions, even when training with very large numbers of distinct instances. A baseline version of an exemplar model captures the major qualitative pattern of results in the experiment, as do prototype models that make allowance for changes in parameter settings across the different training conditions. Based on the modeling results, we hypothesize that although high-variability training does not enhance generalization in the prototype-distortion paradigm, it may do so when participants learn more complex category structures.
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Data availability
The datasets generated and/or analyzed during the current study are available at OSF website https://osf.io/c6wea/.
Code availability
The computer code used in the current study is available from the corresponding author on reasonable request.
Notes
In work in progress, Bowman and Zeithamova (2023b) reported results from an experiment that equated total training trials while manipulating another form of training-instance variability. In this study, the authors used face stimuli that were blends of “parent” faces. Members of the same category shared a common parent face, but each blend also included a unique parent face not shared by any other member in the set of training stimuli. In a high-coherence (low-variability) training condition, each blend was dominated by the shared parent face, whereas in a low-coherence (high-variability) training condition, each blend was dominated by a unique parent face. In accord with the previous results reported by Bowman and Zeithamova (2020, 2023a), generalization test performance was better in the low-variability training condition than in the high-variability one. Future work needs to examine the extent to which this face-blend manipulation maps onto the types of training-instance variability manipulations considered in other studies of category generalization.
Allowing the values of low, medium, and high to vary instead as free parameters led to relatively small improvements in model fit.
We should note that the procedure of fitting these types of models to individual-participant data does not necessarily address the theoretical question of why separate values of the sensitivity parameter may be associated with the different training-instance variability conditions. If the overall distribution of individual-participant sensitivity parameters is found to differ across the four training conditions, there remains the question of why.
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Hu, M., Nosofsky, R.M. High-variability training does not enhance generalization in the prototype-distortion paradigm. Mem Cogn (2024). https://doi.org/10.3758/s13421-023-01516-1
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DOI: https://doi.org/10.3758/s13421-023-01516-1