Classification errors and response times over multiple distributed sessions as a function of category structure

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© 2018, Psychonomic Society, Inc. Learning difficulty orderings for categorical stimuli have long provided an empirical foundation for concept learning and categorization research. The conventional approach seeks to determine learning difficulty orderings in terms of mean classification accuracy. However, it is relatively rare that the stability of such orderings is tested over a period of extended learning. Further, research rarely explores dependent variables beyond classification accuracy that may also indicate relative learning difficulty, such as classification response times (RTs). Using a family of category structures defined over three binary dimensions and four positive examples that is well-known for its robust learning difficulty ordering, we report the results of two experiments that test the stability of the ordering (in terms of both errors and RTs) over multiple category learning sessions. The experimental stimuli consisted of instantiations of each of the six category structures in the family. These take the form of categories consisting of four “flasks” that vary along the binary features of size (large or small), shape (circular or triangular), and color (black or white). Experiment 1 shows that when participants are randomly presented instances of all six types, the difficulty ordering remains stable across all three sessions. This stability is present in terms of mean accuracy (errors) as well as mean RTs. In Experiment 2, participants were repeatedly exposed to category instances of a single type. In terms of errors, the ordering is revealed in the first session and disappears in later sessions. The opposite trend is observed for classification RTs: The ordering is not present in the first session but is revealed in later sessions. This suggests that even when individuals reach a relative degree of expertise in terms of reduced errors, the original degree of difficulty continues to influence processing. We interpret these results in the context of the concept learning and perceptual expertise literatures.