Instance memorization and category influence: Challenging the evidence for multiple systems in category learning

Abstract

A class of dual-system theories of categorization assumes a categorization system based on actively formed prototypes in addition to a separate instance memory system. It has been suggested that, because they have used poorly differentiated category structures (such as the influential “5-4” structure), studies supporting the alternative exemplar theory reveal little about the properties of the categorization system. Dual-system theories assume that the instance memory system only influences categorization behaviour via similarity to single isolated instances, without generalization across instances. However, we present the results of two experiments employing the 5-4 structure to argue against this. Experiment 1 contrasted learning in the standard 5-4 structure with learning in an even more poorly differentiated 5-4 structure. In Experiment 2, participants memorized the 5-4 structure based on a five minute simultaneous presentation of all nine category instances. Both experiments revealed category influences as reflected by differences in instance learnability and generalization, at variance with the dual-system prediction. These results have implications for the exemplars versus prototypes debate and the nature of human categorization mechanisms.

Keywords:

• Category learning
• Exemplars
• Prototypes
• Categorization
• Representation

Acknowledgement

We thank Lewis Bott, Marc Buehner, Stephan Lewandowsky, and several anonymous reviewers for detailed comments and suggestions on an earlier version of this manuscript.

Notes

¹Structural ratio as a measurement does not have a fixed upper bound corresponding to perfect differentiation as, among other reasons, sharing zero features with instances of a contrast category leaves the ratio undefined to avoid division by 0. But some indication of a relevant upper bound can be had by making all of the instances of Category A identical to the category prototype, A 1111, and likewise the B instances to B 2222 except for one B instance that shares a single feature with A, i.e., B2221. In this case the structural ratio is 3.8/0.3 = 12.7, clearly a very long way from the 1.5 for the standard 5-4 structure.

²Fitting the model with RMSD directly rather than G ² resulted in a virtually identical fit and set of parameters as well as predicted values: RMSD = 0.053, with dimensional attention parameters of 0.371, 0.102, 0.301, and 0.226, respectively, and a similarity scaling parameter of c = 4.031. The (unreported) scatterplot of the model's predictions against the data was virtually identical to the left panel of Figure 5.

³ Fitting the model with RMSD directly rather than G ² also resulted in extremely similar parameters, fit, and predictions for these data: with dimensional attention parameters of 0.403, 0.126, 0.321, and 0.150, respectively, and a similarity scaling parameter of c = 2.959. The (unreported) model predictions were also very similar to those in the right panel of Figure 5.