Tracking prototype and exemplar representations in the brain across learning

Abstract

There is a long-standing debate about whether categories are represented by individual category members (exemplars) or by the central tendency abstracted from individual members (prototypes). Neuroimaging studies have shown neural evidence for either exemplar representations or prototype representations, but not both. Presently, we asked whether it is possible for multiple types of category representations to exist within a single task. We designed a categorization task to promote both exemplar and prototype representations and tracked their formation across learning. We found only prototype correlates during the final test. However, interim tests interspersed throughout learning showed prototype and exemplar representations across distinct brain regions that aligned with previous studies: prototypes in ventromedial prefrontal cortex and anterior hippocampus and exemplars in inferior frontal gyrus and lateral parietal cortex. These findings indicate that, under the right circumstances, individuals may form representations at multiple levels of specificity, potentially facilitating a broad range of future decisions.

Keywords: category learning; fmri; generalization; hippocampus; human; long term memory; neuroscience.

Conflict of interest statement

CB, TI, DZ No competing interests declared

© 2020, Bowman et al.

Figures

Figure 1.. Category-learning task.

Conceptual depiction of (A) exemplar and (B) prototype models. Exemplar: categories are represented as individual exemplars. New items are classified into the category with the most similar exemplars. Prototype: categories are represented by their central tendencies (prototypes). New items are classified into the category with the most similar prototype. (C) Example stimuli. The leftmost stimulus is the prototype of category A and the rightmost stimulus is the prototype of category B, which shares no features with prototype A. Members of category A share more features with prototype A than prototype B, and vice versa. (D) During the learning phase, participants completed four study-test cycles while undergoing fMRI. In each cycle, there were two runs of observational study followed by one run of an interim generalization test. During observational study runs, participants saw training examples with their species labels without making any responses. During interim test runs, participants classified training items as well as new items at varying distances. (E) After all study-test cycles were complete, participants completed a final generalization test that was divided across four runs. Participants classified training items as well as new items at varying distances.

Figure 2.. Regions of interest from a representative subject.

Regions were defined in the native space of each subject using automated segmentation in Freesurfer.

Figure 3.. Behavioral accuracy for interim and final tests.

(A) Mean generalization accuracy across each of four interim tests completed during the learning phase. Source data can be found in Figure 3—source data 1. (B) Mean categorization accuracy in the final test. Source data can be found in Figure 3—source data 2. In both cases, accuracies are separated by distance from category prototypes (0–3) and old vs. new (applicable to distance two items only). Error bars represent the standard error of the mean.

Figure 4.. Behavioral model fits.

Scatter plots indicate the relative exemplar vs. prototype model fits for each subject. Fits are given in terms of negative log likelihood (i.e., model error) such that lower values reflect better model fit. Each dot represents a single subject and the trendline represents equal prototype and exemplar fit. Dots above the line have better exemplar relative to prototype model fit. Dots below the line have better prototype relative to exemplar model fit. Pie charts indicate the percentage of individual subjects classified as best fit by the prototype model (in blue), the exemplar model (in red), and those similarly fit by the two models (in grey). Model fits were computed separately for the 1st half of the learning phase (interim tests 1–2, A,D), the 2nd half of the learning phase (interim tests 3–4, B,E), and the final test (C,F). Source data for all phases can be found in Figure 4—source data 1.

Figure 5.. Neural prototype and exemplar model fits.

Neural model fits for each region of interest for (A) the first half of the learning phase, (B) the second half of the learning phase, (C) the overall learning phase (averaged across the first and second half of learning), and (D) the final test. Prototype fits are in blue, exemplar fits in red. Neural model fit is the effect size: the mean/SD of ß-values within each ROI, averaged across appropriate runs. VMPFC = ventromedial prefrontal cortex, ahip = anterior hippocampus, phip = posterior hippocampus, LO = lateral occipital cortex, IFG = inferior frontal gyrus, and Lat. Par. = lateral parietal cortex. Source data for interim tests is in Figure 5—source data 1 and Figure 5—source data 2 for the final test.

Author response image 1.

Author response image 2.

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Author response image 5.