Department of Psychology, University of Oregon, United States; Department of Psychology, University of Wisconsin-Milwaukee, United States;
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.

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Introduction


The ability to form new conceptual knowledge is a key aspect of healthy memory function. There has been a longstanding debate about the nature of the representations underlying conceptual knowledge, which is exemplified in the domain of categorization. Some propose that categories are represented by their individual category members and that generalizing the category label to new examples involves joint retrieval and consideration of individual examples encountered in the past (i.e., exemplar models, Figure 1A; Kruschke, 1992; Medin and Schaffer, 1978; Nosofsky, 1986). Others propose that categories are represented by their central tendency – an abstract prototype containing all the most typical features of the category (i.e., prototype models, Figure 1B; Homa, 1973; Posner and Keele, 1968; Reed, 1972). Category generalization then involves consideration of a new item’s similarity to relevant category prototypes.


Figure 1

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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.


Both the prototype and exemplar accounts have been formalized as quantitative models and fit to behavioral data for decades, with numerous studies supporting each model (exemplar meta-analysis: Nosofsky, 1988; prototype meta-analysis: Smith and Minda, 2000). Neuroimaging studies have also provided support for these models. Studies using univariate contrasts showed overlap between neural systems supporting categorization and recognition (Nosofsky et al., 2012), as well as medial temporal lobe involvement in categorization (Koenig et al., 2008; Lech et al., 2016; Nomura et al., 2007), both of which have been interpreted as indicating a role of exemplar retrieval in categorization. More recently, studies have used parameters generated from formal prototype and exemplar models with neuroimaging data, but with conflicting results. Mack et al., 2013 found similar behavioral fits for the two models, but better fit of the exemplar model to brain data. Parts of the lateral occipital, lateral prefrontal and lateral parietal cortices tracked exemplar model predictors. No region tracked prototype predictors. The authors concluded that categorization decisions are based on memory for individual items rather than abstract prototypes. In contrast, Bowman and Zeithamova, 2018 found better fit of the prototype model in both brain and behavior. The ventromedial prefrontal cortex and anterior hippocampus tracked prototype predictors, demonstrating that neural category representations can involve more than representing the individual category members, even in regions like the hippocampus typically thought to support memory for specific episodes.

Interestingly, the different brain regions identified across these two studies aligned well with the larger literature contrasting memory specificity with memory integration and generalization. Lateral prefrontal regions are thought to resolve interference between similar items in memory (Badre and Wagner, 2005; Bowman and Dennis, 2016; Jonides et al., 1998; Kuhl et al., 2007), and lateral parietal cortex supports recollective experience (Vilberg and Rugg, 2008) and maintains high fidelity representations of individual items during memory retrieval (Kuhl and Chun, 2014; Xiao et al., 2017). That these regions also tracked exemplar predictors suggests that these functions may also support categorization by maintaining representations of individual category members as distinct from one another and from non-category members. In contrast, the VMPFC and hippocampus are known to support episodic inference through memory integration of related episodes (Schlichting et al., 2015; Shohamy and Wagner, 2008; Zeithamova et al., 2012) and encoding of new information in light of prior knowledge (van Kesteren et al., 2012). That these regions also tracked prototype predictions suggests that prototype extraction may involve integrating across category exemplars, linking across items sharing a category label to form an integrated, abstract category representation. However, as neural prototype and exemplar representations were identified across studies that differed in both task details and in the categorization strategies elicited, it has not been possible to say whether differences in the brain regions supporting categorization were due to differential strength of prototype versus exemplar representations or some other aspect of the tasks.

It is possible that the seemingly conflicting findings regarding the nature of category representations arose because individuals are capable of forming either type of representation. Prior studies have compared different category structures and task instructions to identify multiple memory systems supporting categorization (e.g., Aizenstein et al., 2000; Ashby et al., 1998; Ell et al., 2010; Zeithamova et al., 2008). While such findings show that the nature of concept representations depend on task demands, it is unclear if both prototype and exemplar representations can co-exist within the same task. Such mixed representations have been identified in episodic memory tasks, with individuals sometimes forming both integrated and separated representations for the same events (Schlichting et al., 2015) and a single episode sometimes represented at multiple levels of specificity, even within the hippocampus (Collin et al., 2015). We also know that individuals sometimes use a mix of strategies in categorization, for example when most category members are classified according to a simple rule while others are memorized as exceptions to that rule (Davis et al., 2012; Nosofsky et al., 1994). These differing representations may emerge because they allow for flexibility in future decision-making, as abstract representations that discard details of individual items are well suited to making generalization judgments but are poorly suited to judgments that require specificity. Alternatively, prototype representations may emerge as a byproduct of retrieving category exemplars, and they may themselves be encoded via recurrent connections, becoming an increasingly robust part of the concept representation (Hintzman, 1986; Koster et al., 2018; Zeithamova and Bowman, 2020). Thus, under some circumstances, both prototype and exemplar representations may be apparent within the same task.

To test this idea, we used fMRI in conjunction with a categorization task designed to balance encoding of individual examples vs. abstract information. This task used a training set with examples relatively close to the prototype, which has been shown to promote prototype abstraction (Bowman and Zeithamova, 2018; Bowman and Zeithamova, 2020). To promote exemplar encoding, we used an observational training task rather than feedback-based training (Cincotta and Seger, 2007; Heindel et al., 2013; Poldrack et al., 2001). We then looked for evidence of prototype and exemplar representations in the brain and in behavioral responses. In behavior, the prototype model assumes that categories are represented by their prototypes and predicts that subjects should be best at categorizing the prototypes themselves, with decreasing accuracy for items with fewer shared features with prototypes. The prototype model does not make differential predictions for new and old (training) items at the same distance from the prototype. The exemplar model assumes that categories are represented by the previously encountered exemplars and predicts that subjects should be best at categorizing old items and new items closest to the old exemplars. The mathematical formalizations of the models further take into account that a participant may not pay equal attention to all stimulus features and that perceived distance increases non-linearly with physical distance (see Methods for more details). We note that it is sometimes possible to observe behavioral evidence for both types of representations. For example, in our prior study (Bowman and Zeithamova, 2018), participants’ behavior was better explained by the prototype model than the exemplar model, but we also observed an advantage for old items relative to new items at the same distance to prototypes, in line with exemplar but not prototype model predictions.

The key behavioral prediction of each model is the trial-by-trial probability of responding category A vs category B. These probabilities are determined for each trial by the relative similarity of the test item to the category A and category B representations proposed by each model. Once these probabilities are generated for each model, they are compared to the participant’s actual responses to determine which model better predicted the subject’s observed behavior. We also used output from the models to generate subject-specific, trial-by-trial fMRI predictions. These were derived from the similarity of each test item to either an exemplar-based or prototype-based category representation (see Methods for details). We then measured the extent to which prototype- and exemplar-tracking brain regions could be identified, focusing on the VMPFC and anterior hippocampus as predicted prototype-tracking regions, and lateral occipital, prefrontal, and parietal regions as predicted exemplar-tracking regions.

We also asked whether there are shifts across learning in the type of concept representation individuals rely on to make categorization judgments. While some have suggested that memory systems compete with one another during learning (Poldrack and Packard, 2003; Seger, 2005), prior studies fitting exemplar and prototype models to fMRI data have done so only during a categorization test that followed extensive training, potentially missing dynamics occurring earlier in concept formation. Notably, memory consolidation research suggests that memories become abstract over time, often at the expense of memory for specific details (McClelland et al., 1995; Moscovitch et al., 2016; Payne et al., 2009; Posner and Keele, 1970), suggesting that early concept representations may be exemplar-based. In contrast, research on schema-based memory shows that abstract knowledge facilitates learning of individual items by providing an organizational structure into which new information can be incorporated (Bransford and Johnson, 1972; Tse et al., 2007; van Kesteren et al., 2012). Thus, early learning may instead emphasize formation of prototype representations, with exemplars emerging later. Finally, abstract and specific representations need not trade-off in either direction. Instead, the brain may form these representations in parallel (Collin et al., 2015; Schlichting et al., 2015) without trade-off between concept knowledge and memory for individual items (Schapiro et al., 2017), generating the prediction that both prototype and exemplar representations may grow in strength over the course of learning.

In the present study, participants underwent fMRI scanning while learning two novel categories or ‘species,’ which were represented by cartoon animals varying on eight binary dimensions (Figure 1C). The learning phase consisted of two types of runs: observational study runs and interim generalization test runs (Figure 1D). During study runs, participants passively viewed individual category members with their accompanying species label (‘Febble’ or ‘Badoon’). All of the items presented during study runs differed by two features from their respective prototypes (for example, exemplars depicted in Figure 1A). After completing two runs of observational study, participants underwent an interim generalization test run in which participants classified cartoon animals into the two species. Test items included the training items as well as new items at varying distances from category prototypes. Across the entire learning phase, there were four study-test cycles, with different new test items at every cycle. The learning phase was followed by a final generalization test, whose structure was similar to the interim test runs but more extensive (Figure 1E).

To test for evidence of prototype and exemplar representations in behavior across the group, we compared accuracy for items varying in distance from category prototypes and for an accuracy advantage for training items relative to new items matched for distance from category prototypes. We also fit formal prototype and exemplar models to behavior in individual subjects, which involves computing the similarity of a given test item to either the prototype of each category (prototype model) or the individual training items from each category (exemplar model), which is then used to make predictions about how likely it is that an item will be classified into one category versus the other. The model whose predictions better match a given subject’s actual classification responses will have better fit. However, it is also possible that evidence for each of the models will be similar, potentially reflecting a mix of representations.

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To test for co-existing prototype and exemplar correlates in the brain during interim and final generalization tests, we used latent metrics generated from each model as trial-by-trial predictors of BOLD activation in six regions of interest (Figure 2): ventromedial prefrontal cortex, anterior hippocampus, posterior hippocampus, lateral occipital cortex, inferior frontal gyrus, and lateral parietal cortex. To identify potential changes with learning, we tested these effects separately in the first half of the learning phase (interim tests 1 and 2) and second half of the learning phase (interim tests 3 and 4) as well as in the final test.