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The development and application of concepts is a critical component of cognition. Although concepts can be formed on the basis of simple perceptual or semantic features, conceptual representations can also capitalize on similarities across feature relationships. By representing these types of higher-order relationships, concepts can simplify the learning problem and facilitate decisions. Despite this, little is known about the neural mechanisms that support the construction and deployment of these kinds of higher-order concepts during learning. To address this question, we combined a carefully designed associative learning task with computational model-based functional magnetic resonance imaging (fMRI). Participants were scanned as they learned and made decisions about sixteen pairs of cues and associated outcomes. Associations were structured such that individual cues shared feature relationships, operationalized as shared patterns of cue pair-outcome associations. In order to capture the large number of possible conceptual representational structures that participants might employ and to evaluate how conceptual representations are used during learning, we leveraged a well-specified Bayesian computational model of category learning [1]. Behavioral and model-based results revealed that participants who displayed a tendency to link experiences in memory benefitted from faster learning rates, suggesting that the use of the conceptual structure in the task facilitated decisions about cue pair-outcome associations. Model-based fMRI analyses revealed that trial-by-trial integration of cue information into higher-order conceptual representations was supported by an anterior temporal (AT) network of regions previously implicated in representing complex conjunctions of features and meaning-based information.

The development and application of concepts is a critical component of cognition. Although concepts can be formed on the basis of simple perceptual or semantic features, conceptual representations can also capitalize on similarities across feature relationships. By representing these types of higher-order relationships, concepts can simplify the learning problem and facilitate decisions. Despite this, little is known about the neural mechanisms that support the construction and deployment of these kinds of higher-order concepts during learning. To address this question, we combined a carefully designed associative learning task with computational model-based functional magnetic resonance imaging (fMRI). Participants were scanned as they learned and made decisions about sixteen pairs of cues and associated outcomes. Associations were structured such that individual cues shared feature relationships, operationalized as shared patterns of cue pair-outcome associations. In order to capture the large number of possible conceptual representational structures that participants might employ and to evaluate how conceptual representations are used during learning, we leveraged a well-specified Bayesian computational model of category learning [1]. Behavioral and model-based results revealed that participants who displayed a tendency to link experiences in memory benefitted from faster learning rates, suggesting that the use of the conceptual structure in the task facilitated decisions about cue pair-outcome associations. Model-based fMRI analyses revealed that trial-by-trial integration of cue information into higher-order conceptual representations was supported by an anterior temporal (AT) network of regions previously implicated in representing complex conjunctions of features and meaning-based information.

A long-standing debate in decision making has been whetherpeople rely on very little information for making choices, orweigh and add all available information. We propose a newmethod to determine whether a non-compensatory (Take-The-Best) or compensatory strategy (Logistic Regression) is morepsychologically plausible: by looking at peoples active learningqueries. This method goes beyond traditional model selectiontechniques as it reveals the information people choose tolearn early on, which subsequently drives their decisions. Wedeveloped active learning algorithms for both Take-The-Bestand Logistic Regression, and designed an active learning experimentto distinguish between these models. By letting bothmodels and humans actively learn, we could compare theirqueries, and found that people follow a rank-based learningstrategy in non-compensatory environments, but prefer morecertainty-based queries in compensatory environments. Weargue that active learning studies provide a promising newmethodology to distinguish among decision models

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a network model of human category learning. SUSTAIN is a three layer model where learning between the first two layers is unsupervised, while learning between the top two layers is supervised. SUSTAIN clusters inputs in an unsupervised fashion until it groups input patterns inappropriately (as signaled by the supervised portion of the network). When such an error occurs, SUSTAIN alters its architecture, recruiting a new unit that is tuned to correctly classify the exception. Units recruited to capture exceptions can evolve into prototypes/attractors/rules in their own right. SUSTAIN's adaptive architecture allows it to master simple classification problems quickly, while still retaining the capacity to learn difficult mappings. SUSTAIN also adjusts its sensitivity to input dimensions during the course of learning, paying more attention to dimensions relevant to the classification task. SUSTAIN successfully fits item and category learning data from Medin, Dewey, and Murphy (1983). SUSTAIN's performance on other data sets is discussed. SUSTAIN is compared with other models of category learning.

Features differ in their mutability. For example, a robin could still be a robin even if it lacked a red breast; but it would probably not count as one if it lacked bones. I have hypothesized (Love & Sloman, 1995) that features are immutable to the extent other features depend on them. We can view a feature's mutability as a measure of transformational difficulty. In deriving new concepts, we often transform existing concepts (e.g. we can go from thinking about a robin to thinking about a robin without a red breast). The difficulty of this transformation, as measured by reaction time, increases with the immutability of the feature transformed. Conceptual transformations are strongly affected by context, but in a principled manner, also explained by feature dependency structure. A detailed account of context's effect on mutability is given, as well as corroborating data. I conclude by addressing how mutability-dependency theory can be applied to the study of similarity, categorization, conceptual combination, and metaphor.

Features differ in their mutability. For example, a robin could still be a robin even if it lacked a red breast; but it would probably not count as one if it lacked bones. One hypothesis to explain this differential transformability is that having bones is more critical to a biological theory than having a red breast is. W e reject this hypothesis in favor of a theory of mutability based solely on local dependency links and expressed in the form of an iterative equation. W e hypothesize that features are immutable to the extent other features depend on them and offer supporting data.