RUNNING HEAD: ACQUIRING CONTEXTUALIZED CONCEPTS Acquiring Contextualized Concepts: A Connectionist Approach

Conceptual knowledge is acquired through recurrent experiences, by extracting statistical regularities at different levels of granularity. At a fine level, patterns of feature co-occurrence are categorized into objects. At a coarser level, patterns of concept co-occurrence are categorized into contexts. We present and test CONCAT, a connectionist model that simultaneously learns to categorize objects and contexts. The model contains two hierarchically organized CALM modules (Murre, Phaf, & Wolters, 1992). The first module, the Object Module, forms object representations based on co-occurrences between features. These representations are used as input for the second module, the Context Module, which categorizes contexts based on object co-occurrences. Feedback connections from the Context Module to the Object Module send activation from the active context to those objects that frequently occur within this context. We demonstrate that context feedback contributes to the successful categorization of objects, especially when bottom-up feature information is degraded or ambiguous. Whenever we interact with a particular object, we automatically activate stored knowledge about similar objects that we have encountered before. In other words, we activate a concept, a mental representation referring to a particular class of objects in the world. Concepts enable us to recognize and categorize objects, to draw inferences about their characteristics, and to react to them appropriately. Moreover, we can think and talk about objects that are not directly present by using our conceptual knowledge of those objects. Thus, concepts are the basic building blocks of cognition, and they are necessary to understand and interact with the world around us (Murphy, 2004). Most theories of concept learning assume that conceptual knowledge of objects is acquired through experience (e. During recurrent interactions with different exemplars of a class of objects (e.g., balls), the features that are invariant across those exemplars (e.g., shape), and thus relevant for the category, are extracted and stored in memory to form a concept of the category (the concept BALL). The features that vary across exemplars (e.g., color) are discarded, as well as information about the different contexts in which exemplars are encountered. These models thus assume that concepts are primarily based on bottom-up feature information, and they generally ignore the role of context information in conceptual processing (Yeh & Barsalou, 2006). However, across many domains of cognition there is evidence that the context profoundly influences conceptual processing and performance (for an overview, see Yeh & Barsalou, 2006). Studies of visual processing have shown that …