David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Cognitive Science 35 (6):1162-1189 (2011)
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
|Keywords||Top‐down context influence Concept learning Neural network Hierarchical categorization|
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References found in this work BETA
Gregory L. Murphy (2004). The Big Book of Concepts. The MIT Press.
Bernhard Hommel, Jochen Müsseler, Gisa Aschersleben & Wolfgang Prinz (2001). The Theory of Event Coding (TEC): A Framework for Perception and Action Planning. Behavioral and Brain Sciences 24 (5):849-878.
Antonio R. Damasio (1989). Time-Locked Multiregional Retroactivation: A Systems-Level Proposal for the Neural Substrates of Recognition and Recall. Cognition 3 (1-2):25-62.
Aude Oliva & Antonio Torralba (2007). The Role of Context in Object Recognition. Trends in Cognitive Sciences 11 (12):520-527.
Ken McRae, Virginia R. de Sa & Mark S. Seidenberg (1997). On the Nature and Scope of Featural Representations of Word Meaning. Journal of Experimental Psychology 126 (2).
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