Acquiring contextualized concepts: a connectionist approach |
| |
Authors: | van Dantzig Saskia Raffone Antonino Hommel Bernhard |
| |
Affiliation: | Department of Psychology, Leiden University, The Netherlands. saskia@vandantzig.nl |
| |
Abstract: | 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: | Concept learning Top‐down context influence Hierarchical categorization Neural network |
本文献已被 PubMed 等数据库收录! |
|