Ronaldo Vigo, Jay Wimsatt, Charles A. Doan, Derek E. Zeigler
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引用次数: 2
Abstract
ABSTRACT In the past two decades, human categorisation research has achieved significant progress via the rigorous and systematic study of concepts in terms of category structures and their families. The importance of these structure families stems from evidence suggesting that learning and categorisation performance are not only limited by low- and high-level generalisation mechanisms but by the inherent nature of the environmental and mental stimuli entertained by observers during the concept learning process. In this paper, we propose a new direction for concept learning and categorisation research based on several dual paradigmatic tensions that hinge on the inherent nature of the components of stimuli, limitations of the innate abilities of the observer to process such components, and the relationship between the two. The tensions range from the various possible properties and constraints of the dimensions underlying categories of object stimuli to various notions of supervised learning capable of significantly altering concept learnability. The substantial extant literature on concept learning research indicates that rigorous empirical investigations targeting these tensions are either non-existent or, at best, severely lacking despite their ecological significance. We shall argue that future theory building about concept learning should attempt to resolve these tensions and that without the proper empirical and theoretical focus on them, concept learning research will fail to achieve its ultimate goals anytime soon.
期刊介绍:
Journal of Experimental & Theoretical Artificial Intelligence (JETAI) is a world leading journal dedicated to publishing high quality, rigorously reviewed, original papers in artificial intelligence (AI) research.
The journal features work in all subfields of AI research and accepts both theoretical and applied research. Topics covered include, but are not limited to, the following:
• cognitive science
• games
• learning
• knowledge representation
• memory and neural system modelling
• perception
• problem-solving