|Abstract||An alternative account of human concept learning based on an invariance measure of the categorical stimulus is proposed. The categorical invariance model (CIM) characterizes the degree of structural complexity of a Boolean category as a function of its inherent degree of invariance and its cardinality or size. To do this we introduce a mathematical framework based on the notion of a Boolean differential operator on Boolean categories that generates the degrees of invariance (i.e., logical manifold) of the category in respect to its dimensions. Using this framework, we propose that the structural complexity of a Boolean category is indirectly proportional to its degree of categorical invariance and directly proportional to its cardinality or size. Consequently, complexity and invariance notions are formally unified to account for concept learning difficulty. Beyond developing the above unifying mathematical framework, the CIM is significant in that: (1) it precisely predicts the key learning difficulty ordering of the SHJ [Shepard, R. N., Hovland, C. L.,&Jenkins, H. M. (1961). Learning and memorization of classifications. Psychological Monographs: General and Applied, 75(13), 1-42] Boolean category types consisting of three binary dimensions and four positive examples; (2) it is, in general, a good quantitative predictor of the degree of learning difficulty of a large class of categories (in particular, the 41 category types studied by Feldman [Feldman, J. (2000). Minimization of Boolean complexity in human concept learning. Nature, 407, 630-633]); (3) it is, in general, a good quantitative predictor of parity effects for this large class of categories; (4) it does all of the above without free parameters; and (5) it is cognitively plausible (e.g., cognitively tractable)|
|Keywords||No keywords specified (fix it)|
|Categories||No categories specified (fix it)|
|Through your library||Only published papers are available at libraries|
Similar books and articles
Pierre Barbaroux & Gilles Enée (2005). Spontaneous Coordination and Evolutionary Learning Processes in an Agent-Based Model. Mind and Society 4 (2):179-195.
Markus Graf & Werner X. Schneider (2001). Structural Descriptions in HIT – a Problematic Commitment. Behavioral and Brain Sciences 24 (3):483-484.
Alexander Clark & Shalom Lappin (2013). Complexity in Language Acquisition. Topics in Cognitive Science 5 (1):89-110.
Michael D. Lee (2001). Extending Bayesian Concept Learning to Deal with Representational Complexity and Adaptation. Behavioral and Brain Sciences 24 (4):685-686.
Marc Lange (1999). Laws, Counterfactuals, Stability, and Degrees of Lawhood. Philosophy of Science 66 (2):243-267.
Stevan Harnad & Stephen J. Hanson, Learned Categorical Perception in Neural Nets: Implications for Symbol Grounding.
Vuk Uskoković (2009). On the Light Doves and Learning on Mistakes. Axiomathes 19 (1).
Added to index2010-11-24
Total downloads9 ( #113,980 of 549,037 )
Recent downloads (6 months)2 ( #37,272 of 549,037 )
How can I increase my downloads?