David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jonathan Jenkins Ichikawa
Jack Alan Reynolds
Learn more about PhilPapers
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)|
No categories specified
(categorize this paper)
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
Geoffrey P. Goodwin & Philip N. Johnson-Laird (2013). The Acquisition of Boolean Concepts. Trends in Cognitive Sciences 17 (3):128-133.
Colin Allen (2014). Models, Mechanisms, and Animal Minds. Southern Journal of Philosophy 52 (S1):75-97.
Ronaldo Vigo (2013). The Gist of Concepts. Cognition 129 (1):138-162.
Elliott Moreton, Joe Pater & Katya Pertsova (2015). Phonological Concept Learning. Cognitive Science 40 (1).
Richard A. Hullinger, John K. Kruschke & Peter M. Todd (2015). An Evolutionary Analysis of Learned Attention. Cognitive Science 39 (6):1172-1215.
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.
Stevan Harnad (2003). Categorical Perception. In L. Nadel (ed.), Encyclopedia of Cognitive Science. Nature Publishing Group 67--4.
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):17-50.
Added to index2010-11-24
Total downloads28 ( #170,336 of 1,941,073 )
Recent downloads (6 months)1 ( #458,098 of 1,941,073 )
How can I increase my downloads?