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)
References found in this work BETA
No references found.
Citations of this work BETA
The Acquisition of Boolean Concepts.Geoffrey P. Goodwin & Philip N. Johnson-Laird - 2013 - Trends in Cognitive Sciences 17 (3):128-133.
Models, Mechanisms, and Animal Minds.Colin Allen - 2014 - Southern Journal of Philosophy 52 (S1):75-97.
Phonological Concept Learning.Elliott Moreton, Joe Pater & Katya Pertsova - 2016 - Cognitive Science 40 (1).
An Evolutionary Analysis of Learned Attention.Richard A. Hullinger, John K. Kruschke & Peter M. Todd - 2015 - Cognitive Science 39 (6):1172-1215.
Similar books and articles
Spontaneous Coordination and Evolutionary Learning Processes in an Agent-Based Model.Pierre Barbaroux & Gilles Enée - 2005 - Mind and Society 4 (2):179-195.
Learning-in-Practise: The Social Complexity of Learning in Working Life.Elena P. Antonacopoulou - unknown
Structural Descriptions in HIT – a Problematic Commitment.Markus Graf & Werner X. Schneider - 2001 - Behavioral and Brain Sciences 24 (3):483-484.
Categorical Perception.Stevan Harnad - 2003 - In L. Nadel (ed.), Encyclopedia of Cognitive Science. Nature Publishing Group. pp. 67--4.
Complexity in Language Acquisition.Alexander Clark & Shalom Lappin - 2013 - Topics in Cognitive Science 5 (1):89-110.
Extending Bayesian Concept Learning to Deal with Representational Complexity and Adaptation.Michael D. Lee - 2001 - Behavioral and Brain Sciences 24 (4):685-686.
Laws, Counterfactuals, Stability, and Degrees of Lawhood.Marc Lange - 1999 - Philosophy of Science 66 (2):243-267.
Learned Categorical Perception in Neural Nets: Implications for Symbol Grounding.Stevan Harnad & Stephen J. Hanson - unknown
Added to index2010-11-24
Total downloads30 ( #172,861 of 2,177,988 )
Recent downloads (6 months)1 ( #317,698 of 2,177,988 )
How can I increase my downloads?