Cogprints

Towards a Law of Invariance in Human Concept Learning

Vigo, Professor Ronaldo (2011) Towards a Law of Invariance in Human Concept Learning. [Journal (Paginated)]

Full text available as:

[img]
Preview
 PDF (Vigo (2011a))
537Kb

Abstract

Invariance principles underlie many key theories in modern science. They provide the explanatory and predictive framework necessary for the rigorous study of natural phenomena ranging from the structure of crystals, to magnetism, to relativistic mechanics. Vigo (2008, 2009)introduced a new general notion and principle of invariance from which two parameter-free (ratio and exponential) models were derived to account for human conceptual behavior. Here we introduce a new parameterized exponential “law” based on the same invariance principle. The law accurately predicts the subjective degree of difficulty that humans experience when learning different types of concepts. In addition, it precisely fits the data from a large-scale experiment which examined a total of 84 category structures across 10 category families (R-Squared =.97, p < .0001; r= .98, p < .0001). Moreover, it overcomes seven key challenges that had, hitherto, been grave obstacles for theories of concept learning.

Item Type:Journal (Paginated)
Keywords:Concepts; concept learning; categorization; law of invariance; mathematical model; pattern perception; ideotype.
Subjects:Psychology > Applied Cognitive Psychology
Psychology > Cognitive Psychology
Computer Science > Artificial Intelligence
Computer Science > Complexity Theory
Computer Science > Machine Learning
Psychology > Perceptual Cognitive Psychology
Philosophy > Logic
Psychology > Psychophysics
ID Code:7960
Deposited By: Zeigler , Derek
Deposited On:09 Nov 2012 17:47
Last Modified:09 Nov 2012 17:47

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

Bourne, L. E. (1966). Human conceptual behavior. Boston:

Allyn and Bacon.

Estes, W. K. (1994). Classification and Cognition. Oxford

Psychology Series, 22, Oxford University Press, Oxford.

Feldman, J. (2000). Minimization of Boolean complexity in

human concept learning. Nature, 407, 630-633.

Garner, W. R. (1970). Good patterns have few alternatives.

American Scientist, 58, 34-42.

Garner, W. R. (1974). The processing of information and

structure. New York: Wiley.

Gibson, J. J. (1966). The senses considered as perceptual

systems. Boston: Houghton Mifflin.

Haygood, R. C., & Bourne, L. E., Jr. (1965). Attribute-andrule learning aspects of conceptual behavior. Psychological

Review, 72, 175-195.

Kruschke, J. K. (1992). ALCOVE: An exemplar-based

connectionist model of category learning. Psychological

Review, 99, 22-44.

Love, B. C., and Medin, D. L. (1998). SUSTAIN: A model of

human category learning. Proceedings of the Fifteenth

National Conference on Artificial Intelligence, 15, 671-676.

Murphy, G. L. (2002). The big book of concepts. MIT Press.

Nosofsky, R. M. (1984). Choice, similarity, and the context

theory of classification. Journal of Experimental Psychology:

Learning, Memory, and Cognition, 10(1), 104-114.

Nosofsky, R. M. (1991). Typicality in logically defined

categories: Exemplar-similarity versus rule instantiation.

Memory and Cognition, 19(2), 131-150.

Shepard, R. N., Hovland, C. L., & Jenkins, H. M. (1961).

Learning and memorization of classifications. Psychological

Monographs: General and Applied, 75(13), 1-42.

Vigo, R. (2006). A note on the complexity of Boolean

concepts. Journal of Mathematical Psychology, 50(5), 1-10.

Vigo, R. (2009). Categorical invariance and structural

complexity in human concept learning. Journal of

Mathematical Psychology, Vol. 53, 203-221.

Vigo, R. (2011). Towards a General Law of Human

Conceptual Behavior (under review, contact the author for a

copy)

Metadata

Repository Staff Only: item control page

Cogprints is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.