A Formal Psychological Model of Classification Applied to Natural-Science Category Learning

Abstract

The field of psychological science has seen major advances in the development of formal models of perceptual classification learning; however, little work has tested such models in real-world natural-category domains. In our current project, we aim to fill that gap by testing the ability of a formal exemplar model of classification to predict learning of rock categories in the geologic sciences. As a prerequisite for testing the model in this domain, we have conducted extensive work to derive a high-dimensional feature-space representation for the rock stimuli. An eight-dimensional representation yields good accounts of naive participants’ judgments of similarity among a large battery of rock-picture samples; furthermore, the eight dimensions have natural psychological interpretations. We then use the exemplar model in combination with the derived feature-space representation to successfully predict participants’ learning and generalization of a variety of scientifically defined rock categories. We discuss further steps for making use of the model and its associated feature-space representation to search for effective techniques of teaching categories in the science classroom.

Keywords

category learning exemplar model feature-space representation

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References

Anderson

J. R.

(1991). The adaptive nature of human categorization. Psychological Review, 98, 409–429.

Ashby

F. G.

(Ed.). (1992). Multidimensional models of perception and cognition. Hillsdale, NJ: Erlbaum.

Cohen

A. L.

Nosofsky

R. M.

Zaki

S. R.

(2001). Category variability, exemplar similarity, and perceptual classification. Memory & Cognition, 29, 1165–1175.

Heit

(1994). Models of the effects of prior knowledge on category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, 1264–1282.

Kruschke

J. K.

(1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99, 22–44.

Love

B. C.

Medin

D. L.

Gureckis

T. M.

(2004). SUSTAIN: A network model of category learning. Psychological Review, 111, 309–332.

Meagher

B. J.

Carvalho

P. F.

Goldstone

R. L.

Nosofsky

R. M.

(2017). Organized simultaneous displays facilitate learning of complex natural science categories. Psychonomic Bulletin & Review, 24, 1987–1994.

Medin

D. L.

Schaffer

M. M.

(1978). Context theory of classification learning. Psychological Review, 85, 207–238.

Miyatsu

Gouravajhala

Nosofsky

R. M.

McDaniel

M. A.

(in press). Feature highlighting enhances learning of complex natural-science categories. Journal of Experimental Psychology: Learning, Memory, and Cognition.

10.

Murphy

G. L.

(2002). The big book of concepts. Cambridge, MA: MIT Press.

11.

Nosofsky

R. M.

(1986). Attention, similarity, and the identification–categorization relationship. Journal of Experimental Psychology: General, 115, 39–57.

12.

Nosofsky

R. M.

(1987). Attention and learning processes in the identification and categorization of integral stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 87–108.

13.

Nosofsky

R. M.

(1992). Similarity scaling and cognitive process models. Annual Review of Psychology, 43, 25–53.

14.

Nosofsky

R. M.

(2011). The generalized context model: An exemplar model of classification. In Pothos

E. M.

Wills

A. J.

(Eds.), Formal approaches in categorization (pp. 18–39). Cambridge, England: Cambridge University Press.

15.

Nosofsky

R. M.

Sanders

C. A.

Gerdom

Douglas

B. J.

McDaniel

M. A.

(2017). On learning natural-science categories that violate the family-resemblance principle. Psychological Science, 28, 104–114.

16.

Nosofsky

R. M.

Sanders

C. A.

McDaniel

M. A.

(2018). Tests of an exemplar-memory model of classification learning in a high-dimensional natural-science category domain. Journal of Experimental Psychology: General, 147, 328–353.

17.

Nosofsky

R. M.

Sanders

C. A.

Meagher

B. J.

Douglas

B. J.

(2017). Toward the development of a feature-space representation for a complex, natural-category domain. Behavior Research Methods. Advance online publication. doi:10.3758/s13428-017-0884-8

18.

Palmeri

T. J.

Wong

A. C.

Gauthier

(2004). Computational approaches to the development of perceptual expertise. Trends in Cognitive Sciences, 8, 378–386.

19.

Patil

K. R.

Zhu

Kopeć

Love

B. C.

(2014). Optimal teaching for limited-capacity human learners. In Ghahramani

Welling

Cortes

Lawrence

N. D.

Weinberger

K. Q.

(Eds.), Advances in Neural Information Processing Systems 27 (pp. 2465–2473). Retrieved from https://papers.nips.cc/paper/5541-optimal-teaching-for-limited-capacity-human-learners.pdf

20.

Pothos

E. M.

Wills

A. J.

(Eds.). (2011). Formal approaches in categorization. Cambridge, England: Cambridge University Press.

21.

Reed

S. K.

(1972). Pattern recognition and categorization. Cognitive Psychology, 3, 382–407.

22.

Rosch

Mervis

C. B.

(1975). Family resemblances: Studies in the internal structure of categories. Cognitive Psychology, 7, 573–605.

23.

Shepard

R. N.

(1980). Multidimensional scaling, tree-fitting, and clustering. Science, 210, 390–398.

24.

Smith

J. D.

Minda

J. P.

(1998). Prototypes in the mist: The early epochs of category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24, 1411–1436.