Cogprints

The GIST of Concepts

Vigo , Dr. Ronaldo (2013) The GIST of Concepts. [Journal (Paginated)]

This is the latest version of this eprint.

Full text available as:

[img]
Preview
 PDF
2884Kb

Abstract

A unified general theory of human concept learning based on the idea that humans detect invariance patterns in categorical stimuli as a necessary precursor to concept formation is proposed and tested. In GIST (generalized invariance structure theory) invariants are detected via a perturbation mechanism of dimension suppression referred to as dimensional binding. Structural information acquired by this process is stored as a compound memory trace termed an ideotype. Ideotypes inform the subsystems that are responsible for learnability judgments, rule formation, and other types of concept representations. We show that GIST is more general (e.g., it works on continuous, semi-continuous, and binary stimuli) and makes much more accurate predictions than the leading models of concept learning difficulty,such as those based on a complexity reduction principle (e.g., number of mental models,structural invariance, algebraic complexity, and minimal description length) and those based on selective attention and similarity (GCM, ALCOVE, and SUSTAIN). GIST unifies these two key aspects of concept learning and categorization. Empirical evidence from three experiments corroborates the predictions made by the theory and its core model which we propose as a candidate law of human conceptual behavior.

Item Type:Journal (Paginated)
Keywords:Categorization; Invariance; Complexity; Ideotype; Pattern detection; Concept learning
Subjects:Psychology > Cognitive Psychology
Computer Science > Artificial Intelligence
Computer Science > Complexity Theory
Psychology > Perceptual Cognitive Psychology
ID Code:9102
Deposited By: Zeigler , Derek
Deposited On:18 Nov 2013 21:10
Last Modified:18 Nov 2013 21:10

Available Versions of this Item

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.

Allport, A. (1987). Selection for action: some behavioural and neurophysiological considerations of attention and action, In:Perspective on perception and action, H. Heuer and A. Sanders (Ed.),Erlbaum. Ashby, F. G., Alfonso-Reese, L. A., Turken, A. U., & Waldron, E. M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological Review, 105, 442–481.

Bonatti, Luca. (Oct 1994). Propositional reasoning by model? Psychological Review, 101(4), 725–733.

Bourne, L. E. (1966). Human conceptual behavior. Boston: Allyn and Bacon. Bush, R. R., Luce, R D., and Rose, R. M. Learning models for psychophysics.In R. C. Atkinson (Ed), Studies in mathematical psychology. Stanford:Stanford University Press, 1964. Pp. 201–217.

Daellenbach, H. G., & George, A. (1978). Introduction to operations research techniques. Boston: Allyn & Bacon.

Estes, W. K. (1994). Classification and cognition. Oxford psychology series(Vol. 22). Oxford: Oxford University Press.

Feldman, J. (2000). Minimization of Boolean complexity in human concept learning. Nature, 407, 630–633.

Feldman, J. (2003). A catalog of Boolean concepts. Journal of Mathematical Psychology, 47(1), 98–112.

Feldman, J. (2006). An algebra of human concept learning. Journal of Mathematical Psychology, 50, 339–368.

Garner, W. R. (1963). Goodness of pattern and pattern uncertainty. Journal of Verbal Learning and Verbal Behavior, 2, 446–452.

Garner, W. R. (1974). The processing of information and structure. New York: Wiley.

Garner, W. R., & Felfoldy, G. L. (1970). Integrality of stimulus dimensions in

various types of information processing. Cognitive Psychology, 1, 225–241.

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

Houghton Mifflin.

Goodwin, P. G., & Johnson-Laird, P. N. (2011). Mental models of Boolean

concepts. Cognitive Psychology, 63, 34–59.

Haygood, R. C., & Bourne, L. E. Jr., (1965). Attribute-and-rule learning

aspects of conceptual behavior. Psychological Review, 72, 175–195.

Higonnet, R. A., & Grea, R. A. (xxxx). Logical design of electrical circuits. New

York, NY, USA: McGraw-Hill.

Johnson-Laird, P. N. (1983). Mental Models: Towards a Cognitive Science of

Language, Inference, and Consciousness. Cambridge, MA: Harvard

University Press.

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

of category learning. Psychological Review, 99, 22–44.

Kruskal, Joseph. B., & Wish, Myron. (1978). Multidimensional Scaling.

Beverly Hills: Sage.

Lafond, D., Lacouture, Y., & Mineau, G. (2007). Complexity mnimization in

rule-based category learning: Revising the catalog of Boolean

concepts and evidence for non-minimal rules. Journal of

Mathematical Psychology, 51(2), 57–74.

Leyton, M. (1992). Symmetry, causality, mind. The MIT Press.

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

learning. Proceedings of the Fifteenth National Conference on Artificial

Intelligence (AAAI-98), USA, 15, 671–676.

Love, B. C., Medin, D. L., & Gureckis, T. M. (2004). SUSTAIN: A network

model of category learning. Psychological Review, 111, 309–332.

Luce, D. (1995). Four Tensions Concerning Mathematical Modeling in

Psychology. Annual Review of Psychology, 46, 1–27.

Neumann, O. (1987). Beyond capacity: A functional view of attention. In

H. Heuer & A. F. Sanders (Eds.), Perspectives on perception and action.

Hillsdale: Erlbaum.

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. (1986). Attention, similarity, and the identification–

categorization relationship. Journal of Experimental Psychology:

General, 115(1), 39–57.

Nosofsky, R. M., Gluck, M. A., Palmeri, T. J., McKinley, S. C., & Glauthier, P.

G. (1994a). Comparing models of rule-based classification learning: A

replication and extension of Shepard, Hovland, and Jenkins (1961).

Memory and Cognition, 22(3), 352–369.

Nosofsky, R. M., Palmeri, T. J.,& McKinley, S. C. (1994b). Rule-plus-exception

model of classification learning. Psychological Review, 101, 53–79.

O’Brien, D. P., Braine, M. D., & Yang, Y. (1994). Propositional reasoning by

mental models? Simple to refute in principle and in practice.

Psychological Review, 101(4), 711–724.

Pothos, E. M., & Chater, N. (2002). A simplicity principle in unsupervised

human categorization. Cognitive Science, 26, 303–343.

Prinz, W. (1983). Asymmetrical control areas in continuous visual search.

In R. Groner, C. Menz, D. F. Fisher, & R. A. Monty (Eds.), Eye movements

and psychological functions: International views (pp. 85–100). Hillsdale,

N.J.: Erlbaum.

Rehder, B., & Hoffman, A. B. (2005). Eyetracking and selective attention in

category learning. Cognitive Psychology, 51, 1–41.

Schneider, W. X. (1993). Space-based visual attention models and object

selection: Constraints, problems, and possible solutions. Psychological

Research, 56, 35–43.

Shepard, R. N. (1984). Ecological constraints on internal representation:

Resonant kinematics of perceiving, imagining, thinking, and

dreaming. Psychological Review, 91(4).

Shepard, R. N. (1987). Towards a universal law of generalization for

psychological science. Science, 237, 1317–1323.

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

memorization of classifications. Psychological Monographs: General

and Applied, 75(13), 1–42.

Shepard, R. N., Romney, A. K., & Nerlove, S. B. (Eds.). (1972).

Multidimensional scaling: Theory and applications in the behavioral

sciences. Vol. I: Theory. New York: Seminar Press.

Stevens, S. S. (1955). On the psychophysical law. Psychological Review,

64(3), 153–181.

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

Mathematical Psychology, 50(5), 501–510.

Vigo, R. (2009). Categorical invariance and structural complexity in human

concept learning. Journal of Mathematical Psychology, 53, 203–221.

Vigo, R. (2011a). Towards a law of invariance in human concept learning.

In L. Carlson, C. Hölscher, & T. Shipley (Eds.), Proceedings of the 33rd

annual conference of the Cognitive Science Society (pp. 2580–2585).

Austin, TX: Cognitive Science Society.

Vigo, R. (2011b). Representational information: A new general notion and

measure of information. Information Sciences, 181, 4847–4859.

Vigo, R., Zeigler, D. E., & Halsey, P. A. (2013). Gaze and informativeness

during category learning: Evidence for an inverse relation. Visual

Cognition, 21(4), 446–476.

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.