creators_name: Vigo , Ronaldo 
creators_id: vigo@ohio.edu
type: journalp
datestamp: 2013-09-17 14:35:11
lastmod: 2013-11-18 21:10:28
metadata_visibility: no_search
title: The GIST of Concepts 
ispublished: pub
subjects: cog-psy
subjects: comp-sci-art-intel
subjects: comp-sci-complex-theory
subjects: percep-cog-psy
full_text_status: public
keywords: Categorization; Invariance; Complexity; Ideotype; Pattern detection; Concept learning
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.
date: 2013
date_type: published
publication: Cognition 
volume: 129
number: 1
publisher: Elsevier 
pagerange: 138-162
refereed: TRUE
referencetext: 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.
citation:   Vigo , Dr. Ronaldo   (2013) The GIST of Concepts.  [Journal (Paginated)]     
document_url: http://cogprints.org/9055/1/RVigo%20%282013%29%20GIST%20of%20concepts.pdf