Skip to main content
Top
Gepubliceerd in:

27-10-2018 | Original Article

The impact of training methodology and category structure on the formation of new categories from existing knowledge

Auteurs: Sébastien Hélie, Farzin Shamloo, Shawn W. Ell

Gepubliceerd in: Psychological Research | Uitgave 4/2020

Log in om toegang te krijgen
share
DELEN

Deel dit onderdeel of sectie (kopieer de link)

  • Optie A:
    Klik op de rechtermuisknop op de link en selecteer de optie “linkadres kopiëren”
  • Optie B:
    Deel de link per e-mail

Abstract

Categorization decisions are made thousands of times every day, and a typical adult knows tens of thousands of categories. It is thus relatively rare that adults learn new categories without somehow reorganizing pre-existing knowledge. Yet, most perceptual categorization research has investigated the ability to learn new categories without considering they relation to existing knowledge. In this article, we test the ability of young adults to merge already known categories into new categories as a function of training methodology and category structures using two experiments. Experiment 1 tests participants’ ability to merge rule-based or information-integration categories that are either contiguous, semi-contiguous, or non-contiguous in perceptual space using a classification paradigm. Experiment 2 is similar Experiment 1 but uses a YES/NO learning paradigm instead. The results of both experiments suggest a strong effect of the contiguity of the merged categories in perceptual space that depends on the type of category representation that is learned. The type of category representation that is learned, in turn, depends on a complex interaction of the category structures and training task. We conclude by discussing the relevance of these results for categorization outside the laboratory.
Voetnoten
1
The noise distribution was cut so that the mixture parameters could not be negative and summed to 1.
 
2
On any given trial, participants chose one of two response buttons so chance performance was 0.50, and each block had 96 trials.
 
Literatuur
go back to reference Aerts, D., Gabora, L., & Sozzo, S. (2013). Concepts and their dynamics: A quantum-theoretic modeling of human thought. Topics in Cognitive Sciences, 5, 737–772. Aerts, D., Gabora, L., & Sozzo, S. (2013). Concepts and their dynamics: A quantum-theoretic modeling of human thought. Topics in Cognitive Sciences, 5, 737–772.
go back to reference 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(3), 442.CrossRef 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(3), 442.CrossRef
go back to reference Ashby, F. G., & Ell, S. W. (2001). The neurobiology of human category learning. Trends in Cognitive Science, 5, 204–210.CrossRef Ashby, F. G., & Ell, S. W. (2001). The neurobiology of human category learning. Trends in Cognitive Science, 5, 204–210.CrossRef
go back to reference Ashby, F. G., & Gott, R. E. (1988). Decision rules in the perception and categorization of multidimensional stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 33–53.PubMed Ashby, F. G., & Gott, R. E. (1988). Decision rules in the perception and categorization of multidimensional stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 33–53.PubMed
go back to reference Ashby, F. G., & Maddox, W. T. (2010). Human category learning 2.0. Annals of the New York Academy of Sciences, 1224, 147–161.CrossRef Ashby, F. G., & Maddox, W. T. (2010). Human category learning 2.0. Annals of the New York Academy of Sciences, 1224, 147–161.CrossRef
go back to reference Badre, D., Kayser, A. S., & D’Esposito, M. (2010). Frontal cortex and the discovery of abstract action rules. Neuron, 66, 315–326.CrossRef Badre, D., Kayser, A. S., & D’Esposito, M. (2010). Frontal cortex and the discovery of abstract action rules. Neuron, 66, 315–326.CrossRef
go back to reference Bishop, C. (2006). Pattern recognition and machine learning. Singapore: Springer. Bishop, C. (2006). Pattern recognition and machine learning. Singapore: Springer.
go back to reference Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10, 433–436.CrossRef Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10, 433–436.CrossRef
go back to reference Cohen, B., & Murphy, G. L. (1984). Models of concepts. Cognitive Science, 8, 27–58.CrossRef Cohen, B., & Murphy, G. L. (1984). Models of concepts. Cognitive Science, 8, 27–58.CrossRef
go back to reference Ell, S. W., Smith, D. B., Peralta, G., & Hélie, S. (2017). The impact of category structure and training methodology on learning and generalizing within-category representations. Attention, Perception, & Psychophysics, 79, 1777–1794.CrossRef Ell, S. W., Smith, D. B., Peralta, G., & Hélie, S. (2017). The impact of category structure and training methodology on learning and generalizing within-category representations. Attention, Perception, & Psychophysics, 79, 1777–1794.CrossRef
go back to reference Erev, I. (1998). Signal detection by human observers: A cutoff reinforcement learning model of categorization and decisions under uncertainty. Psychological Review, 105, 280–298.CrossRef Erev, I. (1998). Signal detection by human observers: A cutoff reinforcement learning model of categorization and decisions under uncertainty. Psychological Review, 105, 280–298.CrossRef
go back to reference Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127(2), 107.CrossRef Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127(2), 107.CrossRef
go back to reference Feldman, J. (2003). A catalog of Boolean concepts. Journal of Mathematical Psychology, 47, 75–89.CrossRef Feldman, J. (2003). A catalog of Boolean concepts. Journal of Mathematical Psychology, 47, 75–89.CrossRef
go back to reference Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28, 3–71.CrossRef Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28, 3–71.CrossRef
go back to reference Heckler, A. F. (2011). The ubiquitous patterns of incorrect answers to science questions: The role of automatic, bottom-up processes. The Psychology of Learning and Motivation, 55, 227–267.CrossRef Heckler, A. F. (2011). The ubiquitous patterns of incorrect answers to science questions: The role of automatic, bottom-up processes. The Psychology of Learning and Motivation, 55, 227–267.CrossRef
go back to reference Hélie, S., & Ashby, F. G. (2012). Learning and transfer of category knowledge in an indirect categorization task. Psychological Research, 76, 292–303.CrossRef Hélie, S., & Ashby, F. G. (2012). Learning and transfer of category knowledge in an indirect categorization task. Psychological Research, 76, 292–303.CrossRef
go back to reference Hélie, S., Shamloo, F., & Ell, S. W. (2017). The effect of training methodology on knowledge representation in categorization. PLoS ONE, 12, e0183904.CrossRef Hélie, S., Shamloo, F., & Ell, S. W. (2017). The effect of training methodology on knowledge representation in categorization. PLoS ONE, 12, e0183904.CrossRef
go back to reference Hélie, S., Waldschmidt, J. G., & Ashby, F. G. (2010). Automaticity in rule-based and information-integration categorization. Attention, Perception, & Psychophysics, 72(4), 1013–1031.CrossRef Hélie, S., Waldschmidt, J. G., & Ashby, F. G. (2010). Automaticity in rule-based and information-integration categorization. Attention, Perception, & Psychophysics, 72(4), 1013–1031.CrossRef
go back to reference Markman, A. B. (2002). Stimulus categorization. In D. L. Pashler & H. Medin (Eds.), Stevens’ handbook of experimental psychology (3rd ed., Vol. 2, pp. 165–208)., Memory and cognitive processes New York: Wiley. Markman, A. B. (2002). Stimulus categorization. In D. L. Pashler & H. Medin (Eds.), Stevens’ handbook of experimental psychology (3rd ed., Vol. 2, pp. 165–208)., Memory and cognitive processes New York: Wiley.
go back to reference Markman, A. B., & Ross, B. (2003). Category use and category learning. Psychological Bulletin, 129, 529–613.CrossRef Markman, A. B., & Ross, B. (2003). Category use and category learning. Psychological Bulletin, 129, 529–613.CrossRef
go back to reference Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85(3), 207–238.CrossRef Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85(3), 207–238.CrossRef
go back to reference Nosofsky, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39–57.CrossRef Nosofsky, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39–57.CrossRef
go back to reference Posner, M. I., & Keele, S. W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77, 353–363.CrossRef Posner, M. I., & Keele, S. W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77, 353–363.CrossRef
go back to reference Prinz, J. J. (2012). Regaining composure: A defense of prototype compositionality. In M. Werning, W. Hinzen, & E. Machery (Eds.), The oxford handbook of compositionality (pp. 437–453). New York: Oxford University Press. Prinz, J. J. (2012). Regaining composure: A defense of prototype compositionality. In M. Werning, W. Hinzen, & E. Machery (Eds.), The oxford handbook of compositionality (pp. 437–453). New York: Oxford University Press.
go back to reference Reed, S. K. (1972). Pattern recognition and categorization. Cognitive Psychology, 3, 382–407.CrossRef Reed, S. K. (1972). Pattern recognition and categorization. Cognitive Psychology, 3, 382–407.CrossRef
go back to reference Seger, C. A., & Miller, E. K. (2010). Category learning in the brain. Annual Review of Neuroscience, 33, 203–219.CrossRef Seger, C. A., & Miller, E. K. (2010). Category learning in the brain. Annual Review of Neuroscience, 33, 203–219.CrossRef
go back to reference Shepard, R. N., Hovland, C. I., & Jenkins, H. M. (1961). Learning and memorization of classifications. Psychological Monographs: General and Applied, 75, 1–42.CrossRef Shepard, R. N., Hovland, C. I., & Jenkins, H. M. (1961). Learning and memorization of classifications. Psychological Monographs: General and Applied, 75, 1–42.CrossRef
go back to reference Smith, E. E., Osherson, D. N., Rips, L. J., & Keane, M. (1988). Combining prototypes: A selective modification model. Cognitive Science, 12, 485–527.CrossRef Smith, E. E., Osherson, D. N., Rips, L. J., & Keane, M. (1988). Combining prototypes: A selective modification model. Cognitive Science, 12, 485–527.CrossRef
go back to reference Smith, J. D., & Minda, J. P. (2002). Distinguishing prototype-based and exemplar-based processes in dot-pattern category learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 28, 800–811. Smith, J. D., & Minda, J. P. (2002). Distinguishing prototype-based and exemplar-based processes in dot-pattern category learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 28, 800–811.
go back to reference Treisman, M., & Williams, T. C. (1984). A theory of criterion setting with an application to sequential dependencies. Psychological Review, 91, 68–111.CrossRef Treisman, M., & Williams, T. C. (1984). A theory of criterion setting with an application to sequential dependencies. Psychological Review, 91, 68–111.CrossRef
go back to reference Voorspoels, W., Storms, G., & Vanpaemel, W. (2012). An exemplar approach to conceptual combination. Psychologica Belgica, 52, 435–458.CrossRef Voorspoels, W., Storms, G., & Vanpaemel, W. (2012). An exemplar approach to conceptual combination. Psychologica Belgica, 52, 435–458.CrossRef
go back to reference Waldschmidt, J. G., & Ashby, F. G. (2011). Cortical and striatal contributions to automaticity in information-integration categorization. Neuroimage, 56(3), 1791–1802.CrossRef Waldschmidt, J. G., & Ashby, F. G. (2011). Cortical and striatal contributions to automaticity in information-integration categorization. Neuroimage, 56(3), 1791–1802.CrossRef
go back to reference Wisniewski, E. J. (1997). When concepts combine. Psychonomic Bulletin & Review, 4, 167–183.CrossRef Wisniewski, E. J. (1997). When concepts combine. Psychonomic Bulletin & Review, 4, 167–183.CrossRef
go back to reference Zadeh, L. A. (1982). A note on prototype theory and fuzzy sets. Cognition, 12, 291–297.CrossRef Zadeh, L. A. (1982). A note on prototype theory and fuzzy sets. Cognition, 12, 291–297.CrossRef
Metagegevens
Titel
The impact of training methodology and category structure on the formation of new categories from existing knowledge
Auteurs
Sébastien Hélie
Farzin Shamloo
Shawn W. Ell
Publicatiedatum
27-10-2018
Uitgeverij
Springer Berlin Heidelberg
Gepubliceerd in
Psychological Research / Uitgave 4/2020
Print ISSN: 0340-0727
Elektronisch ISSN: 1430-2772
DOI
https://doi.org/10.1007/s00426-018-1115-3

Andere artikelen Uitgave 4/2020

Psychological Research 4/2020 Naar de uitgave