Skip to main content
Log in

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

  • Original Article
  • Published:
Psychological Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  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.

References

  • 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.

    Google Scholar 

  • Ashby, F., & Maddox, W. T. (1993). Relations between prototype, exemplar, and decision bound models of categorization. Journal of Mathematical Psychology37, 372–400. http://homepage.psy.utexas.edu/homepage/group/maddoxlab/Publications/1990-1994/relations.pdf

  • 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.

    Article  Google Scholar 

  • Ashby, F. G., & Ell, S. W. (2001). The neurobiology of human category learning. Trends in Cognitive Science, 5, 204–210.

    Article  Google Scholar 

  • 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  Google Scholar 

  • Ashby, F. G., & Maddox, W. T. (2010). Human category learning 2.0. Annals of the New York Academy of Sciences, 1224, 147–161.

    Article  Google Scholar 

  • Badre, D., Kayser, A. S., & D’Esposito, M. (2010). Frontal cortex and the discovery of abstract action rules. Neuron, 66, 315–326.

    Article  Google Scholar 

  • Bishop, C. (2006). Pattern recognition and machine learning. Singapore: Springer.

    Google Scholar 

  • Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10, 433–436.

    Article  Google Scholar 

  • Cohen, B., & Murphy, G. L. (1984). Models of concepts. Cognitive Science, 8, 27–58.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Erev, I. (1998). Signal detection by human observers: A cutoff reinforcement learning model of categorization and decisions under uncertainty. Psychological Review, 105, 280–298.

    Article  Google Scholar 

  • Erickson, M. A. (2008). Executive attention and task switching in category learning: Evidence for stimulus-dependent representation. Memory & Cognition, 36(4), 749–761. https://doi.org/10.3758/MC.36.4.749.

    Article  Google Scholar 

  • Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127(2), 107.

    Article  Google Scholar 

  • Feldman, J. (2003). A catalog of Boolean concepts. Journal of Mathematical Psychology, 47, 75–89.

    Article  Google Scholar 

  • Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28, 3–71.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hélie, S., & Ashby, F. G. (2012). Learning and transfer of category knowledge in an indirect categorization task. Psychological Research, 76, 292–303.

    Article  Google Scholar 

  • Hélie, S., Roeder, J. L., & Ashby, F. G. (2010). Evidence for cortical automaticity in rule-based categorization. Journal of Neuroscience, 30(42), 14225–14234. https://doi.org/10.1523/JNEUROSCI.2393-10.2010.

    Article  PubMed  Google Scholar 

  • Hélie, S., Shamloo, F., & Ell, S. W. (2017). The effect of training methodology on knowledge representation in categorization. PLoS ONE, 12, e0183904.

    Article  Google Scholar 

  • Hélie, S., Waldschmidt, J. G., & Ashby, F. G. (2010). Automaticity in rule-based and information-integration categorization. Attention, Perception, & Psychophysics, 72(4), 1013–1031.

    Article  Google Scholar 

  • Levering, K. R., & Kurtz, K. J. (2015). Observation versus classification in supervised category learning. Memory & Cognition43(2), 266–282. http://www.ncbi.nlm.nih.gov/pubmed/25190494 https://doi.org/10.3758/s13421-014-0458-2

    Article  Google Scholar 

  • Ling Wu, L., & Barsalou, L. W. (2009). Perceptual simulation in conceptual combination: Evidence from property generation. Acta Psychologica 132(2), 173–189. http://www.sciencedirect.com/science/article/pii/S0001691809000183 (Spatial working memory and imagery: From eye movements to grounded cognition) https://doi.org/10.1016/j.actpsy.2009.02.002

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Markman, A. B., & Ross, B. (2003). Category use and category learning. Psychological Bulletin, 129, 529–613.

    Article  Google Scholar 

  • Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85(3), 207–238.

    Article  Google Scholar 

  • Miles, S. J., & Minda, J. P. (2011). The effects of concurrent verbal and visual tasks on category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37(3), 588–607. https://doi.org/10.1037/a0022309.

    Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

  • Posner, M. I., & Keele, S. W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77, 353–363.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Reed, S. K. (1972). Pattern recognition and categorization. Cognitive Psychology, 3, 382–407.

    Article  Google Scholar 

  • Seger, C. A., & Miller, E. K. (2010). Category learning in the brain. Annual Review of Neuroscience, 33, 203–219.

    Article  Google Scholar 

  • Shepard, R. N., Hovland, C. I., & Jenkins, H. M. (1961). Learning and memorization of classifications. Psychological Monographs: General and Applied, 75, 1–42.

    Article  Google Scholar 

  • Smith, E. E., Osherson, D. N., Rips, L. J., & Keane, M. (1988). Combining prototypes: A selective modification model. Cognitive Science, 12, 485–527.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Treisman, M., & Williams, T. C. (1984). A theory of criterion setting with an application to sequential dependencies. Psychological Review, 91, 68–111.

    Article  Google Scholar 

  • Voorspoels, W., Storms, G., & Vanpaemel, W. (2012). An exemplar approach to conceptual combination. Psychologica Belgica, 52, 435–458.

    Article  Google Scholar 

  • Waldschmidt, J. G., & Ashby, F. G. (2011). Cortical and striatal contributions to automaticity in information-integration categorization. Neuroimage, 56(3), 1791–1802.

    Article  Google Scholar 

  • Wisniewski, E. J. (1997). When concepts combine. Psychonomic Bulletin & Review, 4, 167–183.

    Article  Google Scholar 

  • Zadeh, L. A. (1982). A note on prototype theory and fuzzy sets. Cognition, 12, 291–297.

    Article  Google Scholar 

  • Zeithamova, D., Maddox, W. T., & Schnyer, D. D. M. (2008). Dissociable prototype learning systems: Evidence from brain imaging and behavior. Journal of Neuroscience28(49),13194–13201. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2605650&tool=pmcentrez&rendertype=abstract http://www.jneurosci.org/content/28/49/13194.short https://doi.org/10.1523/JNEUROSCI.2915-08.2008

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported in part by NSF grants #1349677-BCS and #1349737 to SH and SWE (respectively).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sébastien Hélie.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Human participants

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hélie, S., Shamloo, F. & Ell, S.W. The impact of training methodology and category structure on the formation of new categories from existing knowledge. Psychological Research 84, 990–1005 (2020). https://doi.org/10.1007/s00426-018-1115-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00426-018-1115-3

Navigation