Psychonomic Bulletin & Review

, Volume 25, Issue 2, pp 658–666 | Cite as

Category learning in the color-word contingency learning paradigm

  • James R. Schmidt
  • Maria Augustinova
  • Jan De Houwer
Brief Report


In the typical color-word contingency learning paradigm, participants respond to the print color of words where each word is presented most often in one color. Learning is indicated by faster and more accurate responses when a word is presented in its usual color, relative to another color. To eliminate the possibility that this effect is driven exclusively by the familiarity of item-specific word-color pairings, we examine whether contingency learning effects can be observed also when colors are related to categories of words rather than to individual words. To this end, the reported experiments used three categories of words (animals, verbs, and professions) that were each predictive of one color. Importantly, each individual word was presented only once, thus eliminating individual color-word contingencies. Nevertheless, for the first time, a category-based contingency effect was observed, with faster and more accurate responses when a category item was presented in the color in which most of the other items of that category were presented. This finding helps to constrain episodic learning models and sets the stage for new research on category-based contingency learning.


Contingency learning Category learning Item-specificity Episodic memory 


Author Notes

This research was supported by Grant BOF16/MET_V/002 of Ghent University to Jan De Houwer and by the Interuniversity Attraction Poles Program initiated by the Belgian Science Policy Office (IUAPVII/33).

Supplementary material

13423_2018_1430_MOESM1_ESM.xlsx (54 kb)
ESM 1 (XLSX 54.2 kb)
13423_2018_1430_MOESM2_ESM.xlsx (90 kb)
ESM 2 (XLSX 89.8 kb)


  1. Allan, L. G. (2005). Learning of contingent relationships. Learning & Behavior, 33, 127-129.CrossRefGoogle Scholar
  2. Allenmark, F., Moutsopoulou, K., & Waszak, F. (2015). A new look on S-R associations: How S and R link. Acta Psychologica, 160, 161-169.CrossRefPubMedGoogle Scholar
  3. Atalay, N. B., & Misirlisoy, M. (2012). Can contingency learning alone account for item-specific control? Evidence from within- and between-language ISPC effects. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38, 1578-1590.PubMedGoogle Scholar
  4. Beckers, T., De Houwer, J., & Matute, H. (2007). Editorial: Human contingency learning. Quarterly Journal of Experimental Psychology, 60, 289-290.CrossRefGoogle Scholar
  5. Biederman, I., & Cooper, E. E. (1991). Priming contour-deleted images: Evidence for intermediate representations in visual object recognition. Cognitive Psychology, 23, 393-419.CrossRefPubMedGoogle Scholar
  6. Biederman, I., & Gerhardstein, P. C. (1993). Recognizing depth-rotated objects: Evidence and conditions for three-dimensional viewpoint invariance. Journal of Experimental Psychology: Human Perception and Performance, 19, 1162-1182.PubMedGoogle Scholar
  7. Brady, T. F., & Oliva, A. (2008). Statistical learning using real-world scenes: Extracting categorical regularities without conscious intent. Psychological Science, 19, 678-685.CrossRefPubMedPubMedCentralGoogle Scholar
  8. Carlson, K. A., & Flowers, J. H. (1996). Intentional versus unintentional use of contingencies between perceptual events. Perception & Psychophysics, 58, 460-470.CrossRefGoogle Scholar
  9. Colzato, L. S., Raffone, A., & Hommel, B. (2006). What do we learn from binding features? Evidence for multilevel feature integration. Journal of Experimental Psychology: Human Perception and Performance, 32, 705-716.PubMedGoogle Scholar
  10. Conway, C. M., & Christiansen, M. H. (2005). Modality-constrained statistical learning of tactile, visual, and auditory sequences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 24-39.PubMedGoogle Scholar
  11. Conway, C. M., & Christiansen, M. H. (2006). Statistical learning within and between modalities: Pitting abstract against stimulus-specific representations. Psychological Science, 17, 905-912.CrossRefPubMedGoogle Scholar
  12. De Houwer, J., & Beckers, T. (2002). A review of recent developments in research and theories on human contingency learning. Quarterly Journal of Experimental Psychology, 55B, 289-310.CrossRefGoogle Scholar
  13. Emberson, L. L., & Rubinstein, D. Y. (2016). Statistical learning is constrained to less abstract patterns in complex sensory input (but not the least). Cognition, 153, 63-78.CrossRefPubMedPubMedCentralGoogle Scholar
  14. Forrin, N. D., & MacLeod, C. M. (2017). Relative speed of processing determines color-word contingency learning. Memory & Cognition, 45, 1206–1222.CrossRefGoogle Scholar
  15. Forrin, N. D., & MacLeod, C. M. (in press). The influence of contingency proportion on contingency learning. Attention, Perception, & Psychophysics.Google Scholar
  16. Hintzman, D. L. (1984). Minerva 2: A simulation model of human memory. Behavior Research Methods Instruments & Computers, 16, 96-101.CrossRefGoogle Scholar
  17. Hintzman, D. L. (1986). "Schema abstraction" in a multiple-trace memory model. Psychological Review, 93, 411-428.CrossRefGoogle Scholar
  18. Hintzman, D. L. (1988). Judgments of frequency and recognition memory in a multiple-trace memory model. Psychological Review, 95, 528-551.CrossRefGoogle Scholar
  19. Horner, A. J., & Henson, R. N. (2011). Stimulus-response bindings code both abstract and specific representations of stimuli: Evidence from a classification priming design that reverses multiple levels of response representation. Memory & Cognition, 39, 1457-1471.CrossRefGoogle Scholar
  20. Lemercier, C. (2009). Incidental learning of incongruent items in a manual version of the Stroop task. Perceptual and Motor Skills, 108, 705-716.CrossRefPubMedGoogle Scholar
  21. Levin, Y., & Tzelgov, J. (2016). Contingency learning is not affected by conflict experience: Evidence from a task conflict-free, item-specific Stroop paradigm. Acta Psychologica, 164, 39-45.CrossRefPubMedGoogle Scholar
  22. Lewicki, P. (1985). Nonconscious biasing effects of single instances on subsequent judgments. Journal of Personality and Social Psychology, 48, 563-574.CrossRefGoogle Scholar
  23. Lin, O. Y.-H., & MacLeod, C. M. (2018). The acquisition of simple associations as observed in color-word contingency learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 44, 99-106.PubMedGoogle Scholar
  24. Logan, G. D. (1988). Toward an instance theory of automatization. Psychological Review, 95, 492-527.CrossRefGoogle Scholar
  25. Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207-238.CrossRefGoogle Scholar
  26. Mervis, C. B., & Rosch, E. (1981). Categorization of natural objects. Annual Review of Psychology, 32, 89-115.CrossRefGoogle Scholar
  27. Miller, J. (1987). Priming is not necessary for selective-attention failures: Semantic effects of unattended, unprimed letters. Perception & Psychophysics, 41, 419-434.CrossRefGoogle Scholar
  28. Mordkoff, J. T. (1996). Selective attention and internal constraints: There is more to the flanker effect than biased contingencies. In A. Kramer, M. G. H. Coles, & G. D. Logan (Eds.), Converging operations in the study of visual selective attention (pp. 483–502). Washington, DC: APA.CrossRefGoogle Scholar
  29. Mordkoff, J. T., & Halterman, R. (2008). Feature integration without visual attention: Evidence from the correlated flankers task. Psychonomic Bulletin & Review, 15, 385-389.CrossRefGoogle Scholar
  30. Musen, G., & Squire, L. R. (1993). Implicit learning of color-word associations using a Stroop paradigm. Journal of Experimental Psychology: Learning Memory and Cognition, 19, 789-798.Google Scholar
  31. Nissen, M. J., & Bullemer, P. (1987). Attentional requirements of learning: Evidence from performance measures. Cognitive Psychology, 19, 1-32.CrossRefGoogle Scholar
  32. Nosofsky, R. M. (1988a). Exemplar-based accounts of relations between classification, recognition, and typicality. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 700-708.Google Scholar
  33. Nosofsky, R. M. (1988b). Similarity, frequency, and category representations. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 54-65.Google Scholar
  34. Nosofsky, R. M., Little, D. R., Donkin, C., & Fific, M. (2011). Short-term memory scanning viewed as exemplar-based categorization. Psychological Review, 118, 280-315.CrossRefPubMedPubMedCentralGoogle Scholar
  35. Nosofsky, R. M., & Palmeri, T. J. (1997). An exemplar-based random walk model of speeded classification. Psychological Review, 104, 266-300.CrossRefPubMedGoogle Scholar
  36. Schmidt, J. R. (in press). Best not to bet on the horserace: A comment on Forrin and MacLeod (2017) and a relevant stimulus-response compatibility view of color-word contingency learning asymmetries. Memory & Cognition.Google Scholar
  37. Schmidt, J. R., Crump, M. J. C., Cheesman, J., & Besner, D. (2007). Contingency learning without awareness: Evidence for implicit control. Consciousness and Cognition, 16, 421-435.CrossRefPubMedGoogle Scholar
  38. Schmidt, J. R., & De Houwer, J. (2012a). Adding the goal to learn strengthens learning in an unintentional learning task. Psychonomic Bulletin & Review, 19, 723-728.CrossRefGoogle Scholar
  39. Schmidt, J. R., & De Houwer, J. (2012b). Contingency learning with evaluative stimuli: Testing the generality of contingency learning in a performance paradigm. Experimental Psychology, 59, 175-182.CrossRefPubMedGoogle Scholar
  40. Schmidt, J. R., & De Houwer, J. (2012c). Does temporal contiguity moderate contingency learning in a speeded performance task? Quarterly Journal of Experimental Psychology, 65, 408-425.CrossRefGoogle Scholar
  41. Schmidt, J. R., & De Houwer, J. (2012d). Learning, awareness, and instruction: Subjective contingency awareness does matter in the color-word contingency learning paradigm. Consciousness and Cognition, 21, 1754-1768.CrossRefPubMedGoogle Scholar
  42. Schmidt, J. R., & De Houwer, J. (2016a). Contingency learning tracks with stimulus-response proportion: No evidence of misprediction costs. Experimental Psychology, 63, 79-88.CrossRefPubMedGoogle Scholar
  43. Schmidt, J. R., & De Houwer, J. (2016b). Time course of color-word contingency learning: Practice curves, pre-exposure benefits, unlearning, and relearning. Learning and Motivation, 56, 15-30.CrossRefGoogle Scholar
  44. Schmidt, J. R., De Houwer, J., & Besner, D. (2010). Contingency learning and unlearning in the blink of an eye: A resource dependent process. Consciousness and Cognition, 19, 235-250.CrossRefPubMedGoogle Scholar
  45. Schmidt, J. R., De Houwer, J., & Rothermund, K. (2016). The Parallel Episodic Processing (PEP) Model 2.0: A single computational model of stimulus-response binding, contingency learning, power curves, and mixing costs. Cognitive Psychology, 91, 82-108.CrossRefPubMedGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2018

Authors and Affiliations

  • James R. Schmidt
    • 1
  • Maria Augustinova
    • 2
  • Jan De Houwer
    • 1
  1. 1.Department of Experimental Clinical and Health PsychologyGhent UniversityGhentBelgium
  2. 2.Department of PsychologyUniversité de RouenRouenFrance

Personalised recommendations