Memory & Cognition

, Volume 46, Issue 2, pp 261–273 | Cite as

One-back reinforcement dissociates implicit-procedural and explicit-declarative category learning

  • J. David Smith
  • Sonia Jamani
  • Joseph Boomer
  • Barbara A. Church


The debate over unitary/multiple category-learning utilities is reminiscent of debates about multiple memory systems and unitary/dual codes in knowledge representation. In categorization, researchers continue to seek paradigms to dissociate explicit learning processes (yielding verbalizable rules) from implicit learning processes (yielding stimulus–response associations that remain outside awareness). We introduce a new dissociation here. Participants learned matched category tasks with a multidimensional, information-integration solution or a one-dimensional, rule-based solution. They received reinforcement immediately (0-Back reinforcement) or after one intervening trial (1-Back reinforcement). Lagged reinforcement eliminated implicit, information-integration category learning but preserved explicit, rule-based learning. Moreover, information-integration learners facing lagged reinforcement spontaneously adopted explicit rule strategies that poorly suited their task. The results represent a strong process dissociation in categorization, broadening the range of empirical techniques for testing the multiple-process theoretical perspective. This and related methods that disable associative learning—fostering a transition to explicit-declarative cognition—could have broad utility in comparative, cognitive, and developmental science.


Category learning Explicit cognition Associative learning Category rules Procedural learning 


Author note

The preparation of this article was supported by Grants HD-060563 and HD-061455 from NICHD, and Grant BCS-0956993 from the National Science Foundation. We want to thank the research assistants in the Complex Cognition Lab at Georgia State University for their help with data collection. Original data and code is available upon request from the first author.


  1. Alexander, G. E., DeLong, M. R., & Strick, P. L. (1986). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience, 9, 357–381. doi: CrossRefPubMedGoogle Scholar
  2. Arbuthnott, G. W., Ingham, C. A., & Wickens, J. R. (2000). Dopamine and synaptic plasticity in the neostriatum. Journal of Anatomy, 196, 587–596. doi: CrossRefPubMedPubMedCentralGoogle Scholar
  3. Ashby, F. G., & Ell, S. W. (2001). The neurobiology of human category learning. Trends in Cognitive Sciences, 5, 204–210. doi: CrossRefPubMedGoogle Scholar
  4. Ashby, F. G., & Ennis, J. M. (2006). The role of the basal ganglia in category learning. In B. H. Ross (Ed.), The psychology of learning and motivation (Vol. 46, pp. 1–36). San Diego: Academic Press.Google Scholar
  5. Ashby, F. G., Isen, A. M., & Turken, A. U. (1999). A neuropsychological theory of positive affect and its influence on cognition. Psychological Review, 106, 529–550. doi: CrossRefPubMedGoogle Scholar
  6. Ashby, F. G., & Maddox, W. T. (2011). Human category learning 2.0 Annals of the New York Academy of Sciences, 1224, 147–161. doi: CrossRefPubMedGoogle Scholar
  7. Ashby, F. G., Queller, S., & Berretty, P. T. (1999). On the dominance of unidimensional rules in unsupervised categorization. Perception & Psychophysics, 61, 1178–1199. doi: CrossRefGoogle Scholar
  8. Ashby, F. G., & Valentin, V. V. (2005). Multiple systems of perceptual category learning: Theory and cognitive tests. In H. Cohen & C. Lefebvre (Eds.), Handbook of categorization in cognitive science (pp. 547–572). New York: Elsevier.CrossRefGoogle Scholar
  9. Barnes, T. D., Kubota, Y., Hu, D., Jin, D. Z., & Graybiel, A. M. (2005). Activity of striatal neurons reflects dynamic encoding and recoding of procedural memories. Nature, 437, 1158 –1161. doi: CrossRefPubMedGoogle Scholar
  10. Blair, M., & Homa, D. (2003). As easy to memorize as they are to classify: The 5-4 categories and the category advantage. Memory & Cognition, 31, 1293–1301. doi: CrossRefGoogle Scholar
  11. Brooks, L. R. (1978). Nonanalytic concept formation and memory for instances. In E. Rosch & B. B. Lloyd (Eds.), Cognition and categorization (pp. 169–211). Hillsdale: Erlbaum.Google Scholar
  12. Brown, R. G., & Marsden, C. D. (1988). Internal versus external cures and the control of attention in Parkinson’s disease. Brain, 111, 323–345. doi: CrossRefPubMedGoogle Scholar
  13. Bruner, J. S., Goodnow, J. J., & Austin, G. A. (1956). A study of thinking. Oxford: Wiley.Google Scholar
  14. Calabresi, P., Pisani, A., Centonze, D., & Bernardi, G. (1996). Role of Ca2 in striatal LTD and LTP. Seminars in the Neurosciences, 8, 321–328. doi: CrossRefGoogle Scholar
  15. Cerella, J. (1979). Visual classes and natural categories in the pigeon. Journal of Experimental Psychology: Human Perception and Performance, 5, 68–77. doi: PubMedGoogle Scholar
  16. Cook, R. G., & Smith, J. D. (2006). Stages of abstraction and exemplar memorization in pigeons’ category learning. Psychological Science, 17, 1059–1067. doi: CrossRefPubMedGoogle Scholar
  17. Cools, A. R., van den Bercken, J. H., Horstink, M. W., van Spaendonck, K. P., & Berger, H. J. (1984). Cognitive and motor shifting aptitude disorder in Parkinson’s disease. Journal of Neurological and Neurosurgical Psychology, 47, 443–453. Retrieved from CrossRefGoogle Scholar
  18. Divac, I., Rosvold, H. E., & Szwarcbart, M. K. (1967). Behavioral effects of selective ablation of the caudate nucleus. Journal of Comparative and Physiological Psychology, 63, 184–190. doi: CrossRefPubMedGoogle Scholar
  19. Eacott, M. J., & Gaffan, D. (1992). Inferotemporal-frontal disconnection: The uncinate fascicle and visual associative learning in monkeys. European Journal of Neuroscience, 4, 1320–1332. doi: CrossRefPubMedGoogle Scholar
  20. Elliott, R., & Dolan, R. J. (1998). Activation of different anterior cingulate foci in association with hypothesis testing and response selection. NeuroImage, 8, 17–29. doi: CrossRefPubMedGoogle Scholar
  21. Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127, 107–140. doi: CrossRefGoogle Scholar
  22. Feldman, J. (2000). Minimization of Boolean complexity in human concept learning. Nature, 407, 630–633. doi: CrossRefPubMedGoogle Scholar
  23. Filoteo, J. V., Maddox, W. T., Salmon, D. P., & Song, D. D. (2005). Information-integration category learning in patients with striatal dysfunction. Neuropsychology, 19, 212–222. doi: CrossRefPubMedGoogle Scholar
  24. Fukunaga, K. (1972). Introduction to statistical pattern recognition. New York: Academic Press.Google Scholar
  25. Fuster, J. M. (1989). The prefrontal cortex, (2nd ed.). Philadelphia: Lippincott-Raven.Google Scholar
  26. Gaffan, D., & Eacott, M. J. (1995). Visual learning for an auditory secondary reinforcer by macaques is intact after uncinate fascicle section: Indirect evidence for the involvement of the corpus striatum. European Journal of Neuroscience, 7, 1866–1871. doi: CrossRefPubMedGoogle Scholar
  27. Gaffan, D., & Harrison, S. (1987). Amygdalectomy and disconnection in visual learning for auditory secondary reinforcement by monkeys. Journal of Neuroscience, 7, 2285–2292. Retrieved from PubMedGoogle Scholar
  28. Goldman-Rakic, P. S. (1987). Circuitry of the prefrontal cortex and the regulation of behavior by representational knowledge. In F. Plum & V. Mountcastle (Eds.), Handbook of physiology (pp. 373–417). Bethesda: American Physiological Society.Google Scholar
  29. Han, C. J., O’Tuathaigh, C. M., van Trigt, L., Quinn, J. J., Fanselow, M. S., Mongeau, R., …, Anderson, D. J. (2003). Trace but not delay fear conditioning requires attention and the anterior cingulate cortex. Proceedings of the National Academy of Sciences of the United States of America, 100, 13087–13092. doi: CrossRefPubMedPubMedCentralGoogle Scholar
  30. Herrnstein, R. J., Loveland, D. H., & Cable, C. (1976). Natural concepts in pigeons. Journal of Experimental Psychology: Animal Behavioral Processes, 2, 285–302. doi: Google Scholar
  31. Hollerman, J. R., & Schultz, W. (1998). Dopamine neurons report an error in the temporal prediction of reward during learning. Nature Neuroscience, 1, 304–309. doi: CrossRefPubMedGoogle Scholar
  32. Homa, D., Sterling, S., & Trepel, L. (1981). Limitations of exemplar-based generalization and the abstraction of categorical information. Journal of Experimental Psychology: Human Learning and Memory, 7, 418–439. doi: Google Scholar
  33. Knowlton, B. J., Mangels, J. A., & Squire, L. R. (1996). A neostriatal habit learning system in humans. Science, 273, 1399–1402. doi: CrossRefPubMedGoogle Scholar
  34. Knowlton, B. J., & Squire, L. R. (1993). The learning of categories: Parallel memory systems for item memory and category-level knowledge. Science, 262, 1747–1749. doi: CrossRefPubMedGoogle Scholar
  35. Kolb, B., & Whishaw, I. Q. (1990). Fundamentals of human neuropsychology (3rd ed.). New York: Freeman.Google Scholar
  36. Konorski, J. (1967). Integrative activity of the brain. Chicago: University of Chicago Press.Google Scholar
  37. Kryukov, V. I. (2012). Towards a unified model of Pavlovian conditioning: Short review. Cognitive Neuroscience, 6, 377–398. doi: Google Scholar
  38. Maddox, W. T., & Ashby, F. G. (1993). Comparing decision bound and exemplar models of categorization. Perception & Psychophysics, 53, 49–70. doi: CrossRefGoogle Scholar
  39. Maddox, W. T., & Ashby, F. G. (2004). Dissociating explicit and procedural-learning based systems of perceptual category learning. Behavioural Processes, 66, 309–332. doi: CrossRefPubMedGoogle Scholar
  40. Maddox, W. T., Ashby, F. G., & Bohil, C. J. (2003). Delayed feedback effects on rule-based and information-integration category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 650–662. doi: PubMedGoogle Scholar
  41. Maddox, W. T., & Ing, A. D. (2005). Delayed feedback disrupts the procedural-learning system but not the hypothesis testing system in perceptual category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 100–107. doi: PubMedGoogle Scholar
  42. McDonald, R. J., & White, N. M. (1993). A triple dissociation of memory systems: Hippocampus, amygdala, and dorsal striatum. Behavioral Neuroscience, 107, 3–22. doi: CrossRefPubMedGoogle Scholar
  43. McDonald, R. J., & White, N. M. (1994). Parallel information processing in the water maze: Evidence for independent memory systems involving dorsal striatum and hippocampus. Behavioral and Neural Biology, 61, 260–270. doi: CrossRefPubMedGoogle Scholar
  44. Medin, D. L. (1975). A theory of context in discrimination learning. In G. Bower (Ed.), The psychology of learning and motivation (Vol. 9, pp. 263–314). New York: Academic Press.Google Scholar
  45. Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238. doi: CrossRefGoogle Scholar
  46. Minda, J. P., & Smith, J. D. (2001). Prototypes in category learning: The effects of category size, category structure, and stimulus complexity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 775–799. doi: PubMedGoogle Scholar
  47. Mishkin, M., Malamut, B., & Bachevalier, J. (1984). Memories and habits: Two neural systems. In G. Lynch, J. L. McGaugh, & N. M. Weinberger (Eds.), Neurobiology of human learning and memory (pp. 65–88). New York: Guilford Press.Google Scholar
  48. Murphy, G. L. (2003). The big book of concepts. Cambridge: MIT Press.Google Scholar
  49. Nairne, J. S. (1990). A feature model of immediate memory. Memory & Cognition, 18, 251–269. doi: CrossRefGoogle Scholar
  50. Nomura, E. M., Maddox, W. T., Filoteo, J. V., Ing, A. D., Gitelman, D. R., Parrish, T. B., … Reber, P. J. (2007). Neural correlates of rule-based and information-integration visual category learning. Cerebral Cortex, 17, 37–43. doi: CrossRefPubMedGoogle Scholar
  51. Nosofsky, R. M. (1987). Attention and learning processes in the identification and categorization of integral stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 87–108. doi: PubMedGoogle Scholar
  52. Nosofsky, R. M., & Johansen, M. K. (2000). Exemplar-based accounts of multiple-system phenomena in perceptual categorization. Psychonomic Bulletin & Review, 7, 375–402. Retrieved from Google Scholar
  53. Nosofsky, R. M., & Kruschke, J. K. (2002). Single-system models and interference in category learning: Commentary on Waldron and Ashby (2001). Psychonomic Bulletin & Review, 9, 169–174. doi: CrossRefGoogle Scholar
  54. 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. doi: CrossRefPubMedPubMedCentralGoogle Scholar
  55. O’Doherty, J., Dayan, P., Schultz, J., Deichmann, R., Friston, K., & Dolan, R. J. (2004, April 16). Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science, 304, 452–454. doi: CrossRefPubMedGoogle Scholar
  56. Packard, M. G., Hirsh, R., & White, N. M. (1989). Differential effects of fornix and caudate nucleus lesions on two radial maze tasks: Evidence for multiple memory systems. Journal of Neuroscience, 9, 1465–1472. Retrieved from PubMedGoogle Scholar
  57. Packard, M. G., & McGaugh, J. L. (1992). Double dissociation of fornix and caudate nucleus lesions on acquisition of two water maze tasks: Further evidence for multiple memory systems. Behavioral Neuroscience, 106, 439–446. doi: CrossRefPubMedGoogle Scholar
  58. Pavlov, I. P. (1927). Conditioned reflexes: An investigation of the physiological activity of the cerebral cortex. London: Oxford University Press.Google Scholar
  59. Pearce, J. M. (1994). Discrimination and categorization: Animal learning and cognition. In N. J. Mackintosh (Ed.), Handbook of perception and cognition series (2nd ed., Vol 18, pp. 109–134). San Diego: Academic Press.Google Scholar
  60. Posner, M. I., & Petersen, S. E. (1990). Attention systems in the human brain. Annual Review of Neuroscience, 13, 25–42.CrossRefPubMedGoogle Scholar
  61. Pylyshyn, Z. W. (1973). What the mind’s eye tells the mind’s brain: A critique of mental imagery. Psychology Bulletin, 80, 1–24. doi: CrossRefGoogle Scholar
  62. Rao, S. M., Bobholz, J. A., Hammeke, T. A., Rosen, A. C., Woodley, S. J., Cunningham, J. M., … Binder, J. R. (1997). Functional MRI evidence for subcortical participation in conceptual reasoning skills. NeuroReport, 27, 1987–1993. doi: CrossRefGoogle Scholar
  63. Raybuck, J. D., & Lattal, K. M. (2014). Bridging the interval: Theory and neurobiology of trace conditioning. Behavioral Processes, 101, 103–111. doi: CrossRefGoogle Scholar
  64. Reed, S. K. (1978). Category vs. item learning: Implications for categorization models. Memory & Cognition, 6, 612–621. doi: CrossRefGoogle Scholar
  65. Robinson, A. L., Heaton, R. K., Lehman, R. A. W., & Stilson, D. W. (1980). The utility of the Wisconsin Card Sorting Test in detecting and localizing frontal lobe lesions. Journal of Consulting and Clinical Psychology, 48, 605–614. doi: CrossRefPubMedGoogle Scholar
  66. Rolls, E. T. (1994). Neurophysiology and cognitive functions of the striatum. Revue Neurologique, 150, 648–660.Google Scholar
  67. Rosch, E., & Mervis, C. B. (1975). Family resemblances: Studies in the internal structure of categories. Cognitive Psychology, 7, 573–605. doi: CrossRefGoogle Scholar
  68. Rosseel, Y. (2002). Mixture models of categorization. Journal of Mathematical Psychology, 46, 178–210. doi: CrossRefGoogle Scholar
  69. Schultz, W. (1992). Activity of dopamine neurons in the behaving primate. Seminars in Neuroscience, 4, 129–138. doi: CrossRefGoogle Scholar
  70. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464. doi: CrossRefGoogle Scholar
  71. Seger, C. A., & Cincotta, C. M. (2005). The roles of the caudate nucleus in human classification learning. Journal of Neuroscience, 25, 2941–2951. doi: CrossRefPubMedGoogle Scholar
  72. Smith, J. D., Ashby, F. G., Berg, M. E., Murphy, M. S., Spiering, B., Cook, R. G., & Grace, R. C. (2011). Pigeons’ categorization may be exclusively nonanalytic. Psychonomic Bulletin & Review, 18, 414–421. doi: CrossRefGoogle Scholar
  73. Smith, J. D., Beran, M. J., Crossley, M. J., Boomer, J., & Ashby, F. G. (2010). Implicit and explicit category learning by macaques (Macaca mulatta) and humans (Homo sapiens). Journal of Experimental Psychology: Animal Behavior Processes, 36, 54–65. doi: PubMedPubMedCentralGoogle Scholar
  74. Smith, J. D., Boomer, J., Zakrzewski, A. C., Roeder, J. L., Church, B. A., & Ashby, F. G. (2014). Deferred feedback sharply dissociates implicit and explicit category learning. Psychological Science, 25, 447–457. doi: CrossRefPubMedGoogle Scholar
  75. Smith, J. D., Chapman, W. P., & Redford, J. S. (2010). Stages of category learning in monkeys (Macaca mulatta) and humans (Homo sapiens). Journal of Experimental Psychology: Animal Behavior Processes, 36, 39–53. doi: PubMedPubMedCentralGoogle Scholar
  76. Smith, J. D., & Church, B. A. (2017). Dissociable learning processes in comparative psychology. Psychonomic Bulletin and Review. Advance online publication. doi:
  77. Smith, J. D., Coutinho, M. V. C., & Couchman, J. J. (2011). The learning of exclusive-or categories by monkeys (Macaca mulatta) and humans (Homo sapiens). Journal of Experimental Psychology: Animal Behavior Processes, 37, 20–29. doi: PubMedPubMedCentralGoogle Scholar
  78. Smith, J. D., & Minda, J. P. (1998). Prototypes in the mist: The early epochs of category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24, 1411–1436. doi: Google Scholar
  79. Smith, J. D., Murray, M. J., Jr., & Minda, J. P. (1997). Straight talk about linear separability. Journal of Experimental Psychology: Learning, Memory, & Cognition, 23, 659–680. doi: Google Scholar
  80. Smith, J. D., Redford, J. S., & Haas, S. M. (2008). Prototype abstraction by monkeys (Macaca mulatta). Journal of Experimental Psychology: General, 137, 390–401. doi: CrossRefGoogle Scholar
  81. Smith, J. D., Zakrzewski, A. C., Johnson, J. M., & Valleau, J. C. (2016). Ecology, fitness, evolution: New perspectives on categorization. Current Directions in Psychological Science, 25, 266–274. doi: CrossRefPubMedPubMedCentralGoogle Scholar
  82. Thorndike, E. L. (1911). Animal intelligence. New York: Macmillan.Google Scholar
  83. Waldschmidt, J. G., & Ashby, F. G. (2011). Cortical and striatal contributions to automaticity in information-integration categorization. NeuroImage, 56, 1791–1802. doi: CrossRefPubMedPubMedCentralGoogle Scholar
  84. Wasserman, E. A., Kiedinger, R. E., & Bhatt, R. S. (1988). Conceptual behavior in pigeons: Categories, subcategories, and pseudocategories. Journal of Experimental Psychology: Animal Behavior Processes, 14, 235–246. doi: Google Scholar
  85. Wickens, J. (1993). A theory of the striatum. New York: Pergamon Press.Google Scholar
  86. Yagishita, S., Hayashi-Takagi, A., Ellis-Davies, G. C., Urakubo, H., Ishii, S., & Kasai, H. (2014). A critical time window for dopamine actions on the structural plasticity of dendritic spines. Science, 345, 1616–1620. doi: CrossRefPubMedPubMedCentralGoogle Scholar
  87. Yin, H. H., Ostlund, S. B., Knowlton, B. J., & Balleine, B. W. (2005). The role of the dorsomedial striatum in instrumental conditioning. European Journal of Neuroscience, 22, 513–523. doi: CrossRefPubMedGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2017

Authors and Affiliations

  • J. David Smith
    • 1
  • Sonia Jamani
    • 1
  • Joseph Boomer
    • 2
  • Barbara A. Church
    • 3
  1. 1.Department of PsychologyGeorgia State UniversityAtlantaUSA
  2. 2.University at BuffaloThe State University of New YorkNew YorkUSA
  3. 3.Language Research CenterGeorgia State UniversityAtlantaUSA

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