Advertisement

Attention, Perception, & Psychophysics

, Volume 76, Issue 5, pp 1318–1334 | Cite as

Continuous executive function disruption interferes with application of an information integration categorization strategy

  • Sarah J. Miles
  • Kazunaga Matsuki
  • John Paul Minda
Article

Abstract

Category learning is often characterized as being supported by two separate learning systems. A verbal system learns rule-defined (RD) categories that can be described using a verbal rule and relies on executive functions (EFs) to learn via hypothesis testing. A nonverbal system learns non-rule-defined (NRD) categories that cannot be described by a verbal rule and uses automatic, procedural learning. The verbal system is dominant in that adults tend to use it during initial learning but may switch to the nonverbal system when the verbal system is unsuccessful. The nonverbal system has traditionally been thought to operate independently of EFs, but recent studies suggest that EFs may play a role in the nonverbal system—specifically, to facilitate the transition away from the verbal system. Accordingly, continuously interfering with EFs during the categorization process, so that EFs are never fully available to facilitate the transition, may be more detrimental to the nonverbal system than is temporary EF interference. Participants learned an NRD or an RD category while EFs were untaxed, taxed temporarily, or taxed continuously. When EFs were continuously taxed during NRD categorization, participants were less likely to use a nonverbal categorization strategy than when EFs were temporarily taxed, suggesting that when EFs were unavailable, the transition to the nonverbal system was hindered. For the verbal system, temporary and continuous interference had similar effects on categorization performance and on strategy use, illustrating that EFs play an important but different role in each of the category-learning systems.

Keywords

Categorization Attention and executive control Attention in learning 

References

  1. Ashby, F. G. (1992). Multidimensional models of categorization. In F. G. Ashby (Ed.), Multidimensional models of perception and cognition (pp. 449–483). Hillsdale, NJ: Earlbaum.Google Scholar
  2. 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.PubMedCrossRefGoogle Scholar
  3. Ashby, F. G., & Crossley, M. J. (2010). Interactions between declarative and procedural-learning categorization systems. Neurobiology of Learning and Memory, 94, 1–12.PubMedCentralCrossRefGoogle Scholar
  4. Ashby, F. G., & Gott, R. E. (1988). Decision rules in the perception and categorization of multidimensional stimuli. Journal of Experimental Psychology: Learning, Memory, & Cognition, 14, 33–53.Google Scholar
  5. Ashby, F. G., & Maddox, W. T. (2005). Human category learning. Annual Reviews in Psychology, 56, 149–178.CrossRefGoogle Scholar
  6. Ashby, F. G., Maddox, W. T., & Bohil, C. J. (2002). Observational versus feedback training in rule-based and information-integration category learning. Memory & Cognition, 30, 666–677.CrossRefGoogle Scholar
  7. Ashby, F. G., & O’Brien, J. (2005). Category learning and multiple memory systems. Trends in Cognitive Sciences, 9, 83–89.PubMedCrossRefGoogle Scholar
  8. Ashby, F. G., & Valentin, V. V. (2005). Multiple systems of perceptual category learning: Theory and cogntive tests. In H. Cohen & C. Lefebvre (Eds.), Categorization in Cognitive Science Categorization in cognitive science (pp. 16–30). New York, NY: Elsevier.Google Scholar
  9. Buckner, R. L. (2004). Memory and executive function in aging and AD: Multiple factors that cause decline and reserve factors that compensate. Neuron, 44, 195–208.PubMedCrossRefGoogle Scholar
  10. Bunge, S., & Zelazo, P. (2006). A brain-based account of the development of rule use in childhood. Current Directions in Psychological Science, 15, 118–121.CrossRefGoogle Scholar
  11. Casey, B. J., Giedd, J. N., & Thomas, K. M. (2000). Structural and functional brain development and its relation to cognitive development. Biological Psychology, 54, 241–257.PubMedCrossRefGoogle Scholar
  12. Decaro, M. S., Carlson, K. D., Thomas, R. D., & Beilock, S. L. (2009). When and how less is more: Reply to Tharp and Pickering. Cognition, 111, 397–403.PubMedCrossRefGoogle Scholar
  13. Decaro, M. S., Thomas, R. D., Albert, N. B., & Beilock, S. L. (2011). Choking under pressure: Multiple routes to skill failure. Journal of Experimental Psychology: General, 140, 390–406.CrossRefGoogle Scholar
  14. Erickson, M. A. (2008). Executive attention and task switching in category learning: Evidence for stimulus dependent representation. Memory & Cognition, 36, 749–761.CrossRefGoogle Scholar
  15. Filoteo, J. V., Lauritzen, J. S., & Maddox, W. T. (2010). Removing the frontal lobes: The effects of engaging executive functions on perceptual category learning. Psychological Science, 21, 415–423.PubMedCentralPubMedCrossRefGoogle Scholar
  16. Fisk, J. E., & Sharp, C. A. (2004). Age-Related impairment in executive functioning: Updating, inhibition, shifting, and access. Journal of Clinical and Experimental Neuropsychology, 26, 874–890.PubMedCrossRefGoogle Scholar
  17. Friedman, N. P., Miyake, A., Corley, R. P., Young, S. E., DeFries, J. C., & Hewitt, J. K. (2006). Not all executive functions are related to intelligence. Psychological Science, 17, 172–179.PubMedCrossRefGoogle Scholar
  18. Harrison, Y., Horne, J. A., & Rothwell, A. (2000). Prefrontal neuropsychological effects of sleep deprivation in young adults–a model for healthy aging? Sleep, 23, 1067–1073.PubMedGoogle Scholar
  19. Helie, S., Waldschmidt, J. G., & Ashby, F. G. (2010). Automaticity in rule-based and information-integration categorization. Attention, Perception, & Psychophysics, 72, 1013–1031.CrossRefGoogle Scholar
  20. Huang-Pollock, C. L., Maddox, W. T., & Karalunas, S. L. (2011). Development of implicit and explicit category learning. Journal of Experimental Child Psychology, 109, 321–335.PubMedCentralPubMedCrossRefGoogle Scholar
  21. Lamberts, K. F., Evans, J. J., & Spikman, J. M. (2010). A real-life, ecologically valid test of executive functioning: The executive secretarial task. Journal of Clinical and Experimental Neuropsychology, 32, 56–65.PubMedCrossRefGoogle Scholar
  22. Maddox, W. T., & Ashby, F. G. (1993). Comparing decision bound and exemplar models of categorization. Perception and Psychophysics, 53, 49–70.PubMedCrossRefGoogle Scholar
  23. 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, & Cognition, 29, 650–662.Google Scholar
  24. Maddox, W. T., Ashby, F. G., Ing, A. D., & Pickering, A. D. (2004a). Disrupting feedback processing interferes with rule-based but not information-integration category learning. Memory & Cognition, 32, 582–591.CrossRefGoogle Scholar
  25. Maddox, W. T., Bohil, C. J., & Ing, A. D. (2004b). Evidence for a procedural-learning based system in perceptual category learning. Psychonomic Bulletin & Review, 11, 945–952.CrossRefGoogle Scholar
  26. Maddox, W. T., & Filoteo, J. V. (2001). Striatal contributions to category learning: Quantitative modeling of simple linear and complex rule learning in patients with Parkinson’s disease. Journal of the International Neuropsychological Society, 7, 710–727.PubMedCrossRefGoogle Scholar
  27. Maddox, W. T., Filoteo, J. V., Hejl, K. D., & Ing, A. D. (2004c). Category number impacts rule-based but not information-integration category learning: Further evidence for dissociable category-learning systems. Journal of Experimental Psychology: Learning, Memory, & Cognition, 30, 227–245.Google Scholar
  28. Maddox, W. T., Glass, B. D., Wolosin, S. M., Savarie, Z. R., Bowen, C., & Matthews, M. D. (2009). The effects of sleep deprivation on information-integration categorization performance. Sleep, 32, 1439–1449.PubMedCentralPubMedGoogle Scholar
  29. 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, & Cognition, 31, 100–107.Google Scholar
  30. Maddox, W. T., Love, B. C., Glass, B. D., & Filoteo, J. V. (2008). When more is less: Feedback effects in perceptual category learning. Cognition, 108, 578–589.PubMedCentralPubMedCrossRefGoogle Scholar
  31. Maddox, W. T., Pacheco, J., Reeves, M., Zhu, B., & Schnyer, D. M. (2010). Rule-based and information-integration category learning in normal aging. Neuropsychologia, 48, 2998–3008.PubMedCentralPubMedCrossRefGoogle Scholar
  32. Markman, A. B., Maddox, W. T., & Worthy, D. A. (2006). Choking and excelling under pressure. Psychological Science, 17, 944–948.PubMedCrossRefGoogle Scholar
  33. Miles, S. J., & Minda, J. P. (2011). The effects of concurrent verbal and visual tasks on category learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 37, 588–607.Google Scholar
  34. Minda, J. P., Desroches, A. S., & Church, B. A. (2008). Learning rule-described and non-rule-described categories: A comparison of children and adults. Journal of Experimental Psychology: Learning, Memory, & Cognition, 34, 1518–1533.Google Scholar
  35. Minda, J. P., & Miles, S. J. (2010). The influence of verbal and nonverbal processing on category learning. In B. H. Ross (Ed.), The psychology of learning and motivation the psychology of learning and motivation (Vol. 52, pp. 117–162). Burlington: Academic Press.CrossRefGoogle Scholar
  36. Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., & Howerter, A. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41, 49–100.PubMedCrossRefGoogle Scholar
  37. Newell, B. R., Dunn, J. C., & Kalish, M. (2011). Systems of category learning: Fact or fantasy? In B. H. Ross (Ed.), The psychology of learning and motivation the psychology of learning and motivation (Vol. 54, pp. 167–215). Burlington: Elsevier Inc.Google Scholar
  38. Nilsson, J. P., Söderström, M., Karlsson, A., Lekander, M., Kerstedt, T. A., & Lindroth, N. (2005). Less effective executive functioning after one night’s sleep deprivation. Journal of Sleep Research, 14, 51–56.CrossRefGoogle Scholar
  39. Nomura, E. M., Maddox, W. T., Filoteo, J. V., Ing, A. D., Gitelman, D. R., & Parrish, T. B. (2007). Neural correlates of rule-based and information-integration visual category learning. Cerebral Cortex, 17, 37–43.PubMedCrossRefGoogle Scholar
  40. Nomura, E. M., & Reber, P. J. (2008). A review of medial temporal lobe and caudate contributions to visual category learning. Neuroscience and Biobehavioral Reviews, 32, 279–291.PubMedCrossRefGoogle Scholar
  41. Nomura, E. M., & Reber, P. J. (2012). Combining computational modeling and neuroimaging to examine multiple category learning systems in the brain. Brain Sciences, 2, 176–202.PubMedCentralPubMedCrossRefGoogle Scholar
  42. R Core Team. (2012). R: A language and environment for statistical computing [Manuel de logiciel]. Vienna, Austria.Google Scholar
  43. Rehder, B., & Hoffman, A. B. (2005). Eyetracking and selective attention in category learning. Cognitive Psychology, 51, 1–41.PubMedCrossRefGoogle Scholar
  44. Schnyer, D. M., Maddox, W. T., Ell, S. W., Davis, S., Pacheco, J., & Verfaellie, M. (2009). Prefrontal contributions to rule-based and information-integration category learning. Neuropsychologia, 47, 2995–3006.PubMedCentralPubMedCrossRefGoogle Scholar
  45. Waldron, E. M., & Ashby, F. G. (2001). The effects of concurrent task interference on category learning: Evidence for multiple category learning systems. Psychonomic Bulletin & Review, 8, 168–176.CrossRefGoogle Scholar
  46. Worthy, D. A., Maddox, W. T., & Markman, A. B. (2009). Less is more: Stimulus-feedback co-occurrence in perceptual category learning. Proceedings of the Cognitive Science Society, 1–6.Google Scholar
  47. Zeithamova, D., & Maddox, W. T. (2006). Dual-task interference in perceptual category learning. Memory & Cognition, 34, 387–398.CrossRefGoogle Scholar
  48. Zeithamova, D., & Maddox, W. T. (2007). The role of visuospatial and verbal working memory in perceptual category learning. Memory & Cognition, 35, 1380–1398.CrossRefGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2014

Authors and Affiliations

  • Sarah J. Miles
    • 1
    • 2
  • Kazunaga Matsuki
    • 1
  • John Paul Minda
    • 1
  1. 1.Department of PsychologyThe University of Western OntarioLondonCanada
  2. 2.Department of PsychologyThe University of Western OntarioLondonCanada

Personalised recommendations