Introduction to Neurocognitive Learning Therapy

  • Theodore Wasserman
  • Lori Drucker Wasserman


Neurocognitive Learning Therapy (NCLT) is a therapeutic system which targets disorders of mental health. It is designed to work with and make use of our understanding of how the human brain processes and learns information. It is unique in this regard. Some mental health therapies were developed in response to specific etiological hypotheses (psychoanalysis) or operant learning principles (applied behavior analysis). Others had no etiological basis at all but relied on healing concepts such as self-actualization. NCLT is based on information processing theory and mathematically derived brain network models organized along small word hub principles. It incorporates 16 principles that reflect what is known from both learning theory and neuropsychological research. Whatever you call the result of the particular learning that occurs in therapy, self-actualization, behavioral change, spiritual growth, destruction of maladaptive gestalts, or behavior change, the result of therapy should be that the individual engages in more adaptive behavior at the end than when they began. This inevitably means that the individual has learned new ways of behaving.


Therapy Psychotherapy Learning Maladaptive behavior Reward recognition Neurocognitive Learning Therapy 


  1. Aarts, H., & Dijksterhuis, A. (2000). Habits as knowledge structures: Automaticity in goal-directed behavior. Journal of Personality and Social Psychology, 78(1), 53–63. doi: 10.1037/0022-3514.78.1.53.CrossRefPubMedGoogle Scholar
  2. Ablon, S., & Jones, E. (2002). Validity of controlled clinical trials of psychotherapy: Findings from the NIMH treatment of depression collaborative research program. American Journal of Psychiatry, 159(5), 775–783.CrossRefPubMedGoogle Scholar
  3. Bargh, J., & Chartrand, T. (1999). The unbearable automaticity of being. American Psychologist, 54(7), 462–479. doi: 10.1037/0003-066X.54.7.462.CrossRefGoogle Scholar
  4. Bargh, J. A. (1992). The ecology of automaticity: Toward establishing the conditions needed to produce automatic processing effects. American Journal of Psychology, 105, 181–199.CrossRefPubMedGoogle Scholar
  5. Bays, P., & Husain, M. (2008). Dynamic shifts of limited working memory resources in human vision. Science, 321(5890), 851–854. doi: 10.1126/science.1158023.CrossRefPubMedPubMedCentralGoogle Scholar
  6. Bielock, S., Jellison, W., Rydell, R., McConnell, A., & Carr, T. (2006). On the causal mechanisms of stereotype threat: Can skills that don’t rely heavily on working memory still be threatened? Personality and Social Psychology Bulletin, 32(8), 1059–1071. doi: 10.1177/0146167206288489.CrossRefGoogle Scholar
  7. Brewin, C., & Beaton, A. (2002). Thought suppression, intelligence, and working memory capacity. Behavior Research and Therapy, 40(8), 923–930. doi: 10.1016/S0005-7967(01)00127-9.CrossRefGoogle Scholar
  8. Chinn, C., & Brewer, W. (1993). The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction. Review of Educational Research, 63(1), 1–49. doi: 10.3102/00346543063001001.CrossRefGoogle Scholar
  9. Clark, A. (2006). Language, embodiment, and the cognitive niche. Cognitive Sciences, 10(8), 370–374.CrossRefPubMedGoogle Scholar
  10. Cohen, G. L., & Sherman, D. K. (2014). The psychology of change: Self-affirmation and social psychological intervention. Annual Review of Psychology, 65, 333–371.CrossRefPubMedGoogle Scholar
  11. Demaria, S., Aune, J., & Jodlowski, D. (2008). Bruno Bettleheim, autism and the rhetoric of scientific authority. In M. Osteen (Ed.), Autism and representation (pp. 65–77). New York: Routledge.Google Scholar
  12. Diessel, H. (2014). Demonstratives, frames of reference, and semantic universals of space. Language and Linguistics Compass, 8(3), 116–132.CrossRefGoogle Scholar
  13. Dijksterhuis, A., & Aarts, A. (2010). Goals, attention, and (un)consciousness. Annual Review of Psychology, 61, 467–490.CrossRefPubMedGoogle Scholar
  14. Dijksterhuis, A., & Meurs, S. (2006). Where creativity resides: The generative power of unconscious thought. Consciousness and Cognition, 15(1), 135–146. doi: 10.1016/j.concog.2005.04.007.CrossRefPubMedGoogle Scholar
  15. Elman, J., Bates, E., Johnson, M., Kanniloff-Smith, A., Parisi, D., & Plunkett, K. (1996). Rethinking innateness: A connectionist perspective on development. Cambridge: MIT Press.Google Scholar
  16. Endsley, M., & Garland, D. (2000). Situation awareness analysis and measurement. Mahwah, NJ: Erlbaum.Google Scholar
  17. Felder, R., & Brent, R. (2003). Learning by doing. Chemical Engineering Education, 37(4), 282–283.Google Scholar
  18. Greenberg, L., & Safran, J. (1981). Encoding and cognitive therapy: Changing what clients attend to. Psychotherapy: Theory, Research and Practice, 18(2), 163–169. doi: 10.1037/h0086076.CrossRefGoogle Scholar
  19. Haber, S., & Knutson, B. (2010). The reward circuit: Linking primate anatomy and human imaging. Neuropsychopharmacology, 35(1), 4–26.CrossRefPubMedGoogle Scholar
  20. Hart, G., Leung, B. K., & Balleine, B. W. (2014). Dorsal and ventral streams: The distinct role of striatal sub-regions in the acquisition and performance of goal-directed actions. Neurobiology of Learning and Memory, 108, 104–118. doi: 10.1016/j.nlm.2013.11.00.CrossRefPubMedGoogle Scholar
  21. Holtgraves, T., & Kahima, Y. (2008). Language, meaning, and social cognition. Personality and Social Pyschology Review, 12(1), 173–194. doi: 10.1177/1088868307309605.Google Scholar
  22. Klin, A., Shultz, S., & Jones, W. (2015). Social visual engagement in infants and toddlers with autism: Early developmental transitions and a model of pathogenesis. Neuroscience and Biobehavioral Reviews, 50, 189–203. doi: 10.1016/j.neubiorev.2014.10.006.CrossRefPubMedGoogle Scholar
  23. Koziol, L. F., & Budding, D. E. (2009). Subcortical structures and cognition: Implications for neuropsychological assessment. New York: Springer.CrossRefGoogle Scholar
  24. Lipson, M. (1982). Learning new information from text: The role of prior knowledge and reading ability. Journal of Literacy Research, 14(3), 243–261. doi: 10.1080/10862968209547453.Google Scholar
  25. Logan, G. D. (1992). Attention and preattention in theories of automaticity. American Journal of Psychology, 105, 317–339.CrossRefPubMedGoogle Scholar
  26. Lord, C. G., Ross, L. R., & Lepper, M. R. (1979). Biased assimilation and attitude polarization: The effects of prior theories on subsequently considered evidence. Journal of Personality and Social Psychology, 39(11), 2098–2109.CrossRefGoogle Scholar
  27. Majid, A., Bowerman, M., Kits, S., Haun, D., & Levinson, S. (2004). Can language restructure cognition? The case for space. Trends in Cognitive Neuroscience, 8(3), 108–114. doi: 10.1016/j.tics.2004.01.003.CrossRefGoogle Scholar
  28. McGinty, V., Hayden, B., Heilbronner, S., Dumont, E., Graves, S., Mirrione, M., et al. (2011). Emerging, reemerging, and forgotten brain areas of the reward circuit: Notes from the 2010 motivational and neural networks conference. Behavioural Brain Research, 225, 348–357. doi: 10.1016/j.bbr.2011.07.036. Retrieved from National Institute of Health.CrossRefPubMedPubMedCentralGoogle Scholar
  29. Neches, R. (1987). Learning through incremental reinforcement of procedures. In D. Klahr, P. Langley, & R. Neches (Eds.), Production system models of learning and development (pp. 163–222). Boston: Massachusetts Institute of Technology.Google Scholar
  30. Nyhan, B., & Reifler, J. (2010). When corrections fail: The persistence of political misperceptions. Political Behavior, 32(2), 303–330.CrossRefGoogle Scholar
  31. Pennebaker, J., & Francis, M. (1996). Cognitive, emotional, and language processes in disclosure. Cognition and Emotion, 10(6), 601–612.CrossRefGoogle Scholar
  32. Perlmutter, M., & Nyquist, L. (1990). Relationship between self-reported physical and mental health and intelligence performance across adulthood. Journal of Gerontology, 45, 145–155.CrossRefGoogle Scholar
  33. Petrov, A., Dosher, B., & Lu, Z. (2005). The dynamics of perceptual learning: An incremental reweighting model. Psychological Review, 112(4), 715–743. doi: 10.1037/0033-295X.112.4.715.CrossRefPubMedGoogle Scholar
  34. Piaget, J. (1972). Development and learning. In C. S. Lavattelly (Ed.), Reading in child behavior and development. New York: Harcourt Brace Janovich.Google Scholar
  35. Piaget, J. (1974). The child’s conception of the world. London: Paladin Books.Google Scholar
  36. Piaget, J. (1977). Intellectual evolution from adolescence to adulthood. Cambridge: Cambridge University Press.Google Scholar
  37. Regan, A., & Hill, C. (1992). Investigation of what clients and counselors do not say in brief therapy. Journal of Counseling Psychology, 39(2), 168–174. doi: 10.1037/0022-0167.39.2.168.CrossRefGoogle Scholar
  38. Rutter, M. (2006). Genes and behavior: Nature-nurture interplay explained. Malden, MA: Blackwell Publishing.Google Scholar
  39. Shell, D., Brooks, D., Trainin, G., Wilson, K., Kauffman, D., & Herr, L. (2010). The unified learning model how motivational, cognitive, and neurobiological sciences inform best teaching practices. New York: Springer.Google Scholar
  40. Steele, C. M. (1988). The psychology of self-affirmation: Sustaining the integrity of the self. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 21, pp. 261–302). New York: Academic Press.Google Scholar
  41. Taber, C. S., & Lodge, M. (2006). Motivated skepticism in the evaluation of political beliefs. American Journal of Political Science, 50(3), 755–769.CrossRefGoogle Scholar
  42. Thatch, W. (1997). Context-response linkage. International Review of Neurobiology, 41, 599–611. doi: 10.1016/S0074-7742(08)60372-4.CrossRefGoogle Scholar
  43. Thorndike, E. (1932). The fundamentals of learning. New York: Teachers College Press.CrossRefGoogle Scholar
  44. van Merriënboer, J., Kester, L., & Paas, F. (2006). Teaching complex rather than simple tasks: Balancing intrinsic and germane load to enhance transfer of learning. Applied Cognitive Psychology, 20(3), 343–352. doi: 10.1002/acp.1250.CrossRefGoogle Scholar
  45. Vygotsky, L. (1934/1986). Thought and language. Cambridge: MIT Press.Google Scholar
  46. Wampol, B. (2015). How important are the common factors in psychotherapy? An update. World Psychiatry, 14(3), 270–277. doi: 10.1002/wps.20238.CrossRefGoogle Scholar
  47. Wasserman, T., & Wasserman, L. (2015). The misnomer of attention deficit hyperactivity disorder. Applied Neuropsychology: Child, 4(2), 115–122. doi: 10.1080/21622965.2015.1005487.Google Scholar
  48. Watson-Gegeo, K. (2004). Mind, language, and epistemology: Toward a language socialization paradigm for SLA. Modern Language Journal, 88(3), 331–350. doi: 10.1111/j.0026-7902.2004.00233.x.CrossRefGoogle Scholar
  49. Wood, J. V., Perunovic, W. Q. E., & Lee, J. W. (2009). Positive self-statements; power for some peril for others. Psychological Science, 20(7), 860–866.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Theodore Wasserman
    • 1
  • Lori Drucker Wasserman
    • 1
  1. 1.Wasserman and Drucker PABoca RatonUSA

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