Four Computational Models for Investigating Neuropsychological Decision-making

  • Debra L. Long
  • Arthur C. Graesser
  • Charles J. Long
Part of the Human Neuropsychology book series (HN)


Neuropsychological decision-making in the clinical setting can be investigated from the perspective of different computational models derived from cognitive science. In this chapter, we focus on four of these models: Multivariate analyses, expert knowledge-based systems, exemplar-based reasoning models, and connectionist models. All presuppose that neuropsychological decision-making is essentially a complex pattern recognition task. For example, the neuropsychologist might attempt to recognize the locus of a lesion on the basis of a pattern of symptoms, test scores, and historical data. The four models in this chapter provide substantially different solutions for this complex pattern recognition problem.


Expert System Speech Perception Hide Unit Connectionist Model Certainty Factor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Atkins, J.S., Kunz, J.C., Shortliffe, E.H., Fallat, R.S. (1984). PUFF: An expert system for interpretation of pulmonary function data. In B.C. Clancey E.H. Shortliffe, Readings in medical artificial intelligence: The first decade. Reading, Mass.: Addison-Wesley.Google Scholar
  2. Ballard, D.H., Sabbah, D. (1983). View-independent shape recognition. IEEE Trans on Pattern Analysis and Machine Intelligence, 5, 6, 653–660.CrossRefGoogle Scholar
  3. Barr, A., Cohen, P. R., Feigenbaum, E.A. (Eds.) (1981). The handbook of artificial intelligence: Vol. 1 and 2. Los Altos, CA William Kaufman.Google Scholar
  4. Buchanan, B.G., Shortliffe, E.H. (Eds.) (1984). Rule-based expert systems. Reading, Mass.: Addison-Wesley.Google Scholar
  5. Charniak, E. (1983). The Bayesian basis of common sense medical diagnosis. In Proceedings of the 3rd National Conference on Artificial Intelligence (Washington D.C. Aug. 22–26 ). American Association for Artificial Intelligence, Menlo Park, CA, pp. 70–73.Google Scholar
  6. Cheeseman, P.A. (1983). A method for computing Bayesian probability values for expert systems. In Proceedings of the 8th International Joint conference on Artificial Intelligence. (Karlsruhe, West Germany, Aug. 8–12 ) 168–202.Google Scholar
  7. Clancey, D.C., Shortliffe, E.H. (Eds.) (1984). Readings in medical artificial intelligence: The first decade. Reading, Mass.: Addison-Wesley.Google Scholar
  8. Coles, R.A., Rudivicky, A. (1983). What’s new in speech perception? The research and ideas of William Chandler Bagley, 1894–1946. Psychological Review, 90, 94–101.Google Scholar
  9. Crockett, D., Klonoff, H. Bjerning, J. (1969). Factor analyses of neuropsychological tests. Perceptual and Motor Skills, 29, 791–802.Google Scholar
  10. Cureton, E.E., Elder, R.F., Fowler, R.C., Mona, M.A. (1962). Perseveration factor. Science, 135, 794.Google Scholar
  11. Erman, L.D., Lesser, U.R. (1980). The Hearsay-II speech understanding system: A tutorial. In W.A. Lea (Ed.), Trends in speech recognition. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  12. Feldman, J.A. (1985). Four frames suffice: A provisional model of vision and space. The Behavioral and Brain Sciences, 8, 265–219.Google Scholar
  13. Feldman, J.A. (1986). Neural representation of conceptual knowledge. Technical Report 189, June.Google Scholar
  14. Feldman, J.A., Ballard, D.H. (1982). Connectionist models and their properties. Cognitive Science, 6, 205–254.Google Scholar
  15. Goldstein„ G., Shelly, C. H. (1972). Statistical and normative study of the Halstead Neuropsychology Test Battery relative to a neuropsychiatric hospital setting. Perceptual and Motor Skills, 34, 603–620.Google Scholar
  16. Goldstein, G., Shelly, C.H. (1973). Univariate vs. multivariate analyses in neuropsychological test assessment. Cortex, 9, 204–216.Google Scholar
  17. Halstead, W.C. (1947). Brain and intelligence: A quantitative study of the frontal lobes. Chicago: University of Chicago Press.Google Scholar
  18. Harmon, P., King, D. (1985) Artificial intelligence in business. New York: John Wiley Sons.Google Scholar
  19. Hayes-Roth, F. (1984). Knowledge-based expert systems: The technological and commercial state of the art. Computer, Aug.Google Scholar
  20. Hayes-Roth, F., Waterman, D., Lennet, D. (Eds.) (1983). Building expert systems. Reading, Mass.: Addison-Wesley.Google Scholar
  21. Klatt, D. H. (1980). Speech perception: A model of acoustic-phonetic analysis and lexical access. In R. Cole (Ed.), Perception and production of fluent speech. Hillsdale, NJ: Erlbaum.Google Scholar
  22. Lebowitz, M. (1986). Not the path to perdition: The utility of similarity-based learning. In Proceedings of the 5th National Conference on Artificial Intelligence. (Philadelphia, PA, Aug. 11–15 ). American Association for Artificial Intelligence. Menlo Park, CA, p. 533–537.Google Scholar
  23. Long, C.J. (1985). Neuropsychology in private practice: Its changing focus. Psychotherapy in Private Practice, 3, 45–55.Google Scholar
  24. McClelland, J. L., Elman, J. L. (1986). The TRACE model of speech perception. Cognitive Psychology, 18, 1–86.Google Scholar
  25. McClelland, J. L., Rummelhart, D. E. (Eds.) (1986). Parallel distributed processing: Explorations in the microstructures of cognition: Volume 1: Foundations. Cambridge, Mass.: The MIT Press.Google Scholar
  26. Pople, H.E. (1982). Heuristic methods for imposing structure on ill-structured problems: The structuring of medical diagnostics. In P. Szolovits (Ed.), Artificial intelligence in medicine. Boulder, CO: Westview Press.Google Scholar
  27. Reddy, D.R., Erman, L.D., Fennell, R.D., Neely, R.B. (1973). The Hearsay speech understanding system: An example of the recognition process. Proceedings of the International Conference on Artificial Intelligence, 185–194.Google Scholar
  28. Reitan, R.M. (1966). A research program on the psychological effects of brain Lesions in human beings. IN H.R. Ellis (Ed.), International review of research in mental retardation (Vol. 1 ). New York: Academic Press.Google Scholar
  29. Rummelhart, D.E., McClelland, J.A. (Eds.) (1986). Parallel distributed processing: Explorations in the microstructure of cognition: Volume 2: Psychological and biological models. Cambridge, Mass.: The MIT Press.Google Scholar
  30. Salasoo, A., Pissoni, D.R. (1985). Interaction of knowledge sources in spoken word identification. Journal of Memory and Language, 24, 210–231.Google Scholar
  31. Stanfill, C., Waltz, J.D. (1986). Toward memory-based reasoning. Communications of the ACM, 29 (12), 1213–1228.Google Scholar
  32. Stevens, D., Blumstein, S. (1981). The search for invariant acoustic correlates of phonetic features. In P.H. Eimas J.L. Miller (Eds.), Perspectives on the study of speech. Hillsdale, NJ: Erlbaum.Google Scholar
  33. Swiercinsky, D.P., Hallenback, E.0 (1975). A factorial approach to neuropsychological assessment. Journal of Clinical Psychology, 31, 610–618.Google Scholar
  34. Winston, P.H., Prendergast, K.A. (Eds.) (1984). The AI business: The commercial uses of artificial intelligence. Cambridge, Mass.: MIT Press.Google Scholar
  35. Wheeler, L., Burke, C., Reitan, R. M. (1963). An application of discriminant functions to the problem of predicting brain damage using behavioral variables. Perceptual and Motor Skills, 16, 417–440.Google Scholar
  36. Wheeler, L, Reitan, R. M. (1963). Discriminant functions applied to the problem of predicting cerebral damage from behavioral tests: A cross-validation study. Perceptual and Motor Skills, 16, 681–701.Google Scholar
  37. Zipper, D. (1986). Biologically plausible models of place recognition and goal location. In D.E. Rummelhart J.L. McClelland (Eds.), Parallel distributed processing: Explorations in the microstructures of cognition, Volume 1: Foundations. Cambridge, Mass.: The MIT Press.Google Scholar

Copyright information

© Plenum Press, New York 1988

Authors and Affiliations

  • Debra L. Long
  • Arthur C. Graesser
  • Charles J. Long

There are no affiliations available

Personalised recommendations