Four Computational Models for Investigating Neuropsychological Decision-making

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.

[1]  H. Klonoff,et al.  Factor Analysis of Neuropsychological Tests , 1969, Perceptual and motor skills.

[2]  Patrick Henry Winston,et al.  The AI business: commercial uses of artificial intelligence , 1984 .

[3]  Dana H. Ballard,et al.  Viewer Independent Shape Recognition , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Michael Lebowitz,et al.  Not the Path to Perdition: The Utility of Similarity-Based Learning , 1986, AAAI.

[5]  Jerome A. Feldman,et al.  Neural Representation of Conceptual Knowledge. , 1986 .

[6]  Frederick Hayes-Roth,et al.  Building expert systems , 1983, Advanced book program.

[7]  G. Goldstein,et al.  Univariate vs. multivariate analysis in neuropsychological test assessment of lateralized brain damage. , 1973, Cortex; a journal devoted to the study of the nervous system and behavior.

[8]  G. Goldstein,et al.  Statistical and Normative Studies of the Halstead Neuropsychological Test Battery Relevant to a Neuropsychiatric Hospital Setting , 1972, Perceptual and motor skills.

[9]  Barr and Feigenbaum Edward A. Avron,et al.  The Handbook of Artificial Intelligence , 1981 .

[10]  A. Salasoo,et al.  Interaction of Knowledge Sources in Spoken Word Identification. , 1985, Journal of memory and language.

[11]  James L. McClelland,et al.  The TRACE model of speech perception , 1986, Cognitive Psychology.

[12]  Peter D. Eimas,et al.  Perspectives on the study of speech , 1981 .

[13]  James L. McClelland,et al.  Parallel Distributed Processing: Explorations in the Microstructure of Cognition : Psychological and Biological Models , 1986 .

[14]  W. C. Halstead.  Brain and intelligence : a quantitative study of the frontal Lobes , 1948 .

[15]  Jerome A. Feldman,et al.  Connectionist Models and Their Properties , 1982, Cogn. Sci..

[16]  David L. Waltz,et al.  Toward memory-based reasoning , 1986, CACM.

[17]  E. Shortliffe,et al.  Readings in medical artificial intelligence: the first decade , 1984 .

[18]  Eugene Charniak,et al.  The Bayesian Basis of Common Sense Medical Diagnosis , 1983, AAAI.

[19]  R. Reitan.  A Research Program on the Psychological Effects of Brain Lesions in Human Beings1 , 1966 .

[20]  Alexander I. Rudnicky,et al.  What's new in speech perception? The research and ideas of William Chandler Bagley, 1874-1946. , 1983, Psychological review.

[21]  J. Feldman.  Four frames suffice: A provisional model of vision and space , 1985, Behavioral and Brain Sciences.

[22]  D. Zipser,et al.  Biologically plausible models of place recognition and goal location , 1986 .

[23]  An Application of Discriminant Functions to the Problem of Predicting Brain Damage Using Behavioral Variables , 1963, Perceptual and motor skills.

[24]  E H Shortliffe,et al.  PUFF: an expert system for interpretation of pulmonary function data. , 1982, Computers and biomedical research, an international journal.

[25]  Wayne A. Lea,et al.  Trends in Speech Recognition , 1980 .

[26]  D. Swiercinsky,et al.  A factorial approach to neuropsychological assessment. , 1975, Journal of clinical psychology.

[27]  James L. McClelland,et al.  James L. McClelland, David Rumelhart and the PDP Research Group, Parallel distributed processing: explorations in the microstructure of cognition . Vol. 1. Foundations . Vol. 2. Psychological and biological models . Cambridge MA: M.I.T. Press, 1987. , 1989, Journal of Child Language.

[28]  Dennis H. Klatt,et al.  Speech perception: a model of acoustic–phonetic analysis and lexical access , 1979 .

[29]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[30]  Peter C. Cheeseman,et al.  A Method of Computing Generalized Bayesian Probability Values for Expert Systems , 1983, IJCAI.

[31]  R. Reitan,et al.  Discriminant Functions Applied to the Problem of Predicting Cerebral Damage from Behavioral Tests: A Cross-Validation Study , 1963, Perceptual and motor skills.

[32]  Lee D. Erman,et al.  The Hearsay-I Speech Understanding System: An Example of the Recognition Process , 1973, IEEE Transactions on Computers.