Hostname: page-component-77f85d65b8-zzw9c Total loading time: 0 Render date: 2026-04-22T22:33:24.740Z Has data issue: false hasContentIssue false
  • English
  • Français

Computational models and empirical constraints

Published online by Cambridge University Press:  04 February 2010

Zenon W. Pylyshyn
Zenon W. Pylyshyn
Affiliation:
Departments of Psychology and Computer Science, The University of Western Ontario, London, Ontario, Canada N6A 5C2
Get access Rights & Permissions [Opens in a new window]

Abstract

It is argued that the traditional distinction between artificial intelligence and cognitive simulation amounts to little more than a difference in style of research - a different ordering in goal priorities and different methodological allegiances. Both enterprises are constrained by empirical considerations and both are directed at understanding classes of tasks that are defined by essentially psychological criteria. Because of the different ordering of priorities, however, they occasionally take somewhat different stands on such issues as the power/generality trade-off and on the relevance of the sort of data collected in experimental psychology laboratories.

Computational systems are more than a tool for checking the consistency and completeness of theoretical ideas. They are ways of empirically exploring the adequacy of methods and of discovering task demands. For psychologists, computational systems should be viewed as functional models quite independent of (and likely not reducible to) neurophysiological systems, and cast at a level of abstraction appropriate for capturing cognitive generalizations. As model objects, however, they do present a serious problem of interpretation and communication since the task of extracting the relevant theoretical principles from a large complex program may be formidable.

Methodologies for validating computer programs as cognitive models are briefly described. These may be classified as intermediate state, relative complexity, and component analysis methods. Compared with the constraints imposed by criteria such as sufficiency, breadth, and extendability, these experimentally based methods are relatively weak and may be most useful after some top-down progress is made in the understanding of methods sufficient for relevant tasks - such as may be forthcoming from artificial intelligence research.

Keywords

artificial intelligencecognitioncognitive sciencemethodologyempirical constraintscomputer simulation

Information

Type
Target Article
Information
Behavioral and Brain Sciences , Volume 1 , Issue 1 , March 1978 , pp. 91 - 99
Copyright
Copyright © Cambridge University Press 1978

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Anderson, J. R.Language, Memory, and Thought. Hillsdale, N.J.: Lawrence Erlbaum, 1976.Google Scholar
Anderson, J. R. and Bower, G. H.Human associative memory. Washington, D.C.: Winston, 1973.Google Scholar
Burks, A. W. Von Neumann's self-reproducing automatia: In Burks, A. W. (Ed.), Essays on cellular automata. Urbana, 111.: University of Illinois, 1970.Google Scholar
Butterfield, H.The origins of modern science 1300–1800. Toronto: Clark, Irwin and Co., 1957.Google Scholar
Chomsky, N.Reflections on language. New York: Pantheon, 1975.Google Scholar
Colby, K. M., Weber, S., and Hilf, F. D.Artificial Paranoia. Artificial Intelligence, 1971, 2, 1–25.CrossRefGoogle Scholar
Cummins, R.Functional analysi. Journal of Philosophy, 1975, 72, 741–765.CrossRefGoogle Scholar
Dennett, D.Intentional systems. Journal of Philosophy, 1971, 68, 87–106.CrossRefGoogle Scholar
Evans, T. G. A heuristic program to solve geometric analogy problems. In: Minsky, M. (Ed.), Semantic Information processing. Cambridge: M.I.T. Press, 1968.Google Scholar
Feigenbaum, E. A. The simulation of verbal learning behavior. In: Feigenbaum, E. A. and Feldman, J. (Eds.) Computers and thought. New York: McGraw-Hill, 1963.Google Scholar
Feigenbaum, E. A., Buchanan, B. G., and Lederberg, J. Generality and problem solving: A case study using the DENDRAL program. In: Meltzer, B. and Michie, D. (Eds.), Machine Intelligence 6, New York: American Elsevier, 1971.Google Scholar
Fodor, J. A.The language of thought. New York: Thomas Crowell, 1978. Computation and reduction.Google Scholar
In Savage, W. (ed). Minnesota Studies in Philosophy of Science, Vol. IX.Google Scholar
Goodman, N.Fact, fiction and forecast. Cambridge: Harvard University Press, 1955.Google Scholar
Groen, G J. and Parkman, J. M.A chronometric analysis of simple addition. Psychological Review, 1972,79, 329–343.CrossRefGoogle Scholar
Haugeland, J.The nature and plausibility of cognitivism. The Behavioral and Brain Sciences (next issue).Google Scholar
Langer, S.Philosophical sketches. Baltimore: Johns Hopkins Press, 1962.Google Scholar
Neisser, U.The imitation of man by machine. Science, 1963, 139, 193–197.Google Scholar
Newell, A. Heuristic programming: Ill-structured problems. In: Aranofsky, J.S. (Ed.), Progress in operations research, Vol. III. New York: Wiley, 1969.Google Scholar
Remarks on the relationship between artificial intelligence and cognitive psychology. In: Banerji, R. and Mesarovic, M. D. (Eds.), Theoretical approaches to non-numerical problem solving. New York: Springer-Verlag, 1970.CrossRefGoogle Scholar
A theoretical exploration of mechanisms for coding the stimulus. In Melton, A. W. and Martin, E. (Eds.), Coding processes in human memory, New York: Winston, 1972.Google Scholar
Production systems: Models of control structures. In Chase, W. (Ed.), Vtsual information processing. New York: Academic Press, 1973a.Google Scholar
You can't play 20 questions with nature and win. In: Chase, W. (Ed.), Visual information processing. New York: Academic Press, 1973b.Google Scholar
Newell, A. and Simon, H. A.Human problem solving. Englewood Cliffs, N.J.: Prentice-Hall, 1972.Google Scholar
Newell, A and Simon, H. A.Computer science as empirical inquiry: Symbols and search. Communications of the Association for Computing Machinery, 1976, 19, 113–126.CrossRefGoogle Scholar
Pylyshyn, Z. W. Complexity and the Study of Artificial and Human Intelligence. In Ringle, M. (ed.), Philosophical perspective in artificial intelligence. New York: The Humanities Press, 1978), in press. Towards a Foundation for Cognitive Science (in preparation).Google Scholar
Reitman, W. R.Cognition and thought. New York: Wiley, 1965.Google Scholar
Schmidt, C. F. Understanding human action: Recognizing the plans and motives of others. In: Carroll, J. and Payne, J. (Eds.), Cognition and social behavior. Hillsdale, N.J.: Lawrence Erlbaum, in press.Google Scholar
Shortliffe, E. H.Computer-based medical consultations: MYCIN. New York: Elsevier, 1976.Google Scholar
Simon, H. A.The sciences of the artificial. Cambridge, Mass.: M.I.T. Press, 1969.Google Scholar
Waltz, D. Understanding live drawings of scenes with shadows. In: Winston, P. H. (Ed.) The psychology of computer vision. New York: McGraw-Hill, 1975.Google Scholar
Winograd, T.Understanding natural language. New York: Academic Press, 1972.CrossRefGoogle Scholar
Winston, P. H. Learning structural descriptions from examples. In Winston, P. H. (Ed.), The psychology of computer vision. New York: McGraw-Hill, 1975.Google Scholar
Young, R. M.Childrens 's seriation behavior: A production system analysis. Unpublished doctoral dissertation, Carnegie-Mellon University, 1973.Google Scholar