Skip to main content
SpringerLink
Log in

Dynamic Models of Simple Judgments: II. Properties of a Self-Organizing PAGAN (Parallel, Adaptive, Generalized Accumulator Network) Model for Multi-Choice Tasks

  • Published:
Nonlinear Dynamics, Psychology, and Life Sciences

Abstract

This is the second of two papers comparing connectionist and traditional stochastic latency mechanisms with respect to their ability to account for simple judgments. In the first, we reviewed evidence for a self-regulating accumulator module for two- and three-category discrimination. In this paper, we examine established neural network models that have been applied to predicting response time measures, and discuss their representational and adaptational limitations. We go on to describe and evaluate the network implementation of a Parallel Adaptive Generalized Accumulator Network (PAGAN), based on the interconnection of a number of self-regulating, generalized accumulator modules. The enhancement of PAGAN through the incorporation of distributed connectionist representation is briefly discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

REFERENCES

  • Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985). A learning algorithm for Boltzmann machines. Cognitive Science, 9, 147-169.

    Google Scholar 

  • Anderson, J. A. (1991). Why, having so many neurons, do we have so few thoughts? In W. E. Hockley and S. Lewandowsky (Eds.), Relating theory to data: Essays on human memory (pp. 477-507). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Anderson, J. A. (1995). An introduction to neural networks. Cambridge, MA: MIT Press/ Bradford.

    Google Scholar 

  • Anderson, J. R. (1990). The adaptive character of thought. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Arbib, M. A. (Ed.). (1995). Handbook of brain theory and neural networks. Cambridge, MA: MIT Press.

    Google Scholar 

  • Audley, R. J. (1973). Some observations on theories of choice reaction time: Tutorial review. In: S. Kornblum (Ed.). Attention & performance IV. New York: Academic Press, 509-545.

    Google Scholar 

  • Birnbaum, M. H., & Jou, J. (1990). A theory of comparative response times and ''difference'' judgments. Cognitive Psychology, 22, 184-210.

    Google Scholar 

  • Broadbent, D. E. (1965). A reformulation of the Yerkes-Dodson law. British Journal of Mathematical and Statistical Psychology, 18, 145-157.

    Google Scholar 

  • Broadhurst, P. L. (1959). The interaction of task difficulty and motivation: The Yerkes-Dodson law revived. Acta Psychologica, 16, 321-338.

    Google Scholar 

  • Brooks, R. A. (1991a). Intelligence without reason. AI memo 1293, Massachusetts Institute of Technology. (ftp://publications.ai.mit.edu/ai-publications/1000-1499/AIM-1293.ps.Z).

  • Brooks, R. A. (1991b). Intelligence without representation. Artificial Intelligence, 47, 139-159.

    Google Scholar 

  • Carpenter, G. A., & Ross, W. D. (1995). ART EMAP: A neural network architecture for object recognition by evidence accumulation, IEEE Transactions on Neural Networks, 6(4), 805-818.

    Google Scholar 

  • Chappell, M., & Humphreys, M. S. (1994). An auto-associative neural network for sparse representations: Analysis and applications to models of recognition and cued recall. Psychological Review, 101(1), 103-128.

    Google Scholar 

  • Cussins, A. (1990). The connectionist construction of concepts. In M. A. Boden (Ed.), The philosophy of artificial intelligence (pp. 368-441). Oxford: Oxford University Press.

    Google Scholar 

  • Eysenck, H. J. (1955). A dynamic theory of anxiety and hysteria. Journal of Mental Science, 101, 28-51.

    Google Scholar 

  • Falmagne, J. C. (1965). Stochastic models for choice-reaction time with applications to experimental results. Journal of Mathematical Psychology, 2, 77-124.

    Google Scholar 

  • French, R. M. (1991). Using semi-distributed representations to overcome catastrophic forgetting in connectionist networks. Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society (pp. 172-178). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Gigerenzer, G., & Goldstein, D.G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103(2), 650-669.

    Google Scholar 

  • Glenberg, A. M. (1997). What memory is for. Behavioral and Brain Sciences, 20, 1-55.

    Google Scholar 

  • Grossberg, S. (1976). Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors. Biological Cybernetics, 23, 121-134.

    Google Scholar 

  • Grossberg, S. (1980). How does the brain build a cognitive code? Psychological Review, 87, 1-51.

    Google Scholar 

  • Grossberg, S. (1982). Studies of mind and brain: Neural principles of learning, perception, development, cognition, and motor control. Boston, MA: Reidel.

    Google Scholar 

  • Grossberg, S. (Ed.). (1987a). The adaptive brain, I: Cognition, learning, reinforcement and rhythm. Amsterdam: Elsevier/North-Holland.

    Google Scholar 

  • Grossberg, S. (Ed.). (1987b). The adaptive brain, II: Vision, speech, language, and motor control. Amsterdam: Elsevier/North-Holland.

    Google Scholar 

  • Hale, D. J. (1968). The relation of correct and error responses in a serial choice reaction task. Psychonomic Science, 13, 299-300.

    Google Scholar 

  • Hale, D. J. (1969). Speed-error trade-off in a three-choice serial reaction task. Journal of Experimental Psychology, 81, 428-435.

    Google Scholar 

  • Hanson, S. J., & Burr, D. J. (1990). What connectionist models learn: Learning and representation in connectionist networks. Behavioral and Brain Sciences, 13, 471-518.

    Google Scholar 

  • Haykin, S. (1994). Neural networks: A comprehensive foundation. Englewood Cliffs, NJ: Macmillan.

    Google Scholar 

  • Hertz, J., Krogh, A., & Palmer, R. G. (1991). Introduction to the theory of neural computing. Redwood City, CA: Addison-Wesley.

    Google Scholar 

  • Hick, W. E. (1952). On the rate of gain of information. Quarterly Journal of Experimental Psychology, 4, 11-26.

    Google Scholar 

  • Hofstadter, D. R. (1985). Metamagical themas: Questing for the essence of mind and pattern. London: Viking.

    Google Scholar 

  • Kanerva, P. (1988). Sparse distributed memory. Cambridge, MA: MIT Press/Bradford.

    Google Scholar 

  • Keeler, J. D. (1988). Comparison between Kanerva's SDMand Hopfield-type neural networks. Cognitive Science, 12, 299-329.

    Google Scholar 

  • Knapp, A. G., & Anderson, J. A. (1984). Theory of categorization based on distributed memory storage. Journal of Experimental Psychology: Learning, Memory and Cognition, 10, 616-637.

    Google Scholar 

  • Kohonen, T. (1984). Self-organization and associative memory. New York: Springer-Verlag.

    Google Scholar 

  • Kosko, B. (1988). Bidirectional associative memories. IEEE Transactions on Systems, Man, and Cybernetics, 18, 49-60.

    Google Scholar 

  • Kruschke, J. K. (1993). Human category learning: Implications for backpropagation models. Connection Science, 5, 3-36.

    Google Scholar 

  • Lacouture, Y., & Marley, A. J. (1991). A connectionist model of choice and reaction time in absolute identification. Connection Science, 3, 401-433.

    Google Scholar 

  • Lacouture, Y., & Marley, A. J. (1995). A mapping model of bow effects in absolute identification. Journal of Mathematical Psychology, 39, 383-395.

    Google Scholar 

  • Laming, D. R. J. (1966). A new interpretation of the relation between choice reaction time and the number of equiprobable alternatives. British Journal of Mathematical and Statistical Psychology, 19, 139-149.

    Google Scholar 

  • Leth-Steenson, C. (1998). A connectionist, evidence accrual model of response times in symbolic comparison. Unpublished thesis submitted for the degree of Ph.D., McGill University, Montreal.

    Google Scholar 

  • Lippmann, R. P. (1987). An introduction to computing with neural networks. IEEE acoustic speech and signal processing magazine, April, 4-22.

  • Loftus, G. F., Busey, T. A., & Senders, J. (1993). Providing a sensory basis for models of visual information acquisition. Perception & Psychophysics, 54, 535-554.

    Google Scholar 

  • Luce, R. D. (1986). Response times. New York: Oxford University Press.

    Google Scholar 

  • McClelland, J. L. (1991). Toward a theory of information processing in graded, random, and interactive networks. In D. E. Meyer and S. Kornblum (Eds.), Attention and performance 14: Synergies in experimental psychology, artificial intelligence, and cognitive neuroscience (pp. 655-688). Cambridge, MA: MIT Press.

    Google Scholar 

  • McClelland, J. L. & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception: part 1. An account of basic findings. Psychological Review, 88, 375-407.

    Google Scholar 

  • Merkel, J. (1885), Die zeitlichen Verhältniss der Willensthätigkeit. Philosophische Studien, 2, 73-127.

    Google Scholar 

  • Minsky, M. (1986). The society of mind. New York: Simon and Schuster.

    Google Scholar 

  • Nettelbeck, T., & Brewer, N. (1976). Effects of stimulus-response variables on the choicereaction time of mildly retarded adults. American Journal of Mental Deficiency, 81, 85-92.

    Google Scholar 

  • Norman, D. A. (1993). Cognition in the head and in the world: An introduction to the special issue on situated action. Cognitive Science, 17, 1-6.

    Google Scholar 

  • Pachella, R. G., Fisher, D. F., & Karsh, R. (1968). Absolute judgments in speeded tasks: Quantification of the trade-off between speed and accuracy. Psychonomic Science, 12, 225-226.

    Google Scholar 

  • Peterson, C., & Hartman, E. (1989). Explorations of the mean-field theory learning algorithm. Neural Networks, 2, 475-494.

    Google Scholar 

  • Peterson, C., & Söderberg, B. (1995). Neural optimization. In M. A. Arbib (Ed.), Handbook of brain theory and neural networks (pp. 617-621). Cambridge, MA: MIT Press.

    Google Scholar 

  • Pew, R. W. (1969). The speed-accuracy operating characteristic. Acta Psychologica, 30, 16-26.

    Google Scholar 

  • Pietsch, A., & Vickers, D. (1997). Memory capacity and intelligence: Novel techniques for evaluating rival models of a fundamental information processing mechanism. Journal of General Psychology, 124, 229-339.

    Google Scholar 

  • Ratcliff, R. & van Zandt. (1996). Comparing connectionist and diffusion models of reaction time. Unpublished manuscript, Northwestern University, Evanston, IL. [http://www.psych.nwu.edu/proger].

    Google Scholar 

  • Rumelhart, D. E. (1989). The architecture of mind: A connectionist approach. In: M.I. Posner (Ed.), Foundations of cognitive science. Cambridge, MA: MIT Press, 133-159.

    Google Scholar 

  • Rumelhart, D. E., & McClelland, J. L. (1982). An interactive activation model of context effects in letter perception: part 2. The contextual enhancement effect and some tests and extensions of the model. Psychological Review, 89(1), 60-94.

    Google Scholar 

  • Rumelhart, D. E., Smolensky, P., McClelland, J. L., & Hinton, G. E. (1986). Schemata and sequential thought processes in PDP models. In J. L. McClelland and D. E. Rumelhart (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition, vol. 2 (pp. 7-57). Cambridge, MA: MIT Press.

    Google Scholar 

  • Ruppin, E., & Yeshurun, Y. (1991). Recall and recognition in an attractor neural network model of memory retrieval. Connection Science, 3(4), 381-400.

    Google Scholar 

  • Schouten, J. F., & Bekker, J. A. M. (1967). Reaction time and accuracy. Acta Psychologica, 27, 143-153.

    Google Scholar 

  • Shepard, R. N. (1987). Toward a universal law of generalization for psychological science. Science, 237, 1317-1323.

    Google Scholar 

  • Shepard, R. N., & Kannappan, S. (1991). Connectionist implementation of a theory of generalization. In R. P. Lippmann, J. E. Moody, D. S. Touretzky (Eds.), Advances in neural information processing systems 3 (pp. 665-671). San Mateo, CA: Morgan Kauffman.

    Google Scholar 

  • Shepard, R. N., & Tenenbaum, J. (1991). Connectionist modeling of multidimensional generalization. Paper presented at the Thirty Second Annual Meeting of the Psychonomic Society, San Francisco, November.

  • Simon, H. A. (1982). Models of bounded rationality. Cambridge, MA: M.I.T. Press.

    Google Scholar 

  • Smith, G. A. (1977). Studies of compatibility and a new model of choice reaction time. In S. Dornic (Ed.), Attention and Performance VI, (pp. 27-48). Hillsdale, N.J: Lawrence Erlbaum.

    Google Scholar 

  • Smolensky, P. (1988). On the proper treatment of connectionism. Behavioral and Brain Sciences, 11, 1-23.

    Google Scholar 

  • Swensson, R. G. (1972). The elusive trade-off: Speed versus accuracy in visual discrimination tasks. Perception and Psychophysics, 12, 16-32.

    Google Scholar 

  • Taylor, M. M., Lindsay, P. H., & Forbes, S. M. (1967). Quantification of shared capacity processing in auditory and visual discrimination. Acta Psychologica, 27, 223-229.

    Google Scholar 

  • Trabasso, T. R., & Riley, C. A. (1975). On the construction and use of representations involving linear order. In: R. L. Solso (Ed.), Information processing and cognition: The Loyola symposium. Hillsdale, NJ: Lawrence Erlbaum, 201-229.

    Google Scholar 

  • Trabasso, T. R., Riley, C. A., & Wilson, E. G. (1975). The representation of linear order and spatial strategies in reasoning: A developmental study. In: R. Falmagne (Ed.), Reasoning: Representation and process. Hillsdale, NJ: Lawrence Erlbaum, 201-229.

    Google Scholar 

  • Usher, M., & McClelland, J. L. (1995). On the time course of perceptual choice: A model based on the principles of neural computation. Technical report PDP.CNS.95.5: Center for the neural basis of cognition. [ftp://hydra.psy.cmu.edu/pub/pdp.cns/pdp.cns.95.5.ps.Z].

  • Vickers, D. (1979). Decision processes in visual perception. New York: Academic Press.

    Google Scholar 

  • Vickers, D., Caudrey, D., & Willson, R. (1971). Discriminating between the frequency of occurrence of two alternative events. Acta Psychologica, 35, 151-172.

    Google Scholar 

  • Vickers, D., & Lee, M. D. (1998). Dynamic models of simple judgments: I. Properties of a self-regulating accumulator module. Nonlinear Dynamics, Psychology, and Life Sciences, 2, 169-194.

    Google Scholar 

  • Welford, A. T. (1968). Fundamentals of skill. London: Methuen.

    Google Scholar 

  • Welford, A. T. (1971). What is the basis of choice-reaction time? Ergonomics, 14, 679-693.

    Google Scholar 

  • Welford, A. T. (1973). Attention, strategy, and reaction time: A tentative metric. In S. Kornblum (Ed.), Attention and Performance IV, and (pp. 37-53). New York: Academic Press.

    Google Scholar 

  • Welford, A. T. (1980). Reaction times. New York: Academic Press.

    Google Scholar 

  • Wickelgren, W. A. (1977). Speed-accuracy tradeoff and information processing dynamics. Acta Psychologica, 41, 67-86.

    Google Scholar 

  • Willshaw, D. J., Buneman, O. P.,& Longuet-Higgins, H. C. (1969). Non-holographic associative memory. Nature, 222, 960-962.

    Google Scholar 

  • Woocher, F. D., Glass, A. L., & Holyoak, K. J. (1978). Positional discriminability in linear orderings. Memory and Cognition, 6, 165-173.

    Google Scholar 

  • Yuille, A. L. (1995). Constrained optimization and the elastic net. In M.A. Arbib (Ed.), Handbook of brain theory and neural networks (pp. 250-255). Cambridge, MA: MIT Press.

    Google Scholar 

Download references

Author information

Author notes

  1. Michael D. Lee

    Present address: Communications Division, Defence Science and Technology Organisation, PO Box 1500, Salisbury, South Australia, 5108

Authors and Affiliations

  1. Department of Psychology, University of Adelaide, South Australia

    Douglas Vickers & Michael D. Lee

Authors
  1. Douglas Vickers
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael D. Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Vickers, D., Lee, M.D. Dynamic Models of Simple Judgments: II. Properties of a Self-Organizing PAGAN (Parallel, Adaptive, Generalized Accumulator Network) Model for Multi-Choice Tasks. Nonlinear Dynamics Psychol Life Sci 4, 1–31 (2000). https://doi.org/10.1023/A:1009571011764

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1009571011764

Search

Navigation