Parallel Distributed Processing

  • Living reference work entry
  • First Online:
Encyclopedia of Animal Cognition and Behavior
  • 123 Accesses

Introduction

The modern understanding of biological cognition has been heavily influenced by the computer metaphor or the idea that cognitive processes can be construed in terms of information processing. This idea was conceived in the early 1950s of the twentieth century when it was realized that the inventory of newly discovered digital computers provides a powerful framework for understanding the mind. Cognitive scientists readily grasped the idea that performance in tasks such as problem solving or visual object recognition can be described by drafting an algorithm of a series of distinct computations operating on well-defined data structures. The advantage of this approach was that it allowed the development of formal theories of cognition and the generation of testable predictions about behavior. However, it was soon realized that there are aspects of cognition which are hard to be accounted for in this manner. For example, the high granularity of the computer-inspired operations...

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

Access this chapter

Institutional subscriptions

References

  • Abudarham, N., Shkiller, L., & Yovel, G. (2019). Critical features for face recognition. Cognition, 182, 73–83.

    Article  Google Scholar 

  • Bengio, Y., LeCun, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

    Article  Google Scholar 

  • Bowers, J. S. (2017). Parallel distributed processing theory in the age of deep networks. Trends in Cognitive Science, 21, 950–961.

    Article  Google Scholar 

  • Bowers, J. S., Vankov, I. I., Damian, M. F., & Davis, C. J. (2014). Neural networks learn highly selective representations in order to overcome the superposition catastrophe. Psychological Review, 121(2), 248–261.

    Article  Google Scholar 

  • Carey, S., & Bartlett, E. (1978). Acquiring a single new word. Proceedings of the Stanford Child Language Conference, 15, 17–29.

    Google Scholar 

  • Dietrich, E., & Markman, A. B. (2003). Discrete thoughts: Why cognition must use discrete representations. Mind & Language, 18(1), 95–119.

    Article  Google Scholar 

  • Elman, J. L., Bates, E. A., Johnson, M. H., Karmiloff-Smith, A., Parisi, D., & Plunkett, K. (1996). Rethinking innateness: A connectionist perspective on development. Cambridge: Bradford Books/MIT Press.

    Google Scholar 

  • Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28(1–2), 3–71.

    Article  Google Scholar 

  • Forbus, K. D., Liang, C., & Rabkina, I. (2017). Representation and computation in cognitive models. Topics in Cognitive Science, 9(3), 694–718.

    Article  Google Scholar 

  • Gerstner, W., & Kistler, W. M. (2002). Spiking neuron models: Single neurons, populations, plasticity. Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  • Hebb, D. O. (1961). Distinctive features of learning in the higher animal. In J. F. Delafresnaye (Ed.), Brain mechanisms and learning. London: Oxford University Press.

    Google Scholar 

  • Hochreiter, S., & Schmidhuber, J. (1997). LSTM can solve hard long time lag problems. In M. C. Mozer, M. I. Jordan, T. Petsche (Eds.) Advances in Neural Information Processing Systems 9, NIPS’9, 473–479, MIT Press, Cambridge MA.

    Google Scholar 

  • Hubel, D. H., & Wiesel, T. N. (1959). Receptive fields of single neurons in the cat's striate cortex. The Journal of Physiology, 124(3), 574–591.

    Article  Google Scholar 

  • Hummel, J. E. (2016). Putting distributed representations into context. Language, Cognition and Neuroscience, 32(3), 359–365.

    Article  Google Scholar 

  • Kaminski, J., Call, J., & Fischer, J. (2004). Word learning in a domestic dog: Evidence for "Fast Map**". Science, 304(5677), 1682–1683.

    Article  Google Scholar 

  • LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Back-propagation applied to handwritten zip code recognition. Neural Computation, 1(4), 541–551.

    Article  Google Scholar 

  • Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., & Hinton, G. (2020). Backpropagation and the brain. Nature Reviews. Neuroscience. https://doi.org/10.1038/s41583-020-0277-3.

  • McClelland, J. L., McNaughton, B. L., & O’Reilly, R. C. (1995). Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102, 419–457.

    Article  Google Scholar 

  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133.

    Article  Google Scholar 

  • Minsky, M. L., & Papert, S. A. (1969). Perceptrons. Cambridge, MA: MIT Press.

    Google Scholar 

  • Rosenblatt, F. (1957). The Perceptron – A perceiving and recognizing automaton. Report 85–460-1. Cornell Aeronautical Laboratory.

    Google Scholar 

  • Rumelhart, D. E., McClelland, J. L., & The PDP Research Group. (1986). Parallel distributed processing: Explorations in the microstructure of cognition. Cambridge, MA: MIT Press. isbn: 978-026268053.

    Google Scholar 

  • Silver, D., Huang, A., Maddison, C., et al. (2016). Mastering the game of go with deep neural networks and tree search. Nature, 529, 484–489.

    Article  Google Scholar 

  • Vankov, I., & Bowers, J. (2020). Training neural networks to encode symbols enables combinatorial generalization. Philosophical Transactions of the Royal Society B, 375, 20190309.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Vankov, I. (2021). Parallel Distributed Processing. In: Vonk, J., Shackelford, T. (eds) Encyclopedia of Animal Cognition and Behavior. Springer, Cham. https://doi.org/10.1007/978-3-319-47829-6_738-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47829-6_738-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47829-6

  • Online ISBN: 978-3-319-47829-6

  • eBook Packages: Springer Reference Behavioral Science and PsychologyReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

Publish with us

Policies and ethics

Navigation