Skip to main content Accessibility help
  • Print publication year: 2005
  • Online publication date: June 2012

5 - Churchland on Connectionism



Paul Churchland cemented his appointment as Ambassador of Connectionism to Philosophy with the 1986 publication of his paper “Some reductive strategies in cognitive neurobiology.” However, as Churchland tells the story in the preface to his collection of papers, A Neurocomputational Perspective, his relationship with connectionism began three years earlier, when he became acquainted with the model of the cerebellum put forward by Andras Pellionisz and Rodolfo Llinas (1979). The work of Pellionisz and Llinas foreshadows many of the arguments that Churchland makes. They argue that functions of the brain are represented in multidimensional spaces, that neural networks should therefore be treated as “geometrical objects” (323), and that “the internal language of the brain is vectorial” (330). The Pellionisz and Llinas paper also includes an argument for the superiority of neural network organization over von Neumann computer organization on the grounds that the network is more reliable and resistant to damage, a theme to which Churchland often returns.

Over the years, Churchland has applied connectionism to several areas of philosophy, notably: philosophy of mind, epistemology, philosophy of science, and ethics. Churchland's arguments in these areas have a common structure. First, he shows that the predominant positions in the field are (a) based on an assumption that the fundamental objects of study are propositions and logical inferences, and (b) have significant internal difficulties largely attributable to that assumption. Second, he presents a re-construal of the field based on connectionism, giving a “neurocomputational perspective” on the fundamental issues in the field.

Abeles, M., Bergman, H., Margalit, E., & Vaadia, E. (1993). “Spatiotemporal firing patterns in the frontal cortex of behaving monkeys.” Journal of Neurophysiology 70(4): 1629–38
Ashby, F. G., Boynton, G., & Lee, W. W. (1994). “Categorization response time with multidimensional stimuli.” Perception & Psychophysics 55(1): 11–27
Bair, W., & Koch, C. (1996). “Temporal precision of spike trains in extrastriate cortex of the behaving macaque monkey.” Neural Computation 8(6): 1185–202
Ballard, D. H. (1986). Parallel logical inference and energy minimization. Proceedings of the 5th National Conference on Artificial Intelligence (AAAI-86) (Vol. 1, pp. 203–9). Philadelphia, Morgan Kaufmann
Ballard, D. H. (1999). An introduction to natural computation. Cambridge, MA, MIT Press
Barsalou, L. W. (1985). “Ideals, central tendency, and frequency of instantiation as determinants of graded structure in categories.” Journal of Experimental Psychology: Learning, Memory, & Cognition 11(1–4): 629–54
Barsalou, L. W (1991). Deriving categories to acheive goals. Bower, G. H. (Ed.), The psychology of learning and motivation: Advances in research and theory, (Vol. 27, pp. 1–64). San Diego, Academic Press
Bickerton, D . (1995). Language and human behavior. Seattle, University of Washington Press
Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford, Clarendon Press
Churchland, P. M. (1986). “Some reductive strategies in cognitive neurobiology.” Mind 95(379): 279–309
Churchland, P. M. (1988). Folk psychology and the explanation of human behavior. A neurocomputational perspective: The nature of mind and the structure of science (pp. 111–35). Cambridge, MA, MIT Press/Bradford Books
Churchland, P. M. (1989a). Learning and conceptual change. A neurocomputational perspective: The nature of mind and the structure of science (pp. 231–53). Cambridge, MA, MIT Press/Bradford Books
Churchland, P. M. (1989b). Moral facts and moral knowledge. A neurocomputational perspective: The nature of mind and the structure of science (pp. 297–303). Cambridge, MA, MIT Press/Bradford Books
Churchland, P. M. (1989c). A neurocomputational perspective: The nature of mind and the structure of science. Cambridge, MA, MIT Press/Bradford Books
Churchland, P. M. (1989d). On the nature of explanation: A PDP approach. A neurocomputational perspective: The nature of mind and the structure of science (pp. 197–230). Cambridge, MA, MIT Press/Bradford Books
Churchland, P. M. (1989e). Preface. A neurocomputational perspective: The nature of mind and the structure of science (pp. ⅺ–ⅹⅶ). Cambridge, MA, MIT Press/Bradford Books
Churchland, P. M. (1990). On the nature of theories: A neurocomputational perspective. Savage, C. W. (Ed.), Scientific theories (Vol. 14). Minneapolis, University of Minneapolis Press
Churchland, P. M. (1995). The engine of reason, the seat of the soul: A philosophical journey into the brain. Cambridge, MA, MIT Press/Bradford Books
Cottrell, G. (1985). Parallelism in inheritance hierarchies with exceptions. Proceedings of the Eighth International Joint Conference on Artificial Intelligence, Los Angeles, CA
Cottrell, G. W., Bartell, B., & Haupt, C. (1990). Grounding meaning in perception. Marburger, H. (Ed.), Proceedings of the German Workshop on Artificial Intelligence (GWAI) (pp. 307–21). Berlin, Springer-Verlag
Derthick, M. A. (1987). A connectionist architecture for representing and reasoning about structured knowledge. Proceedings of the Ninth Annual Conference of the Cognitive Science Society (pp. 131–42). Hillsdale, NJ, Lawrence Erlbaum Associates
Elman, J. L. (1991). “Distributed representations, simple recurrent networks, and grammatical structure.” Machine Learning 7: 195–225
Fodor, J. A. (1975). The language of thought. Cambridge, MA, Harvard University Press
Gerstner, W. (2001). What's different with spiking neurons? Mastebroek, H. & Vos, H. (Eds.), Plausible neural networks for biological modeling (pp. 23–48). Boston: Kluwer
Gerstner, W ., & Kistler, W. M. (2002). Spiking neuron models: Single neurons, populations, plasticity. Cambridge, UK, Cambridge University Press
Gilmore, G. C., Hersh, H., Caramazza, A., & Griffin, J. (1979). “Multidimensional letter similarity derived from recognition errors.” Perception and Psychophysics, 25: 425–31
Glymour, C. (2001). The mind's arrows: Bayes nets and graphical causal models in psychology. Cambridge, MA, MIT Press
Goldstone, R. L. (1994). “The role of similarity in categorization: providing a groundwork.” Cognition 52: 125–57
Goldstone, R. L., & Son, J. (2005). Similarity. Holyoak, K. & Morrison, R. (Eds.), Cambridge handbook of thinking and reasoning. Cambridge, UK, Cambridge University Press
Gorman, R. P., & Sejnowski, T. J. (1988). “Analysis of hidden units in a layered network trained to classify sonar targets.” Neural Networks 1: 75–89
Hahn, U., Chater, N., & Richardson, L. B. (2002). “Similarity as transformation.” Cognition, 87: 1–32
Hertz, J., Krogh, A., & Palmer, R. G. (1991). Introduction to the theory of neural computation. New York, Addison-Wesley
Holyoak, K. J., & Gordon, P. C. (1983). “Social reference points.” Journal of Personality and Social Psychology 44: 881–7
Hopfield, J. J., & Brody, C. D. (2001). “What is a moment? Transient synchrony as a collective mechanism for spatiotemporal integration.” Proceedings of the National Academy of Sciences 98(3)
Krumhansl, C. L. (1978). “Concerning the applicability of geometric models to similarity data: The interrelationship between similarity and spatial density.” Psychological Review 85: 450–63
Kuhn, T. (1962). The structure of scientific revolutions. Chicago, University of Chicago Press
Laakso, A., & Cottrell, G. W. (2000). “Content and cluster analysis: Assessing representational similarity in neural systems.” Philosophical Psychology 13(1): 77–95
Maass, W. (1998). On the role of time and space in neural computation. Proceedings of the Federated Conference of CLS'98 and MFCS'98 (Vol. 1450, pp. 72–83). Berlin: Springer
Malsburg, C. von. (1995). “Binding in models of perception and brain function.” Current Opinion in Neurobiology 5: 520–6
Nosofsky, R. M. (1991). “Stimulus bias, asymmetric similarity, and classification.” Cognitive Psychology 23: 94–140
Palmeria, T. J., & Nosofsky, R. M. (2001). “Central tendencies, extreme points, and prototype enhancment effects in ill-defined perceptual categorization.” Quarterly Journal of Experimental Psychology: Human Experimental Psychology 54A(1): 197–235
Pellionisz, A., & Llinas, R. (1979). “Brain modeling by tensor network theory and computer simulation. The cerebellum: Distributed processor for predictive coordination.” Neuroscience 4: 323–48
Plate, T. A. (1995). “Holographic reduced representations.” IEEE Transactions on Neural Networks 6(3): 623
Podgorny, P., & Garner, W. R. (1979). “Reaction time as a measure of inter-intraobject visual similarity: Letters of the alphabet.” Perception and Pyschophysics 26(1): 37–52
Pollack, J. B. (1990). “Recursive distributed representations.” Artificial Intelligence 46(1–2): 77–105
Quine, W. V. O. (1951). “Two dogmas of empiricism.” Philosophical Review 60: 20–43
Rao, R. P. N., & Sejnowski, T. J. (2001). “Spike-timing-dependent Hebbian placticity as temporal difference learning.” Neural Computation 13(10): 2221–37
Rosch, E., & Mervis, C. B. (1975). “Family resemblances: Studies in the internal structure of categories.” Cognitive Psychology 7(4): 573–605
Russell, S. J., & Norvig, P. (2003). Artificial intelligence: A modern approach (2nd ed.). Upper Saddle River, NJ, Prentice-Hall
Schlimmer, J. S. (1987a). Concept acquisition through representational adjustment. Unpublished doctoral dissertation, University of California, Irvine
Schlimmer, J. S (1987b). Mushrooms dataset. The UCI Machine Learning Repository. (Retrieved August 11, 2004, from
Sejnowski, T. J., & Rosenberg, C. R. (1987). “NETtalk: Parellel networks that learn to pronounce english text.” Complex Systems 1, 145–68
Shastri, L., & Ajjanagadde, V. (1993). “From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony.” Behavioral and Brain Sciences 16: 417–94
Shon, A. P., Rao, R. P. N., & Sejnowski, T. J. (2004). “Motion detection and prediction through spike-timing dependent plasticity.” Network: Computation in Neural Systems 15: 179–98
Singer, W., & Gray, C. M. (1995). “Visual feature integration and the temporal correlation hypothesis.” Annual Review of Neuroscience 18: 555–86
Smolensky, P. (1990). “Tensor product variable binding and the representation of symbolic structures in connectionist systems.” Artificial Intelligence 46(1–2): 159–216
Tesar, B., & Smolensky, P. (1994). Synchronous-firing variable binding is spatio-temporal tensor product representation. Ram, A. & Eiselt, K. (Eds.), Proceedings of the 16th Annual Conference of the Cognitive Science Society. Hillsdale, NJ, Lawrence Erlbaum Associates
Thorpe, S., Fize, D., & Marlot, C. (1996). “Speed of processing in the human visual system.” Nature 381: 520–2
Touretzky, D. S. (1990). “BoltzCONS: dynamic symbol structures in a connectionist network.” Artificial Intelligence 46: 5–46
Touretzky, D. S., & Hinton, G. E. (1985). Symbols among the neurons: Details of a connectionist inference architecture. Proceedings of the 9th International Joint Conference on Artificial Intelligence (IJCAI 85) (pp. 238–43). San Mateo, CA, Morgan Kaufmann
Tversky, A. (1977). “Features of similarity.” Psychological Review 84(4): 327–52
Tversky, A., & Gati, I. (1978). Studies of similarity. E. Rosch & B. Lloyd (Eds.). Cognition and categorization (pp. 79–98). Hillsdale, NJ, Lawrence Erlbaum Associates
Tversky, A., & Hutchinson, J. W. (1986). “Nearest-neighbor analysis of psychological spaces.” Psychological Review 93: 3–22