Convolution and modal representations in Thagard and Stewart’s neural theory of creativity: a critical analysis
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Abstract
According to Thagard and Stewart (Cogn Sci 35(1):1–33, 2011), creativity results from the combination of neural representations (an idea which Thagard calls ‘the combinatorial conjecture’), and combination results from convolution, an operation on vectors defined in the holographic reduced representation (HRR) framework (Plate, Holographic reduced representation: distributed representation for cognitive structures, 2003). They use these ideas to understand creativity as it occurs in many domains, and in particular in science. We argue that, because of its algebraic properties, convolution alone is ill-suited to the role proposed by Thagard and Stewart. The semantic pointer concept (Eliasmith, How to build a brain, 2013) allows us to see how we can apply the full range of HRR operations while keeping the modal representations so central to Thagard and Stewart’s theory. By adding another combination operation and using semantic pointers as the combinatorial basis, this modified version overcomes the limitations of the original theory and perhaps helps us explain aspects of creativity not covered by the original theory. While a priori reasons cast doubts on the use of HRR operations with modal representations (Fisher et al., Appl Opt 26(23):5039–5054, 1987) such as semantic pointers, recent models point in the other direction, allowing us to be optimistic about the success of the revised version.
Keywords
Creativity Neural network Semantic pointer Modal representation Holographic reduced representation Vector symbolic architectureNotes
Acknowledgments
We thank Paul Thagard for sending us the data on the combinatorial hypothesis, as well as Chris Eliasmith and Terry Stewart for patiently answering the questions of one of us (de Pasquale) about Spaun. We however take full responsibility for any error or misinterpretation in the present article.
References
- Anonymous (2014). Binding Problem. Wikipedia.org. Online. Consulted on January 31, 2014. http://en.wikipedia.org/wiki/Binding_problem
- Binder, J. R., & Desai, R. H. (2011). The neurobiology of semantic memory. Trends in Cognitive Sciences, 15(11), 527–536.CrossRefGoogle Scholar
- Binder, J. R., Desai, R. H., Graves, W. W., & Conant, L. L. (2009). Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cerebral Cortex, 19(12), 2767–2796.CrossRefGoogle Scholar
- Churchland, P. (1992). A neurocomputational perspective. Cambridge, MA: MIT Press.Google Scholar
- De Pasquale, J.-F., & Poirier, P. (2014). Can holographic reduced representations operations be used with modal representations? An experiment. UQAM, Laboratoire d’analyse cognitive de l’information, Montréal, Cahiers du Lanci, 17 p.Google Scholar
- Eliasmith, C. (2004). Learning context sensitive logical inference in a neurobiological simulation. In S. Levy & R. Gayler (Eds.), Compositional connectionism in cognitive science (pp. 17–20). AAAI Fall Symposium. Menlo Park: AAAI Press.Google Scholar
- Eliasmith, C. (2005). Cognition with neurons: A large-scale, biologically realistic model of the Wason task. In G. Bara, L. Barsalou, & M. Bucciarelli (Eds.), Proceedings of the 27th Annual Meeting of the Cognitive Science Society. Italy: Stresa.Google Scholar
- Eliasmith, C. (2013). How to build a brain. Oxford: Oxford University Press.CrossRefGoogle Scholar
- Eliasmith, C. E., & Anderson, C. H. (2002). Neural engineering. Computation, representation, and dynamics in neurobiological systems. Cambridge, MA: MIT Press.Google Scholar
- Eliasmith, C., Stewart, T. C., Choo, X., Bekolay, T., DeWolf, T., Tang, Y., et al. (2012). A large-scale model of the functioning brain. Science, 338(6111), 1202–1205.CrossRefGoogle Scholar
- Fisher, A. D., Lippincott, W. L., & Lee, J. N. (1987). Optical implementations of associative networks with versatile adaptive learning capabilities. Applied Optics, 26(23), 5039–5054.CrossRefGoogle Scholar
- Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28(1), 3–71.CrossRefGoogle Scholar
- Gayler, R. W. (2003). Vector Symbolic Architectures answer Jackendoff’s challenges for cognitive neuroscience. In P. Slezak (Ed.), ICCS /ASCS International Conference on Cognitive Science (pp. 133–138). Sydney, Australia: University of New South Wales.Google Scholar
- Giere, R. (2002). Scientific cognition as distributed cognition. In P. Carruthers, S. Stich, & M. Siegal (Eds.), The cognitive basis of science. Cambridge: Cambridge University Press.Google Scholar
- Gopnik, A. (2000). Explanation as orgasm, and the drive for causal understanding: The evolution, function and phenomenology of the theory-formation system. In F. Keil & R. Wilson (Eds.), Cognition and explanation (pp. 299–323). Cambridge, MA: MIT Press.Google Scholar
- Gross, J. J., & Barrett, L. F. (2011). Emotion generation and emotion regulation: One or two depends on your point of view. Emotion Review, 3, 8–16.CrossRefGoogle Scholar
- Halliday, D., Resnick, R., & Walker, J. (2004). Fundamentals of physics. New York: Wiley.Google Scholar
- Hickok, G. (2014). The myth of mirror neurons: The real neuroscience of communication and cognition. New York: WW Norton & Company.Google Scholar
- Hinton, G. (1990). Mapping part-whole hierarchies into connectionist networks. Artificial Intelligence, 46, 47–75.CrossRefGoogle Scholar
- Hinton, G. E. (2009). Deep belief networks. Scholarpedia, 4(5), 5947.CrossRefGoogle Scholar
- Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.CrossRefGoogle Scholar
- Hunsberger, E., Blouw, P., Bergstra, J., & Eliasmith, C. (2013). A neural model of human image categorization. In 35th Annual Conference of the Cognitive Science Society, pp. 633-638.Google Scholar
- Jones, M. N., & Mewhort, D. J. (2007). Representing word meaning and order information in a composite holographic lexicon. Psychological Review, 114(1), 1–37.CrossRefGoogle Scholar
- Kelly, M. A. (2010). Advancing the theory and utility of holographic reduced representations. Master Thesis, Kingston, ON: Queen’s University.Google Scholar
- Kelly, M. A., Blostein, D., & Mewhort, D. J. K. (2013). Encoding structure in holographic reduced representations. Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale, 67(2), 79.CrossRefGoogle Scholar
- Kim, K. H. (2005). Can only intelligent people be creative? A meta-analysis. Prufrock Journal, 16(2–3), 57–66.Google Scholar
- Levy, S. D. (2007). Changing semantic role representations with holographic memory. In Computational approaches to representation change during learning and development: Papers from the 2007 AAAI Symposium (pp. 40–45). Washington, DC: AAAI Press.Google Scholar
- Levy, S. D., & Gayler, R. W. (2008). Vector symbolic architectures: A new building material for artificial general intelligence. Proceedings of the First Conference on Artificial General Intelligence (AGI-08). IOS Press, pp. 414–418.Google Scholar
- Mahon, B. Z., & Caramazza, A. (2008). A critical look at the embodied cognition hypothesis and a new proposal for grounding conceptual content. Journal of Physiology, Paris, 102(2008), 59–70.CrossRefGoogle Scholar
- Markman, A., & Stilwell, H. (2004). Concepts á la modal: An extended review of Prinz’s Furnishing the mind. Philosophical Psychology, 17(3), 391–401.CrossRefGoogle Scholar
- Milner, P. M. (1974). A model for visual shape recognition. Psychological Review, 81(6), 521–535.CrossRefGoogle Scholar
- Neumann, J. (2001). PhD Thesis. Holistic Processing of Hierarchical Structures in Connectionist Networks. Edinburgh, UK: University of Edinburgh.Google Scholar
- Plate, T. A. (2003). Holographic reduced representation: Distributed representation for cognitive structures. Stanford: CSLI Publications.Google Scholar
- Prinz, J. J. (2002). Furnishing the mind. Cambridge, MA: MIT press.Google Scholar
- Rasmussen, D., & Eliasmith, C. (2011). A neural model of rule generation in inductive reasoning. Topics in Cognitive Science, 3(1), 140–153.CrossRefGoogle Scholar
- Revonsuo, A. (1999). Binding and the phenomenal unity of consciousness. Consciousness and Cognition, 8(2), 173–185.CrossRefGoogle Scholar
- Russell, J. A. (2009). Emotion, core affect, and psychological construction. Cognition and Emotion, 23, 1259–1283.CrossRefGoogle Scholar
- Rutledge-Taylor, M. F., Kelly, M. A., West, R. L., & Pyke, A. A. (2014). Dynamically structured holographic memory. Biologically Inspired Cognitive Architectures, 9, 9–32.CrossRefGoogle Scholar
- Sander, D., Grandjean, D., & Scherer, K. R. (2005). A systems approach to appraisal mechanisms in emotion. Neural Networks, 18(4), 317–352.CrossRefGoogle Scholar
- Schröder, T., Stewart, T. C., & Thagard, P. (2013). Intention, emotion, and action: A neural theory based on semantic pointers. Cognitive Science (published online November 13, 2013).Google Scholar
- Schröder, T., & Thagard, P. (2013). The affective meanings of automatic social behaviors: Three mechanisms that explain priming. Psychological Review, 120(1), 255–280.CrossRefGoogle Scholar
- Shadlen, M. N., & Movshon, J. A. (1999). Synchrony unbound: A critical evaluation of the temporal binding hypothesis. Neuron, 24, 67–77.CrossRefGoogle Scholar
- Sligh, A. C., Conners, F. A., & Roskos-Ewoldsen, Beverly. (2005). Relation of creativity to fluid and crystallized intelligence. The Journal of Creative Behavior, 39(2), 123–136.CrossRefGoogle Scholar
- Smolensky, P. (1990). Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artificial Intelligence, 46, 159–216.CrossRefGoogle Scholar
- Smythies, J. R. (1994). The walls of Plato’s cave. Aldershot: Avebury.Google Scholar
- Stewart, T., & Eliasmith, C. (2012). Compositionality and biologically plausible models. In W. Hinzen, E. Machery, & M. Werning (Eds.), Oxford handbook of compositionality. Oxford: Oxford University Press.Google Scholar
- Stewart, T. C., Tang, Y., & Eliasmith, C. (2011). A biologically realistic cleanup memory: Autoassociation in spiking neurons. Cognitive Systems Research, 12(2), 84–92.CrossRefGoogle Scholar
- Tang, Y., & Eliasmith, C. (2010). Deep networks for robust visual recognition. In Proceedings of the 27th International Conference on Machine Learning (ICML-10) (pp. 1055–1062).Google Scholar
- Thagard, P. (1988). Computational Philosophy of Science. Cambridge, MA: MIT Press.Google Scholar
- Thagard, P. (2012). The cognitive science of science: Explanation, discovery, and conceptual change. Cambridge, MA: MIT Press.Google Scholar
- Thagard, P., & Schröder, T. (2013). Emotions as semantic pointers: Constructive neural mechanism. In L. F. Barrett & J. A. Russell (Eds.), The physiological construction of emotions. New York: Guilford.Google Scholar
- Thagard, P., & Stewart, T. C. (2011). The Aha! experience: Creativity through emergent binding in neural networks. Cognitive Science, 35(1), 1–33; reprinted in Thagard, P. (2012), The cognitive science of science: Explanation, discovery, and conceptual change. Cambridge, MA: MIT Press, Chap. 8.Google Scholar
- Tripp, B. P., & Eliasmith, C. (2010). Population models of temporal differentiation. Neural Computation, 22(3), 621–659.CrossRefGoogle Scholar
- von der Malsburg, C. (1981). The correlation theory of brain function. MPI Biophysical Chemistry, Internal Report 81-2. Reprinted in E. Domany, J.L. van Hemmen, and K. Schulten (Eds.), (1994), Models of neural networks II. Berlin: Springer.Google Scholar