Abstract
Given that original purpose of cognitive architectures was to lead to a unified theory of cognition, this chapter considers the possible contributions that cognitive architectures can make to embodied theories of cognition in particular. This is not a trivial question since the field remains very much divided about what embodied cognition actually means, and we will see some example positions in this chapter. It is then argued that a useful embodied cognitive architecture would be one that can demonstrate (a) what precisely the role of the body in cognition actually is, and (b) whether a body is constitutively needed at all for some (or all) cognitive processes. It is proposed that such questions can be investigated if the cognitive architecture is designed so that consequences of varying the precise embodiment on higher cognitive mechanisms can be explored. This is in contrast with, for example, those cognitive architectures in robotics that are designed for specific bodies first; or architectures in cognitive science that implement embodiment as an add-on to an existing framework (because then, that framework is by definition not constitutively shaped by the embodiment). The chapter concludes that the so-called semantic pointer architecture by Eliasmith and colleagues may be one framework that satisfies our desiderata and may be well-suited for studying theories of embodied cognition further.
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Notes
- 1.
This is arguably the most relevant aim. If the aim is simply to create a robot controller, then there is no particular need to appeal to theories of human cognition, and therefore also no ambiguities due to a lack of an agreed-upon meaning of the terms used.
- 2.
The different flavours of embodied cognition have been extensively reviewed by multiple researchers over the past two decades. It is not the purpose here to produce another such review.
- 3.
One interesting effect resulting from the use of biologically plausible (and therefore constrained) neurons to implement models is that the actual behaviour of the model may differ from the symbolic description, for example, if the latter stipulates computations that cannot be accurately implemented by the neurons. In fact, without this, the case for going through the trouble of creating the neural implementation would be much less compelling.
- 4.
This separates NEF/SPA from most other attempts to create architectures that operate both at symbolic and subsymbolic levels: traditionally, these often start with an arbitrary symbolic framework that is then converted into a neural representation (which is always possible, given that neural networks are universal function approximators, so there is nothing intrinsically insightful in this step alone). Such “arbitrary” marriages have never been particularly compelling [1]. In NEF/SPA, the symbolic language in which a cognitive model is expressed is defined and constrained by an understanding of the underlying neural substrate.
- 5.
One aspect of this compression mechanism and the binding of vectors that we do not go into detail about here is that it is reversible: the compressed encoding is easily manipulable in computations, but should there be a need to recall details about the underlying sensorimotor experience, this can be done through unbinding and decompression in order to re-obtain details of the original experience.
- 6.
This would be unbinding, which, in SPA, is done through convolving with the inverse of that to which a vector is currently bound.
- 7.
It is also worth remembering, as many have pointed out (e.g. [4]), that this position has a long history in theory of mind, and is not a merely a reaction to computationalist approaches that have been arising in cognitive science more recently.
References
Barsalou, L. W. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22(4), 577–660.
Barsalou, L. W., Santos, A., Simmons, W. K., & Wilson, C. D. (2008). Language and simulation in conceptual processing. Symbols, embodiment, and meaning (pp. 245–283). Oxford: Oxford University Press.
Cangelosi, A., & Schlesinger, M. (2015). Developmental robotics: From babies to robots. MIT Press.
Chemero, A. (2009). Radical embodied cognitive science. Cambridge, MA: MIT Press.
Chersi, F., Thill, S., Ziemke, T., & Borghi, A. M. (2010). Sentence processing: Linking language to motor chains. Frontiers in Neurorobotics, 4(4).
Cisek, P., & Kalaska, J. F. (2010). Neural mechanisms for interacting with a world full of action choices. Annual Review of Neuroscience, 33(1), 269–298. PMID: 20345247.
Dove, G. (2011). On the need for embodied and dis-embodied cognition. Frontiers in Psychology, 1(242).
Eliasmith, C. (2013). How to build a brain: A neural architecture for biological cognition. Oxford: Oxford University Press.
Eliasmith, C., & Anderson, C. H. (2002). Neural engineering: Computation, representation, and dynamics in neurobiological systems. Cambridge, MA: MIT Press.
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.
Erlhagen, W., & Schöner, G. (2002). Dynamic field theory of movement preparation. Psychological Review, 109(3), 545–572.
Felleman, D. J., & Van Essen, D. C. (1991). Distributed hierarchical processing in primate visual cortex. Cerebral Cortex, 1, 1–47.
Harnad, S. (1990). The symbol grounding problem. Physica D: Nonlinear Phenomena, 42(1–3), 335–346.
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(1), 59–70. Links and Interactions Between Language and Motor Systems in the Brain.
Pfeifer, R., Bongard, J., & Grand, S. (2007). How the body shapes the way we think: A new view of intelligence. Cambridge, MA: MIT press.
Pfeifer, R., & Iida, F. (2005). Morphological computation: Connecting body, brain and environment. Japanese Scientific Monthly.
Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3(9), 417–424.
Spencer, J. P., Austin, A., & Schutte, A. R. (2012). Contributions of dynamic systems theory to cognitive development. Cognitive Development, 27(4), 401–418. The Potential Contribution of Computational Modeling to the Study of Cognitive Development: When, and for What Topics?
Stapleton, M. (2011). Proper embodiment: The role of the body in affect and cognition. Ph.D. thesis, The University of Edinburgh.
Stapleton, M. (2013). Steps to a “properly embodied” cognitive science. Cognitive Systems Research, 22–23, 1–11.
Stewart, T. C., Tang, Y., & Eliasmith, C. (2010). A biologically realistic cleanup memory: Autoassociation in spiking neurons. Cognitive Systems Research, 12(2), 84–92.
Stramandinoli, F., Cangelosi, A., & Marocco, D. (2011). Towards the grounding of abstract words: A neural network model for cognitive robots. In The 2011 International Joint Conference on Neural Networks (IJCNN) (pp. 467–474).
Sun, R. (2004). Desiderata for cognitive architectures. Philosophical Psychology, 17(3), 341–373.
Thill, S., Caligiore, D., Borghi, A. M., Ziemke, T., & Baldassarre, G. (2013). Theories and computational models of affordance and mirror systems: An integrative review. Neuroscience & Biobehavioral Reviews, 37(3), 491–521.
Thill, S., Padó, S., & Ziemke, T. (2014). On the importance of a rich embodiment in the grounding of concepts: Perspectives from embodied cognitive science and computational linguistics. Topics in Cognitive Science, 6(3), 545–558.
Thill, S., Svensson, H., & Ziemke, T. (2011). Modeling the development of goal-specificity in mirror neurons. Cognitive Computation, 3(4), 525–538.
Thill, S., & Twomey, K. (2016). What’s on the inside counts: A grounded account of concept acquisition and development. Frontiers in Psychology: Cognition, 7(402).
van der Velde, F., & de Kamps, M. (2006). Neural blackboard architectures of combinatorial structures in cognition. Behavioral and Brain Sciences, 29(2), 37–70.
Vernon, D. (2014). Artificial cognitive systems: A primer. Cambridge, MA: MIT Press.
Vernon, D., von Hofsten, C., & Fadiga, L. (2016). Desiderata for developmental cognitive architectures. Biologically Inspired Cognitive Architectures, 18, 116–127.
Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin & Review, 9(4), 625–636.
Ziemke, T. (2003). What’s that thing called embodiment? In Proceedings of the 25th Annual Meeting of the Cognitive Science Society (pp. 1305–1310).
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Thill, S. (2019). What We Need from an Embodied Cognitive Architecture. In: Aldinhas Ferreira, M., Silva Sequeira, J., Ventura, R. (eds) Cognitive Architectures. Intelligent Systems, Control and Automation: Science and Engineering, vol 94. Springer, Cham. https://doi.org/10.1007/978-3-319-97550-4_4
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