Abstract
The remarkable feature of linguistic communications is the use of symbols for transmitting information and mutual understanding. Deacon (1997) pointed out that humans are symbolic species, namely, we show symbolic cognitive activities such as learning, formation, and manipulation of symbols. In research into the origin and the evolution of language, we should elucidate the emerging process of such symbolic cognitive activities.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Note that using a connectionist model does not necessarily mean that no symbolic element is involved. For example, in the simple recurrent network introduced by Elman (1995), sequences of words which are discrete representations are fed to the network as inputs.
- 2.
In Harnad’s original article (1990), the term “shape” is used. We reword this as “form” for clarification.
- 3.
An attractor ruin is a region in a state space of a dynamical system, in which an orbit stays for a while like an attractor, but does not stay forever, and escapes from there.
- 4.
Because of the discretisation of the time variable, the model is represented by difference equations, that is, maps, while the original Hopfield model is represented by differential equations.
- 5.
Refer to Nozawa (1992) for the detailed derivation from the Hopfield model.
- 6.
As described later, the parameters T ii , I i , r and β are chosen for the system to show chaotic behaviour and T ij is determined to store some memory patterns in the system.
References
Adachi, M., Aihara, K.: Associative dynamics in a chaotic neural network. Neural Networks 10 (1997) 83–98
Aihara, K., Matsumoto, G.: Chaotic oscillations and bifurcations in squid giant axons. In Holden, A.V., ed.: Chaos. Princeton University Press (1986) 257–269
Cangelosi, A., Parisi, D., eds.: Simulating the Evolution of Language. Springer (2002)
Deacon, T.: The Symbolic Species: The Co-Evolution of Language and the Brain. W W Norton (1997)
Elman, J.L.: Language as a dynamical system. In Port, R., van Gelder, T., eds.: Mind as Motion: Dynamical Perspectives on Behavior and Cognition. MIT Press (1995) 195–225
Freeman, W.J.: Simulation of chaotic EEG patterns with a dynamic model of the olfactory system. Biological Cybernetics 56 (1987) 139–150
Harnad, S.: The symbol grounding problem. Physica D 42 (1990) 335–346
Hashimoto, T.: The constructive approach to the dynamical view of language. In Cangelosi, A., Parisi, D., eds.: Simulating the Evolution of Language. Springer (2002) 307–324
Hashimoto, T.: Language as dynamics, – a computational study of ontogenetic and glossogenetic loop. In Hurford, J.R., Fitch, T., eds.: Fourth International Conference on the Evolution of Language – Proceedings. (2002)
Hopfield, J.J.: Neurons with graded response have collective computational properties like those of two-state neurons. Proc. Natl. Acad. Sci. USA 81 (1984) 3088–3092
Kaneko, K., Tsuda, I.: Chaotic itinerancy. Chaos 13(3) (2003) 926–936
Kirby, S.: Learning, bottlenecks and evolution of recursive syntax. In Briscoe, T., ed.: Linguistic Evolution through Language Acquisition. Cambridge University Press (2002) 173–203
Kirby, S., Hurford, J.R.: The emergence of linguistic structure: an overview of the iterated learning model. In Cangelosi, A., Parisi, D., eds.: Simulating the Evolution of Language. Springer (2002) 121–147
Matsuo, N., Nozawa, H.: Coupled maps and nonlinear optimization (in japanese). In: Proceedings of The Institute of Electrical Engineers of Japan (IEEJ). Volume IP-97-3. (1997)
Nozawa, H.: A neural network model as a globally coupled map and applications based on chaos. Chaos 2(3) (1992) 377–386
Skarda, C.A., Freeman, W.J.: How brains make chaos in order to make sense of the world. Behavioral and Brain Sciences 10 (1987) 161–195
Tsuda, I.: Toward an interpretation of dynamic neural activity in terms of chaotic dynamical systems. Behavioral and Brain Sciences 24(5) (2001) 793–847
van Gelder, T., Port, R., eds.: Mind as Motion: Dynamical Perspectives on Behavior and Cognition. MIT Press (1995)
van Gelder, T.: The dynamical hypothesis in cognitive science. Brain and Behavioural Sciences 10 (1998) 615–665
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag London Limited
About this chapter
Cite this chapter
Hashimoto, T., Masumi, A. (2007). Learning and Transition of Symbols: Towards a Dynamical Model of a Symbolic Individual. In: Lyon, C., Nehaniv, C.L., Cangelosi, A. (eds) Emergence of Communication and Language. Springer, London. https://doi.org/10.1007/978-1-84628-779-4_11
Download citation
DOI: https://doi.org/10.1007/978-1-84628-779-4_11
Publisher Name: Springer, London
Print ISBN: 978-1-84628-491-5
Online ISBN: 978-1-84628-779-4
eBook Packages: Computer ScienceComputer Science (R0)