Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-30T20:25:53.086Z Has data issue: false hasContentIssue false
  • English
  • Français

Reconciling genetic evolution and the associative learning account of mirror neurons through data-acquisition mechanisms

Published online by Cambridge University Press:  29 April 2014

Arnon Lotem  and
Oren Kolodny
Arnon Lotem
Affiliation:
Department of Zoology, Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv 69978, Israel. [email protected]@gmail.comhttp://www.tau.ac.il/~lotem
Oren Kolodny
Affiliation:
Department of Zoology, Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv 69978, Israel. [email protected]@gmail.comhttp://www.tau.ac.il/~lotem
Get access Rights & Permissions [Opens in a new window]

Abstract

An associative learning account of mirror neurons should not preclude genetic evolution of its underlying mechanisms. On the contrary, an associative learning framework for cognitive development should seek heritable variation in the learning rules and in the data-acquisition mechanisms that construct associative networks, demonstrating how small genetic modifications of associative elements can give rise to the evolution of complex cognition.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 37 , Issue 2 , April 2014 , pp. 210 - 211
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Baron-Cohen, S., Richler, J., Bisarya, D., Gurunathan, N. & Wheelwright, S. (2003) The systemizing quotient: An investigation of adults with Asperger syndrome or high-functioning autism, and normal sex differences. Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences 358:361–74.CrossRefGoogle ScholarPubMed
Bateson, P. P. G. (1990) Is imprinting such a special case? Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences 329:125–31.Google Scholar
Crespi, B., Stead, P. & Elliot, M. (2010) Comparative genomics of autism and schizophrenia. Proceedings of the National Academy of Sciences USA 107:1736–41.CrossRefGoogle ScholarPubMed
Goldstein, M. H., Waterfall, H. R., Lotem, A., Halpern, J. Y., Schwade, J. A., Onnis, L. & Edelman, S. (2010) General cognitive principles for learning structure in time and space. Trends in Cognitive Sciences 14:249–58.CrossRefGoogle ScholarPubMed
Gray, J. R. & Thompson, P. M. (2004) Neurobiology of intelligence: Science and ethics. Nature Reviews Neuroscience 5:471–82.CrossRefGoogle ScholarPubMed
Heyes, C. M. (2012b) Simple minds: A qualified defence of associative learning. Philosophical Transactions of the Royal Society B: Biological Sciences 367(1603):2695–703.CrossRefGoogle ScholarPubMed
Heyes, C. M. (2012c) What's social about social learning? Journal of Comparative Psychology 126:193–202.CrossRefGoogle ScholarPubMed
Kolodny, O. Edelman, S. & Lotem, A. (2014) The evolution of continuous learning of the structure of the environment. Journal of the Royal Society, Interface 11:20131091.CrossRefGoogle ScholarPubMed
Kolodny, O., Lotem, A. & Edelman, S. (in press) Learning a generative probabilistic grammar of experience: A process-level model of language acquisition. Cognitive Science.Google Scholar
Lotem, A. & Halpern, J. Y. (2008) A data-acquisition model for learning and cognitive development and its implications for autism. Cornell Computing and Information Science Technical Reports. Available at: http://hdl.handle.net/1813/10178.Google Scholar
Lotem, A. & Halpern, J. Y. (2012) Coevolution of learning and data-acquisition mechanisms: A model for cognitive evolution. Philosophical Transactions of the Royal Society B: Biological Sciences 367:2686–94.CrossRefGoogle Scholar
Mery, F., Belay, A. T., So, A. K. C., Sokolowski, M. B. & Kawecki, T. J. (2007) Natural polymorphism affecting learning and memory in Drosophila. Proceedings of the National Academy of Sciences USA 104:13051–55.CrossRefGoogle ScholarPubMed
Shettleworth, S. J. (2010) Cognition, evolution and behavior. Oxford University Press.Google Scholar