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The Leabra architecture: Specialization without modularity

Published online by Cambridge University Press:  22 October 2010

Alexander A. Petrov ,
David J. Jilk  and
Randall C. O'Reilly
Alexander A. Petrov
Affiliation:
Department of Psychology, Ohio State University, Columbus, OH 43210. [email protected]://alexpetrov.com
David J. Jilk
Affiliation:
eCortex, Inc., Boulder, CO 80301. [email protected]://www.e-cortex.com
Randall C. O'Reilly
Affiliation:
Department of Psychology and Neuroscience, University of Colorado, Boulder, CO 80309. [email protected]://psych.colorado.edu/~oreilly
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Abstract

The posterior cortex, hippocampus, and prefrontal cortex in the Leabra architecture are specialized in terms of various neural parameters, and thus are predilections for learning and processing, but domain-general in terms of cognitive functions such as face recognition. Also, these areas are not encapsulated and violate Fodorian criteria for modularity. Anderson's terminology obscures these important points, but we applaud his overall message.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 33 , Issue 4 , August 2010 , pp. 286 - 287
Copyright
Copyright © Cambridge University Press 2010

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References

Anderson, J. R. (2007) How can the human mind occur in the physical universe? Oxford University Press.CrossRefGoogle Scholar
Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C. & Qin, Y. (2004) An integrated theory of mind. Psychological Review 111:1036–60.CrossRefGoogle Scholar
Anderson, M. L. (2007a) Evolution of cognitive function via redeployment of brain areas. The Neuroscientist 13:13–21.CrossRefGoogle ScholarPubMed
Anderson, M. L. (2007c) The massive redeployment hypothesis and the functional topography of the brain. Philosophical Psychology 21(2):143–74.CrossRefGoogle Scholar
Atallah, H. E., Frank, M. J. & O'Reilly, R. C. (2004) Hippocampus, cortex, and basal ganglia: Insights from computational models of complementary learning systems. Neurobiology of Learning and Memory 82(3):253–67.CrossRefGoogle ScholarPubMed
Bergeron, V. (2007) Anatomical and functional modularity in cognitive science: Shifting the focus. Philosophical Psychology 20(2):175–95.CrossRefGoogle Scholar
Carruthers, P. (2006) The architecture of the mind: Massive modularity and the flexibility of thought. Clarendon Press/Oxford University Press.CrossRefGoogle Scholar
Cherniak, C., Mokhtarzada, Z., Rodrigues-Esteban, R. & Changizi, K. (2004) Global optimization of cerebral cortex layout. Proceedings of the National Academy of Sciences USA 101:1081–86.CrossRefGoogle ScholarPubMed
Fodor, J. (1983) The modularity of mind. MIT Press.CrossRefGoogle Scholar
Fodor, J. (2000) The mind doesn't work that way. MIT Press.CrossRefGoogle Scholar
Huettel, S. A., Song, A. W. & McCarthy, G. (2008) Functional magnetic resonance imaging. Sinauer.Google Scholar
Jilk, D. J., Lebiere, C., O'Reilly, R. C. & Anderson, J. R. (2008) SAL: An explicitly pluralistic cognitive architecture. Journal of Experimental and Theoretical Artificial Intelligence 20:197–218.CrossRefGoogle Scholar
McClelland, J. L., McNaughton, B. L. & O'Reilly, R. C. (1995) Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review 102:419–57.CrossRefGoogle ScholarPubMed
Mesulam, M.-M. (1990) Large-scale neurocognitive networks and distributed processing for attention, language and memory. Annals of Neurology 28:597–613.CrossRefGoogle ScholarPubMed
Miller, E. K. & Cohen, J. D. (2001) An integrative theory of prefrontal cortex function. Annual Review of Neuroscience 24:167–202.CrossRefGoogle ScholarPubMed
O'Reilly, R. C. (1998) Six principles for biologically based computational models of cortical cognition. Trends in Cognitive Sciences 2:455–62.CrossRefGoogle ScholarPubMed
O'Reilly, R. C. (2006) Biologically based computational models of high-level cognition. Science 314(5796):91–94.CrossRefGoogle ScholarPubMed
O'Reilly, R. C., Braver, T. S. & Cohen, J. D. (1999) A biologically based computational model of working memory. In: Models of working memory: Mechanisms of active maintenance and executive control, ed. Miyake, A & Shah, P, pp. 375–411. Cambridge University Press.CrossRefGoogle Scholar
O'Reilly, R. C. & Frank, M. J. (2006) Making working memory work: A computational model of learning in the prefrontal cortex and basal ganglia. Neural Computation 18:283–328.CrossRefGoogle Scholar
O'Reilly, R. C. & Munakata, Y. (2000) Computational explorations in cognitive neuroscience: Understanding the mind by simulating the brain. MIT Press.CrossRefGoogle Scholar
Poldrack, R. A. (2006) Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Sciences 10:59–63.CrossRefGoogle ScholarPubMed
Prinz, J. (2006) Is the mind really modular? In: Contemporary debates in cognitive science, ed. Stainton, R. J., pp. 22–36. Blackwell.Google Scholar
Samuels, R. (2006) Is the human mind massively modular? In: Contemporary debates in cognitive science, ed. Stainton, R. J., pp. 37–56. Blackwell.Google Scholar
Tooby, J. & Cosmides, L. (1992) The psychological foundations of culture. In: The adapted mind: Evolutionary psychology and the generation of culture, ed. Barkow, J., Cosmides, L. & Tooby, J., pp. 19–136. Oxford University Press.CrossRefGoogle Scholar
Uttal, W. R. (2001) The new phrenology: The limits of localizing cognitive processes in the brain. MIT Press.Google Scholar