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The bottleneck may be the solution, not the problem

Published online by Cambridge University Press:  02 June 2016

Arnon Lotem
Affiliation:
Department of Zoology, Tel Aviv University, Tel Aviv 6997801, [email protected]
Oren Kolodny
Affiliation:
Department of Biology, Stanford University, Stanford, CA [email protected]
Joseph Y. Halpern
Affiliation:
Department of Computer Science, Cornell University, Ithaca, NY [email protected]
Luca Onnis
Affiliation:
Division of Linguistics and Multilingual Studies, Nanyang Technological University, Singapore [email protected]
Shimon Edelman
Affiliation:
Department of Psychology, Cornell University, Ithaca, NY 14853. [email protected]

Abstract

As a highly consequential biological trait, a memory “bottleneck” cannot escape selection pressures. It must therefore co-evolve with other cognitive mechanisms rather than act as an independent constraint. Recent theory and an implemented model of language acquisition suggest that a limit on working memory may evolve to help learning. Furthermore, it need not hamper the use of language for communication.

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2016 

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References

Anderson, M. L. (2010) Neural reuse: A fundamental organizational principle of the brain. Behavioral and Brain Sciences 34:245–66.CrossRefGoogle Scholar
Blokland, G. A. M., McMahon, K. L., Thompson, P. M., Martin, N. G., de Zubicaray, G. I. & Wright, M. J. (2011) Heritability of working memory brain activation. The Journal of Neuroscience 31:10882–90.CrossRefGoogle ScholarPubMed
Burghardt, G. M. (1970) Defining “communication.” In: Communication by chemical signals, ed. Johnston, J. W. Jr., Moulton, D. G. & Turk, A., pp. 518. Appleton-Century-Crofts.Google Scholar
Chater, N. & Christiansen, M. H. (2010) Language acquisition meets language evolution. Cognitive Science 34:1131–57.Google Scholar
Cui, J., Gao, D., Chen, Y., Zou, X. & Wang, Y. (2010) Working memory in early-school-age children with Asperger's syndrome. Journal of Autism and Developmental Disorders 40:958–67.Google Scholar
Edelman, S. (2008a) Computing the mind: How the mind really works. Oxford University Press.Google Scholar
Edelman, S. (2008b) On the nature of minds, or: Truth and consequences. Journal of Experimental and Theoretical AI 20:181–96.Google Scholar
Edelman, S. (2015) The minority report: Some common assumptions to reconsider in the modeling of the brain and behavior. Journal of Experimental and Theoretical Artificial Intelligence 27, doi 10.1080/0952813X.2015.1042534.Google Scholar
Falconer, D. S. (1981) Introduction to quantitative genetics. Longman.Google Scholar
Goldstein, M. H., Waterfall, H. R., Lotem, A., Halpern, J., Schwade, J., Onnis, L. & Edelman, S. (2010) General cognitive principles for learning structure in time and space. Trends in Cognitive Sciences 14:249–58.Google Scholar
Green, S. & Marler, P. (1979) The analysis of animal communication. In: Handbook of behavioral neurobiology: Vol. 3. Social behavior and communication, ed. Marler, P. & Vandenbergh, J. G., pp. 73158. Plenum Press.Google Scholar
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.Google Scholar
Kolodny, O., Edelman, S. & Lotem, A. (2015a) Evolution of protolinguistic abilities as a by-product of learning to forage in structured environments. Proceedings of the Royal Society of London B 282(1811):20150353.Google Scholar
Kolodny, O., Lotem, A. & Edelman, S. (2015b) Learning a generative probabilistic grammar of experience: A process-level model of language acquisition. Cognitive Science 39:227–67.Google Scholar
Lachmann, M., Számadó, S. & Bergstrom, C. T. (2001) Cost and conflict in animal signals and human language. Proceedings of the National Academy of Science 98:13189–94.Google Scholar
Leger, D. W. (1993) Contextual sources of information and responses to animal communication signals. Psychological Bulletin 113:295304.CrossRefGoogle ScholarPubMed
Lind, J., Enquist, M. & Ghirlanda, S. (2015) Animal memory: A review of delayed matching-to-sample data. Behavioural Processes 117:5258.CrossRefGoogle ScholarPubMed
Lotem, A. & Halpern, J. Y. (2008) A data-acquisition model for learning and cognitive development and its implications for autism. Computing and information science technical reports, Cornell University. 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 367:2686–94.Google Scholar
Menyhart, O., Kolodny, O., Goldstein, M. H., Devoogd, T. & Edelman, S. (2015) Juvenile zebra finches learn the underlying statistical regularities in their father's song. Frontiers in Psychology 6:571.Google Scholar
Mery, F., Belay, A. T., So, A. K., Sokolowski, M. B. & Kawecki, T. J. (2007) Natural polymorphism affecting learning and memory in Drosophila. Proceedings of the National Academy of Science 104:13051–55.Google Scholar
Mueller, S. T. & Krawitz, A. (2009) Reconsidering the two-second decay hypothesis in verbal working memory. Journal of Mathematical Psychology 53:1425.CrossRefGoogle Scholar
Odling-Smee, F. J., Laland, K. N. & Feldman, M. W. (2003) Niche construction: The neglected process in evolution, vol. MPB 37. Princeton University Press.Google Scholar
Onnis, L. & Spivey, M. J. (2012) Toward a new scientific visualization for the language sciences. Information 3:124–50.Google Scholar
Onnis, L., Waterfall, H. R. & Edelman, S. (2008) Learn locally, act globally: Learning language from variation set cues. Cognition 109:423–30.CrossRefGoogle ScholarPubMed
Princeton, N. J. & Stromswold, K. (2001) The heritability of language: A review and metaanalysis of twin, adoption, and linkage studies. Language 77:647–23.Google Scholar
Solan, Z., Horn, D., Ruppin, E. & Edelman, S. (2005) Unsupervised learning of natural languages. Proceedings of the National Academy of Science 102:11629–34.Google Scholar
Stephens, D. W. & Krebs, J. R. (1986) Foraging theory. Princeton University Press.Google Scholar
van Soelen, I. L. C., Brouwer, R. M., van Leeuwen, M., Kahn, R. S., Hulshoff Pol, H. E. & Boomsma, D. I. (2011) Heritability of verbal and performance intelligence in a pediatric longitudinal sample. Twin Research and Human Genetics 14:119–28.Google Scholar
Vogler, C., Gschwind, L., Coyne, D., Freytag, V., Milnik, A., Egli, T., Heck, A., de Quervain, D. J. & Papassotiropoulos, A. (2014) Substantial SNP-based heritability estimates for working memory performance. Translational Psychiatry 4:e438.Google Scholar