Natural Recursion Doesn’t Work That Way: Automata in Planning and Syntax

  • Cem BozşahinEmail author
Part of the Synthese Library book series (SYLI, volume 376)


Natural recursion in syntax is recursion by linguistic value, which is not syntactic in nature but semantic. Syntax-specific recursion is not recursion by name as the term is understood in theoretical computer science. Recursion by name is probably not natural because of its infinite typeability. Natural recursion, or recursion by value, is not species-specific. Human recursion is not syntax-specific. The values on which it operates are most likely domain-specific, including those for syntax. Syntax seems to require no more (and no less) than the resource management mechanisms of an embedded push-down automaton (EPDA). We can conceive EPDA as a common automata-theoretic substrate for syntax, collaborative planning, i-intentions, and we-intentions. They manifest the same kind of dependencies. Therefore, syntactic uniqueness arguments for human behavior can be better explained if we conceive automata-constrained recursion as the most unique human capacity for cognitive processes.


Recursion Syntax Planning Mind and computation 



Thanks to PT-AI reviewers and the audience at Oxford, İstanbul, and Ankara, and to Julian Bradfield, Aravind Joshi, Simon Kirby, Vincent Müller, Umut Özge, Geoffrey Pullum, Aaron Sloman, Mark Steedman, and Language Evolution and Computation Research Unit (LEC) at Edinburgh University, for comments and advice. I am to blame for all errors and for not heeding good advice. This research is supported by the GRAMPLUS project granted to Edinburgh University, EU FP7 Grant #249520.


  1. Aaronson, S. (2013). Why philosophers should care about computational complexity. In B. J. Copeland, C. J. Posy, & O. Shagrir (Eds.), Computability: Turing, Gödel, Church, and Beyond. Cambridge: MIT.Google Scholar
  2. Abelson, H., Sussman, G. J., & Sussman, J. (1985). Structure and interpretation of computer programs. Cambridge: MIT.Google Scholar
  3. Berwick, R. C., Okanoya, K., Beckers, G. J., & Bolhuis, J. J. (2011). Songs to syntax: The linguistics of birdsong. Trends in Cognitive Sciences, 15(3), 113–121.CrossRefGoogle Scholar
  4. Berwick, R. C., Friederici, A. D., Chomsky, N., & Bolhuis, J. J. (2013). Evolution, brain, and the nature of language. Trends in Cognitive Sciences, 17(2), 89–98.CrossRefGoogle Scholar
  5. Bozsahin, C. (2012). Combinatory linguistics. Berlin/Boston: De Gruyter Mouton.CrossRefGoogle Scholar
  6. Bratman, M. E. (1992). Shared cooperative activity. The Philosophical Review, 101(2), 327–341.CrossRefGoogle Scholar
  7. Burns, S. R. (2009). The problem of deduction: Hume’s problem expanded. Dialogue 52(1), 26–30.Google Scholar
  8. Chomsky, N. (1995). The minimalist program. Cambridge: MIT.Google Scholar
  9. Chomsky, N. (2005). Three factors in language design. Linguistic Inquiry, 36(1), 1–22.CrossRefGoogle Scholar
  10. Chomsky, N. (2013) Problems of projection. Lingua, 130, 33–49.CrossRefGoogle Scholar
  11. Curry, H. B., & Feys, R. (1958). Combinatory logic. Amsterdam: North-Holland.Google Scholar
  12. Deacon, T. W. (1997). The symbolic species: The co-evolution of language and the human brain. London: The Penguin Press.Google Scholar
  13. Everett, D. L. (2005). Cultural constraints on grammar and cognition in Pirahã. Current Anthropology, 46(4), 621–646.CrossRefGoogle Scholar
  14. Everett, D. L. (2009). Pirahã culture and grammar: A response to some criticisms. Language, 85(2), 405–442.CrossRefGoogle Scholar
  15. Fitch, T., Hauser, M., & Chomsky, N. (2005). The evolution of the language faculty: Clarifications and implications. Cognition, 97, 179–210.CrossRefGoogle Scholar
  16. Gazdar, G. (1988). Applicability of indexed grammars to natural languages. In U. Reyle & C. Rohrer (Eds.), Natural language parsing and linguistic theories (pp. 69–94). Dordrecht: Reidel.CrossRefGoogle Scholar
  17. Ghallab, M., Nau, D., & Traverso, P. (2004) Automated planning: Theory and practice. San Francisco: Morgan Kaufmann.Google Scholar
  18. Gibson, J. (1966). The senses considered as perceptual systems. Boston: Houghton-Mifflin Co.Google Scholar
  19. Grimshaw, J. (1990). Argument structure. Cambridge: MIT.Google Scholar
  20. Grosz, B., & Kraus, S. (1993). Collaborative plans for group activities. In IJCAI, Chambéry (Vol. 93, pp. 367–373).Google Scholar
  21. Grosz, B. J., Hunsberger, L., & Kraus, S. (1999). Planning and acting together. AI Magazine, 20(4), 23.Google Scholar
  22. Hale, K., & Keyser, S. J. (2002). Prolegomenon to a theory of argument structure. Cambridge: MIT.Google Scholar
  23. Hauser, M., Chomsky, N., & Fitch, W. T. (2002). The faculty of language: What is it, who has it, and how did it evolve? Science, 298, 1569–1579.CrossRefGoogle Scholar
  24. Huybregts, R., & van Riemsdijk, H. (1982). Noam Chomsky on the generative enterprise. Dordrecht: Foris.Google Scholar
  25. Jackendoff, R., & Pinker, S. (2005). The nature of the language faculty and its implications for language evolution. Cognition, 97, 211–225.CrossRefGoogle Scholar
  26. Jaynes, J. (1976). The origin of consciousness in the breakdown of the bicameral mind. New York: Houghton Mifflin Harcourt.Google Scholar
  27. Joshi, A. K. (1983). Factoring recursion and dependencies: An aspect of tree adjoining grammars (TAG) and a comparison of some formal properties of TAGs, GPSGs, PLGs, and LPGs. In Proceedings of the 21st Annual Meeting on Association for Computational Linguistics, Cambridge (pp. 7–15)Google Scholar
  28. Joshi, A. (1985). How much context-sensitivity is necessary for characterizing complex structural descriptions—Tree adjoining grammars. In D. Dowty, L. Karttunen, & A. Zwicky (Eds.), Natural language parsing (pp. 206–250). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  29. Joshi, A. (1990). Processing crossed and nested dependencies: An automaton perspective on the psycholinguistic results. Language and Cognitive Processes, 5, 1–27.CrossRefGoogle Scholar
  30. Joshi, A. K. (2004). Starting with complex primitives pays off: Complicate locally, simplify globally. Cognitive Science, 28(5), 637–668.CrossRefGoogle Scholar
  31. Joshi, A., & Schabes, Y. (1992). Tree-adjoining grammars and lexicalized grammars. In M. Nivat & A. Podelski (Eds.), Definability and recognizability of sets of trees. Princeton: Elsevier.Google Scholar
  32. Joshi, A., Vijay-Shanker, K., & Weir, D. (1991). The convergence of mildly context-sensitive formalisms. In P. Sells, S. Shieber, & T. Wasow (Eds.), Foundational issues in natural language processing (pp. 31–81). Cambridge: MIT.Google Scholar
  33. Kanazawa, M., & Salvati, S. (2012). MIX is not a tree-adjoining language. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1, Jeju Island (pp. 666–674). Association for Computational Linguistics.Google Scholar
  34. Knuth, D. E. (1968). Fundamental algorithms (The art of computer programming, Vol. 1). Reading: Addison-Wesley.Google Scholar
  35. Kok, A. (2013). Kant, Hegel, und die Frage der Metaphysik: Über die Möglichkeit der Philosophie nach der Kopernikanischen Wende. Wilhelm Fink.Google Scholar
  36. Lashley, K. (1951). The problem of serial order in behavior. In L. Jeffress (Ed.), Cerebral mechanisms in behavior (pp. 112–136). New York: Wiley. Reprinted in Saporta (1961).Google Scholar
  37. Lochbaum, K. E. (1998). A collaborative planning model of intentional structure. Computational Linguistics, 24(4), 525–572.Google Scholar
  38. Manning, C. D. (1996). Ergativity: Argument structure and grammatical relations. Stanford: CSLI.Google Scholar
  39. Nevins, A., Pesetsky, D., & Rodrigues, C. (2009). Pirahã exceptionality: A reassessment. Language, 85(2), 355–404.CrossRefGoogle Scholar
  40. Parker, A. R. (2006). Evolving the narrow language faculty: Was recursion the pivotal step. In The Evolution of Language: Proceedings of the 6th International Conference on the Evolution of Language (pp. 239–246). Singapore: World Scientific Press.CrossRefGoogle Scholar
  41. Petrick, R. P., & Bacchus, F. (2002). A knowledge-based approach to planning with incomplete information and sensing. In AIPS, Toulouse (pp. 212–222).Google Scholar
  42. Peyton Jones, S. L. (1987). The implementation of functional programing languages. New York: Prentice-Hall.Google Scholar
  43. Quine, W. v. O. (1960). Word and object. Cambridge: MIT.Google Scholar
  44. Saporta, S. (Ed.). (1961). Psycholinguistics: A book of readings. New York: Holt Rinehart Winston.Google Scholar
  45. Searle, J. R. (1990). Collective intentions and actions. In P. R. Cohen, M. E. Pollack, & J. L. Morgan (Ed.), Intentions in communication. Cambridge: MIT.Google Scholar
  46. Shieber, S. (1985). Evidence against the context-freeness of natural language. Linguistics and Philosophy, 8, 333–343.CrossRefGoogle Scholar
  47. Speas, M., & Roeper, T. (Eds.). (2009, forthcoming). Proceedings of the Conference on Recursion: Structural Complexity in Language and Cognition, University of Mass, Amherst.Google Scholar
  48. Stabler, E. (2013). Copying in mildly context sensitive grammar. Informatics Seminars, Institute for Language, Cognition and Computation, University of Edinburgh, October 2013.Google Scholar
  49. Steedman, M. (2000). The syntactic process. Cambridge: MIT.Google Scholar
  50. Steedman, M. (2002). Plans, affordances, and combinatory grammar. Linguistics and Philosophy, 25, 723–753.CrossRefGoogle Scholar
  51. Steedman, M., & Petrick, R. P. (2007). Planning dialog actions. In Proceedings of the 8th SIGDIAL Workshop on Discourse and Dialogue (SIGdial 2007), Antwerp (pp. 265–272)Google Scholar
  52. Tomasello, M., & Call, J. (1997). Primate cognition. New York: Oxford University Press.Google Scholar
  53. Tomasello, M., Call, J., & Hare, B. (2003). Chimpanzees understand psychological states—the question is which ones and to what extent. Trends in Cognitive Sciences, 7(4), 153–156.CrossRefGoogle Scholar
  54. Turing, A. M. (1937). Computability and \(\lambda\)-definability. Journal of Symbolic Logic, 2(4), 153–163.CrossRefGoogle Scholar
  55. Valiant, L. (1984). A theory of the learnable. Communications of the ACM, 27(11), 1134–1142.CrossRefGoogle Scholar
  56. Valiant, L. (2013). Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World. New York: Basic Books.Google Scholar
  57. Van Heijningen, C. A., De Visser, J., Zuidema, W., & Ten Cate, C. (2009). Simple rules can explain discrimination of putative recursive syntactic structures by a songbird species. Proceedings of the National Academy of Sciences, 106(48), 20538–20543.CrossRefGoogle Scholar
  58. Vijay-Shanker, K. (1987). A study of tree adjoining grammars. PhD thesis, University of Pennsylvania.Google Scholar
  59. Vijay-Shanker, K., & Weir, D. (1993). Parsing some constrained grammar formalisms. Computational Linguistics, 19, 591–636.Google Scholar
  60. Watt, D. A. (2004). Programming language design concepts. Chicester: Wiley.Google Scholar
  61. Zettlemoyer, L., & Collins, M. (2005). Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars. In Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, Edinburgh.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Cognitive Science DepartmentThe Informatics Institute, Middle East Technical UniversityAnkaraTurkey

Personalised recommendations