Informational Theories of Content and Mental Representation

  • Marc ArtigaEmail author
  • Miguel Ángel Sebastián


Informational theories of semantic content have been recently gaining prominence in the debate on the notion of mental representation. In this paper we examine new-wave informational theories which have a special focus on cognitive science. In particular, we argue that these theories face four important difficulties: they do not fully solve the problem of error, fall prey to the wrong distality attribution problem, have serious difficulties accounting for ambiguous and redundant representations and fail to deliver a metasemantic theory of representation. Furthermore, we argue that these difficulties derive from their exclusive reliance on the notion of information, so we suggest that pure informational accounts should be complemented with (or perhaps substituted by) functional approaches.



We would like to thank Axel Barceló, the NCH Mind and Brain conference 2016 and two anonymous referees for their helpful comments and criticisms. Financial support was provided by a Postdoctoral Fellowship at the MCMP-LMU, the fellowship ’formación postdoctoral’ from the Ministerio de Economia y Competividad, the UNAM-DGAPA-PAPIIT programs


  1. Artiga, M. 2013. Reliable misrepresentation and teleosemanics. Disputatio. 37.Google Scholar
  2. Artiga, M. 2016. Liberal representationalism. A deflationist defense. Dialectica. 70(3): 407–430.CrossRefGoogle Scholar
  3. Bahrick, L., and R Lickliter. 2000. Intersensory redundancy guides attentional selectivity and perceptual learning in infancy. Developmental Psychology 36: 153–186.CrossRefGoogle Scholar
  4. Bahrick, L., R. Flom, and R. Lickliter. 2002. Intersensory redundancy facilitates discrimination of tempo in 30 month old infants. Developmental Psychobiology 41: 352–363.CrossRefGoogle Scholar
  5. Bahrick, L., R. Lickliter, and R. Flom. 2004. Intersensory redundancy guides infants selective attention, perception, and cognition in infancy. Current Directions in Psychological Science 13: 99– 102.CrossRefGoogle Scholar
  6. Birch, J. 2014. Propositional content in signalling systems. Philosophical Studies 171-3: 493–512.CrossRefGoogle Scholar
  7. Bremner, J.G., A. Slater, S.P. Johnson, U.C. Mason, and J. Spring. 2012. The effects of auditory information on 4 month old infants perception of trajectory continuity. Children development 83(3): 954–964.CrossRefGoogle Scholar
  8. Chun, M., N. Kanwisher, and J. McDermott. 1997. The fusiform face area: a module in human extrastriate cortex specialized for face perception. The Journal of Neuroscience 17(11): 4302–4311.CrossRefGoogle Scholar
  9. Damasio, A. 2010. Self Comes to Mind: Constructing the Conscious Brain, 1st ed. New York: Pantheon.Google Scholar
  10. Desimone, R. 1991. Face-selective cells in the temporal cortex of monkeys. Journal of Cognitive Neuroscience 3: 1–8.CrossRefGoogle Scholar
  11. Dretske, F. 1981a. Knowledge and the Flow of Information. Cambridge: The MIT Press.Google Scholar
  12. Dretske, F. 1981b. Knowledge and the Flow of Information. Cambridge: MIT Press.Google Scholar
  13. Eliasmith, C. 2000. How neurons mean: a neurocomputational theory of representational content. Unpublished Dissertation, Washington University in St. Louis.Google Scholar
  14. Eliasmith, C. 2005a. Neurosemantics and Categories. In Handbook of Categorization in Cognitive Science, eds. H. Cohen, and C. Lafebvre. Amsterdam: Elsevier.Google Scholar
  15. Eliasmith, C. 2005b. A new perspective on representational problemss. Journal of Cognitive Science 6: 97–123.Google Scholar
  16. Eliasmith, C. 2013. How to build a brain: A neural architecture for biological cognition. New YOrk: Oxford University Press.CrossRefGoogle Scholar
  17. Godfrey-Smith, P. 1991. Signal, detection, action. Journal of Philosophy 88 (12): 709–722.CrossRefGoogle Scholar
  18. Hubel, D.H., and T.N. Wiesel. 1959. Receptive fields of single neurones in the cat striate cortex. Journal of Physiology 148: 574–59I.CrossRefGoogle Scholar
  19. Kraemer, D. 2013. Against ”soft” statistical information. Philosophical Psychology 28(1): 139–147.CrossRefGoogle Scholar
  20. Kriegel, U. 2009. Subjective Consciousness: A Self-Representational Theory. New York: Oxford University Press.CrossRefGoogle Scholar
  21. LeDoux, J. 2003. The emotional brain, fear, and the amygdala. Cellular and Mollecular Neurobiology 23(1): 727–738.CrossRefGoogle Scholar
  22. Mendelovici, A. 2013. Reliable misrepresentation and tracking theories of mental representation. Philosophical Studies 165(2): 421–443.CrossRefGoogle Scholar
  23. Mendelovici, A. 2016. Why tracking theories should allow for clean cases of reliable misrepresentation. Disputatio 8(42): 57–92.Google Scholar
  24. Millikan, R.G. 1989. Biosemantics. The Journal of Philosophy 86: 281–297.CrossRefGoogle Scholar
  25. Millikan, R.G. 2000. On Clear and Confused Ideas. Cambridge University Press.Google Scholar
  26. Neander, K. 2017. A Mark of the Mental: In Defense of Informational Teleosemantics. Cambridge: MIT Press.Google Scholar
  27. Oehman, A., and S. Mineka. 2001. Fears, phobias and preparedness: Toward an evolved module of fear and fear learning. Current Biology 17(13): 129–33.Google Scholar
  28. Prinz, J. 2004. Gut Reactions: A Perceptual Theory of Emotion. New York: Oxford University Press.Google Scholar
  29. Rosenthal, D.M. 2005: Consciousness and mind. Oxford University Press.Google Scholar
  30. Rupert, R. 1999. The best test theory of extension: First principle(s). Mind and Language 14(3): 321–355.CrossRefGoogle Scholar
  31. Scarantino, A., and G Piccinini. 2010. Information processing, computation, and cognition. Journal of Biological Physics.Google Scholar
  32. Schulte, P. 2018. Perceiving the world outside: How to solve the distality problem for informational teleosemantics. Philosophical Quarterly 68(271): 349–369.Google Scholar
  33. Sebastián, M. Á., and M. Artiga. forthcoming. Can informational theories account for metarepresentation?. Topoi.Google Scholar
  34. Skyrms, B. 2010a. Signals: Evolution, Learning, & Information. Oxford: Oxford University Press.CrossRefGoogle Scholar
  35. Skyrms, B. 2010b. Signals: Evolution, learning and information. Oxford: Oxford University Press.CrossRefGoogle Scholar
  36. Stegmann, U. 2013. Animal Communication Theory. Information and Influence. In: Edited by U. Stegmann. A primer on information and influence in animal communication. New York: Oxford University Press.Google Scholar
  37. Stegmann, U. 2015. Prospects for probabilistic theories of natural information. Erkenntnis 80: 869–893.CrossRefGoogle Scholar
  38. Usher, M. 2001. A statistical referential theory of content: Using information theory to account for misrepresentation. Mind and Language 16(3): 331–334.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Universitat de ValènciaValènciaSpain
  2. 2.IIF- Universidad Nacional Autónoma de MéxicoCiudad de MéxicoMexico

Personalised recommendations