Strong liberal representationalism

Abstract

The received view holds that there is a significant divide between full-blown representational states and so called ‘detectors’, which are mechanisms set off by specific stimuli that trigger a particular effect. The main goal of this paper is to defend the idea that many detectors are genuine representations, a view that I call ‘Strong Liberal Representationalism’. More precisely, I argue that ascribing semantic properties to them contributes to an explanation of behavior, guides research in useful ways and can accommodate misrepresentation.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

Notes

  1. 1.

    ‘Receptor’ is used for certain biological kinds that might or might not coincide with the set of states that are supposed to be captured by the philosophical use of the term, so this expression is a potential source of misunderstandings. Furthermore, this term misleadingly suggests that these states are purely passive but, as I will argue below, they are also defined by the specific behavior they trigger. ‘Detector’ is not ideal either, since this expression seems to beg the question in favor of representationalism. Finally, ‘C-state’, which has also occasionally been employed in the literature, is not the most elegant proposal and is much less evocative than the other two.

  2. 2.

    For a discussion of the adaptationist explanation of bacteria magnetotaxis, see O’Malley, 2014: 29-38.

  3. 3.

    Strictly speaking, the claim is that certain activity patterns of detectors are representations. For simplicity, however, I will often claim that detectors are representations. When the distinction between mechanisms and activities is relevant, I will explicitly distinguish the two.

  4. 4.

    Let me stress that I do not have an particular example in mind. In any case, all examples I will discuss here qualify as truly representational. I would like to thank a reviewer for pressing me on this point.

  5. 5.

    Millikan (1984) emphasizes the idea of a systematic mapping relationship between a set of representations and a set of entities. So a more precise description of her view would say that the function of the sender is to emit a range of states which are supposed to map onto a range of states according to a certain mapping function, and which might elicit a range of behaviors. Nonetheless, this criterion can be satisfied by a mechanism that produces only two states and behaviors: a cell firing (or not firing) at a time t and place p representing the presence (or absence) of a predator at t near p, which triggers an evasive behavior (or it does not). Consequently, these apparently more stringent conditions can easily be met by detectors as well.

  6. 6.

    The fact that scientists often assert that detectors are representations could be taken as an additional piece of evidence in favor of SLR. Figdor (2018), for example, has interestingly argued that we should interpret these assertions literally. Although these results are sympathetic to the main conclusion of this paper, the arguments differ: here I would like to focus on the explanatory role of SLR, rather than on the linguistic interpretation of scientific claims.

  7. 7.

    Let me hasten to add that questions about the indeterminacy of content are irrelevant here. Certainly, the behavior could be described in various ways (e.g. flying away from bats, avoiding a predator, evading a deadly flying mammal, etc..) and, accordingly, there might be some content indeterminacy (e.g. bat, predator, deadly flying mammal, etc..). However, in any of these descriptions, the ascribed semantic content is equally explanatory.

  8. 8.

    Nonetheless, he accepts that they are still representations. As a result, he might be taken to embrace Liberal Representationalism, but reject Strong Liberal Representationalism.

  9. 9.

    I apologize for reproducing a sexist example.

  10. 10.

    Note that this idea is in tension with Dretkse’s (1988, p.91-92) suggestion that indication in the evolutionary past can qualify as a structuring cause of behavior. If an entity M is a structuring cause of R causing E, then presumably M explains (or at least contributes to an explanation of) R causing E. As a consequence, M explains a property of organism. Thus, the role of past indication as a structuring cause is hard to reconcile with the idea that natural selection can not explain why organisms possess certain features.

  11. 11.

    In a nutshell, here is the reasoning: “natural selection can reshape a population in a way that makes a given variant more likely to be produced by the immediate sources of variation than it otherwise would be. As selection changes the background in which mutation and recombination operate, it changes what those factors can produce” (Godfrey-Smith 2014, p. 29). The idea that still remains controversial is whether natural selection can explain why a particular individual possesses a certain trait.

  12. 12.

    This description does not rely on an explicit distinction between sender and receivers because Shea does not appeal to them (see Shea, 2018, p. 19).

  13. 13.

    Note that even if members of kind K tend to possess property F, it does not follow that F is an important or explanatory property of K; F could be an accidental feature or a side-effect of a truly explanatory property, for example.

  14. 14.

    One might complain that this analysis mischaracterizes Ramsey’s notion of IO-representations, since at some points he seems to suggest that for a process to count as an IO-representation, the system’s output needs to qualify as a representation. This reply, however, is unsatisfactory for various reasons. First, on this reading, the only kinds of representations that do not presuppose the existence of any other sort of representation within the system are S-representations. Furthermore, it seems to be in tension with some of the claims he makes at other places (e.g. Ramsey, 2007, p. 77). Finally, the reasons adduced to support the status of IO-representations as genuine carriers of semantic content do not underpin this constraint. What seems to be crucial for the system’s internal sub-processes to qualify as representational is that that the output be characterized distally. Adding that the output should itself qualify as a genuine representation seems unmotivated.

  15. 15.

    Of course, another aspect that unifies these mechanisms is that they trigger behaviors that deal with corpses. Given the fact that the signal’s content probably depends on the behavior it elicits, one should expect a similarity of content to correlate with some similarity at the level of behavior (at least in simple cases).

  16. 16.

    If one thinks these mechanisms represent proximal features rather than distal events, misrepresentation is still possible. If AN2 neurons represent ultrasound waves rather than bats, then they misrepresent when detectors are directly stimulated by a clever neuroscientist using electrodes or by other cells that fail to work properly.

  17. 17.

    In the last round of revisions for this journal, Ganson’s (2020) paper came to my attention. He defends a similar view, although he provides different arguments and does not discuss some of the issues that I develop in this paper (especially Sections 3 and 4). In any case, a discussion of his interesting paper will have to wait for another occasion.

References

  1. Artiga, M. (2016). Liberal representationalism: a deflationist defense. Dialectica, 70(3), 407–430.

    Article  Google Scholar 

  2. Bechtel, W. (2016). Investigating neural representations: the tale of place cells. Synthese, 193(5), 1287–1321.

    Article  Google Scholar 

  3. Bermudez, J.L. (2003). Thinking without words. Oxford: Oxford University Press.

    Google Scholar 

  4. Bot, A.N.M., Currie, C.R., Hart, A.G., Boomsma, J.J. (2001). Waste management in leafcutting ants. Ethology Ecology and Evolution, 13(3), 225–237.

    Article  Google Scholar 

  5. Braddon-Mitchell, Jackson. (2007). The philosophy of mind and cognition. Hoboken: Blackwell.

    Google Scholar 

  6. Brentano, F. (1874). Psychology from an empirical standpoint. Abingdon: Routledge.

    Google Scholar 

  7. Burge, T. (2010). The origins of objectivity. Oxford: Oxford University Press.

    Google Scholar 

  8. Butlin, P. (forthcoming). Representation and the active consumer. Synthese, 1–18.

  9. Chemero, R. (2009). Radical embodied cognitive science. Bradford Book.

  10. Choe, D., Millar, J.G., Rust, M.K. (2009). Chemical signals associated with life inhibit necrophoresis in Argentine ants. Proceedings of the National Academy of Sciences of the United States of America, 106(20), 8251—8255.

    Google Scholar 

  11. Clark, A. (1997). Being there putting brain, body, and world together again. Bradford Book.

  12. Clark, A., & Grush, R. (1999). Towards a cognitive robotics. Adaptive Behavior, 7, 5–16.

    Article  Google Scholar 

  13. Cummins, R. (1975). Functional analysis. Journal of Philosophy, 72, 741–765.

    Article  Google Scholar 

  14. Cummins, R., & McLaughlin, B. (1991). The role of mental meaning in psychological explanation. In Dretske and his critics: Basil Blackwell.

  15. Dean, I., Harper, N., McAlping, D. (2005). Neural population coding of sound level adapts to stimulus statistics. Nature Neuroscience, 8(12), 1684–1688.

    Article  Google Scholar 

  16. Dennett, D. (1991). Ways of establishing harmony. In McLaughlin, B. (Ed.) Dretske and his critics: Basil Blackwell.

  17. Diez, L., Lejeune, P., Detrain, C. (2014). Keep the nest clean: Survival advantages of corpse removal in ants. Biology Letters, 10(7), 1–4.

    Article  Google Scholar 

  18. Dretske, F. (1988). Explaining behavior. Reasons in a world of causes. Cambridge: MIT Press.

    Google Scholar 

  19. Fidgor, C. (2018). Pieces of mind: the proper domain of psychological predicates. Oxford: Oxford University Press.

    Google Scholar 

  20. Fish, W. (2010). Philosophy of perception: a contemporary introduction. Abingdon: Routledge.

    Google Scholar 

  21. Fodor, J. (1986). Why paramecia don’t have mental representations. Midwest Studies in Philosophy, 10(1), 3–23.

    Article  Google Scholar 

  22. Ganson, T. (2018). The senses as signalling systems. Australasian Journal of Philosophy, 96(3), 519– 531.

    Article  Google Scholar 

  23. Ganson, T. (2020). A role for representations in inflexible behavior. Biology and Philosophy, 35, 1–18.

    Article  Google Scholar 

  24. Gładziejewski, P. (2015). Explaining cognitive phenomena with internal representations: A mechanistic perspective. Studies in Logic, Grammar and Rhetoric, 40(53), 63–90.

    Article  Google Scholar 

  25. Gładziejewski, P., & Milkowski, M. (2017). Structural representations: causally relevant and different from detectors. Biology and Philosophy, 32(3), 337–355.

    Article  Google Scholar 

  26. Godfrey-Smith, P. (1996). Complexity and the function of mind in nature. Cambridge: Cambridge University Press.

    Google Scholar 

  27. Godfrey-Smith, P. (2014). Philosophy of biology. Princeton: Princeton University Press.

    Google Scholar 

  28. Grush, R. (1997). The architecture of representation. Philosophical Psychology, 10, 5–23.

    Article  Google Scholar 

  29. Horgan, T. (1991). Actions, reasons, and the explanatory role of content. In McLaughlin, B. (Ed.) Dretske and his critics: Basil Blackwell.

  30. Howard, D.F., & Tschinkel, W.R. (1976). Aspects of necrophoric behavior in the red imported fire ant, solenopsis invicta. Behaviour, 56(1/2), 157–180.

    Article  Google Scholar 

  31. Hutto, D., & Myin, E. (2017). Evolving enactivism: basic minds meet content. Cambridge: MIT Press.

    Google Scholar 

  32. Hutto, D., & Myin, E. (2012). Radicalizing enactivism: basic minds without content. Cambridge: MIT Press.

    Google Scholar 

  33. Jacob, P. (2013). Intentionality., Stanford Encyclopedia of Philosophy (Winter 2019 Edition), https://plato.stanford.edu/archives/win2019/entries/intentionality/.

  34. Kandel, E., Schwartz, J.H., Jessel, T.M., Siegelbaum, S.A., Hudspeth, A.J. (2013). Principles of Neural Science. New York: McGraw Hill.

    Google Scholar 

  35. Kvale, M.N., & Schreiner, C.E. (2004). Short-term adaptation of auditory receptive fields to dynamic stimuli. J Neurophysiol, 91, 604–612.

    Article  Google Scholar 

  36. Lisman, J.E. (1997). Bursts as a unit of neural information: making unreliable synapse reliable. Trends Neurosci, 20, 38–43.

    Article  Google Scholar 

  37. Marsat, G., & Pollack, G.S. (2006). A behavioral role for feature detection by Sensory Bursts. J Neurosci, 26(41), 10542–10547.

    Article  Google Scholar 

  38. Marsal, G., & Pollack, G. (2012). Bursting neurons and ultrasound avoidance in crickets. Frontiers in Neuroscience, 6(1), 1–9.

    Google Scholar 

  39. Martinez, M. (2013). Teleosemantics and indeterminacy. Dialectica, 67(4), 427–453.

    Article  Google Scholar 

  40. Marsat, G., & Pollack, G. (2013). A behavioral role for feature detection by Sensory Bursts. The Journal of Neuroscience, 26(41), 10542–10547.

    Article  Google Scholar 

  41. Miłkowski, M. (2013). Explaining the computational mind. Cambridge: MIT Press.

    Google Scholar 

  42. Miller, L., & Surllyke, A. (2001). How some insects detect and avoid being eaten by bats: tactics and countertactics of prey and predator. Bioscience, 51 (7), 570–581.

    Article  Google Scholar 

  43. Millikan, R.G. (1984). Language. Cambridge: MIT Press.

    Google Scholar 

  44. Millikan, R.G. (2009). Biosemantics. In Beckermann, A., McLaughlin, B.P., Walter, S. (Eds.) The Oxford handbook of philosophy of mind: OUP.

  45. Morgan, A. (2014). Representations gone mental. Synthese, 191 (12), 213–244.

    Article  Google Scholar 

  46. Morgan, A. (2018). Mindless accuracy: on the ubiquity of content in nature. Synthese, 195(12), 5403–5429.

    Article  Google Scholar 

  47. Neander, K. (1995). Prunning the tree of life. British Journal for the Philosophy of Science, 46(1), 59–80.

    Article  Google Scholar 

  48. Neander, K. (2017). A mark of the mental. Cambridge: MIT Press.

    Google Scholar 

  49. O’Malley, M. (2014). A. Cambridge: Cambridge University Press.

    Google Scholar 

  50. Papineau, D. (2003). Is representation rife?. Ratio, 16(2), 107–123.

    Article  Google Scholar 

  51. Pollack, G. (2015). Neurobiology of acoustically mediated predator detection. The Journal of Comparative Physiology, 201, 99–109.

    Article  Google Scholar 

  52. Price, C. (2001). Functions in mind: A theory of intentional content. Oxford: Clarendon Press.

    Google Scholar 

  53. Ramsey, W. (2003). Are receptors representations?. Journal of Experimental and Theoretical Artifical Intelligence, 15(2), 125–141.

    Article  Google Scholar 

  54. Ramsey, W. (2007). Representation reconsidered. Cambridge: Cambridge University Press.

    Google Scholar 

  55. Rescorla, M. (2013). Millikan on honeybee navigation and communication. In Ryder, D., Kingsbury, J., Williford, K. (Eds.) Millikan and her Critics: Blackwell.

  56. Rowlands, M. (2006). Body Language: Representation in Action. Cambridge: MIT Press.

    Google Scholar 

  57. Sakura, T., Namiki, S., Kanzaki, R. (2014). Molecular and neural mechanisms of sex pheromone reception and processing in the silkmoth bombyx mori. Frontiers in Physiology, 5, 1–29.

    Google Scholar 

  58. Schulte, P. (2015). Perceptual representations: a teleosemantic answer to the breadth-of-application problem. Biology and Philosophy, 30(1), 119–136.

    Article  Google Scholar 

  59. Schulte, P. (2019). Challenging liberal representationalism: a reply to Artiga, Dialectica.

  60. Shea, N. (2013). Naturalising representational content. Philosophy Compass, 8(5), 496–509.

    Article  Google Scholar 

  61. Shea, N. (2018). Representations in Cognitive Science. Oxford: OUP.

    Google Scholar 

  62. Smirnakis, S.M., Berry, M.J., Warland, D.K., Bialek, W, Meister, M. (1997). Adaptation of retinal processing to image contrast and spatial scale. Nature, 386, 69–73.

    Article  Google Scholar 

  63. Sober, E. (1984). Cambridge: MIT Press.

  64. Stampe, D. (1977). Toward a causal theory of linguistic representation. In French, P., Wettstein, H.K., Uehling, T.E., Williford, K. (Eds.) Midwest studies in philosophy, (Vol. 2 pp. 42–63). Minneapolis: University of Minnesota Press.

  65. Sterelny, K. (1990). The representational theory of mind: an introduction. Oxford: Oxford University Press.

    Google Scholar 

  66. Sterelny, K. (1995). Basic minds. Philosophical Perspectives, 9, 251–270.

    Article  Google Scholar 

  67. Sterelny, K. (2003). Thought in a hostile world. Oxford: Blackwell Publishing.

    Google Scholar 

  68. Sun, Q., & Zhou, X. (2013). Corpse management in social insects. International Journal of Biological Sciences, 9, 313–321.

    Article  Google Scholar 

  69. Swadlow, H.A., & Gusev, A.G. (2001). The impact of “bursting” thalamic impulses at a neocortical synapse. Nat Neurosci, 4, 402–408.

    Article  Google Scholar 

  70. Yager, D. (2012). Predator detection and evasion by flying insects. Current Opinion in Neurobiology, 22, 201–207.

    Article  Google Scholar 

  71. Wilson, E. (1971). O. Harvard: Harvard University Press.

    Google Scholar 

Download references

Acknowledgments

I would like to thank Javier González de Prado Salas, Manolo Martínez, Ruth Millikan, Christian Nimtz, David Papineau, Jake Quilty-Dunn, Peter Schulte, Nicholas Shea, Joulia Smortchkova, Ulrich Stegmann and two anonymous reviewers for their helpful comments and criticisms. Earlier versions of this paper were presented at the workshop ‘The Future of Teleosemantics’ at the University of Bielefeld (2018), the IX Conference of the Spanish Society for Logic, Methodology and Philosophy of Science at UNED (2018), the seminar ‘Valencia Colloquium in Philosophy’ at the University of Valencia (2019) and the Work-in-progress Reading Group in philosophy of mind at the University of Oxford (2019). Financial support was provided by the MICIU project ‘Varieties of Information’ (PGC2018-101425-B-I00).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Marc Artiga.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Artiga, M. Strong liberal representationalism. Phenom Cogn Sci (2021). https://doi.org/10.1007/s11097-020-09720-z

Download citation

Keywords

  • Representation
  • Detector
  • Receptor
  • Mental content
  • Teleosemantics
  • Naturalism
  • Explanation