Biology & Philosophy

, Volume 31, Issue 4, pp 527–550 | Cite as

A contextualist approach to functional localization in the brain

Article

Abstract

Functional localization has historically been one of the primary goals of neuroscience. There is still debate, however, about whether it is possible, and if so what kind of theories succeed at localization. I argue for a contextualist approach to localization. Most theorists assume that widespread contextual variability in function is fundamentally incompatible with functional decomposition in the brain, because contextualist accounts will fail to be generalizable and projectable. I argue that this assumption is misplaced. A properly articulated contextualism can ground successful theories of localization even without positing completely generalizable accounts. Via a case study from perceptual neuroscience, I suggest that there is strong evidence for contextual variation in the function of perceptual brain areas. I then outline a version of contextualism that is empirically adequate with respect to this data, and claim that it can still distinguish brain areas from each other according to their functional properties. Finally, I claim that the view does not fail the norms for good theory in the way that anticontextualists suppose. It is true that, on a contextualist view, we will not have theories that are completely generalizable and predictive. We can, however, have successful partial generalizations that structure ongoing investigation and lead to novel functional insight, and this success is sufficient to ground the project of functional localization.

Keywords

Absolutism Contextualism Explanation Functional localization Perceptual neuroscience 

Notes

Acknowledgments

I would like to thank William Bechtel, Jonathan Cohen, Rick Grush, Thomas Albright, and John Serences for invaluable advice during the development of this project. Thanks also to Ann-Sophie Barwich, Olivier Morin, Isabella Sarto-Jackson, and Ben Sheredos for extremely helpful comments on an earlier version of the manuscript. This material was presented to colloquia audiences at Tulane University and Indiana University, Bloomington, and I am grateful to both for helpful discussion.

References

  1. Anderson ML (2010) Neural reuse: a fundamental organizational principle of the brain. Behav Brain Sci 33(4):245–266 (discussion 266-313) CrossRefGoogle Scholar
  2. Anzai A, DeAngelis GC (2010) Neural computations underlying depth perception. Curr Opin Neurobiol 20(3):367–375CrossRefGoogle Scholar
  3. Bechtel W, Richardson RC (1993) Discovering complexity: decomposition and localization as scientific research strategies. Princeton University Press, PrincetonGoogle Scholar
  4. Bergeron V (2007) Anatomical and functional modularity in cognitive science: shifting the focus. Philos Psychol 20(2):175–195CrossRefGoogle Scholar
  5. Britten KH, Newsome WT, Shadlen MN, Celebrini S, Movshon JA (1996) A relationship between behavioral choice and the visual responses of neurons in macaque MT. Vis Neurosci 13:87–100CrossRefGoogle Scholar
  6. Burnston DC (2015) Perceptual Context and the Nature of Neural Function. Doctoral dissertation, University of California, San DiegoGoogle Scholar
  7. Burnston DC (2016) Computational neuroscience and localized neural function. Synthese. doi:10.1007/s11229-016-1099-8
  8. Canolty RT, Ganguly K, Carmena JM (2012) Task-dependent changes in cross-level coupling between single neurons and oscillatory activity in multiscale networks. PLoS Comput Biol 8(12):e1002809CrossRefGoogle Scholar
  9. Cappelen H, Lepore E (2005) Insensitive semantics: a defense of semantic minimalism and speech act pluralism. Wiley, New YorkCrossRefGoogle Scholar
  10. Connor CE, Brincat SL, Pasupathy A (2007) Transformation of shape information in the ventral pathway. Curr Opin Neurobiol 17(2):140–147CrossRefGoogle Scholar
  11. Craver CF (2007) Explaining the brain. Oxford University Press, OxfordCrossRefGoogle Scholar
  12. Craver CF, Darden L (2013) In search of mechanisms: discoveries across the life sciences. University of Chicago Press, ChicagoCrossRefGoogle Scholar
  13. Cummins RC (1975) Functional analysis. J Philos 72(20):741–765CrossRefGoogle Scholar
  14. DeAngelis GC, Newsome WT (2004) Perceptual “read-out” of conjoined direction and disparity maps in extrastriate area MT. PLoS Biol 2:e77CrossRefGoogle Scholar
  15. DeAngelis GC, Cumming BG, Newsome WT (1998) Cortical area MT and the perception of stereoscopic depth. Nature 394(6694):677–680CrossRefGoogle Scholar
  16. DeRose K (1992) Contextualism and knowledge attributions. Philos Phenomenol Res 52(4):913–929CrossRefGoogle Scholar
  17. Dobkins KR, Albright TD (1994) What happens if it changes color when it moves?: the nature of chromatic input to macaque visual area MT. J Neurosci 14(8):4854–4870Google Scholar
  18. Dobkins KR, Albright TD (2004) Merging processing streams: color cues for motion detection and interpretation. In: Chalupa LM, Werner JS (eds) The visual neurosciences. MIT Press, Cambridge, pp 1217–1228Google Scholar
  19. Dobkins KR, Stoner GR, Albright TD (1998) Perceptual, oculomotor, and neural responses to moving color plaids. Perception 27:681–709CrossRefGoogle Scholar
  20. Dodd JV, Krug K, Cumming BG, Parker AJ (2001) Perceptually bistable three-dimensional figures evoke high choice probabilities in cortical area MT. J Neurosci 21(13):4809–4821Google Scholar
  21. Felleman DJ, Van Essen DC (1991) Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex 1(1):1–47CrossRefGoogle Scholar
  22. Klein C (2012) Cognitive ontology and region-versus network-oriented analyses. Philos Sci 79(5):952–960CrossRefGoogle Scholar
  23. Livingstone M, Hubel D (1988) Segregation of form, color, movement, and depth: anatomy, physiology, and perception. Science 240(4853):740–749CrossRefGoogle Scholar
  24. MacFarlane J (2009) Nonindexical contextualism. Synthese 166(2):231–250CrossRefGoogle Scholar
  25. Maunsell JH, Van Essen DC (1983a) Functional properties of neurons in middle temporal visual area of the macaque monkey. I. Selectivity for stimulus direction, speed, and orientation. J Neurophysiol 49(5):1127–1147Google Scholar
  26. Maunsell JH, Van Essen DC (1983b) Functional properties of neurons in middle temporal visual area of the macaque monkey. II. Binocular interactions and sensitivity to binocular disparity. J Neurophysiol 49(5):1148–1167Google Scholar
  27. McCaffrey JB (2015) The brain’s heterogeneous functional landscape. Philos Sci 82(5):1010–1022CrossRefGoogle Scholar
  28. McIntosh AR (2004) Contexts and catalysts: a resolution of the localization and integration of function in the brain. Neuroinformatics 2(2):175–182CrossRefGoogle Scholar
  29. Mishkin M, Ungerleider LG, Macko KA (1983) Object vision and spatial vision: two cortical pathways. Trends Neurosci 6:414–417CrossRefGoogle Scholar
  30. Palanca BJA, DeAngelis GC (2003) Macaque middle temporal neurons signal depth in the absence of motion. J Neurosci 23(20):7647–7658Google Scholar
  31. Pessoa L (2014) Understanding brain networks and brain organization. Phys Life Rev 11(3):400–435CrossRefGoogle Scholar
  32. Poldrack RA (2006) Can cognitive processes be inferred from neuroimaging data? Trends Cognit Sci 10(2):59–63CrossRefGoogle Scholar
  33. Preyer G, Peter G (2005) Contextualism in philosophy: knowledge, meaning, and truth. Oxford University Press, OxfordGoogle Scholar
  34. Price CJ, Friston KJ (2005) Functional ontologies for cognition: the systematic definition of structure and function. Cognit Neuropsychol 22(3):262–275CrossRefGoogle Scholar
  35. Rathkopf CA (2013) Localization and intrinsic function. Philos Sci 80(1):1–21CrossRefGoogle Scholar
  36. Rentzeperis I, Nikolaev AR, Kiper DC, van Leeuwen C (2014) Distributed processing of color and form in the visual cortex. Front Psychol 5:1–14CrossRefGoogle Scholar
  37. Roe AW, Chelazzi L, Connor CE, Conway BR, Fujita I, Gallant JL, Vanduffel W (2012) Toward a unified theory of visual area V4. Neuron 74(1):12–29CrossRefGoogle Scholar
  38. Sanada TM, Nguyenkim JD, DeAngelis GC (2012) Representation of 3-D surface orientation by velocity and disparity gradient cues in area MT. J Neurophysiol 107(8):2109–2122CrossRefGoogle Scholar
  39. Simon HA (1962) The architecture of complexity. In: Proceedings of the American Philosophical Society, pp 467–482Google Scholar
  40. Stanley J (2005) Semantics in context. In: Preyer G, Peter G (eds) Contextualism in philosophy: knowledge, meaning, and truth. Clarendon Press, Oxford, pp 221–253Google Scholar
  41. Stein BE, Stanford TR (2008) Multisensory integration: current issues from the perspective of the single neuron. Nat Rev Neurosci 9(4):255–266CrossRefGoogle Scholar
  42. Treue S, Trujillo JCM (1999) Feature-based attention influences motion processing gain in macaque visual cortex. Nature 399(6736):575–579CrossRefGoogle Scholar
  43. Uka T, DeAngelis GC (2003) Contribution of middle temporal area to coarse depth discrimination: comparison of neuronal and psychophysical sensitivity. J Neurosci 23(8):3515–3530Google Scholar
  44. Watrous AJ, Fell J, Ekstrom AD, Axmacher N (2015) More than spikes: common oscillatory mechanisms for content specific neural representations during perception and memory. Curr Opin Neurobiol 31:33–39CrossRefGoogle Scholar
  45. Zeki SM (1974) Functional organization of a visual area in the posterior bank of the superior temporal sulcus of the rhesus monkey. J Physiol 236(3):549–573CrossRefGoogle Scholar
  46. Zeki SM (1978) Functional specialisation in the visual cortex of the rhesus monkey. Nature 274(5670):423–428CrossRefGoogle Scholar
  47. Zeki S, Watson J, Lueck C, Friston KJ, Kennard C, Frackowiak R (1991) A direct demonstration of functional specialization in human visual cortex. J Neurosci 11(3):641–649Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Philosophy DepartmentTulane UniversityNew OrleansUSA

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