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Synthese

, Volume 195, Issue 5, pp 1909–1940 | Cite as

An interventionist approach to psychological explanation

  • Michael Rescorla
S.I. : Neuroscience and Its Philosophy

Abstract

Interventionism is a theory of causal explanation developed by Woodward and Hitchcock. I defend an interventionist perspective on the causal explanations offered within scientific psychology. The basic idea is that psychology causally explains mental and behavioral outcomes by specifying how those outcomes would have been different had an intervention altered various factors, including relevant psychological states. I elaborate this viewpoint with examples drawn from cognitive science practice, especially Bayesian perceptual psychology. I favorably compare my interventionist approach with well-known nomological and mechanistic theories of psychological explanation.

Keywords

Psychological explanation Interventionism Deductive-nomological model Mechanism Bayesian cognitive science Psychological law 

Notes

Acknowledgements

I presented excerpts from this material at a conference on Bayesian Theories of Perception and Epistemology at Cornell University, July 2015; during a symposium at the Philosophy of Science Association Biennial Meeting in Atlanta, November 2016; and during a symposium at the Society for Philosophy and Psychology Annual Meeting, Baltimore, July 2017. I am grateful to all participants and audience members for helpful feedback, especially David Chalmers, David Danks, Steven Gross, Gualtiero Piccinini, Susanna Siegel, and Scott Sturgeon. Thanks also to Nicholas Shea and to three anonymous referees for this journal for comments that significantly improved the paper. My research was supported by a fellowship from the National Endowment for the Humanities. Any views, findings, conclusions, or recommendations expressed in this publication do not necessarily reflect those of the National Endowment for the Humanities.

References

  1. Adams, W., Graf, E., & Ernst, M. (2004). Experience can change the light-from-above prior. Nature Neuroscience, 7, 1057–1058.CrossRefGoogle Scholar
  2. Alais, D., & Burr, D. (2004). The ventriloquism effect results from near-optimal bimodal integration. Current Biology, 14, 257–262.CrossRefGoogle Scholar
  3. Antony, L. (1995). Law and order in psychology. Philosophical Perspectives, 9, 429–446.CrossRefGoogle Scholar
  4. Aydede, M. (2000). Computation and intentional psychology. Dialogue, 39, 365–379.CrossRefGoogle Scholar
  5. Baker, C., & Tenenbaum, J. (2014). Modeling human plan recognition using Bayesian theory of mind. In G. Sukthankar, R. P. Goldman, C. Geib, D. Pynadath, & H. Bui (Eds.), Plan, activity, and intent recognition: Theory and practice. Waltham: Morgan Kaufmann.Google Scholar
  6. Bays, P., & Wolpert, D. (2007). Computational principles of sensorimotor control that minimize uncertainty and variability. Journal of Physiology, 578, 387–396.CrossRefGoogle Scholar
  7. Bechtel, W. (2008). Mental mechanisms: Philosophical perspectives on cognitive neuroscience. New York: Lawrence Erlbaum Associates.Google Scholar
  8. Bechtel, W., & Wright, C. (2009). What is psychological explanation? In J. Symons & P. Calvo (Eds.), Routledge companion to the philosophy of psychology. New York: Routledge.Google Scholar
  9. Born, R., & Bradley, D. (2005). Structure and function of visual area MT. Annual Review of Neuroscience, 28, 157–189.CrossRefGoogle Scholar
  10. Burge, T. (2010). Origins of objectivity. Oxford: Oxford University Press.CrossRefGoogle Scholar
  11. Campbell, J. (2007). An interventionist approach to causation in psychology. In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation. Oxford: Oxford University Press.Google Scholar
  12. Chater, N., & Manning, C. (2006). Probabilistic models of language processing and acquisition. Trends in Cognitive Science, 10, 335–344.CrossRefGoogle Scholar
  13. Chater, N., & Oaksford, M. (Eds.). (2008). The probabilistic mind. Oxford: Oxford University Press.Google Scholar
  14. Colombo, M., & Hartmann, S. (2017). Bayesian cognitive science: Unification and explanation. The British Journal for the Philosophy of Science, 68, 451–484.Google Scholar
  15. Craver, C. (2006). When mechanistic models explain. Synthese, 153, 355–376.CrossRefGoogle Scholar
  16. Craver, C. (2014). The ontic account of scientific explanation. In M. Kaiser, O. Scholz, D. Plenge, & A. Hütteman (Eds.), Explanation in the special sciences: The case of biology and history. Dordrecht: Springer.Google Scholar
  17. Cummins, R. (2000). “How does it work?” versus “What are the laws?”: Two conceptions of psychological explanation. In F. Keil & R. Wilson (Eds.), Explanation and cognition. Cambridge: MIT Press.Google Scholar
  18. Darley, J., & Latané, B. (1968). Bystander intervention in emergencies: Diffusion of responsibility. Journal of Personality and Social Psychology, 8, 377–383.CrossRefGoogle Scholar
  19. Davidson, D. (1980). Essays on actions and events. Oxford: Clarendon Press.Google Scholar
  20. Dennett, D. (1993). Back from the drawing board. In B. Dahlbom (Ed.), Dennett and his critics. Malden: Blackwell.Google Scholar
  21. Earman, J., Roberts, J., & Smith, S. (2002). Ceteris paribus lost. Erkenntnis, 57, 281–302.CrossRefGoogle Scholar
  22. Ernst, M. (2007). Learning to integrate arbitrary signals from vision and touch. Journal of Vision, 7, 1–14.CrossRefGoogle Scholar
  23. Feldman, J. (2015). Bayesian models of perceptual organization. In J. Wagemans (Ed.), The Oxford handbook of perceptual organization. Oxford: Oxford University Press.Google Scholar
  24. Flanagan, J., Bittner, J., & Johansson, R. (2008). Experience can change distinct size-weight priors engaged in lifting objects and judging their weights. Current Biology, 22, 1742–1747.CrossRefGoogle Scholar
  25. Fletcher, P., & Frith, C. (2009). Perceiving is believing: A Bayesian approach to explaining the positive symptoms of schizophrenia. Nature Reviews Neuroscience, 10, 48–58.CrossRefGoogle Scholar
  26. Fodor, J. (1981). Representations. Cambridge: MIT Press.Google Scholar
  27. Fodor, J. (1987). Psychosemantics. Cambridge: MIT Press.Google Scholar
  28. Fodor, J. (1991a). Replies. In B. Loewer & G. Rey (Eds.), Meaning in mind. Cambridge: Blackwell.Google Scholar
  29. Fodor, J. (1991b). You can fool some of the people all of the time, everything else being equal: Hedged laws and psychological explanation. Mind, 100, 19–34.CrossRefGoogle Scholar
  30. Fodor, J. (1994). The elm and the expert. Cambridge: MIT Press.Google Scholar
  31. Fodor, J., & Lepore, E. (1992). Holism: A shopper’s guide. Cambridge: Blackwell.Google Scholar
  32. Franklin-Hall, L. (2016). High-level explanations and the interventionist’s “variables problem”. The British Journal for the Philosophy of Science, 67, 553–577.CrossRefGoogle Scholar
  33. Garcia, J., & Koelling, R. (1966). The relation of cue to consequence in avoidance learning. Psychonomic Science, 4, 123–124.CrossRefGoogle Scholar
  34. Gauker, C. (2005). The belief-desire law. Facta Philosophica, 7, 121–144.CrossRefGoogle Scholar
  35. Gopnik, A., Glymour, G., Sobel, D., Schulz, L., & Kushnir, T. (2004). A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review, 111, 3–32.CrossRefGoogle Scholar
  36. Griffiths, T., Kemp, C., & Tenenbaum, J. (2008). Bayesian models of cognition. In R. Sun (Ed.), The Cambridge handbook of computational cognitive modeling. Cambridge: Cambridge University Press.Google Scholar
  37. Hempel, C. (1965). Aspects of scientific explanation, and other essays in the philosophy of science. New York: Free Press.Google Scholar
  38. Herschbach, M., & Bechtel, W. (2011). Relating Bayes to cognitive mechanisms. Behavioral and Brain Sciences, 34, 202–203.CrossRefGoogle Scholar
  39. Hershenson, M. (1989). The moon illusion. Hillsdale: Lawrence Erlbaum Associates.Google Scholar
  40. Horgan, T., & Tienson, J. (1990). Soft laws. Midwest Studies in Philosophy, 15, 256–279.CrossRefGoogle Scholar
  41. Jones, M., & Love, B. (2011). Bayesian fundamentalism or enlightenment? On the explanatory status and theoretical contribution of Bayesian models of cognition. Behavioral and Brain Sciences, 34, 169–188.CrossRefGoogle Scholar
  42. Kaufman, L., & Kaufman, J. (2000). Explaining the moon illusion. Proceedings of the National Academy of Sciences, 97, 500–505.CrossRefGoogle Scholar
  43. Kitcher, P. (1989). Explanatory unification and the causal structure of the world. In P. Kitcher & W. Salmon (Eds.), Scientific explanation. Minneapolis: University of Minnesota Press.Google Scholar
  44. Knill, D. (2007). Learning Bayesian priors for depth perception. Journal of Vision, 7, 1–20.Google Scholar
  45. Knill, D., & Richards, W. (Eds.). (1996). Perception as Bayesian inference. Cambridge: Cambridge University Press.Google Scholar
  46. Lange, M. (2016). Because without cause. Oxford: Oxford University Press.CrossRefGoogle Scholar
  47. MacDonald, J., & McGurk, H. (1978). Visual influences on speech perception processes. Perception and Psychophysics, 24, 253–257.CrossRefGoogle Scholar
  48. Madigan, S. (1969). Intraserial repetition and coding processes in free recall. Journal of Verbal Learning and Verbal Behavior, 8, 828–835.CrossRefGoogle Scholar
  49. Madl, T., Franklin, S., Chen, K., Montaldi, D., & Trappl, R. (2014). Bayesian integration of information in hippocampal place cells. PloS One, 9, e89762.CrossRefGoogle Scholar
  50. Mankiw, G. (1997). Macroeconomics (3rd ed.). New York: Worth Publishers.Google Scholar
  51. Moreno-Bote, R., Knill, D., & Pouget, A. (2011). Bayesian sampling in visual perception. Proceedings of National Academy of Sciences, 108, 12491–6.CrossRefGoogle Scholar
  52. Palmer, S. (1999). Vision science. Cambridge: MIT Press.Google Scholar
  53. Pellicano, E., & Burr, D. (2012). When the world becomes too real. Trends in Cognitive Science, 16, 504–510.CrossRefGoogle Scholar
  54. Petzschner, F., & Glasauer, S. (2011). Iterative Bayesian estimation as an explanation for range and regression effects: A study on human path integration. Journal of Neuroscience, 31, 17220–17229.CrossRefGoogle Scholar
  55. Pietroski, P., & Rey, G. (1995). When other things aren’t equal: Saving ceteris paribus laws from vacuity. The British Journal for the Philosophy of Science, 46, 81–110.CrossRefGoogle Scholar
  56. Piccinini, G., & Craver, C. (2011). Integrating psychology and neuroscience: Functional analyses as mechanism sketches. Synthese, 183, 283–311.CrossRefGoogle Scholar
  57. Pouget, A., Beck, J., Ma, W. J., & Latham, P. (2013). Probabilistic brains: Knowns and unknowns. Nature Neuroscience, 16, 1170–1178.CrossRefGoogle Scholar
  58. Rescorla, M. (2014). The causal relevance of content to computation. Philosophy and Phenomenological Research, 88, 173–208.CrossRefGoogle Scholar
  59. Rescorla, M. (2015). Bayesian perceptual psychology. In M. Matthen (Ed.), The Oxford handbook of the philosophy of perception. Oxford: Oxford University Press.Google Scholar
  60. Rescorla, M. (2016). Bayesian sensorimotor psychology. Mind and Language, 31, 3–36.CrossRefGoogle Scholar
  61. Rock, I., & Kaufman, L. (1962). The moon illusion, II. Science, 136, 1023–1031.CrossRefGoogle Scholar
  62. Saatsi, J., & Pexton, M. (2013). Reassessing Woodward’s account of explanation: Regularities, counterfactuals, and noncausal explanations. Philosophy of Science, 80, 613–624.CrossRefGoogle Scholar
  63. Salmon, W. (1971). Statistical explanation. In W. Salmon (Ed.), Statistical explanation and statistical relevance. Pittsburgh: University of Pittsburgh Press.CrossRefGoogle Scholar
  64. Salmon, W. (1989). Four decades of scientific explanation. In P. Kitcher & W. Salmon (Eds.), Scientific explanations: Minnesota studies in philosophy of science XIII. Minneapolis: University of Minnesota Press.Google Scholar
  65. Sanborn, A., Masinghka, J., & Griffiths, T. (2013). Reconciling intuitive physics and Newtonian mechanics for colliding objects. Psychological Review, 120, 411–437.CrossRefGoogle Scholar
  66. Sato, Y., & Kording, K. (2014). How much to trust the senses: Likelihood learning. Journal of Vision, 14, 1–13.Google Scholar
  67. Schiffer, S. (1991). Ceteris paribus laws. Mind, 100, 1–17.CrossRefGoogle Scholar
  68. Schneider, S. (2005). Direct reference, psychological explanation, and Frege cases. Mind and Language, 20, 423–447.CrossRefGoogle Scholar
  69. Seydell, A., Knill, D., & Trommershäuser, J. (2010). Adapting internal statistical models for interpreting visual cues to depth. Journal of Vision, 10, 1–27.CrossRefGoogle Scholar
  70. Sotiropoulos, G., Seitz, A., & Seriès, P. (2011). Changing expectations about speed alters perceived motion direction. Current Biology, 21, R883–R884.CrossRefGoogle Scholar
  71. Stinson, C. (2016). Mechanisms in psychology: Ripping natures at its seams. Synthese, 193, 1585–1614.CrossRefGoogle Scholar
  72. Stocker, A., & Simoncelli, E. (2006). Noise characteristics and prior expectations in human visual speed perception. Nature Neuroscience, 4, 578–585.CrossRefGoogle Scholar
  73. Stone, J. (2011). Footprints sticking out of the sand, part 2: Children’s Bayesian priors for shape and lighting direction. Perception, 40, 175–190.CrossRefGoogle Scholar
  74. Stroop, J. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643–662.CrossRefGoogle Scholar
  75. Strevens, M. (2008). Depth. Cambridge: Harvard University Press.Google Scholar
  76. von Helmholtz, H. (1867). Handbuch der Physiologischen Optik. Leipzig: Voss.Google Scholar
  77. Weiskopf, D. (2011). Models and mechanisms in psychological explanation. Synthese, 181, 313–338.CrossRefGoogle Scholar
  78. Weiss, Y., Simoncelli, E., & Adelson, E. (2002). Motion illusions as optimal percepts. Nature Neuroscience, 5, 598–604.CrossRefGoogle Scholar
  79. Wolpert, D. (2007). Probabilistic models in human sensorimotor control. Human Movement Science, 26, 511–524.CrossRefGoogle Scholar
  80. Woodward, J. (2003). Making things happen. Oxford: Oxford University Press.Google Scholar
  81. Woodward, J. (2008). Mental causation and neural mechanisms. In J. Hohwy & J. Kallestrup (Eds.), Being reduced. Oxford: Oxford University Press.Google Scholar
  82. Woodward, J. (2008). Cause and explanation in psychiatry: An interventionist perspective. In K. Kendler & J. Parnas (Eds.), Philosophical issues in psychiatry: Explanation, phenomenology, and nosology. Baltimore: Johns Hopkins Press.Google Scholar
  83. Woodward, J. (forthcoming). Explanation in neurobiology: An interventionist perspective. In D. Kaplan (Ed.) Integrating psychology and neuroscience: Prospects and problems. Oxford: Oxford University Press.Google Scholar
  84. Woodward, J., & Hitchcock, C. (2003a). Explanatory generalizations, part I: A counterfactual account. Nous, 37, 1–24.CrossRefGoogle Scholar
  85. Woodward, J., & Hitchcock, C. (2003b). Explanatory generalizations, part II: Plumbing explanatory depth. Nous, 37, 181–199.CrossRefGoogle Scholar
  86. Zeki, S. (2015). Area V5—A microcosm of the visual brain. Frontiers in Integrative Neuroscience, 9, 1–18.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Department of PhilosophyUniversity of California Los AngelesLos AngelesUSA

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