Skip to main content
SpringerLink
Log in

A Cautionary Contribution to the Philosophy of Explanation in the Cognitive Neurosciences

  • Published:
Minds and Machines Aims and scope Submit manuscript

Abstract

I propose a cautionary assessment of the recent debate concerning the impact of the dynamical approach on philosophical accounts of scientific explanation in the cognitive sciences and, particularly, the cognitive neurosciences. I criticize the dominant mechanistic philosophy of explanation, pointing out a number of its negative consequences: In particular, that it doesn’t do justice to the field’s diversity and stage of development, and that it fosters misguided interpretations of dynamical models’ contribution. In order to support these arguments, I analyze a case study in computational neuroethology and show why it should not be understood through a mechanistic lens; I specially focus on Zednik’s mechanistic interpretation of the case study. In addition, I argue for a greater appreciation of the relation between explanation and other epistemic goals in the field, as well as an increased sensitivity towards the associated contextual factors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Notes

  1. However, in the case of Zednik, this does not imply that there is no space for non-mechanistic dynamical explanation in cognitive science, as is shown by his analysis of Haken, Kelso and Bunz’s model of bimanual coordination as a covering law explanation –despite his comments on the known philosophical problems of this view on explanation (cf., Zednik 2011: 245–246). As we will see, my criticism towards Zednik lies specifically in what I see as an inadequate rendering of dynamical models’ rationale and contribution to the field.

  2. It is worth mentioning that I do not endorse the position Revonsuo (2001: 57–58) presents regarding dynamicism, which he sees as a recent version of functionalism in cognitive science; however, I highlight his warning on the importance of directing our reflections to more defined scientific fields.

  3. It is interesting to note how Bickle (2006) appeals to what he sees as an unfitting assessment of neuroscience's state of the art (in this case, molecular and cellular neuroscience) facing cognitive phenomena, as part of the motives behind the poor adherence to his radical reductionism in philosophy of neuroscience. Clearly, selecting the neuroscientific area that is object of philosophical reflection and subsequent evaluation has a major impact on the epistemological conclusions finally obtained.

  4. Although other aspects can be highlighted: For example, in a philosophical take on this same model by Beer, Chemero (cf., 2009: 38) highlights its predictive benefits and its ability to support counterfactuals.

  5. Similar considerations to the ones I developed in Beer’s case also apply to Zednik’s interpretation of the A-not-B error model by Thelen and collaborators. Again, I don’t see why the contribution here must be strictly understood in terms of functional decomposition of a complex task into low-level (perceptual and motor) activities. Analyzing this case exceeds my purposes here.

  6. In line with my remarks, I would agree with rejecting the dichotomy framed in this way, but my proposal is very different from the authors’.

References

  • Abrahamsen, A., & Bechtel, W. (2006). Phenomena and mechanisms: Putting the symbolic, connectionist, and dynamical systems debate in broader perspective. In R. Stainton (Ed.), Contemporary debates in cognitive science (pp. 159–185). Malden, MA: Blackwell.

    Google Scholar 

  • Abrahamsen, A., & Bechtel, W. (2012). From reactive to endogenously active dynamical conceptions of the brain. In K. Plaisance & T. Reydon (Eds.), Philosophy of behavioral biology (pp. 329–366). Amsterdam: Springer.

    Chapter  Google Scholar 

  • Aminoff, E., Balslev, D., Borroni, P., Bryan, R., Chua, E., Cloutier, J., et al. (2009). The landscape of cognitive neuroscience: Challenges, rewards, and new perspectives. In M. Gazzaniga (Ed.), The cognitive neurosciences IV (pp. 1253–1260). Cambridge, MA: MIT Press.

    Google Scholar 

  • Bechtel, W. (1998). Representations and cognitive explanations: Assessing the dynamicist’s challenge in cognitive science. Cognitive Science, 22(3), 295–318.

    Article  Google Scholar 

  • Bechtel, W. (2001). The compatibility of complex systems and reduction. Minds and Machines, 11(4), 483–502.

    Article  Google Scholar 

  • Bechtel, W. (2002). Aligning multiple research techniques in cognitive neuroscience. Philosophy of Science, 69, S48–S58.

    Article  Google Scholar 

  • Bechtel, W. (2008). Mental mechanisms. London: Routledge.

    Google Scholar 

  • Bechtel, W., & Richardson, R. (2010). Discovering complexity. Cambridge, MA: MIT Press.

    Google Scholar 

  • Beer, R. (1996). Toward the evolution of dynamical neural networks for minimally cognitive behavior. In P. Maes, M. Matarić, J.-A. Meyer, J. Pollack, & S. Wilson (Eds.), From animals to animats (Vol. 4, pp. 421–429). Cambridge, MA: MIT Press.

    Google Scholar 

  • Beer, R. (1997). The dynamics of adaptive behavior: A research program. Robotics and Autonomous Systems, 20(2), 257–289.

    Article  MathSciNet  Google Scholar 

  • Beer, R. (2000). Dynamical approaches in cognitive science. Trends in Cognitive Sciences, 4(3), 91–99.

    Article  MathSciNet  Google Scholar 

  • Beer, R. (2003). The dynamics of active categorical perception in an evolved model agent. Adaptive Behavior, 11(4), 209–243.

    Article  Google Scholar 

  • Bickle, J. (2006). Reducing mind to molecular pathways: Explicating the reductionism implicit in current cellular and molecular neuroscience. Synthese, 151(3), 411–434.

    Article  MathSciNet  Google Scholar 

  • Bickle, J., & Hardcastle, V. (2012). Philosophy of neuroscience. Elsevier Life Sciences Reviews. doi:10.1002/9780470015902.a0024144.

    Google Scholar 

  • Buzsáki, G. (2006). Rhythms of the brain. Oxford: Oxford University Press.

    Book  MATH  Google Scholar 

  • Chemero, A. (2009). Radical embodied cognitive science. Cambridge, MA: MIT Press.

    Google Scholar 

  • Chemero, A., & Silberstein, M. (2008). After the philosophy of mind. Philosophy of Science, 75(1), 1–27.

    Article  Google Scholar 

  • Cooper, R., & Shallice, T. (2010). Cognitive neuroscience: The troubled marriage of cognitive science and neuroscience. Topics in Cognitive Science, 2(3), 398–406.

    Article  Google Scholar 

  • Craver, C. (2005). Beyond reduction: Mechanisms, multifield integration, and the unity of science. Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 373–396.

    Article  Google Scholar 

  • Craver, C. (2006). What mechanistic models explain. Synthese, 153(3), 355–376.

    Article  MathSciNet  Google Scholar 

  • Craver, C. (2008). Physical law and mechanistic explanation in the Hodgkin and Huxley model of the action potential. Philosophy of Science, 75(5), 1022–1033.

    Article  Google Scholar 

  • Cummins, R. (1975). Functional analysis. Journal of Philosophy, 72(20), 741–764.

    Article  Google Scholar 

  • Dale, R. (2008). The possibility of a pluralist cognitive science. Journal of Experimental and Theoretical Artificial Intelligence, 20(3), 155–179.

    Article  Google Scholar 

  • Dale, R., Dietrich, E., & Chemero, A. (2009). Explanatory pluralism in cognitive science. Cognitive Science, 33(5), 739–742.

    Article  Google Scholar 

  • Deco, G., Tononi, G., Boly, M., & Kringelbach, M. (2015). Rethinking segregation and integration: Contributions of whole-brain modelling. Nature Reviews Neuroscience, 16(7), 430–439.

    Article  Google Scholar 

  • Dietrich, E., & Markman, A. (2001). Dynamical description versus dynamical modeling. Trends in Cognitive Sciences, 5(8), 332.

    Article  Google Scholar 

  • Dupré, J. (2013). Living causes. Aristotelian Society Supplementary, 87(1), 19–37.

    Article  Google Scholar 

  • Engel, A., Fries, P., & Singer, W. (2001). Dynamic predictions: Oscillations and synchrony in top-down processing. Nature Reviews Neuroscience, 2(10), 704–716.

    Article  Google Scholar 

  • Engel, A., Friston, K., Kelso, S., König, P., Kovács, I., MacDonald, A., et al. (2010). Coordination in behavior and cognition. In C. von der Malsburg, W. Phillips, & W. Singer (Eds.), Dynamic coordination in the brain: From neurons to mind (pp. 267–299). Cambridge, MA: MIT Press.

    Chapter  Google Scholar 

  • Freeman, W. (2005). A field-theoretic approach to understanding scale-free neocortical dynamics. Biological Cybernetics, 92(6), 350–359.

    Article  MathSciNet  MATH  Google Scholar 

  • Gazzaniga, M., Doron, K., & Funk, C. (2009). Looking toward the future: Perspectives on examining the architecture and function of the human brain as a complex system. In M. Gazzaniga (Ed.), The cognitive neurosciences IV (pp. 267–299). Cambridge, MA: MIT Press.

    Google Scholar 

  • Gervais, R. (2015). Mechanistic and non-mechanistic varieties of dynamical models in cognitive science: Explanatory power, understanding, and the ‘mere description’ worry. Synthese, 192(1), 43–66.

    Article  MathSciNet  Google Scholar 

  • Glennan, S. (2002). Rethinking mechanistic explanation. Philosophy of Science, 69(3), 342–353.

    Article  Google Scholar 

  • Glennan, S. (2005). Modeling mechanisms. Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 443–464.

    Article  Google Scholar 

  • Harnad, S. (1987). Introduction: Psychophysical and cognitive aspects of categorical perception. In S. Harnad (Ed.), Categorical perception: The groundwork of cognition (pp. 1–25). Cambridge: Cambridge University Press.

    Google Scholar 

  • Huster, R., Debener, S., Eichele, T., & Herrmann, C. (2012). Methods for simultaneous EEG-fMRI: An introductory review. The Journal of Neuroscience, 32(18), 6053–6060.

    Article  Google Scholar 

  • Izhikevich, E. (2007). Dynamical systems in neuroscience. Cambridge, MA: MIT Press.

    Google Scholar 

  • Kaplan, D., & Bechtel, W. (2011). Dynamical models: An alternative or complement to mechanistic explanations? Topics in Cognitive Science, 3(2), 438–444.

    Article  Google Scholar 

  • Kaplan, D., & Craver, C. (2011). The explanatory force of dynamical and mathematical models in neuroscience. Philosophy of Science, 78(4), 601–627.

    Article  MathSciNet  Google Scholar 

  • Kelso, S. (1995). Dynamic patterns: The self-organization of brain and behavior. Cambridge, MA: MIT Press.

    Google Scholar 

  • Koertge, N. (1992). Explanation and its problems. British Journal for the Philosophy of Science, 43(1), 85–98.

    Article  Google Scholar 

  • Matthewson, J. (2011). Trade-offs in model-building: A more target-oriented approach. Studies in History and Philosophy of Science, 42(2), 324–333.

    Article  Google Scholar 

  • Mitchell, M. (2009). Complexity: A guided tour. Oxford: Oxford University Press.

    MATH  Google Scholar 

  • Revonsuo, A. (2001). On the nature of explanation in the neurosciences. In P. Machamer, P. McLaughlin, & R. Grush (Eds.), Theory and method in the neurosciences (pp. 45–69). Pittsburgh: University of Pittsburgh Press.

    Google Scholar 

  • Rodriguez, E., George, N., Lachaux, J.-P., Martinerie, J., Renault, B., & Varela, F. (1999). Perception’s shadow: Long-distance synchronization of human brain activity. Nature, 397(9718), 430–433.

    Google Scholar 

  • Rosa, M., Daunizeau, J., & Friston, K. (2010). EEG/fMRI integration: A critical review of biophysical modeling and data analysis approaches. Journal of Integrative Neuroscience, 9(4), 453–476.

    Article  Google Scholar 

  • Ross, L. (2015). Dynamical models and explanation in neuroscience. Philosophy of Science, 82(1), 32–54.

    Article  Google Scholar 

  • Schöner, G., & Reimann, H. (2009). Understanding embodied cognition through dynamical systems thinking. In J. Symons & F. Calvo (Eds.), The Routledge companion to philosophy of psychology (pp. 450–473). New York: Routledge.

    Google Scholar 

  • Silberstein, M., & Chemero, A. (2013). Constraints on localization and decomposition as explanatory strategies in the biological sciences. Philosophy of Science, 80(5), 958–970.

    Article  Google Scholar 

  • Slocum, A., Downey, D., & Beer, R. (2000). Further experiments in the evolution of minimally cognitive behavior. In J.-A. Meyer, A. Berthoz, D. Floreano, H. Roitblat, & S. Wilson (Eds.), From animals to animats (Vol. 6, pp. 430–439). Cambridge, MA: MIT Press.

    Google Scholar 

  • Smith, L., & Thelen, E. (2003). Development as a dynamic system. Trends in Cognitive Sciences, 7(8), 343–348.

    Article  Google Scholar 

  • Stepp, N., Chemero, A., & Turvey, M. (2011). Philosophy for the rest of cognitive science. Topics in Cognitive Science, 3(2), 425–437.

    Article  Google Scholar 

  • Stewart, L., & Walsh, V. (2006). Transcranial magnetic stimulation in human cognition. In C. Senior, T. Russell, & M. Gazzaniga (Eds.), Methods in mind (pp. 1–26). Cambridge, MA: MIT Press.

    Google Scholar 

  • Strogatz, S. (1994). Nonlinear dynamics and chaos. Reading: Addison-Wesley.

    Google Scholar 

  • Sullivan, J. (2009). The multiplicity of experimental protocols. Synthese, 167(3), 511–539.

    Article  Google Scholar 

  • Thelen, E., Schöner, G., Scheier, C., & Smith, L. (2001). The dynamics of embodiment. Behavioral and Brain Sciences, 24(1), 1–86.

    Article  Google Scholar 

  • van Gelder, T. (1997). Dynamics and cognition. In J. Haugeland (Ed.), Mind design II: Philosophy, psychology, artificial intelligence (pp. 421–450). Cambridge, MA: MIT Press.

    Google Scholar 

  • van Gelder, T. (1998). The dynamical hypothesis in cognitive science. Behavioral and Brain Sciences, 21(5), 615–665.

    Google Scholar 

  • van Gelder, T., & Port, R. (1995). It’s about time. In R. Port & T. van Gelder (Eds.), Mind as motion (pp. 1–43). Cambridge, MA: MIT Press.

    Google Scholar 

  • van Leeuwen, M. (2005). Questions for the dynamicist. Minds and Machines, 15(3), 271–333.

    Article  Google Scholar 

  • Venturelli, N. (2012). ¿Puede hablarse de una explicación dinamicista en las ciencias cognitivas? Ludus Vitalis, 37, 151–174.

  • Venturelli, N. (2015). Un abordaje epistemológico de la integración neurocientífica: el caso de los estudios EEG / RMf. In V. Rodríguez, M. Velasco, & P. García (Eds.), Epistemología y prácticas científicas (pp. 41–71). Córdoba: Editorial Universitaria.

  • Walmsley, J. (2008). Explanation in dynamical cognitive science. Minds and Machines, 18(3), 331–348.

    Article  Google Scholar 

  • Weiskopf, D. (2011). Models and mechanisms in psychological explanation. Synthese, 183(3), 313–338.

    Article  Google Scholar 

  • Woodward, J. (Forthcoming). Explanation in neurobiology: An interventionist perspective. In D. Kaplan (Ed.), Integrating psychology and neuroscience: Prospects and problems. Oxford: Oxford University Press. http://philsci-archive.pitt.edu/10974/2/jw._8.23._Kaplan.Explanation_in_Neurobiologyx.pdf. Accessed 12 March 2015.

  • Zednik, C. (2011). The nature of dynamical explanation. Philosophy of Science, 78(2), 236–263.

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

I wish to thank two anonymous reviewers for their insightful feedback on my work. Also, thanks to Itatí Branca for her input on an earlier version of the paper.

Author information

Authors and Affiliations

  1. Instituto de Humanidades (CONICET/Universidad Nacional de Córdoba), Córdoba, Argentina

    A. Nicolás Venturelli

Authors
  1. A. Nicolás Venturelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Nicolás Venturelli.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Venturelli, A.N. A Cautionary Contribution to the Philosophy of Explanation in the Cognitive Neurosciences. Minds & Machines 26, 259–285 (2016). https://doi.org/10.1007/s11023-016-9395-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11023-016-9395-0

Keywords

Search

Navigation