Advertisement

Heuristics, Descriptions, and the Scope of Mechanistic Explanation

  • Carlos ZednikEmail author
Part of the History, Philosophy and Theory of the Life Sciences book series (HPTL, volume 11)

Abstract

The philosophical conception of mechanistic explanation is grounded on a limited number of canonical examples. These examples provide an overly narrow view of contemporary scientific practice, because they do not reflect the extent to which the heuristic strategies and descriptive practices that contribute to mechanistic explanation have evolved beyond the well-known methods of decomposition, localization, and pictorial representation. Recent examples from evolutionary robotics and network approaches to biology and neuroscience demonstrate the increasingly important role played by computer simulations and mathematical representations in the epistemic practices of mechanism discovery and mechanism description. These examples also indicate that the scope of mechanistic explanation must be re-examined: With new and increasingly powerful methods of discovery and description comes the possibility of describing mechanisms far more complex than traditionally assumed.

Keywords

Mechanistic explanation Scientific discovery Evolutionary robotics Mathematical representation Dynamical systems theory Systems neuroscience Decomposability Heuristics of explanation 

References

  1. Abrahamsen, A., & Bechtel, W. (2006). Phenomena and mechanisms: Putting the symbolic, connectionist, and dynamical systems debate in broader perspective. In R. J. Stainton (Ed.), Contemporary debates in cognitive science (pp. 159–185). Oxford: Blackwell.Google Scholar
  2. Baetu, T. (2015). From mechanisms to mathematical models and back to mechanisms: Quantitative mechanistic explanations. In P.-A. Braillard & C. Malaterre (Eds.), Explanation in biology. An enquiry into the diversity of explanatory patterns in the life sciences (pp. 345–363). Dordrecht: Springer.Google Scholar
  3. Banks, E., Nabieva, E., Chazelle, B., & Singh, M. (2008). Organization of physical interactomes as uncovered by network schemas. PLoS Computational Biology, 4(10), e1000203. doi: 10.1371/journal.pcbi.1000203.CrossRefGoogle Scholar
  4. Bargmann, C. I., & Horvitz, H. R. (1991). Chemosensory neurons with overlapping functions direct chemotaxis to multiple chemicals in C. elegans. Neuron, 7(5), 729–742.CrossRefGoogle Scholar
  5. Bechtel, W. (2006). Discovering cell mechanisms. Cambridge: Cambridge University Press.Google Scholar
  6. Bechtel, W. (2008). Mental mechanisms: Philosophical perspectives on cognitive neuroscience. London: Routledge.Google Scholar
  7. Bechtel, W. (2015). Generalizing mechanistic explanations using graph-theoretic representations. In P.-A. Braillard & C. Malaterre (Eds.), Explanation in biology. An enquiry into the diversity of explanatory patterns in the life sciences (pp. 199–225). Dordrecht: Springer.Google Scholar
  8. Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 421–441. doi: 10.1016/j.shpsc.2005.03.010.CrossRefGoogle Scholar
  9. Bechtel, W., & Abrahamsen, A. (2010). Dynamic mechanistic explanation: Computational modeling of circadian rhythms as an exemplar for cognitive science. Studies in History and Philosophy of Science Part A, 41(3), 321–333. doi: 10.1016/j.shpsa.2010.07.003.CrossRefGoogle Scholar
  10. Bechtel, W., & Richardson, R. C. (1993). Discovering complexity: Decomposition and localization as strategies in scientific research. Princeton: Princeton University Press.Google Scholar
  11. Beer, R. D. (2003). The dynamics of active categorical perception in an evolved model agent. Adaptive Behavior, 11(4), 209–243. doi: 10.1177/1059712303114001; discussion 244–305.CrossRefGoogle Scholar
  12. Braillard, P.-A. (2015). Prospect and limits of explaining biological systems in engineering terms. In P.-A. Braillard & C. Malaterre (Eds.), Explanation in biology. An enquiry into the diversity of explanatory patterns in the life sciences (pp. 319–344). Dordrecht: Springer.Google Scholar
  13. Brigandt, I. (2015). Evolutionary developmental biology and the limits of philosophical accounts of mechanistic explanation. In P.-A. Braillard & C. Malaterre (Eds.), Explanation in biology. An enquiry into the diversity of explanatory patterns in the life sciences (pp. 135–173). Dordrecht: Springer.Google Scholar
  14. Chemero, A. (2009). Radical embodied cognitive science. Cambridge, MA: MIT Press.Google Scholar
  15. Chemero, A., & Silberstein, M. (2008). After the philosophy of mind: Replacing scholasticism with science. Philosophy of Science, 75(1), 1–27. doi: 10.1086/587820.CrossRefGoogle Scholar
  16. Craver, C. F. (2006). When mechanistic models explain. Synthese, 153(3), 355–376. doi: 10.1007/s11229-006-9097-x.CrossRefGoogle Scholar
  17. Craver, C. F. (2007). Explaining the brain. Oxford: Oxford University Press.CrossRefGoogle Scholar
  18. Craver, C. F. (2008). Physical law and mechanistic explanation in the Hodgkin and Huxley model of the action potential. Philosophy of Science, 75, 1022–1033.CrossRefGoogle Scholar
  19. Craver, C. F. (2013). Functions and mechanisms: A perspectivalist view. In P. Huneman (Ed.), Functions: Selection and mechanisms. Dordrecht: Springer.Google Scholar
  20. Cummins, R. (1983). The nature of psychological explanation. Cambridge, MA: MIT Press.Google Scholar
  21. Gigerenzer, G. (1991). From tools to theories: A heuristic of discovery in cognitive psychology. Psychological Review, 98(2), 254–267.CrossRefGoogle Scholar
  22. Glauer, R. (2012). Emergent mechanism: Reductive explanation for limited beings. Mentis: Münster.Google Scholar
  23. Glennan, S. (2002). Rethinking mechanistic explanation. Philosophy of Science, 69(S3), S342–S353. doi: 10.1086/341857.CrossRefGoogle Scholar
  24. Harvey, I., di Paolo, E. A., Tuci, E., Wood, R., & Quinn, M. (2005). Evolutionary robotics: A new scientific tool for studying cognition. Artificial Life, 11, 79–98.CrossRefGoogle Scholar
  25. Hempel, C. G. (1965). Aspects of scientific explanation and other essays in the philosophy of science. New York: Free Press.Google Scholar
  26. Hubel, D., & Wiesel, T. (1959). Receptive fields of single neurones in the cat’s striate cortex. Journal of Physiology, 148, 574–591.CrossRefGoogle Scholar
  27. Hubel, D., & Wiesel, T. (1968). Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology, 195, 215–243.CrossRefGoogle Scholar
  28. Iino, Y., & Yoshida, K. (2009). Parallel use of two behavioral mechanisms for chemotaxis in Caenorhabditis elegans. Journal of Neuroscience, 29(17), 5370–5380.CrossRefGoogle Scholar
  29. Issad, T., & Malaterre, C. (2015). Are dynamic mechanistic explanations still mechanistic? In P.-A. Braillard & C. Malaterre (Eds.), Explanation in biology. An enquiry into the diversity of explanatory patterns in the life sciences (pp. 265–292). Dordrecht: Springer.Google Scholar
  30. Izquierdo, E. J., & Beer, R. D. (2013). Connecting a connectome to behavior: An ensemble of neuroanatomical models of C. elegans Klinotaxis. PLoS Computational Biology, 9(2), e1002890. doi: 10.1371/journal.pcbi.1002890.CrossRefGoogle Scholar
  31. Izquierdo, E. J., & Lockery, S. R. (2010). Evolution and analysis of minimal neural circuits for klinotaxis in Caenorhabditis elegans. The Journal of Neuroscience, 30(39), 12908–12917. doi: 10.1523/JNEUROSCI.2606-10.2010.CrossRefGoogle Scholar
  32. Kaplan, D. M. (2011). Explanation and description in computational neuroscience. Synthese, 183, 339–373. doi: 10.1007/s11229-011-9970-0.CrossRefGoogle Scholar
  33. Kaplan, D. M. (2012). How to demarcate the boundaries of cognition. Biology & Philosophy. doi: 10.1007/s10539-012-9308-4.Google Scholar
  34. Kaplan, D. M., & Craver, C. F. (2011). The explanatory force of dynamical and mathematical models in neuroscience: A mechanistic perspective. Philosophy of Science, 78, 601–627.CrossRefGoogle Scholar
  35. Kelso, J. A. S. (1995). Dynamic patterns: The self-organization of brain and behavior. Cambridge, MA: MIT Press.Google Scholar
  36. Kocabas, A., Shen, C. H., Guo, Z. V., & Ramanathan, S. (2012). Controlling interneuron activity in Caenorhabditis elegans to evoke chemotactic behaviour. Nature, 940, 273–277.CrossRefGoogle Scholar
  37. Leuridan, B. (2011). Three problems for the mutual manipulability account of constitutive relevance in mechanisms. The British Journal for the Philosophy of Science, 63(2), 399–427. doi: 10.1093/bjps/axr036.CrossRefGoogle Scholar
  38. Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25.CrossRefGoogle Scholar
  39. Marr, D., & Hildreth, E. (1980). Theory of edge detection. Proceedings of the Royal Society of London. Series B, Biological Sciences, 207(1167), 187–217.CrossRefGoogle Scholar
  40. Mekios, C. (2015). Explanation in systems biology: Is it all about mechanisms? In P.-A. Braillard & C. Malaterre (Eds.), Explanation in biology. An enquiry into the diversity of explanatory patterns in the life sciences (pp. 47–72). Dordrecht: Springer.Google Scholar
  41. Mitchell, M. (1996). An introduction to genetic algorithms. Cambridge, MA: MIT Press.Google Scholar
  42. Schlitt, T., & Brazma, A. (2007). Current approaches to gene regulatory network modelling. BMC Bioinformatics, 8(Supplement 6), S9. doi: 10.1186/1471-2105-8-S6-S9.CrossRefGoogle Scholar
  43. Silberstein, M., & Chemero, A. (2013). Constraints on localization and decomposition as explanatory strategies in the biological sciences. Philosophy of Science, 80(5), 958–970.CrossRefGoogle Scholar
  44. Simon, H. A. (1996). The sciences of the artificial (3rd ed.). Cambridge, MA: MIT Press.Google Scholar
  45. Spencer, J. P., & Schöner, G. (2006). An embodied approach to cognitive systems: A dynamic neural field theory of spatial working memory. In Proceedings of the 28th annual conference of the Cognitive Science Society (pp. 2180–2185), Vancouver.Google Scholar
  46. Suzuki, H., Thiele, T. R., Faumont, S., Ezcurra, M., Lockery, S. R., & Schafer, W. R. (2008). Functional asymmetry in Caenorhabditis elegans taste neurons and its computational role in chemotaxis. Nature, 454(7200), 114–117. doi: 10.1038/nature06927.CrossRefGoogle Scholar
  47. Varshney, L. R., Chen, B. L., Paniagua, E., Hall, D. H., & Chklovskii, D. B. (2011). Structural properties of the Caenorhabditis elegans neuronal network. PLoS Computational Biology, 7(2), e1001066.CrossRefGoogle Scholar
  48. Webb, B. (2009). Animals versus animats: Or why not model the real iguana? Adaptive Behavior, 17(4), 269–286. doi: 10.1177/1059712309339867.CrossRefGoogle Scholar
  49. Wheeler, M. (2005). Reconstructing the cognitive world. Cambridge, MA: MIT Press.Google Scholar
  50. White, J. G., Southgate, E., Thomson, J. N., & Brenner, S. (1986). The structure of the nervous system of the nematode Caenorhabditis elegans. Philosophical Transactions of the Royal Society, B: Biological Sciences, 314, 1–340. doi: 10.1098/rstb.1986.0056.CrossRefGoogle Scholar
  51. Williams, P. L., & Beer, R. D. (2010). Information dynamics of evolved agents. In S. Doncieux, B. Girard, A. Guillot, J. Hallam, J.-A. Meyer, & J.-B. Mouret (Eds.), From animals to animats 11: Proceedings of the 11th international conference on simulation of adaptive behavior (pp. 38–49). Springer.Google Scholar
  52. Wimsatt, W. C. (1986). Forms of aggregativity. In A. Donagan, A. N. Perovich, & M. V. Wedin (Eds.), Human nature and natural knowledge: Festschrift for Marjorie Grene (pp. 259–293). Dordrecht: Reidel.CrossRefGoogle Scholar
  53. Wright, C., & Bechtel, W. (2007). Mechanisms and psychological explanation. In P. Thagard (Ed.), Philosophy of psychology and cognitive science (pp. 31–79). New York: Elsevier.CrossRefGoogle Scholar
  54. Zednik, C. (2011). The nature of dynamical explanation. Philosophy of Science, 78(2), 238–263.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Institute of Cognitive ScienceUniversity of OsnabrückOsnabrückGermany

Personalised recommendations