Sage Journals: Discover world-class research

Abstract

Radical enactivism (REC) and similar embodied and enactive approaches to the mind deny that cognition is fundamentally representational, skull-bound and mechanistic in its organisation. In this article, I argue that modellers may still adopt a mechanistic strategy to produce explanations that are compatible with REC. This argument is scaffolded by a multi-agent model of the true slime mould Physarum polycephalum.

Keywords

Minimal cognition radical enactivism mechanistic explanation

Get full access to this article

View all access options for this article.

References

Adams

Aizawa

(2010). Defending the bounds of cognition. In Menary

(Ed.), The extended mind (pp. 67–80). Cambridge, MA: MIT Press.

Ball

(2008). Cellular memory hints at the origins of intelligence. Nature News, 451, 385–385. doi:10.1038/451385a

Bechtel

(2011). Mechanism and biological explanation. Philosophy of Science, 78, 533–557.

Bechtel

(2012). Understanding endogenously active mechanisms: A scientific and philosophical challenge. European Journal for Philosophy of Science, 2, 233–248.

Bechtel

Abrahamsen

(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, 421–441.

Bechtel

Abrahamsen

(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, 321–333. doi:10.1016/j.shpsa.2010.07.003

Bechtel

Abrahamsen

(2013). Thinking dynamically about biological mechanisms: Networks of coupled oscillators. Foundations of Science, 18, 707–723.

Bechtel

Richardson

R. C.

(2010). Discovering complexity: Decomposition and localization as strategies in scientific research. Cambridge, MA: MIT Press.

Beekman

Latty

(2015). Brainless but multi-headed: Decision-making by the acellular slime mould Physarum Polycephalum. Journal of Molecular Biology, 427, 3734–3743. doi:10.1016/j.jmb.2015.07.007

10.

Boisseau

R. P.

Vogel

Dussutour

(2016). Habituation in non-neural organisms: Evidence from slime moulds. Proceedings of the Royal Society B: Biological Sciences, 283, 20160446.

11.

Bruineberg

Rietveld

(2014). Self-organization, free energy minimization, and optimal grip on a field of affordances. Frontiers in Human Neuroscience, 8, 599.

12.

Chemero

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

13.

Chemero

Silberstein

(2008). After the philosophy of mind: Replacing scholasticism with science. Philosophy of Science, 75(1), 1–27.

14.

Clark

Chalmers

(1998). The extended mind. Analysis, 58(1), 7–19.

15.

Craver

C. F.

Darden

(2013). In search of mechanisms: Discoveries across the life sciences. Chicago, IL: University of Chicago Press.

16.

Di Paolo

. (2009). Extended life. Topoi, 28(1), 9.

17.

Elliott-Graves

(2018). Generality and causal interdependence in ecology. Philosophy of Science, 85, 1102–1114.

18.

Froese

Di Paolo

E. A.

(2011). The enactive approach: Theoretical sketches from cell to society. Pragmatics & Cognition, 19(1), 1–36.

19.

Gao

Liu

Schenz

Zhang

Jusup

. . . Nakagaki

(2018). Does being multi-headed make you better at solving problems? A survey of Physarum-based models and computations. Physics of Life Reviews. Advance online publication. doi:10.1016/j.plrev.2018.05.002

20.

Giere

R. N.

(2010). Scientific perspectivism. Chicago, IL: University of Chicago Press.

21.

Glennan

(2005). Modeling mechanisms. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 36, 443–464.

22.

Glennan

(2017). The new mechanical philosophy. Oxford, UK: Oxford University Press.

23.

Godfrey-Smith

(1998). Complexity and the function of mind in nature. Cambridge, UK: Cambridge University Press.

24.

Godfrey-Smith

(2002). Environmental complexity and the evolution of cognition. In Sternberg

R. J.

Kaufman

(Eds.), The evolution of intelligence (pp. 233–249). Hoboken, NJ: Lawrence Erlbaum.

25.

Haken

Kelso

J. A. S.

Bunz

(1985). A theoretical model of phase transitions in human hand movements. Biological Cybernetics, 51, 347–356.

26.

Hempel

C. G.

(1965). Aspects of scientific explanation. New York, NY: Free Press.

27.

Hempel

C. G.

Oppenheim

(1948). Studies in the logic of explanation. Philosophy of Science, 15, 135–175.

28.

Hutto

D. D.

Kirchhoff

M. D.

Myin

(2014). Extensive enactivism: Why keep it all in? Frontiers in Human Neuroscience, 8, 706.

29.

Hutto

D. D.

Myin

(2012). Radicalizing enactivism: Basic minds without content. Cambridge, MA: MIT Press.

30.

Hutto

D. D.

Myin

(2017). Evolving enactivism: Basic minds meet content. Cambridge, MA: MIT Press.

31.

Jones

(2015). From pattern formation to material computation: Multi-agent modelling of physarum polycephalum (Vol. 15). Cham, Switzerland: Springer. doi:10.1007/978-3-319-16823-4

32.

Kaplan

D. M.

(2015). Moving parts: The natural alliance between dynamical and mechanistic modeling approaches. Biology & Philosophy, 30, 757–786. doi:10.1007/s10539-015-9499-6

33.

Kaplan

D. M.

Bechtel

(2011). Dynamical models : An alternative or complement to mechanistic explanations? Topics in Cognitive Science, 3, 438–444. doi:10.1111/j.1756-8765.2011.01147.x

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. doi:10.1086/661755

35.

Keijzer

(2001). Representation and behavior. Cambridge, MA: MIT Press.

36.

Lamb

Chemero

(2014). Structure and application of dynamical models in cognitive science. In Bello.

Guarini.

McShane.

Scassellati

(Eds.), Proceedings of the 36th annual meeting of the cognitive science society (pp. 809–814). Austin, TX: Cognitive Science Society.

37.

Levins

(1966). The strategy of model building in population biology. American Naturalist, 54, 421–431. doi:10.2307/27836590

38.

Levy

(2013). Three kinds of new mechanism. Biology & Philosophy, 28, 99–114.

39.

Levy

(2014). Machine-likeness and explanation by decomposition. Philosopher’s Imprint, 14(6), 1–15.

40.

Levy

Bechtel

(2013). Abstraction and the organization of mechanisms. Philosophy of Science, 80, 241–261.

41.

Lyon

(2006). The biogenic approach to cognition. Cognitive Processing, 7(1), 11–29.

42.

Machamer

Darden

Craver

C. F.

(2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25.

43.

Matthewson

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

44.

Matthewson

Calcott

(2011). Mechanistic models of population-level phenomena. Biology & Philosophy 26, 737–756.

45.

Menary

(2006). Radical enactivism: Intentionality, phenomenology and narrative. Focus on the philosophy of Daniel D. Hutto (Vol. 2). Amsterdam, The Netherlands: John Benjamins Publishing.

46.

Meyer

(2018). The non-mechanistic option: Defending dynamical explanations. The British Journal for the Philosophy of Science. Advance online publication. doi:10.1093/bjps/axy034

47.

Mitchell

S. D.

(2003). Biological complexity and integrative pluralism. Cambridge, UK: Cambridge University Press.

48.

Mitchell

S. D.

(2009). Unsimple truths: Science, complexity, and policy. Chicago, IL: University of Chicago Press.

49.

Moore

P. B.

(2012). How should we think about the ribosome? Annual Review of Biophysics, 41, 1–19.

50.

Morgan

M. S.

(2012). The world in the model: How economists work and think. Cambridge, UK: Cambridge University Press.

51.

Pearl

(2009). Causality. Cambridge, UK: Cambridge University Press.

52.

Pershin

Y. V.

La Fontaine

Di Ventra

(2008). Memristive model of amoeba’s learning. Physical Review E, 80(2), 1–6. doi:10.1103/PhysRevE.80.021926

53.

Potochnik

(2017). Idealization and the aims of science. Chicago, IL: University of Chicago Press.

54.

Ray

Heyes

(2011). Imitation in infancy: The wealth of the stimulus. Developmental Science, 14, 92–105.

55.

Reid

C. R.

Beekman

(2013). Solving the Towers of Hanoi: How an amoeboid organism efficiently constructs transport networks. The Journal of Experimental Biology, 216, 1546–1551. doi:10.1242/jeb.081158

56.

Reid

C. R.

Beekman

Latty

Dussutour

(2013). Amoeboid organism uses extracellular secretions to make smart foraging decisions. Behavioral Ecology, 24, 812–818. doi:10.1093/beheco/art032

57.

Reid

C. R.

Latty

Dussutour

Beekman

(2012). Slime mold uses an externalized spatial ‘memory’ to navigate in complex environments. Proceedings of the National Academy of Sciences, 109, 17490–17494. doi:10.1073/pnas.1215037109

58.

Rosen

(1999). Essays on life itself (Complexity in Ecological Systems Series). New York, NY: Columbia University Press.

59.

Saigusa

(2008). Amoebae anticipate periodic events. Physical Review Letters, 100, 018101. doi:10.1103/PhysRevLett.100.018101

60.

Schenz

Shima

Kuroda

Nakagaki

Ueda

K.-I.

(2017). A mathematical model for adaptive vein formation during exploratory migration of Physarum polycephalum: Routing while scouting. Journal of Physics D: Applied Physics, 50(43), 434001.

61.

Skillings

D. J.

(2015). Mechanistic explanation of biological processes. Philosophy of Science, 82, 1139–1151.

62.

Sterelny

(2003). Thought in a hostile world: The evolution of human cognition. Oxford, UK: Blackwell. doi:10.1093/bjps/axi162

63.

Tero

Takagi

Ito

Bebber

D. P.

Fricker

M. D.

Yumiki

. . . Nakagaki

(2010). Rules for biologically inspired adaptive network design. Science, 327, 439–442.

64.

Thompson

(2007). Mind in life: Biology, phenomenology, and the sciences of mind. Cambridge, MA: Harvard University Press.

65.

Van Duijn

. (2006). Principles of minimal cognition: Casting cognition as sensorimotor coordination. Adaptive Behavior, 14, 157–170. doi:10.1177/105971230601400207

66.

Van Gelder

. (1995). What might cognition be, if not computation? Journal of Philosophy, 92, 345–381. doi:10.2307/2026571

67.

Van Gelder

. (1998). The dynamical hypothesis in cognitive science. Behavioral and Brain Sciences, 21, 615–628.

68.

Van Gelder

Port

R. F

. (1995). It’s about time: An overview of the dynamical approach to cognition. In Port

van Gelder

(Eds.), Mind as motion: Explorations in the dynamics of cognition (pp. 1–43). Cambridge, MA: MIT Press.

69.

Van Orden

G. C.

Holden

J. G

. (2002). Intentional contents and self-control. Ecological Psychology, 14, 87–109.

70.

Van Orden

G. C.

Holden

J. G.

Turvey

M. T

. (2003). Self-organization of cognitive performance. Journal of Experimental Psychology: General, 132, 331–350.

71.

Varela

F. J.

Thompson

Rosch

(2017). The embodied mind: Cognitive science and human experience. Cambridge, MA: MIT Press.

72.

Vogel

Nicolis

S. C.

Perez-Escudero

Nanjundiah

Sumpter

D. J. T.

Dussutour

(2015). Phenotypic variability in unicellular organisms: From calcium signalling to social behaviour. Proceedings of the Royal Society B: Biological Sciences, 282, 20152322. doi:10.1098/rspb.2015.2322

73.

Weisberg

(2013). Simulation and similarity: Using models to understand the world. Oxford, UK: Oxford University Press. Retrieved from http://books.google.com/books?id=rDu5e532mIoC&pgis=1

74.

Wimsatt

W. C.

(2007). Re-engineering philosophy for limited beings: Piecewise approximations to reality. Cambridge, MA: Harvard University Press.

75.

Woodward

(2003). Making things happen: A theory of causal explanation. Oxford, UK: Oxford University Press. doi:10.1080/10705510709336742

76.

Zhang

Adamatzky

Chan

F. T. S.

Deng

Yang

X.-S.

. . . Mahadevan

(2015). A biologically inspired network design model. Scientific Reports, 5, 10794. doi:10.1038/srep10794

Lessons from a virtual slime: marginal mechanisms,minimal cognition and radical enactivism

Abstract

Keywords

Get full access to this article

References