Abstract
Existing accounts of mechanistic causation are not suited for understanding causation in biological and neural mechanisms because they do not have the resources to capture the unique causal structure of control heterarchies. In this paper, we provide a new account on which the causal powers of mechanisms are grounded by time-dependent, variable constraints. Constraints can also serve as a key bridge concept between the mechanistic approach to explanation and underappreciated work in theoretical biology that sheds light on how biological systems channel energy to actively respond to the environment in adaptive ways, perform work, and fulfill the requirements to maintain themselves far from equilibrium. We show how the framework applies to several concrete examples of control in simple organisms as well as the nervous system of complex organisms.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Notes
The use of levels in the context of control is distinct from the notion of levels of organization or levels in a mechanism (as discussed by Craver 2007). Although control can be exercised by a mechanism on its component parts, control can also be exercised by completely separate mechanisms or even by parts of a given mechanism.
Philosophers presenting mechanistic explanation have discussed examples of control mechanisms, such as negative feedback (Bechtel 2011; Bechtel and Abrahamsen 2011) and circadian mechanisms (Bechtel 2010, 2013), but they have said little about how these mechanisms effect control on other mechanisms. The analysis presented in this paper is intended to fill that lacuna.
We are not suggesting that control is non-causal, but that it is something more than merely one process causally producing another.
In defending top-down causation, Craver and Bechtel (2007) argue that conditions that affect whole mechanisms also affect their parts, thereby providing a sense in which activities of whole mechanisms control those of their constituents. More recently, Bechtel (2017) has further characterized top-down causation in terms of activity in a larger system imposing constraints on individual units in the network. While this proposal resembles in some respects the one we advance here, it is limited to the context of top-down causation, while the account we offer applies more generally to cases in which one mechanism exercises control over another.
For that matter, the causal efficacy of the whole mechanism remains mysterious as well. Machamer e al. (2000) appeal to the productive continuity from one activity to the next and Bechtel and Abrahamsen (2005) appeal to the ability of researchers to simulate mentally the component operations to show how they generate the overall phenomenon. But actual accounts of mechanism are typically incomplete, and the gaps in the mechanism are sometimes only revealed much later after the explanation has been widely accepted.
See Kuhlmann and Glennan (2014) for further discussion about how the classical mechanical causation of macro-level mechanisms can be understood as compatible with quantum mechanics on the Copenhagen interpretation. Rather than seeing theirs as a competing account, we believe that their paper fits well with this account because it is essentially a discussion about the relationship between classical and non-classical types of constraints.
It has been common to focus on organisms, especially single-celled organisms, as the locus of biological autonomy (Moreno and Mossio 2015). However, many organisms live in symbiotic relations in which crucial activities are shared between numerous organisms, often from multiple species (O’Malley 2014). Control relations such as we discuss later can involve entities in the environment with which an organism is tightly coupled. Accordingly, when considering autonomy, we speak of biological systems, not organisms.
An approach to mechanistic causation along similar Aristotelian lines was defended recently by Cartwright and Pemberton (2013).
For a mechanism to produce a phenomenon it must undergo changes induced by the activities of its parts and so fits the broad conception of a ‘dynamical system’ as “a structure of mutually and simultaneously influencing change” unfolding in real time (van Gelder and Port 1995, p. 3).
See chapter 1 of their book, where they rail against “esoteric debates about substance, universals, identity, time, properties, and so on, which make little or no reference to science, and worse, which seem to presuppose that science must be irrelevant to their resolution” and the associated tendency to prioritize “armchair intuitions about the nature of the universe over scientific discoveries” and to attach “epistemic significance to metaphysical intuitions” (Ross et al. 2007, p. 10).
Time-invariant and time-dependent constraints are represented in analytical mechanics by scleronomic and rheonomic constraint equations, respectively.
In analytical mechanics, integrable constraint equations, yielding a state-determined dynamics (e.g., particles of a rigid object, or a series of tightly intermeshing gears), are called holonomic, whereas non-integrable constraints, yielding a flexibly constrained system (e.g., particles free to move but confined within a box, or loosely intermeshing gears), are called non-holonomic.
When negative feedback is implemented in devices such as thermostats, there is once again a role for humans in setting the thermostat. The Watt governor does not permit such setting.
Like any representational system, the clock can misrepresent, for example, by indicating dawn when it is midday.
It is, in fact, more coupled than Fig. 3 indicates, since the synthesis of KaiA, KaiB, and KaiC is also under control of the clock mechanism. Nonetheless, the phosphorylation process is distinct from the synthesis process.
The ambiguous notion of a ‘functional level’ or a ‘functional hierarchy’ may have sometimes resulted in these distinctions being blurred, especially that between ordinary causal dependence and control.
References
Ardiel, E. L., & Rankin, C. H. (2010). An elegant mind: Learning and memory in Caenorhabditis elegans. Learning & Memory, 17, 191–201.
Atkins, P. W. (1984). The second law. New York: Scientific American Books.
Bechtel, W. (2010). The downs and ups of mechanistic research: Circadian rhythm research as an exemplar. Erkenntnis, 73, 313–328.
Bechtel, W. (2011). Mechanism and biological explanation. Philosophy of Science, 78, 533–557.
Bechtel, W. (2013). From molecules to networks: Adoption of systems aproaches in circadian rhythm research. In H. Andersen, D. Dieks, W. J. Gonzalez, T. Uebel, & G. Wheeler (Eds.), New challenges to philosophy of science (Vol. 4, pp. 211–223). Dordrecht: Springer.
Bechtel, W. (2017). Explicating top-down causation using networks and dynamics. Philosophy of Science, 84, 253–274.
Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Sciences, 36, 421–441.
Bechtel, W., & Abrahamsen, A. (2011). Complex biological mechanisms: Cyclic, oscillatory, and autonomous. In C. A. Hooker (Ed.), Philosophy of complex systems. Handbook of the philosophy of science (Vol. 10, pp. 257–285). New York: Elsevier.
Bechtel, W., & Richardson, R. C. (1993/2010). Discovering complexity: Decomposition and localization as strategies in scientific research. Cambridge, MA: MIT Press. 1993 edition published by Princeton University Press.
Bich, L., & Moreno, A. (2016). The role of regulation in the origin and synthetic modelling of minimal cognition. Biosystems, 148, 12–21.
Briggman, K. L., Abarbanel, H. D. I., & Kristan, W. B. (2005). Optical imaging of neuronal populations during decision-making. Science, 307, 896–901.
Cartwright, N. (1989). Nature’s capacities and their measurement. Oxford: Clarendon.
Cartwright, N. (2008). Reply to Stathis Psillos. In S. Hartmann, C. Hoefer, & L. Bovens (Eds.), Nancy Cartwright’s philosophy of science (pp. 195–197). New York: Routledge.
Cartwright, N., & Pemberton, J. (2013). Aristotelian powers: Without them, what would modern science do? In R. Groff & J. Greco (Eds.), Powers and capacities in philosophy: The new Aristotelianism (pp. 93–112). New York: Routledge.
Cohen, S. E., & Golden, S. S. (2015). Circadian rhythms in cyanobacteria. Microbiology and Molecular Biology Reviews, 79(4), 373–385.
Craver, C. F. (2007). Explaining the brain: Mechanisms and the mosaic unity of neuroscience. New York: Oxford University Press.
Craver, C. F., & Bechtel, W. (2007). Top-down causation without top-down causes. Biology and Philosophy, 22, 547–563.
Craver, C. F., & Darden, L. (2013). In search of mechanisms: Discoveries across the life sciences. Chicago: University of Chicago Press.
Darden, L., & Craver, C. (2002). Strategies in the interfield discovery of the mechanism of protein synthesis. Studies in History and Philosophy of Biological and Biomedical Sciences, 33(1), 1–28.
Esfeld, M. (2009). The modal nature of structures in ontic structural realism. International Studies in the Philosophy of Science, 23(2), 179–194.
Glennan, S. (1996). Mechanisms and the nature of causation. Erkenntnis, 44, 50–71.
Glennan, S. (2009). Mechanisms. In H. Beebee, C. Hitchcock, & P. Menzies (Eds.), The Oxford handbook of causation (pp. 315–325). Oxford: Oxford University Press.
Glennan, S. (2017). The new mechanical philosophy. Oxford: Oxford University Press.
Hooker, C. A. (2013). On the import of constraints in complex dynamical systems. Foundations of Science, 18, 757–780.
Jackson, J. H. (1884). Evolution and dissolution of the nervous system (The Croonian Lectures). Lancet, 123, 555–558, 649–652, 739–744.
Juarrero, A. (1999). Dynamics in action: Intentional behavior as a complex system. Cambridge, MA: MIT Press.
Keijzer, F. (2015). Moving and sensing without input and output: Early nervous systems and the origins of the animal sensorimotor organization. Biology and Philosophy, 30, 311–331.
Kistler, M. (2009). Mechanisms and downward causation. Philosophical Psychology, 22(5), 595–609.
Kuhlmann, M., & Glennan, S. (2014). On the relation between quantum mechanical and neo-mechanistic ontologies and explanatory strategies. European Journal of Philosophy of Science, 4, 337–359.
Machamer, P. (2004). Activities and causation: The metaphysics and epistemology of mechanisms. International Studies in the Philosophy of Science, 18(1), 27–39.
Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67, 1–25.
Maxwell, J. C. (1868). On governors. Proceedings of the Royal Society of London, 16, 270–283.
Mayr, O. (1970). The origins of feedback control. Cambridge, MA: MIT Press.
McCulloch, W. S. (1945). A heterarchy of values determined by the topology of nervous nets. Bulletin of Mathematical Biophysics, 7, 89–93.
Moreno, A., & Mossio, M. (2015). Biological autonomy: A philosophical and theoretical inquiry. Dordrecht: Springer.
Nagel, E. (1961). The structure of science. New York: Harcourt, Brace.
Nicolis, G., & Prigogine, I. R. (1977). Self-organization in nonequilibrium systems: From dissipative structures to order through fluctuations. New York: Wiley.
Nielsen, K. (2010). Representation and dynamics. Philosophical Psychology, 23, 759–773.
North, G., & Greenspan, R. J. (2007). Invertebrate neurobiology. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press.
O’Malley, M. (2014). Philosophy of microbiology. Cambridge: Cambridge University Press.
Pattee, H. H. (1970). The problem of biological hierarchy. In C. H. Waddington (Ed.), Towards a theoretical biology 3: Drafts (pp. 117–136). Edinburgh: Edinburgh University Press.
Pattee, H. H. (1971). Physical theories of biological co-ordination. Quarterly Reviews of Biophysics, 4(2–3), 255–276.
Pattee, H. H. (1991). Measurement-control heterarchical networks in living systems. International Journal of General Systems, 18(3), 213–221.
Rosen, R. (1991). Life itself: A comprehensive inquiry into the nature, origin, and fabrication of life. New York: Columbia.
Ross, D., Ladyman, J., & Spurrett, D. (2007). In defence of scientism. In J. Ladyman & D. Ross (Eds.), Every thing must go: Metaphysics naturalized (pp. 1–65). Oxford: Oxford University Press.
Ruiz-Mirazo, K., & Moreno, A. (2004). Basic autonomy as a fundamental step in the synthesis of life. Artificial Life, 10, 235–259.
Ruiz-Mirazo, K., Peretó, J., & Moreno, A. (2004). A universal definition of life: Autonomy and open-ended evolution. Origins of Life and Evolution of the Biosphere, 34, 323–346.
Sklar, L. (2013). Philosophy and the foundations of dynamics. Cambridge: Cambridge University Press.
Smart, J. J. C. (1963). Philosophy and scientific realism. London: Routledge & Kegan Paul.
Stein, P. S. G., Grillner, S., Selverston, A. I., & Stuart, D. G. (Eds.). (1997). Neurons, networks, and motor behavior. Cambridge, MA: MIT Press.
Turvey, M. T. (1977). Preliminaries to a theory of action with reference to vision. In R. Shaw & J. Bransford (Eds.), Perceiving, acting, and knowing (pp. 211–265). Hillsdale, NJ: Erlbaum.
van Gelder, T., & Port, R. F. (1995). It’s about time: An overview of the dynamical approach to cognition. In R. F. Port & T. van Gelder (Eds.), Mind as motion (pp. 1–43). Cambridge, MA: MIT Press.
Varela, F. J. (1979). Principles of biological autonomy. New York: North Holland.
Winning, J. (forthcoming). Mechanistic causation and constraints: Perspectival parts and powers, non-perspectival modal patterns.
Woodward, J. (2003). Making things happen: A theory of causal explanation. Oxford: Oxford University Press.
Woodward, J. (2008). Response to Strevens. Philosophy and Phenomenal Research, 77(1), 193–212.
Yates, F. E. (1979). Physical biology: A basis for modeling living systems. Journal of Cybernetics and Information Science, 2, 57–70.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Winning, J., Bechtel, W. Rethinking Causality in Biological and Neural Mechanisms: Constraints and Control. Minds & Machines 28, 287–310 (2018). https://doi.org/10.1007/s11023-018-9458-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11023-018-9458-5