Abstract
Dynamical systems play a central role in explanations in cognitive neuroscience. The grounds for these explanations are hotly debated and generally fall under two approaches: non-mechanistic and mechanistic. In this paper, I first outline a neurodynamical explanatory schema that highlights the role of dynamical systems in cognitive phenomena. I next explore the mechanistic status of such neurodynamical explanations. I argue that these explanations satisfy only some of the constraints on mechanistic explanation and should be considered pseudomechanistic explanations. I defend this argument against three alternative interpretations of the neurodynamical explanatory schema. The independent interpretation holds that neurodynamical explanations and mechanisms are independent. The constitutive interpretation holds that neurodynamical explanations are constitutive but otherwise non-mechanistic. Both the independent and constitutive interpretations fail to account for all the features of neurodynamical explanations. The partial interpretation assumes that the targets of dynamical systems models are mechanisms and so holds that neurodynamical explanations are incomplete because they lack mechanistic details. I contend instead that the targets of those models are dynamical systems distinct from mechanisms and defend this claim against several objections. I conclude with a defense of the pseudomechanistic interpretation and a discussion of the source of their explanatory power in relation to a causal-mechanical description of the world.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Of course, one could adopt a pluralistic attitude toward cognitive neuroscientific explanations, where some explanations are mechanistic and some not.
Object here is used in a very general sense. Also, a system need not include elements from all three categories; a system could be a collection of properties only, for example.
I have specifically used the phrase ‘organized collections’ because cognitive neurobiologists refer to systems that feature some temporal or topological structure. However, in principle, the collections need not feature this structure.
The dynamical properties are themselves subsets of the set of all properties of such systems. Neurodynamical systems are typically proper subsets of dynamical properties of neurophysiological systems because usually not every dynamical property of such a system gets included in the neurodynamical system.
I thank an anonymous reviewer for soliciting this comparison to Lyre.
On some views, all properties, or all scientific or physical properties, are relational. On such views, Lyre’s approach and my own will collapse with regard to this second point.
Of course, to fully explain a cognitive phenomenon, each subcapacity must be accounted for. I skip over this complication in the following discussion.
See Gold and Shadlen (2007) for extensive discussion of this research. Note that many aspects of this case are still actively researched and hotly debated (Latimer et al. 2015; Shadlen et al. 2016). For my present purposes, the still unsettled details do not matter, as I am merely illustrating how such explanations are constructed.
Different analyses of this task will yield different sets of functions; the specific set selected does not matter for the moment.
This equation is often used to describe the firing rate of a pool of neurons. Here I use it to describe a single neuron.
While I do not have space to delve deeper into this mapping, note that a 1:1 mapping within a range is insufficient.
Recent research has called into question whether area LIP in fact causally influences evidence integration during motion discrimination (Katz et al. 2016). Nonetheless, this example serves to illustrate the explanatory structure.
The defense and scope of the schema are presented elsewhere (Barack 2019), where I contend that the schema has broad application in cognitive neurobiology.
Bechtel and Abrahamsen (2005, p. 424ff) discuss the heart as mechanism. In general, Bechtel and colleagues are more liberal in their approach to mechanisms than Craver, Kaplan, Piccinini and colleagues, and so are more amenable to some of the points discussed below.
Temporal sequence in biological m-mechanism may be less important, as they often exhibit more complex organization (Bechtel and Abrahamsen 2005; Abrahamsen and Bechtel 2012; Bechtel 2012). More generally, I include the more dynamically oriented definitions of mechanisms under the m-mechanism rubric.
While many of the definitions cited are older, this physically focused view of mechanisms is still prevalent. Glennan, for example, says that “[e]ntities and activities are not abstract; they are fully determinate particulars located somewhere in space and time; they are part of the causal structure of the world. Sometimes there are abstract structures that can be characterized with mechanistic metaphors—but they are not mechanisms” (Glennan 2017, p. 20).
Other, similar violations on the conditions of mechanisms are discussed by Levy and Bechtel (2016), who advocate expanding the concept of a mechanism to include problematic borderline cases.
I will usually elide the relativity of completeness in the following.
I include processes here as a recent alternative to mechanisms. A discussion of the relationship between processes in the Dupré sense and dynamical systems goes beyond the scope of this essay.
Many thanks to a reviewer who pointed out that these arguments are originally aimed at the independent interpretation.
I don’t mean to imply that Craver, Piccinini, Kaplan and other mechanists would endorse the mapping argument. Rather, I am taking their stated positions on the proper role of dynamical systems models in cognitive neuroscience explanations as one way of arguing for the partial interpretation, which may or may not be a use of such mappings which these philosophers would endorse.
All three arguments are compatible with each other and I don’t mean to suggest that philosophers who endorse one could not also endorse another.
Cf: “Organization is… a necessary part of most moderately complex mechanisms such that perturbing either the spatial organization or temporal dynamics of a mechanism, even while the components and their activities remain unchanged, can have appreciable (even catastrophic) effects on its performance. Thinking about mechanistic explanation, then, it is clearly insufficient to describe only the properties and activities of the component parts in a given mechanism without giving adequate weight or attention to the spatial and/or temporal organization of those parts and activities. Often this point is underappreciated or lost when considering the nature of mechanistic explanation…. understanding the dynamical “structure” of a mechanism can be just as important as understanding its physical structure” (Kaplan 2015, pp. 774–775).
They go on to assert that “[t]he idea that functional description is somehow autonomous from details about mechanisms involves a fundamental misunderstanding of the nature of functional attribution in sciences like cognitive neuroscience” (Piccinini and Craver 2011, p. 307). This overlooks at least one clear alternative, that cognitive neuroscientists are concerned with functional descriptions sensu dynamics, and neuroscientists simpliciter are concerned with m-mechanisms. A helpful analogy here is between a car designer or engineer and a car builder or mechanic. The designer or engineer might only care about the functional descriptions of the parts, leaving it to the builder or mechanic to determine the appropriate m-mechanisms. Something similar could be said about cognitive neuroscience and neuroscience. On such an analysis, there is some sense in which functional description is autonomous from m-mechanisms. I will forego further discussion of the issue of autonomy for another time, but see the very nice discussions in Kaplan (2017).
See below in Sect. 6 for an extended discussion of functional analysis.
I thank an anonymous reviewer for this objection.
Ceteris paribus, of course.
Recall that I am simplifying the true complexity of explanations of cognitive phenomena, which require many dynamical systems executing many functions.
There may also be non-cognitive performances performed for the system.
In order to avoid debates over representation, I am deliberately imprecise about whether these parts are representations.
I thank a reviewer for their request for a response to Piccinini and Craver’s arguments regarding functional analysis.
Piccinini and Craver present a second argument as well. They claim that “…explanations that capture these mechanistic details are deeper than those that do not” (Piccinini and Craver 2011, p. 307) for two reasons: first, “…it allows one to expand the range of phenomena that the model can save” and second, “…knowledge of the underlying components and the structural constraints on their activities affords more opportunities for the restoration of function and the prevention of calamity or disease” (Piccinini and Craver 2011, p. 307). I do not have space to adequately address this argument. However, note that neurodynamical explanations can be seen to capture more phenomena than those captured by m-mechanisms in virtue of different m-mechanisms giving rise to the same neurodynamical system. Their pragmatic point is well-taken, though one can intervene on dynamics too.
New evidence questions the role of the integrate-to-bound system in LIP, as inactivating the region does not affect behavior (Katz et al. 2016). But there are other areas that exhibit these dynamics during the task (Ding and Gold 2011; Ding and Gold 2012; Ding and Gold 2013; Hanks et al. 2015; Brody and Hanks 2016), so this may simply suggest that the system is not actually instantiated in LIP or that the dynamical properties in LIP are a read-out of those properties elsewhere in the brain. Also, the nature of the explanatory enterprise can be revealed even if the specifics of the case study are false.
References
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). Dordrecht: Springer.
Austin, C. J. (2016). The ontology of organisms: Mechanistic modules or patterned processes? Biology & Philosophy, 31(5), 639–662.
Barack, D. L. (2019). Mental machines. Biology & Philosophy, 34(6), 63.
Bechtel, W. (2002). Decomposing the mind–brain: A long-term pursuit. Brain and Mind, 3(2), 229–242.
Bechtel, W. (2008). Mental mechanisms: Philosophical perspectives on cognitive neuroscience. Routledge: Taylor & Francis.
Bechtel, W. (2012). Understanding endogenously active mechanisms: A scientific and philosophical challenge. European Journal for Philosophy of Science, 2(2), 233–248.
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.
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.
Beer, R. D. (2000). Dynamical approaches to cognitive science. Trends in Cognitive Sciences, 4(3), 91–99.
Blatt, G. J., Andersen, R. A., & Stoner, G. R. (1990). Visual receptive field organization and cortico-cortical connections of the lateral intraparietal area (area LIP) in the macaque. Journal of Comparative Neurology, 299(4), 421–445.
Bokulich, A. (2011). How scientific models can explain. Synthese, 180(1), 33–45.
Boone, W., & Piccinini, G. (2016). Mechanistic abstraction. Philosophy of Science, 83(5), 686–697.
Britten, K. H., Shadlen, M. N., Newsome, W. T., & Movshon, J. A. (1992). The analysis of visual motion: A comparison of neuronal and psychophysical performance. Journal of Neuroscience, 12(12), 4745–4765.
Britten, K. H., Shadlen, M. N., Newsome, W. T., & Movshon, J. A. (1993). Responses of neurons in macaque MT to stochastic motion signals. Visual Neuroscience, 10(6), 1157–1169.
Brody, C. D., & Hanks, T. D. (2016). Neural underpinnings of the evidence accumulator. Current Opinion in Neurobiology, 37, 149–157.
Campbell, J. (2008). Interventionism, control variables and causation in the qualitative world. Philosophical Issues, 18(1), 426–445.
Chemero, A. (2011). Radical embodied cognitive science. Cambridge: MIT Press.
Chemero, A., & Silberstein, M. (2008). After the philosophy of mind: Replacing scholasticism with science*. Philosophy of Science, 75(1), 1–27.
Chirimuuta, M. (2014). Minimal models and canonical neural computations: The distinctness of computational explanation in neuroscience. Synthese, 191(2), 127–153.
Chirimuuta, M. (2017). Explanation in computational neuroscience: Causal and non-causal. The British Journal for the Philosophy of Science, 69, 849–880.
Craver, C. F. (2001). Role functions, mechanisms, and hierarchy. Philosophy of Science, 68(1), 53–74.
Craver, C. F. (2007a). Constitutive explanatory relevance. Journal of Philosophical Research, 32, 3–20.
Craver, C. F. (2007b). Explaining the brain. Oxford: Oxford University Press.
Craver, C. F., & Kaplan, D. M. (2018). Are more details better? On the norms of completeness for mechanistic explanations. The British Journal for the Philosophy of Science, 71, 287–319.
Darden, L. (2006). Reasoning in biological discoveries. Cambridge: Cambridge University Press.
Ding, L., & Gold, J. I. J. C. C. (2011). Neural correlates of perceptual decision making before, during, and after decision commitment in monkey frontal eye field. Cerebral Cortex, 22(5), 1052–1067.
Ding, L., & Gold, J. I. J. N. (2012). Separate, causal roles of the caudate in saccadic choice and execution in a perceptual decision task. Neuron, 75(5), 865–874.
Ding, L., & Gold, J. I. J. N. (2013). The basal ganglia’s contributions to perceptual decision making. Neuron, 79(4), 640–649.
Ditterich, J. (2006). Stochastic models of decisions about motion direction: Behavior and physiology. Neural Networks, 19(8), 981–1012.
Dupré, J. (2013). I—Living causes. Aristotelian Society Supplementary, 87(1), 19–37.
Egan, F. (2017). Function-theoretic explanation. In D. M. Kaplan (Ed.), Explanation and integration in mind and brain science (pp. 145–163). Oxford: Oxford University Press.
Felline, L. (2018). Mechanisms meet structural explanation. Synthese, 195(1), 99–114.
Franklin-Hall, L. R. (2016). New mechanistic explanation and the need for explanatory constraints. In K. Aizawa & C. Gillett (Eds.), Scientific composition and metaphysical ground (pp. 41–74). Berlin: Springer.
Giunti, M. (1997). Computation, dynamics, and cognition. Oxford: Oxford University Press.
Glennan, S. S. (1996). Mechanisms and the nature of causation. Erkenntnis, 44(1), 49–71.
Glennan, S. (2008). Mechanisms. In S. Glennan & P. Illari (Eds.), The Routledge companion to philosophy of science (pp. 404–412). Abingdon: Routledge.
Glennan, S. (2017). The new mechanical philosophy. Oxford: Oxford University Press.
Gold, J. I., & Shadlen, M. N. (2007). The neural basis of decision making. Annual Review of Neuroscience, 30, 535–574.
Goldman, M. S., Compte, A., & Wang, X.-J. (2010). Neural integrator models. In L. R. Squire (Ed.), Encyclopedia of neuroscience (pp. 165–178). Amsterdam: Elsevier.
Hanks, T. D., Kopec, C. D., Brunton, B. W., Duan, C. A., Erlich, J. C., & Brody, C. D. J. N. (2015). Distinct relationships of parietal and prefrontal cortices to evidence accumulation. Nature, 520(7546), 220.
Hempel, C. G., & Oppenheim, P. (1948). Studies in the logic of explanation. Philosophy of Science, 15(2), 135–175.
Huk, A. C., & Shadlen, M. N. (2005). Neural activity in macaque parietal cortex reflects temporal integration of visual motion signals during perceptual decision making. Journal of Neuroscience, 25(45), 10420–10436.
Huneman, P. (2018). Outlines of a theory of structural explanations. Philosophical Studies, 175(3), 665–702.
Illari, P. M., & Williamson, J. (2012). What is a mechanism? Thinking about mechanisms across the sciences. European Journal for Philosophy of Science, 2(1), 119–135.
Kaplan, D. M. (2011). Explanation and description in computational neuroscience. Synthese, 183(3), 339–373.
Kaplan, D. M. (2015). Moving parts: The natural alliance between dynamical and mechanistic modeling approaches. Biology and Philosophy, 30(6), 757–786.
Kaplan, D. M. (2017). Explanation and integration in mind and brain science. Oxford: Oxford University Press.
Kaplan, D. M., & Craver, C. F. (2011). The explanatory force of dynamical and mathematical models in neuroscience: A mechanistic perspective*. Philosophy of Science, 78(4), 601–627.
Katz, L. N., Yates, J. L., Pillow, J. W., & Huk, A. C. (2016). Dissociated functional significance of decision-related activity in the primate dorsal stream. Nature, 535(7611), 285–288.
Klein, C. (2017). Brain regions as difference-makers. Philosophical Psychology, 30(1–2), 1–20.
Kuhlmann, M. (2014). Explaining financial markets in terms of complex systems. Philosophy of Science, 81(5), 1117–1130.
Latimer, K. W., Yates, J. L., Meister, M. L., Huk, A. C., & Pillow, J. W. (2015). Single-trial spike trains in parietal cortex reveal discrete steps during decision-making. Science, 349(6244), 184–187.
Levy, A., & Bechtel, W. (2016). Towards mechanism 2.0: Expanding the scope of mechanistic explanation.
Lyre, H. (2017). Structures, dynamics and mechanisms in neuroscience: An integrative account. Synthese, 195, 5141–5158.
Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25.
Mazurek, M. E., Roitman, J. D., Ditterich, J., & Shadlen, M. N. (2003). A role for neural integrators in perceptual decision making. Cerebral Cortex, 13(11), 1257–1269.
Meister, M. L., Hennig, J. A., & Huk, A. C. (2013). Signal multiplexing and single-neuron computations in lateral intraparietal area during decision-making. Journal of Neuroscience, 33(6), 2254–2267.
Miłkowski, M. (2016). Explanatory completeness and idealization in large brain simulations: A mechanistic perspective. Synthese, 193(5), 1457–1478.
Paz, A. W. (2017). A mechanistic perspective on canonical neural computation. Philosophical Psychology, 30, 213–234.
Piccinini, G. (2007). Computing mechanisms. Philosophy of Science, 74(4), 501–526.
Piccinini, G. (2010). The mind as neural software? Understanding functionalism, computationalism, and computational functionalism. Philosophy and Phenomenological Research, 81(2), 269–311.
Piccinini, G., & Craver, C. (2011). Integrating psychology and neuroscience: Functional analyses as mechanism sketches. Synthese, 183(3), 283–311.
Platt, M. L., & Glimcher, P. W. (1999). Neural correlates of decision variables in parietal cortex. Nature, 400(6741), 233–238.
Port, R. F., & van Gelder, T. (1995). Mind as motion: Explorations in the dynamics of cognition. Cambridge: MIT Press.
Quine, W. V. O. (1960). Word and object. Cambridge: MIT Press.
Roitman, J. D., & Shadlen, M. N. (2002). Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. Journal of Neuroscience, 22(21), 9475–9489.
Salmon, W. C. (1984). Scientific explanation and causal structure of the world. Princeton: Princeton University Press.
Shadlen, M. N., Kiani, R., Newsome, W. T., Gold, J. I., Wolpert, D. M., Zylberberg, A., et al. (2016). Comment on “Single-trial spike trains in parietal cortex reveal discrete steps during decision-making”. Science, 351(6280), 1406–1406.
Shagrir, O., & Bechtel, W. (2017). Marr’s computational level and delineating phenomena. In D. M. Kaplan (Ed.), Explanation and integration in mind and brain science (pp. 190–214). Oxford: Oxford University Press.
Shapiro, L. A. (2013). Dynamics and cognition. Minds and Machines, 23(3), 353–375.
Shapiro, L. A. (2016). Mechanism or bust? Explanation in psychology. The British Journal for the Philosophy of Science, 68(4), 1037–1059.
Silberstein, M., & Chemero, A. (2012). Complexity and extended phenomenological-cognitive systems. Topics in Cognitive Science, 4(1), 35–50.
Silberstein, M., & Chemero, A. (2013). Constraints on localization and decomposition as explanatory strategies in the biological sciences. Philosophy of Science, 80(5), 958–970.
Strawson, P. F. (1959). Individuals. London: Methuen.
Strogatz, S. (2001). Nonlinear dynamics and chaos: With applications to physics, biology, chemistry, and engineering (studies in nonlinearity). Boulder: Westview Press.
Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: The leaky, competing accumulator model. Psychological Review, 108(3), 550.
van Gelder, T. (1995). What might cognition be, if not computation? The Journal of Philosophy, 92(7), 345–381.
van Gelder, T. (1998). The dynamical hypothesis in cognitive science. Behavioral Brain Sciences, 21(5), 615–628.
Wald, A., & Wolfowitz, J. (1948). Optimum character of the sequential probability ratio test. The Annals of Mathematical Statistics, 19(3), 326–339.
Walmsley, J. (2008). Explanation in dynamical cognitive science. Minds and Machines, 18(3), 331–348.
Wang, X. J. (2002). Probabilistic decision making by slow reverberation in cortical circuits. Neuron, 36(5), 955–968.
Weisberg, M. (2013). Simulation and similarity: Using models to understand the world. Oxford: Oxford University Press.
Weiskopf, D. A. (2011). The functional unity of special science kinds. The British Journal for the Philosophy of Science, 62, 233–258.
Weiskopf, D. A. (2017). The explanatory autonomy of cognitive models. In D. M. Kaplan (Ed.), Explanation and integration in mind and brain science. Oxford: Oxford University Press.
Wimsatt, W. C. (1997). Aggregativity: Reductive heuristics for finding emergence. Philosophy of Science, 64, S372–S384.
Wong, K.-F., & Huk, A. C. (2008). Temporal dynamics underlying perceptual decision making: Insights from the interplay between an attractor model and parietal neurophysiology. Frontiers in Neuroscience, 2, 245.
Wong, K.-F., & Wang, X.-J. (2006). A recurrent network mechanism of time integration in perceptual decisions. The Journal of Neuroscience, 26(4), 1314–1328.
Woodward, J. (2003). Making things happen: A theory of causal explanation. Oxford: Oxford University Press.
Woodward, J. (2017). Explanation in neurobiology. In D. M. Kaplan (Ed.), Explanation and integration in mind and brain science (pp. 70–100). Oxford: Oxford University Press.
Wright, C. D., & Bechtel, W. P. (2007). Mechanisms and psychological explanation. In P. Thagard (Ed.), Philosophy of psychology and cognitive science. Amsterdam: Elsevier.
Zednik, C. (2011). The nature of dynamical explanation*. Philosophy of Science, 78(2), 238–263.
Zeki, S. M. (1974). Functional organization of a visual area in the posterior bank of the superior temporal sulcus of the rhesus monkey. The Journal of Physiology, 236(3), 549.
Zeki, S. (1991). Cerebral akinetopsia (visual motion blindness). Brain, 114(2), 811–824.
Acknowledgements
Many thanks to several anonymous reviewers at multiple different journals. Thanks also goes out to the members of my dissertation committee, who all commented on very early versions of these ideas, including Karen Neander, Felipe De Brigard, Alex Rosenberg, and Walter Sinnott-Armstrong. Special thanks goes to Gualtiero Piccinini and the philosophy of neuroscience reading group at Columbia University.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Barack, D.L. Mental kinematics: dynamics and mechanics of neurocognitive systems. Synthese 199, 1091–1123 (2021). https://doi.org/10.1007/s11229-020-02766-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11229-020-02766-1