Abstract
Kaplan and Craver (Philos Sci 78(4):601–627, 2011) and Piccinini and Craver (Synthese, 183(3):283–311, 2011) argue that only mechanistic explanations of cognition are genuine causal explanations, because only evidence of mechanisms reveals the causal structure of cognition. I first argue that this claim is grounded in a commitment to the mechanistic account of causality, which cannot be endorsed by a defender of causal-nonmechanistic explanations. Then, I defend the epistemic theory of causality, which holds that causal explanations are not genuine to the extent that they reveal mechanistic causal structure, but, rather, to the extent that they have evidential support and yield successful prediction, explanation, and control inferences. Finally, I enact an epistemic unification of causal explanation in cognitive science, according to which both mechanistic and nonmechanistic explanations of cognition can be genuine causal explanations.
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Notes
Kaplan and Craver (2011, p. 603) accept that there are “domains of science in which mechanistic explanation is inappropriate.” The examples they give are of “certain areas of physics [...] that do not involve decomposing phenomena into component parts (Bechtel and Richardson 2010; Glennan 1996)” and of “mental phenomena, such as belief and inference, [that] are fundamentally normative and so demand noncausal forms of explanation (McDowell 1996).” However, the first is not clearly an explanation of cognition, because physical systems are just as likely non-cognitive. And the second is explicitly a “noncausal” explanation, because the form of explanation is “fundamentally normative.”
I assume here that the firing of a neuron will be an important feature of the implementation of a wide range of—if not all—cognitive competences.
A feature can also be inexplicable via structural decomposition, but this is less important here, since most explanations of cognition are concerned with cognitive performance at the level of functionality. That said, we know that structural decomposition of features of the brain presents an additional challenge, because “the brain’s organization is variable across people: different types of regions do not stand in constant spatial relationships to one another” (Ward 2019, p. 9).
Following this line of thinking, one might suppose that causal-nonmechanistic explanations cite information that counts as a “mechanism sketch” (cf. Darden 2002). On this way of thinking, causal-nonmechanistic explanations are taken to provide “an incomplete representation of a mechanism that specifies some of the relevant entities, activities, and organizational features but leaves gaps that cannot yet be filled” (Craver and Tabery 2019).
Non-causal explanations of cognition will not be constrained by the causal methodological or causal ontological principles. Irvine (2015, p. 3953), for example, argues that, in the case of some cognitive scientific explanations, “the abstract structure of the model, and of the target system (via model-target fit), is the only thing left that can do any explaining with respect to certain key questions.” Likewise, Chirimuuta (2018) argues that non-causal explanations play an important role in computational neuroscience. Notably, however, both accept that causal explanations of cognition play a central role in cognitive science, because there is at best:
a division of explanatory labour: some neuroscientists will focus on the non-causal, mathematical explanation of the efficiency of a feature, while it is the job of others to find out about the aetiology of that feature (Chirimuuta 2018, p. 875).
Defenders of causal-nonmechanistic explanations may recognise both causal-mechanistic and causal-nonmechanistic explanations, only causal-nonmechanistic explanations, or only certain kinds of causal-nonmechanistic explanations (e.g. dynamical explanations). I will have nothing more to say about these distinctions, because in this paper I assume only that we can predicate ‘being a defender of causal-nonmechanistic explanations’ (D) of all individuals (\(x_i\)) that ‘recognise some kind of causal-nonmechanistic explanation’ (R); e.g. \(\forall x(Rx \rightarrow Dx)\).
Note that those who endorse a mechanistic account of explanations may still take the possibility of specifying potential interventions to be an important theoretical virtue of explanations (cf. Piccinini and Craver 2011). However, this does not amount to an endorsement of an interventionist account of causality.
For further discussion of the difficulties of reconciling mechanistic accounts of explanation and interventionist accounts of causality, see Runhardt (2015).
Equivocation is not uncommon in debates about cognition and cognitive science. Taylor and Vosgerau (2019), for example, identify a problematic equivocation concerning the meaning and explanatory role of perhaps the most important posit in cognitive science: the kind concept.
I confine myself here to consideration of the weak inferentialist account of causality, according to which community usage or commitments determine the inferential base and target (this seems to have been the view endorsed by, e.g., Wittgenstein (1953)). The strong inferentialist account assumes that factors above and beyond community usage or commitments determine the inferential base and target. However, there is an open question as to whether this amounts to an inferentialist account of causality at all, because whatever explains a causal relation is not inference at all, but, rather, some non-specified standard of inferential success (cf. Williamson 2013).
In recognition of this state of affairs, Darden (2013) undertakes an analysis of cystic fibrosis and goes so far as to argue that mechanistic explanations are not causal explanations at all. As such, a commitment to the mechanistic account of causality may inspire not only the rejection of causal-nonmechanistic explanations, but of causal-mechanistic explanations as well.
A further problem with the mechanistic account of causality is that it fails to explain causality between absences. For example, when not pushing a button causes a door to open. It remains an open question whether or not there are causal relations between absences in cognitive systems. Recent research in cognitive science has suggested that the experience of absences may cause us to make certain inferences (Hsu et al. 2017). However, it is unclear that the experience of an absence counts as a true absence in this instance, because the experience itself could be understood as a representational state that features as a component in a mechanism.
This evidential norm pertains to a version of epistemic causality that Williamson (2019) calls “precise.” But one could also defend an “imprecise” version of epistemic causality, whereby causal beliefs are represented as a set of causal relations, \({\mathbb {C}}_E\), that should lie within the convex hull \(\langle {\mathbb {C}}^*\rangle \) of \({\mathbb {C}}^*\), \({\mathbb {C}}_E \subseteq \langle {\mathbb {C}}^*\rangle \).
Note, that the relevant sense of entailment here is not logical entailment.
Having said that causal relations are not explanatory, there is no need to go so far as to say that there is no causal ‘oomph’ in the world. In fact, one might argue that it is only in virtue of the presence of non-epistemic causal relations that our causal beliefs are able to motivate successful PEC-inferences in the first place. Equally, however, one might argue that all talk of non-epistemic causal relations is superfluous, since ETC submits that there can be no characterisation of causal relations independent of our epistemological considerations. The point, then, is that when we take up ETC, the question of whether we should be realists or anti-realists about causality is left behind.
To be clear, we do beg one question against all who develop causal explanations: does the explanation in question support a causal belief that is grounded in evidence and licenses successful PEC-inferences?
Some philosophers explicitly endorse a pluralistic account of causality and so would object to my characterisation of causal pluralism as problematic (cf. Cartwright 2004; Godfrey-Smith 2008; Hall 2004). I will not engage with this view any further here, except to note that, intuitively at least, we do not seem to have lots of different kinds of cause and that causal pluralism does not seem to be presupposed by most working cognitive scientists (cf. Braun 1991; Williamson 2006).
There is a further question about how any appropriate body of evidence is constituted in this instance; that is, about the nature of the mapping \(B_{CogSci} \rightarrow E_x\). It might be the case, for instance, that this mapping is sensitive to other factors; for example, theoretical, sociopolitical, or even ethical factors. For lack of space, however, I will have to bracket this question for future research.
Evidential pluralism is the view that:
In order to establish that A is a cause of B in medicine one normally needs to establish two things. First, that A and B are suitably correlated—typically, that A and B are probabilistically dependent, conditional on B’s other known causes. Second, that there is some underlying mechanism linking A and B that can account for the difference that A makes to B (Russo and Williamson 2007).
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Acknowledgements
I would like to thank two anonymous reviewers for their comments, critique, and advice about how the paper could be improved. Thanks to all members of the Centre for Reasoning at Kent, Ruben Noorloos, and Gottfried Vosgerau for their generous feedback. Finally, thanks to Jon Williamson and Yafeng Shan for their guidance and encouragement.
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Taylor, S.D. Causation and cognition: an epistemic approach. Synthese 199, 9133–9160 (2021). https://doi.org/10.1007/s11229-021-03197-2
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DOI: https://doi.org/10.1007/s11229-021-03197-2
Keywords
- Causation
- Cognition
- Epistemic causality
- Mechanistic explanation
- Nonmechanistic explanation
- Evidence