Abstract
Interventionism is a theory of causal explanation developed by Woodward and Hitchcock. I defend an interventionist perspective on the causal explanations offered within scientific psychology. The basic idea is that psychology causally explains mental and behavioral outcomes by specifying how those outcomes would have been different had an intervention altered various factors, including relevant psychological states. I elaborate this viewpoint with examples drawn from cognitive science practice, especially Bayesian perceptual psychology. I favorably compare my interventionist approach with well-known nomological and mechanistic theories of psychological explanation.
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Notes
Hempel supplements the DN model with the deductive-statistical (DS) model and the inductive-statistical (IS) model. The DS model is really just a special case of the DN model, in which the laws take a statistical form. For the IS model, one does not deduce the explanandum from the explanantia. Rather, one shows that the explanandum was likely to occur (at least to some degree) given the explanantia. Technically speaking, the “nomological conception of scientific explanation” includes the IS model along with the DN model. However, addressing the IS model would complicate my exposition without affecting the main thrust of my argument.
Bechtel and Wright (2009) note that the phrase “law” is seldom used in scientific psychology. This does not strike me as an important datum for philosophical theorizing, since a psychological generalization might satisfy the traditional philosophical criteria for lawhood even though psychologists do not call it a law.
The literature offers several additional theories of scientific explanation, such as the unificationist conception (Kitcher 1989) and the kairetic conception (Strevens 2008). There is not enough space to discuss all existing theories in a single paper, so I have focused upon the two rival theories that seem to have been most influential within philosophy of cognitive science: the nomological and mechanistic conceptions. The unificationist conception faces serious problems (e.g. Woodward 2003, pp. 358–373), and in any event it never found wide application among philosophers concerned with psychological explanation. Strevens (2008, pp. 464–468) briefly addresses how the kairetic conception applies to psychological explanation. He focuses exclusively on high-level propositional attitudes. He holds that psychological properties of propositional attitudes are noncausally explanatorily relevant to mental and behavioral outcomes. In contrast, I think that many good causal explanations found within cognitive science cite causally relevant psychological properties of mental states. My discussion is devoted to developing that viewpoint. More detailed discussion of the unificationist and kairetic conceptions must await another occasion.
Woodward (2003, pp. 209–220) extends interventionism to encompass singular causal claims, such as “The short circuit caused the fire.” Woodward holds that singular causal claims answer certain w-questions and hence are minimally explanatory. We need not evaluate this aspect of Woodward’s position.
Strictly speaking, one can embrace interventionism about causal explanation without embracing interventionism about causal relevance (Saatsi and Pexton 2013). However, much of the motivation for interventionism about causal explanation lies in the nexus with interventionism about causal relevance.
If we regiment explanation using variables, then there are at least four putative explanations of the bridge collapse to consider. The first cites a binary variable T whose two values reflect whether the weight on the bridge exceeds 5000 kg. The second cites a binary variable U one value of which is 8356 kg and the other value of which corresponds to all other possible weights. The third cites a binary variable V whose two values reflect whether the weight was less than 8356 kg. The fourth cites a continuum valued variable W whose values are all possible weights. T supports a good explanation of why the bridge collapsed. U does not: there is no determinate answer as to whether the bridge would collapse if an intervention altered the weight from 8356 kg, because the answer depends on whether the altered weight exceeded 5000 kg. Similarly for V. W can figure in good explanations of why the bridge collapsed, since each possible value of W has a determinate implication for whether the bridge collapses. The intuitive statement “The bridge collapsed because the weight on it was 8356 kg” is misleading to the extent that it suggests a regimented explanation using U or V, acceptable to the extent that it suggests a suitable regimented explanation using W. The question remains: how do explanations that cite T compare to explanations that cite W? Woodward (2008b) suggests that causal explanations are better when they are “proportional,” meaning roughly that they describe the explanans in just enough detail to explain the explanandum. From this perspective, an explanation that cites T is superior to an explanation that cites W. Franklin-Hall (2016) argues against proportionality. I remain neutral regarding proportionality. In particular, I remain neutral as to whether T is a better explanans variable than W. What matters for my purposes is that W does not seem like a better explanans variable than T. We gain no explanatory benefit by citing the fine-grained W rather than the binary T. (Thanks to an anonymous referee for suggesting that I discuss the bridge example.)
Craver writes in one passage that “phenomenal models are at best shallow explanations” (2006, p. 374), which suggests that they may be explanations after all (even if not particularly satisfying ones). However, this passage clashes with Craver’s repeated, emphatic insistence that phenomenal models are unexplanatory.
Cummins (2000) emphasizes a mode of psychological explanation that he calls functional analysis, which explains a psychological capacity (e.g. the capacity to perceive depth, or to speak English) by decomposing it into less sophisticated capacities. Functional analysis is arguably an example of non-causal psychological explanation: it explains a psychological capacity not by specifying causal influences upon the capacity but rather by revealing how the capacity decomposes into more basic capacities. While I agree with Cummins that functional analysis plays a role in cognitive science practice, I do not think that it exhausts psychological explanation. In many cases, the explananda of interest to scientific psychology are not capacities but particular states or events. For example, we might want to explain why someone perceived an object as having a certain depth, or why she understood a particular English utterance as expressing a particular propositional content. Cummins does not say what it takes to explain such outcomes. He focuses exclusively upon functional analysis of psychological capacities.
Neural variables describe possible neural states. Are any neural variables also psychological variables? That depends on whether any neural states are psychological states—more carefully, on whether any neural state-types are psychological state-types. This is a controversial question. Luckily, I do not need to take a stand here. Nothing in my treatment turns upon whether any neural variables are psychological variables.
Folk psychology offers numerous singular psychological explanations of mental and behavioral outcomes (e.g. “John went to the restaurant because he wanted to meet Sam there.”) How interventionists should assess these singular explanations depends, in part, on the general issues about singular causal explanation raised in note 4. These matters deserve their own dedicated paper. But it seems clear that anything resembling scientific psychological explanation requires generalizations rather than mere singular causal statements. Folk psychology also offers various platitudes, such as the belief-desire law. Whether those platitudes are (or can be converted into) generalizations that conform to the interventionist template (3) is a tricky question that I will not address here.
Experimental manipulation of the lighting prior alters additional mental states, especially ancillary beliefs. For example, the subject comes to believe that she participated in a psychology experiment. If Bayesian perceptual models are on the right track, then a change in ancillary belief influences the percept (if at all) only by altering the priors, the prior likelihoods, or the cost function. In the experimental manipulation performed by Adams et al. (2004), \(p(\theta )\) changes but p(s) and \(p(e {\vert } s, \theta )\) do not. The cost function also remains fixed. Thus, any changes in ancillary belief influence the percept (if at all) only by altering \(p(\theta )\). For this reason, the experimental manipulation can still count as an intervention on \(p(\theta )\) with respect to the percept even though it alters various ancillary beliefs.
See (Campbell 2007) for discussion of what it is to intervene on an intention.
Some well-confirmed test counterfactuals relate V5 to perceived velocity. Researchers have confirmed counterfactuals of the form: If we microstimulate certain cells in V5, then certain changes in the velocity percept will occur (Zeki 2015). Accordingly, I count some mechanistic details about V5 as explanatory. My point in the main text is that some notable mechanistic details about V5 are not explanatory.
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Acknowledgements
I presented excerpts from this material at a conference on Bayesian Theories of Perception and Epistemology at Cornell University, July 2015; during a symposium at the Philosophy of Science Association Biennial Meeting in Atlanta, November 2016; and during a symposium at the Society for Philosophy and Psychology Annual Meeting, Baltimore, July 2017. I am grateful to all participants and audience members for helpful feedback, especially David Chalmers, David Danks, Steven Gross, Gualtiero Piccinini, Susanna Siegel, and Scott Sturgeon. Thanks also to Nicholas Shea and to three anonymous referees for this journal for comments that significantly improved the paper. My research was supported by a fellowship from the National Endowment for the Humanities. Any views, findings, conclusions, or recommendations expressed in this publication do not necessarily reflect those of the National Endowment for the Humanities.
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Rescorla, M. An interventionist approach to psychological explanation. Synthese 195, 1909–1940 (2018). https://doi.org/10.1007/s11229-017-1553-2
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DOI: https://doi.org/10.1007/s11229-017-1553-2