Abstract
A popular view presents explanations in the cognitive sciences as causal or mechanistic and argues that an important feature of such explanations is that they allow us to manipulate and control the explanandum phenomena. Nonetheless, whether there can be explanations in the cognitive sciences that are neither causal nor mechanistic is still under debate. Another prominent view suggests that both causal and non-causal relations of counterfactual dependence can be explanatory, but this view is open to the criticism that it is not clear how to distinguish explanatory from non-explanatory relations. In this paper, I draw from both views and suggest that, in the cognitive sciences, relations of counterfactual dependence that allow manipulation and control can be explanatory even when they are neither causal nor mechanistic. Furthermore, the ability to allow manipulation can determine whether non-causal counterfactual dependence relations are explanatory. I present a preliminary framework for manipulation relations that includes some non-causal relations and use two examples from the cognitive sciences to show how this framework distinguishes between explanatory and non-explanatory, non-causal relations. The proposed framework suggests that, in the cognitive sciences, causal and non-causal relations have the same criterion for explanatory value, namely, whether or not they allow manipulation and control.
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Notes
One can also be a pluralist and argue that there is no single, unifying framework that can accommodate all these explanations. In this paper, I assume that, were it to be possible, such a framework would be preferable.
This issue has been addressed in several papers that develop such frameworks. Saatsi and Pexton (2013) reply that the explanation of regularities, rather than a singular event, can be symmetrical, and therefore non-causal. For example, the fact that the length of pendulums is proportional to the gravitational field can be explained by the mathematical equation that relates the two. Jansson (2015) and Jansson and Saatsi (2017) describe specific dependence or determination relations and argue that they are not symmetrical.
Throughout the paper, I treat ‘manipulation’ and ‘control’ as having the same meaning in this context. They are often found together in the literature. To avoid redundancy, generally, I will only speak of manipulation.
Although they do not discuss manipulation and control directly, some of the studies that address the issue of the asymmetry of explanation in symmetrical dependence relations suggest solutions that seem consistent with this idea. Woodward (2018) proposes, when describing one example, that if one side of a dependence relation can be explained by other ordinary causes, the direction of explanation runs from this side to the other (Jansson and Saatsi 2017), for their part, claim that, in some mathematical relations, the dependence runs only in one direction when fixing a value of one variable determines the value of the other, but not vice versa.
This requirement makes the manipulation* framework non-reductive; a manipulation* relation cannot be described without appeal to other manipulation* relations. In this respect, it is similar to Woodward’s framework (Woodward 2003).
The requirement that any manipulation* variable can be used to keep X constant may seem too strong. However, note that for causal relations, the effect of the intervention variable on Y must be mediated through X by definition. Hence, however we keep X constant, while keeping all other variables that can manipulate* Y constant, Y will not change. This is also the case for mathematical relations, where the value of Y is determined by the value of X and by the other variables that mathematically define Y.
See Baron et al. (2017) for a discussion of counterfactuals regarding mathematical relations.
Throughout this discussion I assume that we have no information about the prior probability of the bar length.
As discussed in the introduction, non-causal by popular opinion that considers simultaneous, mathematical relations to be non-causal.
This occurs because the sum is proportional to \( n \) and the fluctuations are proportional to \( \sqrt n \), so the sum and its fluctuations differ by a magnitude of \( \sqrt n \).
Some may be surprised that scientific explanations of phenomena can be given in terms of optimality. In the cognitive sciences, where behavior and neuronal activity are often explained by underlying computational models, such explanations are very common. Generally, these explanations assume that the cognitive system has evolved enough by evolution to reach some (at least locally) optimal strategy regarding perception and decision-making problems.
See Kuorikoski and Ylikoski (2015) for a discussion of the relation between counterfactual dependences in models and in real phenomena.
Another baffling issue in Craver’s mutual manipulability criterion is that Craver takes the direction of manipulation to go both from phenomenon to its components and from the components to the phenomenon, while the direction of explanation goes only from the components to the phenomenon. Franklin-Hall’s (2016) interpretation of mutual manipulability suggests a solution to this issue: top-down manipulation amounts to manipulation of the input conditions of the phenomenon. So, we can consider this top-down manipulation a causal manipulation of components by the inputs.
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Acknowledgements
I am thankful to Nir Fresco, Jens Harbecke, Arnon Levy, Jonathan Najenson, Oron Shagrir and three anonymous referees for thoughtful and helpful comments on this manuscript. For their insightful comments, I am also grateful to the participants of ‘Explanatory Reasoning in the Sciences (The Second Jerusalem-MCMP Workshop in the Philosophy of Science)’ in Munich 2017, and to participants in the workshops of the German-Israeli Foundation research group ‘Causation and Computation in Neuroscience’ in Jerusalem 2017, Cologne 2016 and Witten 2015. This research was supported by a Grant from the GIF, the German-Israeli Foundation for Scientific Research and Development.
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Elber-Dorozko, L. Manipulation is key: on why non-mechanistic explanations in the cognitive sciences also describe relations of manipulation and control. Synthese 195, 5319–5337 (2018). https://doi.org/10.1007/s11229-018-01901-3
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DOI: https://doi.org/10.1007/s11229-018-01901-3