Abstract
In this paper, I argue that explaining cognitive behavior can be achieved through what I call hybrid explanatory inferences: inferences that posit mechanisms, but also draw on observed regularities. Moreover, these inferences can be used to achieve unification, in the sense developed by Allen Newel in his work on cognitive architectures. Thus, it seems that explanatory pluralism and unification do not rule out each other in cognitive science, but rather that the former represents a way to achieve the latter.
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Notes
The new mechanist movement initially focused on biology, and more generally, the life sciences. Here, the chief target is cognitive science. I do not think this application is problematic, since many proponents of mechanistic explication explicitly discuss mechanisms behind cognitive behavior (cf. Bechtel 2009 for mechanistic explanations in psychology).
These mechanistic explanations, which explain a phenomenon in terms of an organized, interacting set of entities and activities, are themselves partially causal (often, the mechanists explicitly endorse Woodward’s theory of causal explanation, see for instance Craver 2006 p. 372), but they also include references to other, e.g. mereological dependency relations.
Another take on explanatory pluralism is allowing for explanations to exist at multiple levels vis-à-vis the explanandum; here, explanatory pluralism is opposed to various positions on inter-theoretical relations, such as (various forms of) reductionism and eliminative materialism (McCauley 1996 is usually cited as the starting point for this). Of course, there is a connection between explanatory form and level (cf. van Bouwel et al. 2011 p. 36), but for this paper, I will consider the variety of explanatory pluralism that focuses on different forms of explanation.
Not everyone agrees though (cf. Schiffer 1991). However, regardless of their stance on ceteris paribus laws, everyone does agree that there are no strict laws in cognitive science, which is sufficient at this point.
One might insist, of course, that the generalizations such as the law of contrast, are only a form of law-talk, and that if these generalizations break down when descending to lower levels of description (e.g. from the mammalian visual system to biochemistry to physics), the only true law-like generalizations are to be had at the level of physics. Some time ago, this issue was debated in the context of biology, specifically with respect to the Hodgkin and Huxley model of the action potential, where Weber (2005) held that the explanatory force of this model ultimately derives from physical laws (e.g. Nernst’s equation), while Craver (2006) argued that at least part of the explanatory force of the model vis-à-vis the action potential, can only be realized by including a specification of at least some of the biological properties involved. Here, we touch upon the issue of reduction and the status of ‘special sciences’. These issues cannot be settled here, but I will remark that one of the features of mechanistic explanations, as commonly explicated in the literature (e.g. Bechtel and Abrahamsen 2005), is that explanatory levels in a mechanistic explanation can only be assigned locally, with respect to the mechanism responsible for the given explanandum-phenomenon. As Bechtel notes (2007 p. 182): “The local character of the treatment of levels also has a rather surprising consequence that distinguishes mechanistic reduction from traditional views of reduction (…) if the notion of levels is defined only locally, then on the mechanistic account we are not confronted by the prospect of a comprehensive lower level that is causally complete”..
As it happens, the prime example of a cognitive architecture considered below, the Soar cognitive architecture, is aimed at modelling human behavior, but this is just a feature of that particular example.
Although the following inferences are specifically about comparing the performance of cognitive capacities by humans and artificial systems, they can also be used to explain performance of other (e.g. animal) systems.
See (Gervais 2020) for additional examples.
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Gervais, R. The Multiplicity of Explanation in Cognitive Science. Found Sci 26, 1089–1104 (2021). https://doi.org/10.1007/s10699-020-09653-5
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DOI: https://doi.org/10.1007/s10699-020-09653-5