On computational explanations

Abstract

Computational explanations focus on information processing required in specific cognitive capacities, such as perception, reasoning or decision-making. These explanations specify the nature of the information processing task, what information needs to be represented, and why it should be operated on in a particular manner. In this article, the focus is on three questions concerning the nature of computational explanations: (1) What type of explanations they are, (2) in what sense computational explanations are explanatory and (3) to what extent they involve a special, "independent" or "autonomous" level of explanation. In this paper, we defend the view computational explanations are genuine explanations, which track non-causal/formal dependencies. Specifically, we argue that they do not provide mere sketches for explanation, in contrast to what for example Piccinini and Craver (Synthese 183(3):283-311, 2011) suggest. This view of computational explanations implies some degree of "autonomy" for the computational level. However, as we will demonstrate that does not make this view "computationally chauvinistic" in a way that Piccinini (Synthese 153:343-353, 2006b) or Kaplan (Synthese 183(3):

Integrating computation into the mechanistic hierarchy in the cognitive and neural sciences

“…This simple solution implies that computational and implementational properties figure together in the same explanation and in the same levels of the mechanistic hierarchy. This solution is in tension with the view that computational explanations are autonomous from implementation and therefore do not require implementation details to be complete, but fits quite nicely with the picture on which computational explanations are sketches of mechanisms (some people, e.g., (Rusanen and Lappi, 2016;Shagrir, 2016) interpret (Kaplan and Craver, 2011;Piccinini and Craver, 2011) as advocates of this position). On this picture, the computational sketches turn into a full-fledged mechanistic explanation only when we complement the sketches with the samelevel implementational properties.…”

Integrating Philosophy of Understanding With the Cognitive Sciences

“…Such cases consist of an explanation where the negative or assimilation strategy seems apt but stands in tension with other considerations that suggest the model is both explanatory and non-mechanistic. On this latter front, several pluralists argue that computational, topological, and dynamical explanations’ formal and mathematical properties are not merely abstract representations of mechanisms ( Weiskopf, 2011 ; Serban, 2015 ; Rusanen and Lappi, 2016 ; Egan, 2017 ; Lange, 2017 ; Chirimuuta, 2018 ; Darrason, 2018 ; Huneman, 2018 ; van Rooij and Baggio, 2021 ). Others argue that these explanations cannot ( Chemero, 2009 ; Silberstein and Chemero, 2013 ; Rathkopf, 2018 ) or need not ( Shapiro, 2019 ) be decomposed into mechanistic components or that they cannot be intervened upon in the same way that mechanisms are intervened upon ( Woodward, 2013 ; Meyer, 2020 ; Ross, 2020 ).…”

“…Mechanists' assimilation strategy becomes more plausible than the UBI-inspired alternative if there are good grounds for thinking that the criteria that pluralists use to establish putatively non-mechanistic explanations as genuine explanations Rodieck, 1965;Marr, 1982Shagrir, 2010Kaplan, 2011;Kaplan and Craver, 2011*;Bechtel and Shagrir, 2015;Rusanen and Lappi, 2016;Egan, 2017;Shapiro, 2019 Exhaustive search Recall (memory) Sternberg, 1969Shapiro, 2017 Gain field encoding Hand-eye coordination Zipser and Andersen, 1988;Pouget and Sejnowski, 1997;Pouget et al, 2002;Shadmehr andWise, 2005 Shagrir, 2006*;Kaplan, 2011*;Serban, 2015;Rusanen and Lappi, 2016;Egan, 2017 Geon composition Object recognition Hummel andBiederman, 1992 Weiskopf, 2011;Buckner, 2015…”

Manipulation is key: on why non-mechanistic explanations in the cognitive sciences also describe relations of manipulation and control

“…A variety of models in the cognitive sciences have already been presented as explanatory despite the fact 97 that they do not satisfy the mechanistic requirement of describing relevant causal structures (Chirimuuta 98 2014;Egan 2017;Huneman 2010;Rusanen and Lappi 2016;Silberstein and Chemero 2013). Unlike these 99 studies, I do not aim to argue that some explanations in the cognitive sciences are non-causal.…”