Refining the Inferential Model of Scientific Understanding

Abstract

In this article, I use a mental models computational account of representation to illustrate some details of my previously presented inferential model of scientific understanding. The hope is to shed some light on possible mechanisms behind the notion of scientific understanding. I argue that if mental models are a plausible approach to modelling cognition, then understanding can best be seen as the coupling of specific rules. I present our beliefs as ‘ordinary’ conditional rules, and the coupling process as one where the consequent of one ordinary rule (OR) matches and activates the antecedent of the rule to which it is coupled in virtue of the activation of an intermediate ‘inference’ rule. I argue that on this approach knowledge of an explanation is the activation of ORs in a cognitive hierarchy, while understanding is achieved when those activated ORs are also coupled via correct inference rules. I do not directly address issues regarding the plausibility of mental models themselves. This article should therefore be seen as an exercise in refining the inferential model within an already presupposed computational setting, not one of arguing for the psychological adequacy of computational approaches.

Acknowledgements

Thanks to two anonymous referees of this journal, the editor James McAllister, and Pat Shade for very helpful comments on an earlier draft of this article.

Notes

[1] This law says the orbit of each planet is an ellipse with the sun at one focus.

[2] For a full argument supporting this claim, see Thagard (2012).

[3] For the full argument, see Newman (2012). Nothing in this section depends on any particular account of how to represent cognition.

[4] This is a little long-winded, I am afraid, but all these steps are necessary for what follows.

[5] See, for example, Way (1991), Otero, Léon, and Graesser (2002), Holyoak and Morrison (2005), and Tapiero (2007).

[6] That understanding hangs crucially on making connections is a very common assumption in the literature, made by among others Zagzebski (2001), Kvanvig (2003), Grimm (2006, 2010), and Elgin (2007).

[7] I am assuming that inferences between mathematical steps in this explanation are still causal since the mathematics reflects physical properties and relations, rather than abstract objects.

[8] Here I am addressing an issue raised by one reviewer and the editor of this journal. I very much thank them for pressing me on this point.

[9] See, for instance, Anderson (1983, 1993), Holland et al. (1986), Newell (1990), Thagard and Litt (2008), and Thagard (2012). The same computational architechture can be found used in learning classifier systems, a branch of machine learning. For that use, see Wyatt (2002), Bull and Kovacs (2005), and Urbanowicz and Moore (2009).

[10] Notice however that here the disjunctive antecedent would actually be manifested as a cluster of rules with identical consequents.

[11] Thanks again to one of my reviewers and to the editor for raising this concern.

[12] This is a sticking point for one of internalism's most well developed theories, evidentialism. See especially Connee and Feldman (2012).

[13] Horgan's views on this issue can be found throughout most of his work on epistemology and mind over the last couple of decades. A nice place to find them collected is his recent co-authored book (Henderson and Horgan 2011).