Anna-Mari Rusanen

On computational explanations

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):339–373, 2011) have charged it to be.

Neural Network Templates and Their Interpretation

The wide applicability of neural networks poses an interesting challenge for the philosophy of science. This paper argues that when used for explanatory purposes, the interpretation of the neural networks computational template is crucial. The dynamics of debate about the interpretation of a model are illustrated with examples from the recent history of the neurocognitive sciences.

Self-Organizing Maps as Traveling Computational Templates

In this article we approach neural networks as computational templates that travel across various sciences. Traditionally, it has been thought that models are primarily models of some target systems: they are assumed to represent partially or completely their target systems. We argue, instead, that many computational models cannot easily be conceived of in representational terms. Rather, they can be seen as models for various epistemic endeavors. Apart from dealing with the question of representation, we discuss also what implications the genuinely cross-disciplinary computational templates such as neural networks have for the organization of science. We use Self-organizing maps as an example through which we study the aforementioned questions.

Making too many enemies: Hutto and Myin’s attack on computationalism†

We analyse Hutto & Myins three arguments against computationalism [Hutto, D., E. Myin, A. Peeters, and F. Zahnoun. Forthcoming. “The Cognitive Basis of Computation: Putting Computation In Its Place.” In The Routledge Handbook of the Computational Mind, edited by M. Sprevak, and M. Colombo. London: Routledge.; Hutto, D., and E. Myin. 2012. Radicalizing Enactivism: Basic Minds Without Content. Cambridge, MA: MIT Press; Hutto, D., and E. Myin. 2017. Evolving Enactivism: Basic Minds Meet Content. Cambridge, MA: MIT Press]. The Hard Problem of Content targets computationalism that relies on semantic notion of computation, claiming that it cannot account for the natural origins of content. The Intentionality Problem is targeted against computationalism using non-semantic accounts of computation, arguing that it fails in explaining intentionality. The Abstraction Problem claims that causal interaction between concrete physical processes and abstract computational properties is problematic. We argue that these arguments are flawed and are not enough to rule out computationalism.