Varieties of Difference-Makers: Considerations on Chirimuuta’s Approach to Non-Causal Explanation in Neuroscience

Abstract

Causal approaches to explanation often assume that a model explains by describing features that make a differen ce regarding the phenomenon. Chirimuuta claims that this idea can be also used to understand non-causal explanation in computational neuroscien ce. She argues that mathematical prin ciples that figure in efficient coding explanations are non-causal differen cemakers. Although these prin ciples cannot be causally altered, efficient coding models can be used to show how would the phenomenon change if the prin ciples were modified in counterpossible situations. The problem is that efficient coding models also involve differen ce-makers that, prima facie, cannot be ch aracterized as non-causal in this sense. Mathematical p rin ciples alw ays involve variables which have counterfactual (instead of counterpossible)