Abstract

David Marr famously argued that computational theory (i.e., analysis at the computational level) was required to explain both “what the device does and why”. In a series of papers, Oron Shagrir and William Bechtel argue that computational theory explains how certain mechanisms are appropriate for certain tasks by showing that identity holds between the corresponding mechanisms and the tasks at an abstract, computational level of description. Call this the “computational identity account” of “mechanism-task fit” (or “M/T fit”). Inspired by their work, I propose an alternative account that grounds M/T fit in constraint satisfaction, where the mechanism is appropriate to the task because the mechanism’s properties satisfy all the task-related constraints. I use retinal edge detection and sound localisation as two cases to demonstrate that constraint satisfaction may be a better way to ground M/T fit than identity. This account of M/T fit isn’t confined to the computational level of description, so I describe it as “task-fitting explanation” rather than computational theory. I argue that task-fitting explanation is a species of constraint-based explanation: it is interested in which features of a mechanism make possible above-chance correct task performance for the mechanism. As such, it is “modally complementary” to mechanistic explanation, which, I argue, is interested in which activities done by a mechanism’s parts make actual competent task performance for the mechanism.