Linking across levels of computation in model-based cognitive neuroscience

Abstract

Computational approaches to cognitive neuroscience encompass multiple levels of analysis, from detailed biophysical models of neural activity to abstract algorithmic or normative models of cognition, with several levels in between. Despite often strong opinions on the ‘right’ level of modeling, there is no single panacea: attempts to link biological with higher level cognitive processes require a multitude of approaches. Here I argue that these disparate approaches should not be viewed as competitive, nor should they be accessible to only other researchers already endorsing the particular level of modeling. Rather, insights gained from one level of modeling should inform modeling endeavors at the level above and below it. One way to achieve this synergism is to link levels of modeling by quantitatively fitting the behavioral outputs of detailed mechanistic models with higher level descriptions. If the fits are reasonable (e.g., similar to those achieved when applying high level models to human behavior), one can then derive plausible links between mechanism and computation. Model-based cognitive neuroscience approaches can then be employed to manipulate or measure neural function motivated by the candidate mechanisms, and to test whether these are related to high level model parameters. I describe several examples of this approach in the domain of reward-based learning, cognitive control, and decision making and show how neural and algorithmic models have each informed or refined the other.