Metabolic considerations for cognitive modeling

The human brain makes up just 2% of body mass but consumes closer to 20% of the body's energy. Nonetheless, it is significantly more energy-efficient than most modern computers. Although these facts are well-known, models of cognitive capacities rarely account for metabolic factors. In this paper, we argue that metabolic considerations should be integrated into cognitive models. We distinguish two uses of metabolic considerations in modeling. First, metabolic considerations can be used to evaluate models. Evaluative metabolic considerations function as explanatory constraints. Metabolism limits which types of computation are possible in biological brains. Further, it structures and guides the flow of information in neural systems. Second, metabolic considerations can be used to generate new models. They provide: a starting point for inquiry into the relation between brain structure and information processing, a proof-of-concept that metabolic knowledge is relevant to cognitive modeling, and potential explanations of how a particular type of computation is implemented. Evaluative metabolic considerations allow researchers to prune and partition the space of possible models for a given cognitive capacity or neural system, while generative considerations populate that space with new models. Our account suggests cognitive models should be consistent with the brain's metabolic limits, and modelers should assess how their models fit within these bounds. Our account offers fresh insights into the role of metabolism for cognitive models of mental effort, philosophical views of multiple realization and medium independence, and the comparison of biological and artificial computational systems.