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ABSTRACT In the context of slow–fast behavioral variation, fast individuals are hypothesized to be those who prioritize speed over accuracy while slow individuals are those which do the opposite. Since energy metabolism is a critical component of neural and cognitive functioning, this predicts such differences in cognitive style to be reflected at the level of the brain. We tested this idea in honeybees by first classifying individuals into slow and fast cognitive phenotypes based on a learning assay and then measuring their brain respiration with high-resolution respirometry. Our results broadly show that inter-individual differences in cognition are reflected in differences in brain mass and accompanying energy use at the level of the brain and the whole animal. Larger brains had lower mass-specific energy usage and bees with larger brains had a higher metabolic rate. These differences in brain respiration and brain mass were, in turn, associated with cognitive differences, such that bees with larger brains were fast cognitive phenotypes whereas those with smaller brains were slow cognitive phenotypes. We discuss these results in the context of the role of energy in brain functioning and slow–fast decision making and speed accuracy trade-off. 

The allometric scaling of metabolic rate and what drives it are major questions in biology with a long history. Since the metabolic rate at any level of biological organization is an emergent property of its lower-level constituents, it is an outcome of the intrinsic heterogeneity among these units and the interactions among them. However, the influence of lower-level heterogeneity on system-level metabolic rate is difficult to investigate, given the tightly integrated body plan of unitary organisms. In this context, social insects such as honeybees can serve as important model systems because unlike unitary organisms, these superorganisms can be taken apart and reassembled in different configurations to study metabolic rate and its various drivers at different levels of organization. This commentary discusses the background of such an approach and how combining it with artificial selection to generate heterogeneity in metabolic rate with an analytical framework to parse out the different mechanisms that contribute to the effects of heterogeneity can contribute to the various models of metabolic scaling. Finally, the absence of the typical allometric scaling relationship among different species of honeybees is discussed as an important prospect for deciphering the role of top-down ecological factors on metabolic scaling. This article is part of the theme issue ‘The evolutionary significance of variation in metabolic rates’.