Research conducted by Dr. Philipp Haueis, a philosopher of science at Bielefeld University, and David J. Colaço, Ph.D., from Ludwig-Maximilians-Universität Munich, has unveiled a compelling connection between brain metabolism and cognitive functions such as memory, perception, and attention. Their findings, published in the journal Behavioral and Brain Sciences, challenge existing cognitive models by emphasizing the brain’s energy requirements.
Haueis highlights the importance of considering the brain not merely as a computational device but as a biological organ that operates within energy constraints. “We wanted to find out what happens when you take the brain’s energy demands seriously,” he stated. The human brain, while constituting only about 2% of body mass, consumes an impressive 20% of the body’s energy. Despite this substantial energy use, it demonstrates efficiency that surpasses modern computers.
Metabolism’s Role in Cognitive Models
The researchers argue that metabolism plays a dual role in cognitive science. Firstly, it acts as a benchmark for assessing the biological plausibility of existing cognitive models. Any model demanding more energy than the brain can supply is deemed unrealistic. Secondly, insights into metabolic processes can facilitate the development of new models that better explain the relationship between brain structure and cognitive functions. This can lead to a deeper understanding of how neural networks utilize energy for efficient learning.
Haueis and Colaço’s research is the first comprehensive study to systematically integrate metabolic insights into cognitive modeling. By utilizing the journal’s “Open Peer Commentary” format, they anticipate a robust discourse among interdisciplinary researchers. This discussion is expected to extend beyond the academic sphere, touching on broader implications for understanding mental effort and the differences between biological and artificial intelligence.
Implications for Society and Technology
The study’s findings underscore the significance of energy in cognitive processes. As Dr. Haueis points out, recognizing that thinking requires energy helps clarify why attention is limited and why machine learning—unconstrained by biological factors—takes different paths than human cognition. This perspective not only enhances basic research but also stimulates important conversations around artificial intelligence, energy efficiency, and the essence of intelligence itself.
Conducted under the auspices of the Institute for Studies of Science (ISoS) at Bielefeld University, this research reflects the institute’s commitment to fostering interdisciplinary approaches to science, medicine, and technology. The ISoS received central academic unit status in May 2025, further solidifying its role in exploring how scientific practices intersect with societal needs.
For further details, refer to the study by Philipp Haueis et al, titled “Metabolic considerations for cognitive modeling,” published in Behavioral and Brain Sciences. The DOI for accessing the article is 10.1017/s0140525x25103956.