The commentators point out elaborations and challenges to incorporating metabolic considerations into cognitive modeling. We agree with many of them and highlight that these issues can only be addressed when we actively try to include metabolism while evaluating and generating models. The main message of our response is thus that metabolic considerations are epistemically valuable, regardless of the particular modeling approach, conceptualization, or specific ontological commitments regarding cognition. Yet, the commentator’s contributions also reveal that metabolism alone is never the decisive factor in cognitive modeling, and that its value for particular projects needs to be demonstrated in the modeling process, rather than assumed from the outset.

Haueis and Colaço argue that metabolic constraints should prune and generate cognitive models. Cognitive load theory offers an applied framework for testing these constraints. Working memory load can be viewed as the behavioral expression of local ATP limits. Primary–secondary knowledge, embodied offloading, and instructional efficiency map onto bioenergetic trade-offs. Extending CLT to human–AI symbiosis further shows how shared systems can support dynamic load balancing and testable metabolic predictions.

Haueis and Colaço argue that metabolic considerations should constrain and generate cognitive models. Research in cognitive fatigue provides a test case. Passive fatigue induction reveals specific representational degradation that points to local rather than global metabolic mechanisms, pruning potential models. We argue that local aerobic glycolysis offers a coherent, testable generative account of these findings.

Haueis and Colaço distinguish evaluative and generative roles for metabolic considerations in cognitive modeling. I extend this framework to the default mode network, arguing that this network’s elevated aerobic glycolysis constitutes a metabolic constraint on spontaneous thought. This constraint shapes the richness of spontaneous thought across varying task demands and predicts load-dependent phenomenological shifts through inter-network resource competition.

Haueis and Colaço (H&C) label all models as cognitive. However, just because cognitive science is so-called doesn’t mean that all models are. When type differences are ignored, an argument runs the risk of prescribing a monist modeling strategy. In their case: mechanistic. Such a prescription is empirically inadequate and overly restrictive, as modeling in cognitive science relies on many modeling types.

Metabolic constraints are important for brain modeling. But to be useful, they have to be integrated with an understanding of the architecture of human-level cognition. Otherwise, they could even lead modeling in the wrong direction.

The authors argue convincingly that metabolic considerations are highly relevant to evaluating and generating models of cognition. However, they understate their relevance to the mind–body problem. Metabolic reasoning about energy consumption should be combined with other constraints of speed, space, and history, providing an alternative to multiple realizability and medium independence: coherent materialism.

We reorient Haueis and Colaço’s metabolic framework of cognitive modeling by drawing on neuroscientific evidence from animal motor control, reinforcement learning, and decision-making to show that cognition emerged to serve the metabolic demands of the whole organism, rather than those of the brain alone. We also illustrate the generative value of our reframing to model cognition in artificial systems.

This commentary extends recent accounts of metabolic constraints in cognitive modelling by considering their role in brain development. We argue that shifting energy availability both constrains and drives the emergence of functional specialisation and network organisation. Integrating interactive specialisation, we show how metabolic factors shape competitive and cooperative interactions, and how atypical energy metabolism may contribute to neurodivergent developmental trajectories.

The central distinction – between evaluative and generative metabolic considerations – appears less theoretically secure than Haueis and Colaço suggest. First, the generative examples may ultimately reduce to evaluative reasoning applied earlier in the modeling process. Second, the principal generative examples do not clearly show metabolic considerations functioning within a cognitive model.

Haueis and Colaço’s framework can be inscribed into a positive theory of metabolic rationality where cognitive strategy selection is governed by allostatic energy management across sequential decisions under uncertainty. Heuristics emerge as solutions to a metabolic opportunity cost problem with distinct empirical predictions.

The target article underscores that metabolic considerations can accommodate the standard functionalist account of cognition. However, the energetic makeup of (biological) information processing systems in fact supports the claim of medium dependence of cognitive capacities. We explore two case studies from active matter physics and basal cognition to highlight the philosophical implications of the physicality of information processing.

Although abstract computational models in neuroscience omit biochemical detail, metabolic considerations still apply through abstract measures of efficient information processing. Against Haueis and Colaço’s claim that such measures are too generic to discriminate among competing models, I argue that coding efficiency and related measures are commonly applied to evaluate and generate specific cognitive models.

Metabolic considerations are argued to drive cognitive models. I explore the related high-level hypothesis that the brain favours the most anatomically specialised system that still meets demands for computational flexibility in a given task. The hypothesis points to specific claims across a hierarchy of specialisation, from inter-neuronal connection structure to neuronal cell structure, through to non-neuronal cells and analogue mechanisms.

Survival depends on efficient energy management, and neural systems are highly metabolically demanding biological structures. We fully endorse Haueis and Colaço’s call to integrate metabolism as a fundamental constraint on cognitive modelling. However, by focusing primarily on the neuronal level, their analysis underplays system-level approaches that have already incorporated metabolic constraints into successful and predictive accounts of cognition.

Haueis and Colaço claim that metabolic considerations function as explanatory constraints. I argue the explanatory contribution of metabolic constraints for synaptic models of memory is not well delineated. While metabolic constraints determine which synaptic architectures are biologically possible, they do not structure the outcome of synaptic consolidation. Metabolic constraints are explanatory since they account for the background conditions of memory formation.

Building on Haueis and Colaço, we argue that metabolic considerations should also constrain cognitive architecture. Globally shared and dynamic resources make strong modularity difficult to justify. Rather than fixed features, modules may reflect local optima within a metabolically constrained landscape – stable under typical conditions but liable to break down under perturbation. Metabolic perturbations thus provide diagnostic tools for architectural assumptions.

This commentary emphasizes the importance of how we individuate cognitive capacities, operations, and vehicles. It challenges the target article’s reliance on mechanistic notions of computation and medium-independence and uses examples from the memory literature to suggest that the role played by metabolic considerations in cognitive models will depend on questions of taxonomy and cognitive ontology.