Majid Davoody Beni

This paper critically examines the challenges in consciousness research, with a focus on natural kind (NK) reasoning. It proposes supplementing NK reasoning with a structural realist framework, shifting the focus from identifying a natural kind of consciousness to understanding invariant structural relationships across diverse theories, thereby addressing divergence and ensuring coherence. By using predictive processing as a formal framework that underlies the diversity of consciousness accounts and characterises common structural patterns, the structural realist approach aims to integrate divergent theories, offering a more robust, objective, and unified understanding of consciousness that can supplement and guide natural kind reasoning.

This article challenges the assumption that the science of consciousness can proceed from a theory-neutral foundation. I argue that even ostensibly theory-neutral (or theory-light) programmes inevitably rely on substantive background commitments that cannot be cleanly bracketed. The analysis demonstrates that the aspiration to eliminate or minimize theory-dependence in favour of pure observation risks collapsing into naïve empiricism. More broadly, the paper contends that there is no context-independent scientific method—certainly not one that seeks to purge theoretical commitments from the neuroscience of consciousness without significant epistemic cost.

This paper challenges traditional accuracy-centric accounts of rationality by synthesising the Free Energy Principle (FEP) with Charles Peirce’s pragmatist epistemology. Whereas the FEP frames cognition as a biological imperative to minimise surprise through predictive models, we argue that its normative force emerges when integrated with Peircean abduction and skill-based metrics. By reinterpreting rationality through skill scores—Peirce’s 1884 method for evaluating rare-event predictions—we demonstrate that survival-driven inference prioritises context-sensitive skill over abstract accuracy. The FEP’s variational free-energy minimisation aligns with abduction’s dynamic conjecture-making, revealing rationality as a pragmatic negotiation between organismic survival and environmental complexity. Critically, we show that Bayesian accuracy measures (e.g., Kullback–Leibler divergence) fail to capture the adequacy conditions for skillful forecasting, whereas Peirce’s skill score satisfies constraints such as error weighting and directionality. This fusion of FEP and pragmatism advances a naturalistic-normative framework in which rationality is grounded in adaptive, enactive inference rather than idealised coherence, bridging computational neuroscience and theoretical biology with philosophical accounts of inquiry.