Marcin Miłkowski

Procedural memory—the system underlying skills and habits—is inherently contentful. We argue, drawing on standard accounts of intentionality, that it exhibits a world‐to‐mind direction of fit and is governed by satisfaction conditions that normatively guide skilled action. Four convergent arguments support this: the conceptual structure of procedural memory's directive function; the phenomenon of misrepresentation in apraxia; the constitutive role of contentful goal states in motor control models; and the semantic interface between verbal instruction and action. This challenges anti‐representationalist accounts in embodied cognition and philosophy of memory—including radical enactivism and the skilled intentionality framework—and supports a unified representationalist framework for embodied agency.

Digital philosophy of science has split into a normative, model-driven first wave and a descriptive, text-driven second wave. This paper argues for a synthesis of these traditions. We propose a framework grounded in normative naturalism for evaluating scientific norms based on their role in solving local, context-specific problems. This framework is advanced through three complementary digital methods: tracking norm effectiveness, computational modeling research systems, and analyzing scientific discourse. To demonstrate our approach, we conduct a detailed analysis of how reviewers use the term “pseudoscience” in a large corpus of open peer review reports. This case study shows how combining quantitative methods with close reading can uncover the operative, and often implicit, norms that guide scientific communities. Our approach suggests that digital philosophy of science, by systematically examining scientific communication, can open new possibilities for empirically-informed normative guidance in practice.

This paper augments Mark Bickhard’s interactivist model by incorporating mechanistic explanation, computation, and a non-encodingist correspondence. It argues for a mechanistic framework (drawing on the new mechanism’s focus on organized entities and activities) that preserves core interactivist principles: process ontology, representation as emergent from interaction, rejection of encodingism, and the centrality of system-detectable error. However, grounding representational normativity solely in action outcomes or self-maintenance faces limitations. This paper proposes instead that normativity arises from epistemic norms within cognitive problem-solving, encompassing alethic, pragmatic, and economic dimensions addressed through bounded rationality under functional constraints. A novel framework integrating interactivism with this mechanistic view, computation, and infocorrespondence (a specific, non-encodingist correspondence based on information channels) is developed. This framework emphasizes semantic information processed by computational mechanisms, with inconsistency detection between information vehicles (leveraging informational redundancy) serving as a key system-accessible proxy for representational error (falsity). This synthesis offers a more robust account of error detection, the truth-success relationship, and the grounding of epistemic normativity, thereby enriching interactivist theory.

Representational mechanisms are responsible for processing information to modify readiness for action. While structural similarity has been proposed as foundational to neural representation, how these mechanisms systematically harness correspondence-based information remains undertheorized. This paper introduces a Correspondence Network Framework that elucidates how representational systems exploit multiple channels of structural similarity concurrently. We demonstrate that structural similarity engenders networks of infocorrespondences between informational structures, allowing representational mechanisms to evaluate information across channels, detect inconsistencies, and establish satisfaction conditions for representational content. Our framework extends beyond single-channel accounts by explaining how multiple classification systems can operate simultaneously over the same neural vehicles, enabling content-sensitive processing. To illustrate the framework's utility, we analyze representational similarity analysis (RSA) in cognitive neuroscience as detecting correspondence networks. This analysis reveals why taxonomic organization consistently emerges in neural representational spaces and provides principled responses to anti-representationalist critiques. While RSA alone cannot fully characterize representational mechanisms, the Correspondence Network Framework delineates its interpretive constraints and provides valuable heuristics for further empirical inquiry.

This commentary critiques Mougenot and Matheson's proposal to integrate embodied cognition with mechanistic explanations in cognitive neuroscience. We suggest more promising directions for embodied cognitive neuroscience, focusing on neuroethological research and evolutionary studies of nervous systems. These approaches, compatible with wide mechanistic explanations, offer a robust path forward by examining central nervous system function within whole organisms in their environments.

This paper examines the interplay between integrative explanatory pluralism and the quest for unified theories. We argue that when grounded in virtues associated with satisfactory explanations, integrative pluralism exhibits an inherent instability stemming from the conflict between the demand for unity and the commitment to preserving a patchwork of disparate partial explanations. A case study in cognitive science illuminates the challenges of maintaining both systematicity and depth in explanations within this framework. While this instability does not render integrative pluralism fundamentally flawed, it stresses the importance of a diachronic analysis of scientific dynamics and norms. The conclusion highlights the continued value of integrative pluralism in interdisciplinary research programs, while emphasizing its role as a temporary rather than permanent approach.