Jacopo Colelli
Affiliation: Università degli studi di Roma Tre
Category: Philosophy
Keywords: Psychological theories, Phenomenological analyses, Neurophenomenology
Date: Tuesday 2nd of September
Time: 17:00
Location: Room 154 (154)
View the full session: Functional Analysis & Mechanisms
This paper argues that the theoretical status of psychological theories in cognitive science remains conceptually underdeveloped, particularly in their role of informing and constraining neurocomputational and mechanistic models. Current approaches rely excessively on empirical effects and inductive reasoning (Van Rooij & Baggio, 2021; De Brigard & Gessell, 2024), often overlooking the need for a structured conceptual foundation that defines cognitive capacities in terms of essential properties and constitutive structures embedded in real-world experiential dynamics. I define this issue as the Missing Theory Problem. A key challenge lies in how psychological frameworks establish the explanandum before functional decomposition and localization. These frameworks frequently assume a fixed taxonomy of cognitive functions without a foundational analysis of the structural dependencies that theoretically ground cognitive dynamics in their proper domain- experience. While some argue that theoretical grounding can emerge from refining computational models within iterative cycles (Van Rooij & Baggio, 2021) or through inter-theoretical reductions (Churchland, 1986), I propose an alternative: expanding Marr’s tripartite framework by introducing a fourth, phenomenological level. This level provides a structured, top-down characterization of cognitive explananda, ensuring that model-building is guided by a prior analysis of psychological capacities within experiential regularities and intentional functioning. Rooted in Husserlian phenomenology, phenomenological functional analysis offers a systematic method for describing cognitive capacities beyond folk psychology and common-sense intuitions (Pokropski, 2020, 2021, 2023). Unlike statistical and computational approaches that derive cognitive categories from empirical correlations or formal coherence, phenomenological analysis identifies constitutive and essential differences between cognitive dynamics through rigorous a priori conceptual analysis on the condition of possibility for each specific experiential manifestation. Crucially, it captures how cognitive functions can operate across modalities and different domains of application—not through classical causal explanations, but by revealing how different intentional functions can be directed toward distinct object categories or types, uncovering phenomenological essential distinctions otherwise overlooked. This approach surpasses existing neurophenomenological models, which struggle to offer a viable alternative to cognitive psychology. Experimental phenomenology remains limited to perceptual structures without generalizing to higher-order cognition (Albertazzi, 2019, 2021); microphenomenology and generative neurophenomenology depend on introspective reports, restricting their ability to define universal cognitive structures (Bitbol & Petitmengin, 2017; Ramstead et al., 2022); and front-loaded phenomenology embeds phenomenological categories into experiments without reformulating cognitive ontologies (Gallagher, 2025). In contrast, phenomenological cognitivism integrates phenomenological descriptions into cognitive modeling, offering a principled framework for restructuring theories of cognitive performance. Rather than treating phenomenology as a supplementary tool, this approach establishes it as a fundamental framework for refining psychological theories, clarifying functional and representational dependencies that shape cognitive operations. By systematically uncovering phenomenological differences, it ensures that neurocomputational and mechanistic models are constrained by conceptually rigorous analyses rather than ad hoc theoretical assumptions. In doing so, it addresses foundational epistemological and methodological gaps in cognitive science, offering a more precise and integrable framework for interdisciplinary research on cognitive dynamics.
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