From pure experience to cognitive models: constitutive explanations and modalization in phenomenological cognitivism

Summary

Phenomenological Cognitivism is a new framework that connects psychological explanations to pure experience, focusing on intentional properties within first-person experiences. It differs from traditional phenomenology by using functional analyses to align cognitive neuroscience objectives with these properties. The framework emphasizes constitutive explanations for cognitive capacities and how they adapt within various experiential domains. Additionally, it supports epistemological model structuring by using phenomenological descriptions to guide the creation of computational models and understanding neurobiological mechanisms.

Study at a glance

Abstract

Abstract This paper introduces Phenomenological Cognitivism, a methodological framework grounding psychological explanations within pure experience to elucidate their intentional properties and their embeddness within the layered structure of first-person-experience. Unlike practical phenomenological approaches, which primarily employ phenomenology as a heuristic for data collection, concept formation, or experimental design, Phenomenological Cognitivism utilizes phenomenological functional analyses to directly align sensitivity to intentional properties with the explanatory objectives of cognitive neuroscience. Central to this framework are constitutive explanations, which identify minimal phenomenological structures necessary for cognitive capacities to operate correctly, and modalization, which describes why and how these capacities must adapt functionally within diverse functional architectures and representational dependencies when enacted within different experiential domains or classes of objects. Finally, the paper explains how Phenomenological Cognitivism uniquely supports epistemological model structuring—a methodological approach that can directly employ phenomenological descriptions to inform and constrain the development of computational models and the identification of underlying neurobiological mechanisms.

Tags