An Alternative to Cognitivism: Computational Phenomenology for Deep Learning

Abstract We propose a non-representationalist framework for deep learning relying on novel method computational phenomenology, dialogue between the first-person perspective (relying phenomenology) and mechanisms of models. thereby an alternative to modern cognitivist interpretation learning, according which artificial neural networks encode representations external entities. This mainly relies neuro-representationalism, position that combines strong ontological commitment towards scientific theoretical entities idea brain operates symbolic these proceed as follows: after offering review cognitivism neuro-representationalism in field we first elaborate phenomenological critique positions; then sketch out phenomenology distinguish it from existing alternatives; finally apply this new models trained specific tasks, order formulate conceptual deep-learning, allows one think networks’ terms lived experience.