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We thereby propose an alternative to the modern cognitivist interpretation of deep learning, according to which artificial neural networks encode representations of external entities. This interpretation mainly relies on neuro-representationalism, a position that combines a strong ontological commitment towards scientific theoretical entities and the idea that the brain operates on symbolic representations of these entities. We proceed as follows: after offering a review of cognitivism and neuro-representationalism in the field of deep learning, we first elaborate a phenomenological critique of these positions; we then sketch out computational phenomenology and distinguish it from existing alternatives; finally we apply this new method to deep learning models trained on specific tasks, in order to formulate a conceptual framework of deep-learning, that allows one to think of artificial neural networks\u2019 mechanisms in terms of lived experience.<\/jats:p>","DOI":"10.1007\/s11023-023-09638-w","type":"journal-article","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T09:02:15Z","timestamp":1688029335000},"page":"397-427","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["An Alternative to Cognitivism: Computational Phenomenology for Deep Learning"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9247-4841","authenticated-orcid":false,"given":"Pierre","family":"Beckmann","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guillaume","family":"K\u00f6stner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"In\u00eas","family":"Hip\u00f3lito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,29]]},"reference":[{"issue":"3","key":"9638_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10539-021-09807-0","volume":"36","author":"M Andrews","year":"2021","unstructured":"Andrews, M. 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