Rejecting Cognitivism: Computational Phenomenology for Deep Learning
- URL: http://arxiv.org/abs/2302.09071v1
- Date: Thu, 16 Feb 2023 20:05:06 GMT
- Title: Rejecting Cognitivism: Computational Phenomenology for Deep Learning
- Authors: Pierre Beckmann, Guillaume K\"ostner, In\^es Hip\'olito
- Abstract summary: We propose a non-representationalist framework for deep learning relying on a novel method: computational phenomenology.
We reject the modern cognitivist interpretation of deep learning, according to which artificial neural networks encode representations of external entities.
- Score: 5.070542698701158
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose a non-representationalist framework for deep learning relying on a
novel method: computational phenomenology, a dialogue between the first-person
perspective (relying on phenomenology) and the mechanisms of computational
models. We thereby reject 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'
mechanisms in terms of lived experience.
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