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The empiricist theory of abstraction is well understood to entail infinite regress and circularity in content constitution (Husserl,\u00a0Logical Investigations. Routledge, 2001). This paper argues these entailments hold a fortiori for deep CNNs. Two theses result:\u00a0deep CNNs require supplementation by Quine\u2019s \u201capparatus of identity and quantification\u201d in order to (1) achieve concepts, and (2) represent objects, as opposed to \u201chalf-entities\u201d corresponding to similarity amalgams (Quine, Quintessence, Cambridge, 2004, p. 107). Similarity amalgams are also called \u201capproximate meaning[s]\u201d (Marcus &amp; Davis,\u00a0Rebooting AI, Pantheon, 2019, p. 132). Although Husserl inferred the \u201ccomplete abandonment of the empiricist theory of abstraction\u201d (a fortiori deep CNNs) due to the infinite regress and circularity arguments examined in this paper, I argue that the statistical learning of deep CNNs may be incorporated into a Fodorian hybrid account that supports Quine\u2019s \u201csortal predicates, negation, plurals, identity, pronouns, and quantifiers\u201d which are representationally necessary to overcome the regress\/circularity in content constitution and achieve objective (as opposed to similarity-subjective) representation (Burge, Origins of Objectivity. Oxford, 2010, p. 238). I base myself initially on Yoshimi\u2019s (New Frontiers in Psychology, 2011) attempt to explain Husserlian phenomenology with neural networks but depart from him due to the arguments and consequently propose a two-system view which converges with Weiskopf\u2019s proposal (\u201cObservational Concepts.\u201d The Conceptual Mind. MIT, 2015. 223\u2013248).<\/jats:p>","DOI":"10.1007\/s11023-023-09642-0","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T17:02:12Z","timestamp":1689613332000},"page":"507-524","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Can Deep CNNs Avoid Infinite Regress\/Circularity in Content Constitution?"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4659-0048","authenticated-orcid":false,"given":"Jesse","family":"Lopes","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,17]]},"reference":[{"key":"9642_CR2","volume-title":"Towards compositional understanding of the world by deep learning","author":"Y Bengio","year":"2019","unstructured":"Bengio, Y. (2019). Towards compositional understanding of the world by deep learning. 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