001049019 001__ 1049019
001049019 005__ 20251209202152.0
001049019 037__ $$aFZJ-2025-05114
001049019 1001_ $$0P:(DE-Juel1)176538$$aRathkopf, Charles$$b0$$eCorresponding author
001049019 1112_ $$aColloquium of the Department of Linguistics at the University of Tübingen$$d2025-12-08 - 2025-12-08$$wGermany
001049019 245__ $$aArtificial Competence
001049019 260__ $$c2025
001049019 3367_ $$033$$2EndNote$$aConference Paper
001049019 3367_ $$2DataCite$$aOther
001049019 3367_ $$2BibTeX$$aINPROCEEDINGS
001049019 3367_ $$2ORCID$$aLECTURE_SPEECH
001049019 3367_ $$0PUB:(DE-HGF)31$$2PUB:(DE-HGF)$$aTalk (non-conference)$$btalk$$mtalk$$s1765287880_6389$$xInvited
001049019 3367_ $$2DINI$$aOther
001049019 502__ $$cTübingen
001049019 520__ $$aAI systems increasingly match or surpass humans on complex tasks, yet they often exhibit surprising failure modes or inconsistent behavior across evaluation contexts. While cognitive science relies on the distinction between competence and performance to explain similar discrepancies in humans, this distinction is often framed in terms that preclude its straightforward application to artificial neural networks. This paper develops a unified account of competence applicable to both biological and artificial systems, locating competence at the algorithmic level of analysis. On this view, a system is competent in a domain when it implements an algorithm that reliably generalizes across that domain. Importantly, the relevant notion of implementation applies to neural networks when formalized under causal abstraction: a neural network implements an algorithm if there exists a mapping between the network's components and the algorithm's variables such that both respond identically to causal interventions. This framework provides a principled way to distinguish competence from auxiliary factors that affect performance across systems with very different constraints and architectures. It thereby accounts for double dissociations between performance and competence in both humans and AI systems, and offers a template for designing competence-sensitive evaluation in cognitive science and AI.
001049019 536__ $$0G:(DE-HGF)POF4-5255$$a5255 - Neuroethics and Ethics of Information (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001049019 909CO $$ooai:juser.fz-juelich.de:1049019$$pVDB
001049019 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176538$$aForschungszentrum Jülich$$b0$$kFZJ
001049019 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5255$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
001049019 9141_ $$y2025
001049019 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
001049019 980__ $$atalk
001049019 980__ $$aVDB
001049019 980__ $$aI:(DE-Juel1)INM-7-20090406
001049019 980__ $$aUNRESTRICTED