Calibration and transfer in indicator-based assessments of artificial consciousness

Abstract:Research on artificial consciousness increasingly shifts evaluation from behaviour to internal architecture. Theory-based indicators are used to update probability assignments. This improves on behavioural tests but raises two distinct problems. First, these assignments cannot currently be calibrated against independently established artificial consciousness outcomes. Second, their evidential relevance is transferred from biological cases without independent support that indicator-consciousness relations remain stable across substrates. This commentary distinguishes calibration from transfer and adapts the iterative natural-kind strategy by proposing a preliminary, theory-relative comparative space for cross-substrate assessment.

5 pages, 1 figure. Author manuscript of the article published in Neuroscience of Consciousness. Revised following peer review; title and abstract updated

Artificial Intelligence (cs.AI); Neurons and Cognition (q-bio.NC)

arXiv:2603.27597 [cs.AI]

(or arXiv:2603.27597v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2603.27597 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1093/nc/niag061 Focus to learn more DOI(s) linking to related resources

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.