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Patrick Krauss

FAU Erlangen-Nürnberg

Functionalist What matters is the organisation of the processing, not the material

Patrick Krauss, at the Department of Neurosurgery of Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, works at the junction of deep learning and consciousness science. His research tests artificial agents against criteria drawn from neuroscientific theories of consciousness, most prominently frameworks in the lineage of Antonio Damasio’s core consciousness, a program adjacent to the indicator discipline this site tracks. The 2021 paper reporting this work (PMID 33414712) belongs to a small class of studies that take a specific theory of consciousness and operationalize it as a test for a neural network rather than as commentary.

The position his work defends is methodological. Claims about machine consciousness should be adjudicated by implementations that satisfy or fail the criteria of named theories, not by intuitions about fluent output. That is the same discipline the site’s indicator posts demand, and Krauss’s contribution is to have run it against a theory grounded in affective neuroscience rather than in workspace or higher-order accounts.

Krauss also co-authored the Butlin and Long indicator-properties collaboration, placing his evaluation work inside the field’s main assessment framework. His profile anchors the site’s coverage of the neuroscientific-testing corner of AI consciousness research.

Known for. Deep learning versus core consciousness, neuroscientifically grounded tests for AI agents, consciousness metrics for neural networks

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