CFP: What counts as scientific understanding in cognitive science?

The conference will take place on the 22nd and 24th of May, 2026 at the University of Bucharest, Faculty of Philosophy. Regular presentations will be 30 minutes long, followed by 20 minutes Q&A

It will have a mixed format; speakers may choose whether they present online or face to face (although face to face presentations are preferred).

Abstracts will receive full consideration if sent before May 5th 2026 at the following email address [email protected], word or pdf, with the message titled “Abstract Submission”. The abstracts should be written in English, should have 300-500 words (references not included), a title and 5 keywords.

Please write your identifying details in the body of the email, and leave the attached abstract anonymised. We intend notifications of acceptance to be sent out on or before May 10th 2026. The conference programme will be announced as soon as the review is completed. 

Conference panels

Scientific understanding in cognitive science

•Can cognitive science produce a scientific explanation of understanding? Do we expect a mechanistic one, a computational (representational) one, one based on dynamic systems, or should one better aim for a Bayesian approach?

•Do we need a unifying theory about the mind in cognitive science, or should we settle for pluralism (at the level of explanations, models and scientific practices)?

•What does a grand unified theory in cognitive science afford that pluralism does not

•Should we strive for a unified explanation (one that should account for cognitive, neural, phenomenological and behavioral aspects alike)?

•Do we want integration at the level of explanations? Should we also integrate at the level of models (is that even possible)? Do we need explanations or models to account for phenomenal aspects of understanding? If so, what does explanatory pluralism imply for the phenomenology of understanding? What use for a unifying theory when finer-grained, multilevel, partial analyses might be more explanatory? 

•What epistemic desiderata do cognitive-scientific models meet - approximate truth, explanatory or predictive power, simplicity, empirical adequancy, others? Which such desiderata matter more in which cognitive-scientific contexts?

•If different ensuing models impact different branches of cognitive science differently, how does this bear on the field's interdisciplinary unity?

Scientific understanding and interdisciplinarity

•Does integrating multiple levels of analysis require new forms of explanation and, if so, which? What are the limits of integration? Is integration desirable whenever achievable?

•What roles do models play in interdisciplinary understanding? How do these models function when integrating assumptions from multiple domains (with different ontologies)?

•How can experts communicate their understanding to an audience of non-experts? Does “translation” between multiple disciplines affect understanding? Are there aspects or nuances/features that get lost or transformed when concepts “migrate” between fields?

•If interactional expertise is required for interdisciplinary approaches, does it grant contributory abilities? Is it sufficient for researchers in an interdisciplinary community of experts to be spectatorial cognizers? Is scientific understanding something individuals possess when part of an interdisciplinary effort, or is understanding distributed across research teams, maybe even split between specific research fields?

•Are epistemic standards transferable between fields in interdisciplinary studies, or are they  bound to specific fields?

•Does interdisciplinary research require new epistemic virtues (tolerance for ambiguity, transferable and translatable knowledge) or norms?

•Can understanding at one level of analysis substitute for another level of analysis? If so, in what circumstances?

•What is the epistemic value of interdisciplinarity? Does combining models from multiple fields increase scientific understanding, or does it sometimes obscure it?

Benchmarking scientific understanding

• How can scientific understanding be operationalized?

• Is (scientific) understanding (just) a peak performance? Can we benchmark (scientific) understanding and if so, should we include AI systems? If AI systems understand, does AI understanding bear on how we conceive of human understanding?

• What distinguishes understanding from mere predictive success?

• What role does explainability play in benchmarking?

• Can human and AI understanding be compared? If any, which shared metrics would apply across biological and artificial entities?

• Can interdisciplinary scientific understanding be benchmarked? How could it be evaluated?

• Do different models strike different trade-offs? If so, how does it impact benchmarking model-based understanding? 

Please register before May 5th 2026 at the email addresses below:

[email protected] and [email protected]