Séminaire - DIC-ISC-CRIA - 24 septembre 2026 par Reto Gubelmann

Séminaire - DIC-ISC-CRIA - 24 septembre 2026 par Reto Gubelmann

🎙 Reto Gubelmann 👥 313 📅 September 24, 2026 ⏱ 84 min 👁 0 📄 expert opinion 🧭 2026-09-24
Available in: English (current) Français

Keywords

groundingreasoningcognitionconationaffect

Summary

In this seminar, Reto Gubelmann presents a philosophical account of reasoning as an action of the whole animal, integrating cognition, affect, and conation. He contrasts Platonic-Cartesian dualist views with Aristotelian hylomorphism, using Raphael’s ‘School of Athens’ to illustrate the dialectic. He discusses Anselm’s ontological argument to show how ancient and medieval texts mixed emotional and logical elements, suggesting a more holistic reasoning process. He then introduces the system 1/system 2 distinction, arguing that genuine reasoning requires effortful system 2 thinking, which involves willpower (conation) and emotional attunement (affect). Drawing on Aristotle’s concept of practical wisdom, he claims that good reasoning depends on character and situational judgment. He illustrates this with a courage example where failing to confront a rude peer may be a conative or affective failure rather than a cognitive one. Finally, he hints at implications for large language models, suggesting that offloading reasoning to AI may overlook the embodied, affective, and volitional dimensions of human reason.

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Critical Evaluation

Value of the Information & Strength of the Argument

The talk provides a valuable perspective by reframing reasoning as a holistic action involving not just cognition but also affect and conation. Gubelmann builds a coherent argument using classical sources (Aristotle, Anselm) and contemporary psychology (system 1/2), illustrating with concrete examples like the HR evaluation scenario and the courage confrontation. The argument is logically structured, and the distinction between cognitive, conative, and affective failures is insightful. However, as a work-in-progress, the implications for AI reasoning are only briefly sketched, and the empirical basis for some claims (e.g., emotions affecting reasoning) is assumed rather than detailed.

Scientific Rigor, Source Quality, Title Accuracy

Gubelmann references his own papers (Gubelmann & Hongler 2026; Gubelmann 2026) which are relevant to the topic. He also draws on well-known philosophical sources without extensive citations, but the narrative is academically informed. The title ‘Grounding Reasoning’ accurately reflects the talk’s focus on making explicit the embodied and holistic nature of human reasoning. The video title ‘Séminaire - …’ correctly indicates an academic seminar format, and the content matches the announced topic. No public comments were analyzed.

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Title / Content Match

The video title indicates a seminar talk and is accurate; the talk indeed presents a philosophical exploration of grounding reasoning.

Quality & Reliability

7/10

The talk by an expert in philosophy and NLP presents original ideas grounded in classical philosophy and personal research. Work in progress but well-argued.

Key Moments

Cited Sources

  • Too Fast, Too Shallow–LLMs, Including Reasoning LLMs, Are Unreliable Constitutional Reasoners — Referenced as a paper on legal AI, mentioned in the talk and description.
  • Putting reasons back into reasoning: how genuine reasoning is inference-based and why neuro-symbolic NLI could achieve it — Referenced as a paper connecting philosophy and NLP, mentioned in talk and description.

Contribution & Novelties

The talk offers a novel integration of cognitive science and Aristotelian ethics to redefine reasoning, arguing that good reasoning depends on character and situation, not just logical cognition. This perspective challenges current AI approaches that treat reasoning as purely computational.

Pour aller plus loin :

85 words

Radar Profile

The radar shows high quality and moderate quantity, with technical depth and reliability balanced. The score profile indicates a conceptually rich talk with some gaps in empirical support, fitting an expert opinion format.

Reliability 7/10