--- title: "Cognitive Effects and Biases in Large Language Models (2026)" description: "It first explains how psychology defines, validates and disputes such effects. Among them: 0." authors: ["Markus Schedl", "Ralph Hertwig", "Antonela Tommasel", "Shahed Masoudian"] year: 2026 venue: "Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts)" source: "https://aclanthology.org/2026.eacl-tutorials.2/" doi: "10.18653/v1/2026.eacl-tutorials.2" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/schedl-2026-cognitive-effects-biases-large-language/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/schedl-2026-cognitive-effects-biases-large-language/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/27/) / Cognitive Effects and Biases in Large Language Models # Cognitive Effects and Biases in Large Language Models **Authors:** Markus Schedl, Ralph Hertwig, Antonela Tommasel and Shahed Masoudian · **Year:** 2026 · **Venue:** [Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts)](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2026](https://md.research.elamur.ai/free/en/venues/eacl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.eacl-tutorials.2/) · [PDF](https://aclanthology.org/2026.eacl-tutorials.2.pdf) · [DOI 10.18653/v1/2026.eacl-tutorials.2](https://doi.org/10.18653/v1/2026.eacl-tutorials.2) · [OpenAlex](https://openalex.org/W7140134945) **Citations:** 0 citations (OpenAlex, 2026-10-14) ## Summary This entry is a tutorial abstract (EACL 2026, Volume 6), not a research paper, so it reports no data, experiments or results. It addresses an interdisciplinary problem. NLP researchers increasingly probe large language models for cognitive phenomena such as anchoring, order or position effects, and confirmation bias, often by borrowing psychology experiments. The organizers argue that these adaptations rely on specific experimental paradigms with their own assumptions about human performance. They also argue that NLP work tends to treat every cognitive effect as a "bias," ignoring that some of these phenomena may be adaptive. The tutorial is co-organized by NLP researchers and a decision scientist. It first explains how psychology defines, validates and disputes such effects. It then surveys LLM bias studies and compares their methods with psychological practice. In a hands-on session, participants try to detect one effect, positional bias, in multilingual LLMs and run into the pitfalls of doing so. It closes with open research questions. Its value lies in shared concepts and methods across the two fields, not in a new resource or benchmark. ## Key points - Tutorial T2 at EACL 2026 (Proceedings Vol. 6: Tutorial Abstracts, pp. 4–6) took place on 28 March 2026, 14:00–17:30, in Rabat. Materials are at https://cpjku.github.io/cobis_llm_eacl2026/. - Three of the organizers, Markus Schedl, Antonela Tommasel and Shahed Masoudian, are from Johannes Kepler University Linz. The fourth, Ralph Hertwig, works in Berlin at the Max Planck Institute for Human Development's Center of Adaptive Rationality. Tommasel is also affiliated with UNCPBA/CONICET, Argentina. - The central conceptual claim is that cognitive *effects* should be separated from *biases*, since some effects may serve a useful function. - The practical part is an exercise on detecting positional bias in multilingual LLMs. - The abstract reports no empirical findings, datasets, models or metrics. *Our summary of the paper, in our own words — not its text. 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Cognitive Effects and Biases in Large Language Models. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts). The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{schedl2026cognitive, title = {{Cognitive Effects and Biases in Large Language Models}}, author = {Markus Schedl, Ralph Hertwig, Antonela Tommasel and Shahed Masoudian}, booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts)}, year = {2026}, doi = {10.18653/v1/2026.eacl-tutorials.2}, url = {https://doi.org/10.18653/v1/2026.eacl-tutorials.2} } ``` - [← previous Cognitive Analysis Graph-Guided Multi-Turn Safety Enhancement for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-cognitive-analysis-graph-guided-multi/) - [next → CogToM: A Comprehensive Theory of Mind Benchmark inspired by Human Cognition for Large Language Models](https://md.research.elamur.ai/free/en/papers/tong-2026-cogtom-comprehensive-theory-mind-benchmark/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/27/) *Updated 1 November 2026*