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        <title>Do LLMs Choose Like Humans? Using Cognitive Theory to Evaluate LLM Decision-Making</title>
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        <namePart type="given">Johnathan</namePart>
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    <name type="personal">
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            <namePart type="given">Hung-Yi</namePart>
            <namePart type="family">Lee</namePart>
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            <namePart type="given">Sunipa</namePart>
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            <namePart type="given">Jessy</namePart>
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    <abstract>Large language models (LLMs) exhibit a range of human-like decision-making behaviors, but whether these reflect similar underlying mechanisms or surface-level mimicry remains unclear. We evaluate whether LLM context sensitivity aligns with a cognitive economic theory that explains human behavior through problem categorization and attention allocation. Across 12 open-source and commercial LLMs on a novel 140,000-trial product choice benchmark, context induces human-like shifts in choice and problem categorization, but does not reliably reweight attention between features like price and quality. Neither scale nor chain-of-thought reasoning reliably attenuates context sensitivity or generates human-like behavior. These results suggest that LLM decision mechanisms are distinct from human ones.</abstract>
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    <identifier type="doi">10.18653/2026.findings-emnlp.2177</identifier>
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%0 Conference Proceedings
%T Do LLMs Choose Like Humans? Using Cognitive Theory to Evaluate LLM Decision-Making
%A Sun, Johnathan
%A Shleifer, Andrei
%A Belinkov, Yonatan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F sun-etal-2026-llms
%X Large language models (LLMs) exhibit a range of human-like decision-making behaviors, but whether these reflect similar underlying mechanisms or surface-level mimicry remains unclear. We evaluate whether LLM context sensitivity aligns with a cognitive economic theory that explains human behavior through problem categorization and attention allocation. Across 12 open-source and commercial LLMs on a novel 140,000-trial product choice benchmark, context induces human-like shifts in choice and problem categorization, but does not reliably reweight attention between features like price and quality. Neither scale nor chain-of-thought reasoning reliably attenuates context sensitivity or generates human-like behavior. These results suggest that LLM decision mechanisms are distinct from human ones.
%R 10.18653/2026.findings-emnlp.2177
%U https://aclanthology.org/2026.findings-emnlp.2177/
%U https://doi.org/10.18653/2026.findings-emnlp.2177
%P 46552-46569

Markdown (Informal)

[Do LLMs Choose Like Humans? Using Cognitive Theory to Evaluate LLM Decision-Making](https://aclanthology.org/2026.findings-emnlp.2177/) (Sun et al., Findings 2026)

• Do LLMs Choose Like Humans? Using Cognitive Theory to Evaluate LLM Decision-Making (Sun et al., Findings 2026)

ACL

• Johnathan Sun, Andrei Shleifer, and Yonatan Belinkov. 2026. Do LLMs Choose Like Humans? Using Cognitive Theory to Evaluate LLM Decision-Making. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 46552–46569, Budapest, Hungary. Association for Computational Linguistics.