The psychophysics of affect in large language models

Authors: Joseph Marks (1), Marius C. Vollberg (2 and 3) ((1) NatWest Group, London, UK, (2) University of Zurich, Zurich, Switzerland, (3) Swiss Center for Affective Sciences, Geneva, Switzerland)

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Abstract: Good decisions often require considering how outcomes will feel. Yet large language models (LLMs) increasingly enter decision workflows, despite little being known about how they map outcomes to feelings or use those mappings in choice. We tested how LLMs anticipated gambling or stock-trading outcomes would feel and whether they incorporated these ratings into choice. When outcomes were rated in isolation, ratings exhibited diminishing sensitivity to magnitude, resembling human value functions; but models rated gains more intensely than equivalent losses, an inversion of canonical loss aversion we call silicon gain amplification. When outcomes were rated together, enabling direct comparison, ratings linearised. In choice, the gain-loss asymmetry largely disappeared, and models' own ratings did not reliably explain their choices better than monetary utility did. These findings reveal malleable mappings from outcomes to affect, raising questions about whether and how agentic LLMs should incorporate anticipated feelings into decisions.

Comments:

64 pages (26 main text, 25 Supplementary Information, 13 GUIDE-LLM reporting checklist), 5 figures. Code: this https URL ; data: this https URL

Subjects:

Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL)

ACM classes:

I.2.7; J.4

Cite as:

arXiv:2610.40985 [cs.AI]

(or

arXiv:2610.40985v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.40985

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Joseph Marks [ view email ]

[v1]

Thu, 29 Oct 2026 21:09:12 UTC (1,491 KB)

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