Representation Without Control: Testing the Realization Effect in Language Models
Large language models are increasingly used as behavioral simulators, but it remains unclear when their outputs reflect human-like cognitive mechanisms rather than prompt-sensitive surface patterns. We study this question through the realization effect, a well-characterized finding in behavioral economics in which risk-taking differs systematically after paper versus realized gains and losses. We evaluate LLM behavior at three levels: prompt-only behavioral sensitivity, linear readout of internal representations, and causal control via activation steering. Prompt-only results show systematic condition sensitivity, but the directional pattern does not reproduce human realization-effect pre...
| Authors | Ciarán Walsh, Emilio Barkett |
|---|---|
| Primary category | cs.AI |
| Published | 24 May 2026 |
| Last updated at source | 24 May 2026 |
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