Narrative Context, Direct Behavioral Scripts, and Downstream Shopping Choices in Large Language Models: A Preregistered Two-Study Audit

This record contains the complete preprint and analysis reproducibility package for “Narrative Context, Direct Behavioral Scripts, and Downstream Shopping Choices in Large Language Models: A Preregistered Two-Study Audit.” Reports that emotionally evocative narratives alter later large-language-model choices leave at least three explanations unresolved: a state-like contextual effect, semantic content, or an executable behavioral script embedded in the prompt. We conducted two preregistered studies of shopping choices. Study 1 was a high-fidelity procedural bridge using DeepSeek V4 Flash in 147 fresh sessions. Study 2 used 2,120 fresh sessions split equally between GLM-5.2 and DeepSeek V4 Flash to separate matched neutral context, emotion labels, objective adverse-event content, rich narrative, abstract decision-bias cues, and direct shopping instructions. Rich narrative did not support the predicted reduction in terminal-basket health relative to matched neutral context, whereas direct shopping instruction produced a large, cross-model and measurement-robust decrease. No preregistered rich-narrative persistence signature was established. Under the tested models, prompts, catalog, and measurement rules, the evidence favors sensitivity to explicit task-specific instructions over a robust narrative-induced deterioration in shopping composition. These findings do not establish the absence of internal model state or subjective emotion and do not invalidate prior work. Files included: paper2_preprint_v0.2.2.pdf: complete 15-page preprint. paper2_public_release_v0.2.3.zip: analysis-ready data, code, figures, protocols, claim-evidence documentation, integrity manifests, and reproduction instructions. The archive is an analysis reproducibility package rather than a complete collection of raw vendor API logs. API credentials, account information, private configuration, and unrelated pilot or superseded execution materials are excluded.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-28
DOI
https://doi.org/10.5281/zenodo.22140826
Primary Topic
Mental Health via Writing
Type
preprint
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preprint

Narrative Context, Direct Behavioral Scripts, and Downstream Shopping Choices in Large Language Models: A Preregistered Two-Study Audit

Zhifeng TAN
Zenodo (CERN European Organization for Nuclear Research)
Mental Health via Writing
preprint

Narrative Context, Direct Behavioral Scripts, and Downstream Shopping Choices in Large Language Models: A Preregistered Two-Study Audit

Zhifeng TAN
preprint en

Abstract

This record contains the complete preprint and analysis reproducibility package for “Narrative Context, Direct Behavioral Scripts, and Downstream Shopping Choices in Large Language Models: A Preregistered Two-Study Audit.” Reports that emotionally evocative narratives alter later large-language-model choices leave at least three explanations unresolved: a state-like contextual effect, semantic content, or an executable behavioral script embedded in the prompt. We conducted two preregistered studies of shopping choices. Study 1 was a high-fidelity procedural bridge using DeepSeek V4 Flash in 147 fresh sessions. Study 2 used 2,120 fresh sessions split equally between GLM-5.2 and DeepSeek V4 Flash to separate matched neutral context, emotion labels, objective adverse-event content, rich narrative, abstract decision-bias cues, and direct shopping instructions. Rich narrative did not support the predicted reduction in terminal-basket health relative to matched neutral context, whereas direct shopping instruction produced a large, cross-model and measurement-robust decrease. No preregistered rich-narrative persistence signature was established. Under the tested models, prompts, catalog, and measurement rules, the evidence favors sensitivity to explicit task-specific instructions over a robust narrative-induced deterioration in shopping composition. These findings do not establish the absence of internal model state or subjective emotion and do not invalidate prior work. Files included: paper2_preprint_v0.2.2.pdf: complete 15-page preprint. paper2_public_release_v0.2.3.zip: analysis-ready data, code, figures, protocols, claim-evidence documentation, integrity manifests, and reproduction instructions. The archive is an analysis reproducibility package rather than a complete collection of raw vendor API logs. API credentials, account information, private configuration, and unrelated pilot or superseded execution materials are excluded.

Zenodo (CERN European Organization for Nuclear Research)
Peace, Justice and strong institutions
Mental Health via Writing
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