The study, conducted by independent researcher Vera Wilde, introduces what she calls a capability-gap test. The logic is deceptively simple. Certain problems, such as calculating a positive predictive value from base-rate information, have been studied so extensively in humans that scientists know, with meta-analytic precision, exactly how well people can perform. When participants in an online study dramatically exceed those well-established human ceilings, the most plausible explanation is not superhuman cognition but machine assistance — a canary in the data coalmine, signaling that the sample may be contaminated by large language models.

The empirical basis for the proposal comes from two preregistered pilot studies of a Bayesian reasoning training tool, with a combined sample of 148 participants recruited through the Prolific platform. The tool was designed to teach people how to solve Bayesian inference problems, the kind of task exemplified by medical diagnosis questions: given a disease with a certain prevalence, a test with a certain sensitivity and false-positive rate, what is the probability that a person who tests positive actually has the disease? Decades of research, dating back to classic work by Daniel Kahneman and Amos Tversky and extended by Gerd Gigerenzer and Ulrich Hoffrage, have shown that most people fail such problems, even when the numbers are presented in natural frequency formats that make the underlying logic easier to grasp.

That failure is precisely what makes the task useful as a detector. A meta-analysis by McDowell and Jacobs found that only about 24 percent of people can solve a single Bayesian reasoning problem presented in natural frequency format — and that figure represents a ceiling, a level at which achieving a perfect score on a battery of five such problems is effectively unattainable for genuine human respondents. Yet in Wilde’s pilots, participants’ accuracy on positive predictive value calculation problems reached roughly three times the established human performance ceiling. In the second pilot, 57 percent of participants achieved perfect 5-for-5 scores, a result that should be extraordinarily rare in an uncontaminated human sample.

The technical heart of the approach lies in distinguishing two outcome measures: accuracy and algorithm use. Accuracy refers simply to whether the participant produced the correct numerical answer. Algorithm use, by contrast, refers to evidence in the participant’s response that they actually followed the Bayesian reasoning process — for example, constructing a frequency tree, counting cases, or showing the intermediate steps of the calculation. This distinction matters because a training intervention designed to improve Bayesian reasoning should, if it works, change both measures in tandem. A large language model, however, can produce correct answers without any visible reasoning process, or with a reasoning process that does not respond to the training manipulation in the way human learning does.

By tracking both measures simultaneously, researchers can separate two rival explanations for suspiciously high performance. If accuracy spikes but algorithm use does not, contamination is the likelier culprit, because the model supplies answers without the participant acquiring the underlying skill. If both accuracy and algorithm use rise together, the pattern is consistent with authentic learning effects, and the treatment signal can be preserved rather than discarded. In this way, the capability-gap test does not merely flag bad data; it helps researchers decide which parts of their dataset reflect genuine psychological phenomena and which parts reflect machine-generated noise.

Wilde frames the detection problem itself as a signal detection problem, structurally analogous to the mass screenings for low-prevalence conditions — such as disease screening — around which the Bayesian reasoning literature was originally developed. Just as a medical test must balance hits against false alarms, a contamination detector must catch bot-driven responses without wrongly excluding honest participants who happen to be statistically savvy. The known reference distributions from the Bayesian reasoning literature make this calibration possible in a way that ad hoc attention checks cannot. Rather than relying on generic screening questions, researchers can compare observed performance against quantified human benchmarks and estimate the probability that a given response pattern arose from machine assistance.

The approach offers four practical advantages over existing data-quality tools. First, the human performance ceilings are grounded in meta-analyses rather than informal intuition, giving researchers a defensible threshold for suspicion. Second, the human–large language model performance gap on these problems is large, which increases the sensitivity of the test. Third, the known reference distributions allow nuanced assessment rather than crude pass–fail judgments. Fourth, Bayesian reasoning problems are easy to embed in existing surveys, requiring no special software or platform cooperation. Together, these properties make the method deployable at scale across the many fields — psychology, marketing, political science, epidemiology — that increasingly depend on online samples.

The stakes are considerable. Prior research on crowd work has documented substantial and growing use of large language models by online workers, and studies of data contamination in machine learning itself show how memorized content can masquerade as genuine capability. If a meaningful fraction of respondents in an online study are completing tasks with chatbot help, effect sizes may be distorted, replication attempts may fail for reasons that have nothing to do with the underlying science, and the credibility of entire literatures built on crowdsourced data could be undermined. The problem echoes an older statistical concern: John Tukey’s foundational work on sampling from contaminated distributions warned that even small amounts of contamination can seriously mislead inference drawn from nominally clean data.

Wilde is careful to note the provenance of the idea: neither pilot study was originally designed to validate a contamination detection method, and the proposal emerged from post hoc analysis of unexpectedly strong results. That origin makes the capability-gap test a promising hypothesis rather than a fully validated diagnostic, and the author provides practical recommendations for researchers who wish to use these problems as data-quality diagnostics while the validation literature matures. Both studies were preregistered on the Open Science Framework, and all data, materials, and analysis code are publicly available, allowing other teams to scrutinize and extend the approach. If the method holds up under broader testing, Bayesian reasoning problems — long a symbol of human statistical frailty — may find a second career as guardians of scientific integrity, ensuring that the data feeding behavioral science come from human minds rather than the machines trained on those minds’ collective output.

Subject of Research: Detecting large language model contamination in online behavioral research samples using Bayesian reasoning problems as capability-gap tests

Article Title: An LLM canary in the online data coalmine: Bayesian reasoning problems as a capability-gap test for LLM contamination in online samples

Article References: Wilde, V. (2026). An LLM canary in the online data coalmine: Bayesian reasoning problems as a capability-gap test for LLM contamination in online samples. Behavior Research Methods, 58(11), Article 301. https://doi.org/10.3758/s13428-026-03184-w

Image Credits: AI Generated

DOI: 10.3758/s13428-026-03184-w

Keywords: large language models, Bayesian reasoning, data contamination, online research, data quality, signal detection, natural frequencies, positive predictive value, crowdsourcing, behavioral research methods, capability-gap test, Prolific