2026/04/26 by Marius Mercier, Ruby de Lanerolle, Olivier Morin +2 · 2 voices
Decision Sciences · Mathematics · Psychology · #Bayesian inference #Bayesian probability #Cognitive and developmental aspects of mathematical skills #Competence (human resources) #Decision-Making and Behavioral Economics #Inference #Task (project management) #Task analysis #Visual and Cognitive Learning Processes
paper · doi:10.31234/osf.io/bp9uc_v1
openalex publication_date 2026/04/26 · openalex created_date 2026/04/27 · openalex updated_date 2026/07/14
People routinely infer others' competence under uncertainty, often relying on cues such as task difficulty and past accuracy. An emerging body of research suggests that people approximate Bayesian inference when doing so. We extend these results by testing whether people can infer others' numerical ability in a way that is consistent with a rational Bayesian model. In Study 1, we find that participants accurately predict the arithmetic performance of another individual from information about their past performance. Computational modeling shows that participants' inferences are better described by Bayesian processes than by plausible heuristics. Study 2 introduces a modified paradigm, in which participants are told about both past performance and time taken to solve problems. We find that, although participants are quite accurate in their predictions, they do not seem to take into account information about speed.