Abstract
Cognitive biases, which come from the shortcuts our brains use to make decisions, often lead people to misjudge sizes, amounts, or other magnitudes. However, it is still unclear how these biases evolved. Nonhuman animals like primates and corvids share with humans a nonsymbolic number sense. Whether biases in magnitude estimation are evolutionarily widespread in animals remains an open question. We investigated numerical estimation in macaque monkeys and carrion crows-two distantly related but numerically proficient vertebrates-using a delayed match-to-numerosity task with dot arrays. Both species exhibited key biases commonly observed in humans during numerical estimation tasks: scalar variability (increasing response variability with numerosity), regression to the mean (overestimation of small and underestimation of large numerosities), and a sequential effect (estimates biased toward previous trial numerosities). Traditionally, these biases have been attributed either to static influences of overall task history or to dynamic effects from recent trials. Here, we demonstrate that a Bayesian model incorporating dynamic priors based on multiple previously encountered numerosities can account for all observed behavioral patterns. Our findings suggest that numerical judgments in both species are shaped by uncertainty and the integration of recent experience, revealing shared Bayesian-like mechanisms for cognitive biases across distantly related animals.