  This paper proposes a unified theoretical model to identify and test a
comprehensive set of probabilistic updating biases within a single framework.
The model achieves separate identification by focusing on the updating of
belief distributions, rather than classic point-belief measurements. Testing
the model in a laboratory experiment reveals significant heterogeneity at the
individual level: All tested biases are present, and each participant exhibits
at least one identifiable bias. Notably, motivated-belief biases (optimism and
pessimism) and sequence-related biases (gambler's fallacy and hot hand fallacy)
are identified as key drivers of biased inference. Moreover, at the population
level, base rate neglect emerges as a persistent influence. This study
contributes to the belief-updating literature by providing a methodological
toolkit for researchers examining links between different conflicting biases,
or exploring connections between updating biases and other behavioural
phenomena.
