Precise/not precise (PNP): A Brunswikian model that uses judgment error distributions to identify cognitive processes.

ABSTRACT: In 1956, Brunswik proposed a definition of what he called intuitive and analytic cognitive processes, not in terms of verbally specified properties, but operationally based on the observable error distributions. In the decades since, the diagnostic value of error distributions has generally been overlooked, arguably because of a long tradition to consider the error as exogenous (and irrelevant) to the process. Based on Brunswik's ideas, we develop the precise/not precise (PNP) model, using a mixture distribution to model the proportion of error-perturbed versus error-free executions of an algorithm, to determine if Brunswik's claims can be replicated and extended. In Experiment 1, we demonstrate that the PNP model recovers Brunswik's distinction between perceptual and conceptual tasks. In

SUBMITTER: Sundh J 

PROVIDER: S-EPMC8062428 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature