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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 Experiment 2, we show that also in symbolic tasks that involve no perceptual noise, the PNP model identifies both types of processes based on the error distributions. In Experiment 3, we apply the PNP model to confirm the often-assumed "quasi-rational" nature of the rule-based processes involved in multiple-cue judgment. The results demonstrate that the PNP model reliably identifies the two cognitive processes proposed by Brunswik, and often recovers the parameters of the process more effectively than a standard regression model with homogeneous Gaussian error, suggesting that the standard Gaussian assumption incorrectly specifies the error distribution in many tasks. We discuss the untapped potentials of using error distributions to identify cognitive processes and how the PNP model relates to, and can enlighten, debates on intuition and analysis in dual-systems theories.

SUBMITTER: Sundh J

PROVIDER: S-EPMC8062428 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundSituational judgment tests (SJTs) are used in health sciences education to measure examinee knowledge using case-based scenarios. Despite their popularity, there is a significant gap in the validity research on the response process that demonstrates how SJTs measure their intended constructs. A model of SJT response processes has been proposed in the literature by Robert Ployhart; however, few studies have explored and expanded the factors. The purpose of this study was to describe the factors involved in cognitive processes that examinees use as they respond to SJT items in a health professions education context.MethodsThirty participants-15 student pharmacists and 15 practicing pharmacists-completed a 12-item SJT designed to measure empathy. Each participant engaged in a think-aloud interview while completing the SJT, followed by a cognitive interview probing their decision-making processes. Interviews were transcribed and independently coded by three researchers to identify salient factors that contributed to response processes.ResultsThe findings suggest SJT response processes include all four stages (comprehension, retrieval, judgment, and response selection) as initially proposed by Ployhart. The study showed factors from other published research were present, including job-specific knowledge and experiences, emotional intelligence, and test-taking. The study also identified new factors not yet described, including identifying a task objective in the scenario, assumptions about the scenario, perceptions about the scenario, and the setting of the item.ConclusionsThis study provides additional SJT validity evidence by exploring participants' response processes through cognitive and think-aloud interviews. It also confirmed the four-stage model previously described by Ployhart and identified new factors that may influence SJT response processes. This study contributes to the literature with an expanded SJT response process model in a health professions education context and offers an approach to evaluate SJT response processes in the future.

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

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