ArticleFrontiers in psychology2020
The Effects of Working Memory and Probability Format on Bayesian Reasoning.
Article in Frontiers in psychology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Gone fishin'… for distinct patterns of belief-updating in late-life worry and rumination.Frontiers in psychiatry · 2026Article
- Transforming clinical reasoning-the role of AI in supporting human cognitive limitations.Frontiers in digital health · 2025Review
- Evaluating the Effects of Different Cognitive Tasks on Autonomic Nervous System Responses: Implementation of a High-Precision, Low-Cost Complementary Method.Brain and behavior · 2024Article
- High vagally mediated resting-state heart rate variability is associated with superior working memory function.Frontiers in neuroscience · 2023Article
- Which cognitive individual differences predict good Bayesian reasoning? Concurrent comparisons of underlying abilities.Memory & cognition · 2021Article
- Improving Diagnostic Efficiency with Frequency Double-Trees and Frequency Nets in Bayesian Reasoning.MDM policy & practiceArticle
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bayesian reasoning is common and critical in everyday life while the performance on Bayesian reasoning is rather poor. Previous studies showed that people could enhance their performance by applying cognitive resources under the natural frequency format condition. Working memory is one of the crucial cognitive resources in the reasoning process. However, the role of working memory on Bayesian reasoning remains unclear. In our study, we verified the effect of working memory on Bayesian reasoning by evaluating the performance of participants with high and low working memory span (WMS); we also investigated if working memory as a kind of cognitive resource can affect Bayesian reasoning performance by manipulating the cognitive load in a dual-task paradigm among participants with no-, low-, and high-loads. We found the following: (1) The Bayesian reasoning performance of high WMS participants was significantly higher than that of low WMS participants. (2) Performance under natural frequency condition was noticeably higher than that in standard probability condition. (3) Interaction between working memory and probability format was significant, and the performance of participants with high-load in natural frequency condition was higher when compared to those of participants with no- and low-load. Therefore, we can conclude that: (1) Working memory resource is a major factor in Bayesian reasoning. The performance of Bayesian reasoning is influenced by working memory span and working memory load. (2) A Bayesian facilitation effect exists, and replacing the standard probability format with a natural frequency format can significantly improve Bayesian performance. (3) Bayesian facilitation occurs only in participants with sufficient working memory resources.
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