The Effects of Working Memory and Probability Format on Bayesian Reasoning

Yin, Lin; Shi, Zifu; Liao, Zixiang; Tang, Ting; Xie, Yuntian; Shu-n, Peng

doi:10.3389/fpsyg.2020.00863

Cited by 6 publications

(5 citation statements)

References 48 publications

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“…Indeed, there are several precedents for looking at working memory as an underlying factor in reasoning and judgments under uncertainty (e.g., Capon, Handley, & Dennis, 2003;Copeland & Radvansky, 2004). Shi et al (2006) and Yin et al (2020) have in fact specifically found that working-memory ability predicts Bayesian reasoning performance, based on this NSA view interpretation. Experiment 3 uses a reading span test of working memory that is both performance-based and completely without numbers or mathematical calculations.…”

Section: Methodsmentioning

confidence: 99%

“…Yet another version of the NSA view (e.g., Sirota et al, 2014;Yin et al, 2020) is that ability to understand and reason about nested sets is a reflection of general cognitive processing ability. If one takes this view, then individual differences in general cognitive processing ability (separately measured) should dominate in accounting for variations in Bayesian reasoning performance.…”

Section: Theoretical Implicationsmentioning

confidence: 99%

“…Another specific trait that appears to predict Bayesian reasoning is visuospatial ability (Brase & Hill, 2017;Kellen et al, 2013). Other studies have looked at more general constructs associated with Bayesian reasoning such as cognitive resources (Lesage et al, 2013), cognitive ability (Stanovich & West, 2000), cognitive reflection (Sirota & Juanchich, 2011), cognitive processing (Sirota et al, 2014), and working memory (Yin et al, 2020). A subsequent section develops how the diversity of these accounts, and the broad generality of their proposed predictors, present practical and theoretical challenges.…”

Section: Introductionmentioning

confidence: 98%

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Which cognitive individual differences predict good Bayesian reasoning? Concurrent comparisons of underlying abilities

Brase

2020

Mem Cogn

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We know a lot about how to present Bayesian reasoning tasks in order to aid performance, but less about underlying individual differences that can account for interindividual variability on the same tasks. Such information would be useful for both theoretical and practical reasons. Two theoretical positions, ecological rationality and nested set views, generate multiple hypotheses about which individual difference traits should be most relevant as underlying Bayesian reasoning performance. However, because many of these traits are somewhat overlapping, testing variables in isolation can yield misleading results. The present research assesses Bayesian reasoning abilities in conjunction with multiple individual different measures. Across three experiments, Bayesian reasoning was best predicted by measures of numerical literacy and visuospatial ability, as opposed to several different measures of cognitive thinking dispositions/styles, ability to conceptually model set-theoretic relationships, or cognitive processing ability (working memory span). These results support an ecological rationality view of Bayesian reasoning, rather than nested sets views. There also was some predictive ability for the Cognitive Reflection Task, which was only partially due to the numeracy aspects of that instrument, and further work is needed to clarify if this is a distinct factor. We are now beginning to understand not only how to build Bayesian reasoning tasks, but also how to build good Bayesian reasoners.

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Section: Methodsmentioning

confidence: 99%

Section: Theoretical Implicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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Which cognitive individual differences predict good Bayesian reasoning? Concurrent comparisons of underlying abilities

Brase

2020

Mem Cogn

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“…Moreover, there are findings about the influence of working memory in Bayesian reasoning: Brase and Hill (2015) 45 stated that there should be no influence. However, Yin et al (2020) 46 found that the interaction between working memory and probability format was significant. Therefore, it would be interesting to implement cognitive load tasks additionally in future work.…”

Section: Limitations and Outlookmentioning

confidence: 98%

Improving Diagnostic Efficiency with Frequency Double-Trees and Frequency Nets in Bayesian Reasoning

Kunzelmann

Binder

Fischer³

et al. 2022

MDM Policy & Practice

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Background. Medical students often have problems with Bayesian reasoning situations. Representing statistical information as natural frequencies (instead of probabilities) and visualizing them (e.g., with double-trees or net diagrams) leads to higher accuracy in solving these tasks. However, double-trees and net diagrams (which already contain the correct solution of the task, so that the solution could be read of the diagrams) have not yet been studied in medical education. This study examined the influence of information format (probabilities v. frequencies) and visualization (double-tree v. net diagram) on the accuracy and speed of Bayesian judgments. Methods. A total of 142 medical students at different university medical schools (Munich, Kiel, Goettingen, Erlangen, Nuremberg, Berlin, Regensburg) in Germany predicted posterior probabilities in 4 different medical Bayesian reasoning tasks, resulting in a 3-factorial 2 × 2 × 4 design. The diagnostic efficiency for the different versions was represented as the median time divided by the percentage of correct inferences. Results. Frequency visualizations led to a significantly higher accuracy and faster judgments than did probability visualizations. Participants solved 80% of the tasks correctly in the frequency double-tree and the frequency net diagram. Visualizations with probabilities also led to relatively high performance rates: 73% in the probability double-tree and 70% in the probability net diagram. The median time for a correct inference was fastest with the frequency double tree (2:08 min) followed by the frequency net diagram and the probability double-tree (both 2:26 min) and probability net diagram (2:33 min). The type of visualization did not result in a significant difference. Discussion. Frequency double-trees and frequency net diagrams help answer Bayesian tasks more accurately and also more quickly than the respective probability visualizations. Surprisingly, the effect of information format (probabilities v. frequencies) on performance was higher in previous studies: medical students seem also quite capable of identifying the correct solution to the Bayesian task, among other probabilities in the probability visualizations. Highlights Frequency double-trees and frequency nets help answer Bayesian tasks not only more accurately but also more quickly than the respective probability visualizations. In double-trees and net diagrams, the effect of the information format (probabilities v. natural frequencies) on performance is remarkably lower in this high-performing sample than that shown in previous studies.

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“…It is considered to be a complex but limited-capacity workspace that temporarily stores and processes information during cognitive tasks ( Baddeley, 1992 , 2001 ). As an important part of cognitive function, working memory plays an important role in learning, reasoning, problem-solving, and intellectual activities ( Sumowski et al, 2010 ; Shipstead and Yonehiro, 2016 ; Chuderski and Jastrzębski, 2018 ; Yin et al, 2020 ; Yoo and Collins, 2022 ). Therefore, clarifying the relationship between the vagal nerve and working memory is of great significance.…”

Section: Introductionmentioning

confidence: 99%

High vagally mediated resting-state heart rate variability is associated with superior working memory function

et al. 2023

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BackgroundHeart rate variability (HRV), a cardiac vagal tone indicator, has been proven to predict performance on some cognitive tasks that rely on the prefrontal cortex. However, the relationship between vagal tone and working memory remains understudied. This study explores the link between vagal tone and working memory function, combined with behavioral tasks and functional near-infrared spectroscopy (fNIRS).MethodsA total of 42 undergraduate students were tested for 5-min resting-state HRV to obtain the root mean square of successive differences (rMSSD) data, and then divided into high and low vagal tone groups according to the median of rMSSD data. The two groups underwent the n-back test, and fNIRS was used to measure the neural activity in the test state. ANOVA and the independent sample t-test were performed to compare group mean differences, and the Pearson correlation coefficient was used for correlation analysis.ResultsThe high vagal tone group had a shorter reaction time, higher accuracy, lower inverse efficiency score, and lower oxy-Hb concentration in the bilateral prefrontal cortex in the working memory tasks state. Furthermore, there were associations between behavioral performance, oxy-Hb concentration, and resting-state rMSSD.ConclusionOur findings suggest that high vagally mediated resting-state HRV is associated with working memory performance. High vagal tone means a higher efficiency of neural resources, beneficial to presenting a better working memory function.

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The Effects of Working Memory and Probability Format on Bayesian Reasoning

Cited by 6 publications

References 48 publications

Which cognitive individual differences predict good Bayesian reasoning? Concurrent comparisons of underlying abilities

Which cognitive individual differences predict good Bayesian reasoning? Concurrent comparisons of underlying abilities

Improving Diagnostic Efficiency with Frequency Double-Trees and Frequency Nets in Bayesian Reasoning

High vagally mediated resting-state heart rate variability is associated with superior working memory function

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