The Effects of Working Memory and Probability Format on Bayesian Reasoning

Stress, working memory, and academic performance: a neuroscience perspective

The relationship between stress and working memory (WM) is crucial in determining students' academic performance, but the interaction between these factors is not yet fully understood. WM is a key cognitive function that is important for learning academic skills, such as reading, comprehension, problem-solving, and math. Stress may negatively affect cognition, including WM, via various mechanisms; these include the deleterious effect of glucocorticoids and catecholamines on the structure and function of brain regions that are key for WM, such as the prefrontal cortex and hippocampus. This review explores the mechanisms underlying how stress impacts WM and how it can decrease academic performance. It highlights the importance of implementing effective stress-management strategies to protect WM function and improve academic performance.

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

Background

Heart 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).

Methods

A 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.

Results

The 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.

Conclusion

Our 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.

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

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.

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

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.