Models of verbal working memory capacity: What does it take to make them work?

Theories of working memory (WM) capacity limits will be more useful when we know what aspects of performance are governed by the limits and what aspects are governed by other memory mechanisms. Whereas considerable progress has been made on models of WM capacity limits for visual arrays of separate objects, less progress has been made in understanding verbal materials, especially when words are mentally combined to form multiword units or chunks. Toward a more comprehensive theory of capacity limits, we examined models of forced-choice recognition of words within printed lists, using materials designed to produce multiword chunks in memory (e.g., leather brief case). Several simple models were tested against data from a variety of list lengths and potential chunk sizes, with test conditions that only imperfectly elicited the interword associations. According to the most successful model, participants retained about 3 chunks on average in a capacity-limited region of WM, with some chunks being only subsets of the presented associative information (e.g., leather brief case retained with leather as one chunk and brief case as another). The addition to the model of an activated long-term memory component unlimited in capacity was needed. A fixed-capacity limit appears critical to account for immediate verbal recognition and other forms of WM. We advance a model-based approach that allows capacity to be assessed despite other important processing contributions. Starting with a psychological-process model of WM capacity developed to understand visual arrays, we arrive at a more unified and complete model

Working in the Cloud? : best practices for sharing data and writing collaboratively

These slides support the oral presentation of Jeff Rouder and Mike Watson delivered at Cyberinfrastructure Day 2013 on the University of Missouri campus. The presentation dealt with the challenges of safely and accurately preserving research data, and the currently available control systems

Effects of Violent Video Game Exposure on Aggressive Behavior, Aggressive thought Accessibility, and Aggressive Affect among Adults with and without Autism Spectrum Disorder

Recent mass shootings have prompted the idea among some members of the public that exposure to violent video games can have a pronounced effect on individuals with autism spectrum disorder (ASD). Empirical evidence for or against this claim currently is absent. To address this issue, adults with and without ASD were assigned to play a violent or nonviolent version of a customized first-person shooter video game, after which responses on three aggression-related outcome variables (aggressive behavior, aggressive thought accessibility, and aggressive affect) were assessed. Results showed strong evidence that adults with ASD are not differentially affected by acute exposure to violent video games compared to typically developing adults. Moreover, model comparisons showed modest evidence against any effect of violent game content whatsoever. Findings from the current experiment suggest that societal concerns over whether violent game exposure has a unique effect on adults with autism are not supported by evidence

Teaching Bayes' Theorem: strength of evidence as predictive accuracy

Although teaching Bayes’ theorem is popular, the standard approach—targeting posterior distributions of parameters—may be improved. We advocate teaching Bayes’ theorem in a ratio form where the posterior beliefs relative to the prior beliefs equals the conditional probability of data relative to the marginal probability of data. This form leads to an interpretation that the strength of evidence is relative predictive accuracy. With this approach, students are encouraged to view Bayes’ theorem as an updating mechanism, to obtain a deeper appreciation of the role of the prior and of marginal data, and to view estimation and model comparison from a unified perspective

Commentary: Unlearning implicit social biases during sleep

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The philosophy of Bayes’ factors and the quantification of statistical evidence

A core aspect of science is using data to assess the degree to which data provide evidence for competing claims, hypotheses, or theories. Evidence is by definition something that should change the credibility of a claim in a reasonable person’s mind. However, common statistics, such as significance testing and confidence intervals have no interface with concepts of belief, and thus it is unclear how they relate to statistical evidence. We explore the concept of statistical evidence, and how it can be quantified using the Bayes factor. We also discuss the philosophical issues inherent in the use of the Bayes factor

Using Bayes to get the most out of non-significant results

No scientific conclusion follows automatically from a statistically non-significant result, yet people routinely use non-significant results to guide conclusions about the status of theories (or the effectiveness of practices). To know whether a non-significant result counts against a theory, or if it just indicates data insensitivity, researchers must use one of: power, intervals (such as confidence or credibility intervals), or else an indicator of the relative evidence for one theory over another, such as a Bayes factor. I argue Bayes factors allow theory to be linked to data in a way that overcomes the weaknesses of the other approaches. Specifically, Bayes factors use the data themselves to determine their sensitivity in distinguishing theories (unlike power), and they make use of those aspects of a theory’s predictions that are often easiest to specify (unlike power and intervals, which require specifying the minimal interesting value in order to address theory). Bayes factors provide a coherent approach to determining whether non-significant results support a null hypothesis over a theory, or whether the data are just insensitive. They allow accepting and rejecting the null hypothesis to be put on an equal footing. Concrete examples are provided to indicate the range of application of a simple online Bayes calculator, which reveal both the strengths and weaknesses of Bayes factors