Experimental Design Modulates Variance in BOLD Activation: The Variance Design General Linear Model

Typical fMRI studies have focused on either the mean trend in the blood-oxygen-level-dependent (BOLD) time course or functional connectivity (FC). However, other statistics of the neuroimaging data may contain important information. Despite studies showing links between the variance in the BOLD time series (BV) and age and cognitive performance, a formal framework for testing these effects has not yet been developed. We introduce the Variance Design General Linear Model (VDGLM), a novel framework that facilitates the detection of variance effects. We designed the framework for general use in any fMRI study by modeling both mean and variance in BOLD activation as a function of experimental design. The flexibility of this approach allows the VDGLM to i) simultaneously make inferences about a mean or variance effect while controlling for the other and ii) test for variance effects that could be associated with multiple conditions and/or noise regressors. We demonstrate the use of the VDGLM in a working memory application and show that engagement in a working memory task is associated with whole-brain decreases in BOLD variance.Comment: 18 pages, 7 figure

Item Response Models of Probability Judgments: Application to a Geopolitical Forecasting Tournament

In this article, we develop and study methods for evaluating forecasters and forecasting questions in dynamic environments. These methods, based on item response models, are useful in situations where items vary in difficulty, and we wish to evaluate forecasters based on the difficulty of the items that they forecasted correctly. In addition, the methods are useful in situations where we need to compare forecasters who make predictions at different points in time or for different items. We first extend traditional models to handle subjective probabilities, and we then apply a specific model to geopolitical forecasts. We evaluate the model’s ability to accommodate the data, compare the model’s estimates of forecaster ability to estimates of forecaster ability based on scoring rules, and externally validate the model’s item estimates. We also highlight some shortcomings of the traditional models and discuss some further extensions. The analyses illustrate the models’ potential for widespread use in forecasting and subjective probability evaluation

How ideal are we? Incorporating human limitations into Bayesian models of word segmentation

Word segmentation is one of the first problems infants must solve during language acquisition, where words must be identified in fluent speech. A number of weak cues to word boundaries are present in fluent speech, and there is evidence that infants are able to use many of these, including phonotactic

Interactions Between Perceptual and Conceptual Learning

Confusions arise when 'stable' is equated with 'foundational.' Spurred on by the image of a house`s foundation, it is tempting to think that something provides effective support to the extent that it is rigid and stable. We will argue that when considering the role of perception in grounding our concepts, exactly the opposite is true. Our perceptual system supports our ability to acquire new concepts by being flexibly tuned to these concepts. Whereas the concepts that we learn are certainly influenced by our perceptual representations, we will argue that these perceptual representations are also influenced by the learned concepts. In keeping with one of the central themes of this book, behavioral adaptability is completely consistent with representationalism. In fact, the most straightforward account of our experimental results is that concept learning can produce changes in perceptual representations, the 'vocabulary' of perceptual features, that are used by subsequent tasks. This chapter reviews theoretical and empirical evidence that perceptual vocabularies used to describe visual objects are flexibly adapted to the demands of their user. We will extend arguments made elsewhere for adaptive perceptual representations (Goldstone, Schyns, & Medin, 1998; Schyns, Goldstone, & Thibaut, 1998), and discuss research from our laboratory illustrating specific interactions between perceptual and conceptual learning. We will describe computer simulations that provide accounts of these interactions using neural network models. These models have detectors that become increasingly tuned to the set of perceptual features that support concept learning. The bulk of the chapter will be organized around mechanisms of human perceptual learning, and computer simulations of these mechanisms

Should We Trust our Judgments about the Proficiency of Motivational Interviewing Counselors?A Glimpse at the Impact of Low Inter-rater Reliability

Standardized rating systems are often used to evaluate the proficiency of Motivational Interviewing (MI) counselors. The published inter-rater reliability (degree of coder agreement)  in many studies using these instruments has varied a great deal; some studies report MI proficiency scores that have only fair inter-rater reliability, and others report scores with excellent reliability. How much can we to trust the scores with fair versus excellent reliability? Using a Monte Carlo statistical simulation, we compared the impact of fair (0.50) versus excellent (0.90) reliability on the error rates of falsely judging a given counselor as MI proficient or not proficient. We found that improving the inter-rater reliability of any given score from 0.5 to 0.9 would cause a marked reduction in proficiency judgment errors, a reduction that in some MI evaluation situations would be critical. We discuss some practical tradeoffs inherent in various MI evaluation situations, and offer suggestions for applying findings from formal MI research to problems faced by real-world MI evaluators, to help them minimize the MI proficiency judgment errors bearing the greatest cost