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