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This study examined and compared various statistical methods for detecting individual differences in change. Considering 3 issues including test forms (specific vs. generalized), estimation procedures (constrained vs. unconstrained), and nonnormality, we evaluated 4 variance tests including the specific Wald variance test, the generalized Wald variance test, the specific likelihood ratio (LR) variance test, and the generalized LR variance test under both constrained and unconstrained estimation for both normal and nonnormal data. For the constrained estimation procedure, both the mixture distribution approach and the alpha correction approach were evaluated for their performance in dealing with the boundary problem. To deal with the nonnormality issue, we used the sandwich standard error (SE) estimator for the Wald tests and the Satorra-Bentler scaling correction for the LR tests. Simulation results revealed that testing a variance parameter and the associated covariances (generalized) had higher power than testing the variance solely (specific), unless the true covariances were zero. In addition, the variance tests under constrained estimation outperformed those under unconstrained estimation in terms of higher empirical power and better control of Type I error rates. Among all the studied tests, for both normal and nonnormal data, the robust generalized LR and Wald variance tests with the constrained estimation procedure were generally more powerful and had better Type I error rates for testing variance components than the other tests. Results from the comparisons between specific and generalized variance tests and between constrained and unconstrained estimation were discussed.

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Emotion perception is known to involve multiple operations and waves of analysis, but specific nature of these processes remains poorly understood. Combining psychophysical testing and neurometric analysis of event-related potentials (ERPs) in a fear detection task with parametrically-varied fear intensities (N=45), we sought to elucidate key processes in fear perception. Building on psychophysics marking fear perception thresholds, our neurometric model fitting identified several putative operations and stages: four key processes arose in sequence following face presentation—fear-neutral categorization (P1 at 100 ms), fear detection (P300 at 320 ms), valuation (early subcomponent of the late positive potential/LPP at 400–500 ms) and conscious awareness (late subcomponent LPP at 500–600 ms). Furthermore, within-subject brain-behavior association suggests that initial emotion categorization was mandatory and detached from behavior whereas valuation and conscious awareness directly impacted behavioral outcome (explaining 17% and 31% of the total variance, respectively). The current study thus reveals the chronometry of fear perception, ascribing psychological meaning to distinct underlying processes. The combination of early categorization and late valuation of fear reconciles conflicting (categorical versus dimensional) emotion accounts, lending support to a hybrid model. Importantly, future research could specifically interrogate these psychological processes in various behaviors and psychopathologies (e.g., anxiety and depression).

The indispensability of visual working memory (VWM) in human daily life suggests its importance in higher cognitive functions and neurological diseases. However, despite the extensive research efforts, most findings on the neural basis of VWM are limited to a unimodal context (either structure or function) and have low generalization. To address the above issues, this study proposed the usage of multimodal neuroimaging in combination with machine learning to reveal the neural mechanism of VWM across a large cohort (N = 547). Specifically, multimodal magnetic resonance imaging features extracted from voxel‐wise amplitude of low‐frequency fluctuations, gray matter volume, and fractional anisotropy were used to build an individual VWM capacity prediction model through a machine learning pipeline, including the steps of feature selection, relevance vector regression, cross‐validation, and model fusion. The resulting model exhibited promising predictive performance on VWM (r = .402, p < .001), and identified features within the subcortical‐cerebellum network, default mode network, motor network, corpus callosum, anterior corona radiata, and external capsule as significant predictors. The main results were then compared with those obtained on emotional regulation and fluid intelligence using the same pipeline, confirming the specificity of our findings. Moreover, the main results maintained well under different cross‐validation regimes and preprocess strategies. These findings, while providing richer evidence for the importance of multimodality in understanding cognitive functions, offer a solid and general foundation for comprehensively understanding the VWM process from the top down.