Attractive and repulsive history biases in visual perception occur simultaneously, yet over dissociable timescales, and are explained by efficient encoding and Bayesian decoding of visual information in a stable environment.
A novel method and software provides researchers with the capability to rapidly, flexibly, and robustly perform Bayesian parameter estimation of theoretically meaningful models in cognitive neuroscience that were heretofore intractable.
Deep neural networks can be trained to automatically find mechanistic models which quantitatively agree with experimental data, providing new opportunities for building and visualizing interpretable models of neural dynamics.
High-resolution risk estimates of opisthorchiasis were produced in major endemic countries of Southeast Asia, providing valuable information for guiding disease control and serving as a baseline for future progress assessment.
Neural correlates of somatosensory target detection are restricted to secondary somatosensory cortex, whereas activity in insular, cingulate, and motor regions reflects stimulus uncertainty and overt reports.
Hierarchical modeling of internalizing symptoms and task performance reveals that difficulty adapting probabilistic learning to second-order uncertainty is common to anxiety and depression and holds across rewarding and punishing outcomes.