Improving Predictability, Reliability and Generalisability of Brain-Wide Associations for Cognitive Abilities via Multimodal Stacking.

Brain-wide association studies (BWASs) have attempted to relate cognitive abilities with brain phenotypes, but have been challenged by issues such as predictability, test-retest reliability, and cross-cohort generalisability. To tackle these challenges, we proposed a machine-learning "stacking" approach that draws information from whole-brain magnetic resonance imaging (MRI) across different modalities, from task-fMRI contrasts and functional connectivity during tasks and rest to structural measures, into one prediction model. We benchmarked the benefits of stacking, using the Human Connectome Projects: Young Adults (n=873, 22-35 years old) and Human Connectome Projects-Aging (n=504, 35-100 years old) and the Dunedin Multidisciplinary Health and Development Study (Dunedin Study, n=754, 45 years old).

Suboptimal choice in nonhuman animals: rats commit the sunk cost error.

The present experiments investigated the sunk cost error, an apparently irrational tendency to persist with an initial investment, in rats. This issue is of interest because some have argued that nonhuman animals do not commit this error. Two or three fixed-ratio (FR) response requirements were arranged on one lever, and an escape option was arranged on a second lever.