Leave-One-Trial-Out (LOTO) is a general, efficient and easily implementable approach for inferring trial-by-trial measures of computational model parameters in order to link these measures to neural mechanisms.
The dorsolateral prefrontal cortex integrates concurrent externally and internally generated predictions of task demand to guide information processing, while the medial prefrontal cortex corrects its prediction error based on actual task demand.
A biologically plausible learning rule allows recurrent neural networks to learn nontrivial tasks, using only sparse, delayed rewards, and the neural dynamics of trained networks exhibit complex dynamics observed in animal frontal cortices.
Regulatory success operates by goal-consistent increases and decreases of distinct attribute representations in generic neural hubs and in domain-specific brain regions, explaining when and why regulatory success generalizes across domains and contexts.
Cognitively normal older adults show a positive relationship between neural activity during memory encoding and brain β-amyloid deposition rate over the subsequent 3-4 years, supporting preclinical data that associates neural activity with β-amyloid deposition.
A non-invasive cognitive assistant for blind people endows objects in the environment with voices, allowing users to explore the scene, localize objects, and navigate through a building with minimal training.