These changes are known to be in part the result of small tweaks to the representation of visual stimuli in sensory cortex, but are also the result of context-dependent selection occurring after sensory processing has gone to completion.
How attention implements this balance of sensory change and selection is a central problem for the neuroscience of vision. Human languages are a powerful solution to this challenging coordination problem.
Participants were told members are free to attend events when they choose and that this ensures a highly engaged community.
In the accountability foregrounding ("opt-out") condition, participants were told members are expected to attend every meeting, unless they are unable to do so, and that this creates a tight-knit community.
The ability to manipulate behavior in service of long-term goals, self-regulation, has a long history of research and thought behind it.
Yet despite its long history and the multitude of related concepts and measures self-regulation research suffers from a lack of transferable measures, models and interventions.
Regarding between-subjects designs we show how extensive model comparisons can reveal conflicting narratives and how neural group differences might lie in properties other than the central tendency of distributions.
To sample the important parts of the visual world observers make saccades, moving the high-resolution and color-sensitive fovea to informative locations.
Community-level expectations provide a stable prior, and dynamics within an interaction are driven by partner-specific learning.
Chapter 3 exploits recent connections between this hierarchical Bayesian framework and continual learning in deep neural networks to propose and evaluate a computationally efficient algorithm implementing this same model at scale in an adaptive neural image-captioning agent.