The representational hierarchy in human and artificial visual systems in the presence of object-scene regularities.

Human vision is still largely unexplained. Computer vision made impressive progress on this front, but it is still unclear to which extent artificial neural networks approximate human object vision at the behavioral and neural levels. Here, we investigated whether machine object vision mimics the representational hierarchy of human object vision with an experimental design that allows testing within-domain representations for animals and scenes, as well as across-domain representations reflecting their real-world contextual regularities such as animal-scene pairs that often co-occur in the visual environment.

Measuring Thought-Control Failure: Sensory Mechanisms and Individual Differences.

The ability to control one's thoughts is crucial for attention, focus, ideation, and mental well-being. Although there is a long history of research into thought control, the inherent subjectivity of thoughts has made objective examination, and thus mechanistic understanding, difficult. Here, we report a novel method to objectively investigate thought-control success and failure by measuring the sensory strength of visual thoughts using binocular rivalry, a perceptual illusion.