A generative recurrent neural network is quickly trained in an unsupervised manner to
model popular reinforcement learning environments through compressed spatio-temporal …

Because of the delays inherent in neural transmission, the brain needs time to process
incoming visual information. If these delays were not somehow compensated, we would …

We explore building generative neural network models of popular reinforcement learning
environments. Our world model can be trained quickly in an unsupervised manner to learn a …

Despite their tremendous success in many applications, large language models often fall
short of consistent reasoning and planning in various (language, embodied, and social) …

Everybody loves illusions. At times, the content on the internet seems to be mostly about
illusions—shoes, dresses, straight lines looking bent. This attraction has a long history …

The ability to discover abstract physical concepts and understand how they work in the world
through observing lies at the core of human intelligence. The acquisition of this ability is …

Despite growing evidence for perceptual interactions between motion and position, no
unifying framework exists to account for these two key features of our visual experience. We …

When perception differs from the physical stimulus, as it does for visual illusions and
binocular rivalry, the opportunity arises to localize where perception emerges in the visual …

Due to the delays inherent in neuronal transmission, our awareness of sensory events
necessarily lags behind the occurrence of those events in the world. If the visual system did …

Due to its inherent neural delays, the visual system has an outdated access to sensory
information about the current position of moving objects. In contrast, living organisms are …