Learning About the World by Learning About Images

• Published in: Current directions in psychological science : a journal of the American Psychological Society Vol. 30; no. 2; pp. 120 - 128
• Main Authors: Storrs, Katherine R., Fleming, Roland W.
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.04.2021; SAGE PUBLICATIONS, INC
• Subjects: Deep learning; efficient coding; deep learning; statistical learning; visual perception; human vision; unsupervised learning
• ISSN: 0963-7214; 1467-8721
• DOI: 10.1177/0963721421990334

One of the deepest insights in neuroscience is that sensory encoding should take advantage of statistical regularities. Humans’ visual experience contains many redundancies: Scenes mostly stay the same from moment to moment, and nearby image locations usually have similar colors. A visual system that knows which regularities shape natural images can exploit them to encode scenes compactly or guess what will happen next. Although these principles have been appreciated for more than 60 years, until recently it has been possible to convert them into explicit models only for the earliest stages of visual processing. But recent advances in unsupervised deep learning have changed that. Neural networks can be taught to compress images or make predictions in space or time. In the process, they learn the statistical regularities that structure images, which in turn often reflect physical objects and processes in the outside world. The astonishing accomplishments of unsupervised deep learning reaffirm the importance of learning statistical regularities for sensory coding and provide a coherent framework for how knowledge of the outside world gets into visual cortex.