Event Details

At early stages of processing, the human visual system extracts and encodes information in a manner that most efficiently represents visual scenes as they are projected onto the retina.  That is, within biophysical constraints, early visual representations faithfully encode images in a compact format so that the image information can be communicated to higher-level visual areas that are more specialized.  Higer-level visual representations should be efficient as well, but they must also be designed with specific behaviors in mind.  These representations must consider not efficient representation of information, but rather efficient representation of the information that supports specific behaviors.  One important behavior is visual shape recognition.  I will present a theoretical framework for studying shape representation that considers both efficient coding principles and specific behaviors.  I will then use this framework to demonstrate why some shapes are easier to learn to recognize than other shapes of equivalent complexity.