Deep neural networks (DNNs) are not just inadequate models of the visual system but are so different in their structure and functionality that they are not even on the same playing field. DNN units have almost nothing in common with neurons, and, unlike visual neurons, they are often fully connected. At best, DNNs can label inputs, while our object perception is both holistic and detail preserving. A feat that no computational system can achieve.

The integrated information theory equates levels of consciousness with the amount of information integrated over the elements that constitute a system. Conscious visual perception provides two observations that contradict the IIT. First, objects are accurately perceived when presented for ≪100 ms during which time no neural integration is possible. Second, an object is seen as an integrated whole and, concurrently, all constituent elements are evident. Because integration destroys information about details, IIT cannot account for perceptual detail preservation.