However, these concepts are still prevalent in much current work and are deeply ingrained in many architectures for intelligent systems. In this section, we summarize some of the evidence that has led us to abandon those assumptions about intelligence that classical AI continues to uphold. We then briefly review the alternative methodology that we have been using in constructing humanoid robotic systems. A modern understanding of cognitive science and neuroscience refutes these assumptions.
Humans have no full monolithic internal models.
There is evidence that in normal tasks humans tend to minimize their internal representation of the world. There is also evidence that there are multiple internal representations, which are not mutually consistent. For example, in the phenomena of blindsight, cortically blind patients can discriminate different visual stimuli, but report seeing nothing. This inconsistency would not be a feature of a single central model of visual space. These experiments and many others like it, such as the work by Gazzaniga and LeDoux on split brain patients or Rensink, O'Regan, and Clark on changes in visual scenes, convincingly demonstrate that humans do not construct a full, monolithic model of the environment.