I posted the following comment on neuroskeptic about this article:
`"Noteworthy was the high frequency of agent-slotting exchanges between the hospital boss, Joe, and the Mafia boss, Vito, and parallel confusions between the “I” self-reference and underling Mafia members, suggesting generalization of boss/underling relationships."
For the model to recognize these types of relationships, the authors would have had to explicitly tag these agents as possessing either these qualities the constituent elements of these qualities. In either case, it's easy to imagine post-hoc biases in the model's "memory encoder" that generate just-so results without actually reflecting the biological or theoretical underpinnings.
How these relationships are assessed by the "memory encoder" and the "story parser" has much to do with the way features are associated with lexemes. From http://nn.cs.utexas.edu/?miikkulainen:phd:
"Processing in DISCERN is based on hierarchically-organized backpropagation modules, communicating through a central lexicon of word representations. The lexicon is a double feature map, which transforms the orthographic word symbol into its semantic representation and vice versa."`
(http://neuroskeptic.blogspot.com/2011/04/schizophrenic-computer.html)
A judgement of this article depends largely on whether the parser assigns meaning with a result (at the very least, or, given that the goal is to model schizophrenia, in a way) that's compatible with the output (or processes) of human linguistic cognition.