File(s) stored somewhere else
Please note: Linked content is NOT stored on Carnegie Mellon University and we can't guarantee its availability, quality, security or accept any liability.
Attractor dynamics in word recognition: converging evidence from errors by normal subjects, dyslexic patients and a connectionist model.
Any type of content formally published in an academic journal, usually following a peer-review process.
People make both semantic and visual errors when trying to recognise the meaning of degraded words. This result mirrors the finding that deep dyslexic patients make both semantic and visual errors when reading aloud. We link the results with the demonstration that a recurrent connectionist network which produces the meaning of words in response to their spelling pattern produces this distinctive combination of errors both when its input is degraded and when it is lesioned. The reason why the network can simulate the errors of both normal subjects and patients lies in the nature of the attractors which it develops as it learns to map orthography to semantics. The key role of attractor structure in the successful simulation suggests that the normal adult semantic reading route may involve attractor dynamics.