A computational language approach to modeling prose recall in schizophrenia.
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Date
2014-05-29
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Abstract
Many cortical disorders are associated with memory problems In schizophrenia verbal memory deficits are a hallmark feature However the exact nature of this deficit remains elusive Modeling aspects of language features used in memory recall have the potential to provide means for measuring these verbal processes We employ computational language approaches to assess time varying semantic and sequential properties of prose recall at various retrieval intervals immediate 30 min and 24 h later in patients with schizophrenia unaffected siblings and healthy unrelated control participants First we model the recall data to quantify the degradation of performance with increasing retrieval interval and the effect of diagnosis i e group membership on performance Next we model the human scoring of recall performance using an n gram language sequence technique and then with a semantic feature based on Latent Semantic Analysis These models show that automated analyses of the recalls can produce scores that accurately mimic human scoring The final analysis addresses the validity of this approach by ascertaining the ability to predict group membership from models built on the two classes of language features Taken individually the semantic feature is most predictive while a model combining the features improves accuracy of group membership prediction slightly above the semantic feature alone as well as over the human rating approach We discuss the implications for cognitive neuroscience of such a computational approach in exploring the mechanisms of prose recall