Dataset Information

A computational language approach to modeling prose recall in schizophrenia.

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.

SUBMITTER: Rosenstein M

PROVIDER: S-EPMC4039570 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

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A computational language approach to modeling prose recall in schizophrenia.

Rosenstein Mark M Diaz-Asper Catherine C Foltz Peter W PW Elvevåg Brita B

Cortex; a journal devoted to the study of the nervous system and behavior 20140208

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 late ...[more]

PMID: 24709122

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Project description:Schizophrenia (Sz) is associated with deficits in fluent reading ability that compromise functional outcomes. Here, we utilize a combined eye-tracking, neurophysiological, and computational modeling approach to analyze underlying visual and oculomotor processes. Subjects included 26 Sz patients (SzP) and 26 healthy controls. Eye-tracking and electroencephalography data were acquired continuously during the reading of passages from the Gray Oral Reading Tests reading battery, permitting between-group evaluation of both oculomotor activity and fixation-related potentials (FRP). Schizophrenia patients showed a marked increase in time required per word (d = 1.3, P < .0001), reflecting both a moderate increase in fixation duration (d = .7, P = .026) and a large increase in the total saccade number (d = 1.6, P < .0001). Simulation models that incorporated alterations in both lower-level visual and oculomotor function as well as higher-level lexical processing performed better than models that assumed either deficit-type alone. In neurophysiological analyses, amplitude of the fixation-related P1 potential (P1f) was significantly reduced in SzP (d = .66, P = .013), reflecting reduced phase reset of ongoing theta-alpha band activity (d = .74, P = .019). In turn, P1f deficits significantly predicted increased saccade number both across groups (P = .017) and within SzP alone (P = .042). Computational and neurophysiological methods provide increasingly important approaches for investigating sensory contributions to impaired cognition during naturalistic processing in Sz. Here, we demonstrate deficits in reading rate that reflect both sensory/oculomotor- and semantic-level impairments and that manifest, respectively, as alterations in saccade number and fixation duration. Impaired P1f generation reflects impaired fixation-related reset of ongoing brain rhythms and suggests inefficient information processing within the early visual system as a basis for oculomotor dyscontrol during fluent reading in Sz.

Dataset Information

A computational language approach to modeling prose recall in schizophrenia.

Publications

A computational language approach to modeling prose recall in schizophrenia.

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