Literature DB >> 24861212

Predicting vulnerability to sleep deprivation using diffusion model parameters.

Amiya Patanaik1, Vitali Zagorodnov, Chee Keong Kwoh, Michael W L Chee.   

Abstract

We used diffusion modelling to predict vulnerability to decline in psychomotor vigilance task (PVT) performance following a night of total sleep deprivation (SD). A total of 135 healthy young adults (69 women, age = 21.9 ± 1.7 years) participated in several within-subject cross-over design studies that incorporated the PVT. Participants were classified as vulnerable (lower tertile) or non-vulnerable (upper tertile) according to their change in lapse rate [lapse = reaction time (RT) ≥ 500 ms] between the evening before (ESD) and the morning after SD. RT data were fitted using Ratcliff's diffusion model. Although both groups showed significant change in RT during SD, there was no significant group difference in RT during the ESD session. In contrast, during ESD, the mean diffusion drift of vulnerable subjects was significantly lower than for non-vulnerable subjects. Mean drift and non-decision times were both adversely affected by sleep deprivation. Both mean drift and non-decision time showed significant state × vulnerability interaction. Diffusion modelling appears to have promise in predicting vulnerability to vigilance decline induced by a night of total sleep deprivation.
© 2014 European Sleep Research Society.

Entities:  

Keywords:  fatigue; human factors; inter-individual differences; predicting performance

Mesh:

Year:  2014        PMID: 24861212     DOI: 10.1111/jsr.12166

Source DB:  PubMed          Journal:  J Sleep Res        ISSN: 0962-1105            Impact factor:   3.981


  15 in total

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9.  Cognitive throughput and working memory raw scores consistently differentiate resilient and vulnerable groups to sleep loss.

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10.  Classifying Vulnerability to Sleep Deprivation Using Resting-State Functional MRI Graph Theory Metrics.

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