Literature DB >> 19515359

Detecting phasic lapses in alertness using pupillometric measures.

Sean D Kristjansson1, John A Stern, Timothy B Brown, John W Rohrbaugh.   

Abstract

Small, nonreflexive pupillary changes are robust physiological indicators of cognitive activity. In the present paper, we examined whether measures of pupillary changes could be used to detect phasic lapses in alertness during a vigilance task. A polynomial curve-fitting method for quantifying parameters from single task-evoked pupillary responses (TEPRs) is described. The TEPR parameters associated with long latency responses (indicating low alertness) were compared to the TEPR parameters associated with normal latency responses (indicating an alert state) within a multilevel modeling framework. Three parameters, pupil diameter, linear pupil dilation rate and curvilinear pupil dilation rate, significantly differed between the long latency and normal latency response types. The results provide preliminary evidence that these parameters would be useful neurocognitive markers of operator state in a bio-behavioral alertness monitoring system.

Mesh:

Year:  2009        PMID: 19515359     DOI: 10.1016/j.apergo.2009.04.007

Source DB:  PubMed          Journal:  Appl Ergon        ISSN: 0003-6870            Impact factor:   3.661


  17 in total

1.  Individual differences in the allocation of attention to items in working memory: Evidence from pupillometry.

Authors:  Nash Unsworth; Matthew K Robison
Journal:  Psychon Bull Rev       Date:  2015-06

Review 2.  A locus coeruleus-norepinephrine account of individual differences in working memory capacity and attention control.

Authors:  Nash Unsworth; Matthew K Robison
Journal:  Psychon Bull Rev       Date:  2017-08

3.  Individual differences in baseline oculometrics: Examining variation in baseline pupil diameter, spontaneous eye blink rate, and fixation stability.

Authors:  Nash Unsworth; Matthew K Robison; Ashley L Miller
Journal:  Cogn Affect Behav Neurosci       Date:  2019-08       Impact factor: 3.282

4.  Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method.

Authors:  Jian-Hua Zhang; Xiao-Di Peng; Hua Liu; Jörg Raisch; Ru-Bin Wang
Journal:  Cogn Neurodyn       Date:  2013-01-23       Impact factor: 5.082

5.  Cortical Membrane Potential Signature of Optimal States for Sensory Signal Detection.

Authors:  Matthew J McGinley; Stephen V David; David A McCormick
Journal:  Neuron       Date:  2015-06-11       Impact factor: 17.173

6.  Pupil fluctuations track fast switching of cortical states during quiet wakefulness.

Authors:  Jacob Reimer; Emmanouil Froudarakis; Cathryn R Cadwell; Dimitri Yatsenko; George H Denfield; Andreas S Tolias
Journal:  Neuron       Date:  2014-10-22       Impact factor: 17.173

7.  Brain State Dependence of Hippocampal Subthreshold Activity in Awake Mice.

Authors:  Brad K Hulse; Evgueniy V Lubenov; Athanassios G Siapas
Journal:  Cell Rep       Date:  2017-01-03       Impact factor: 9.423

8.  Coupling between spontaneous pupillary fluctuations and brain activity relates to inattentiveness.

Authors:  A L Breeden; G J Siegle; M E Norr; E M Gordon; C J Vaidya
Journal:  Eur J Neurosci       Date:  2016-11-06       Impact factor: 3.386

Review 9.  Waking State: Rapid Variations Modulate Neural and Behavioral Responses.

Authors:  Matthew J McGinley; Martin Vinck; Jacob Reimer; Renata Batista-Brito; Edward Zagha; Cathryn R Cadwell; Andreas S Tolias; Jessica A Cardin; David A McCormick
Journal:  Neuron       Date:  2015-09-23       Impact factor: 17.173

10.  Pupillary correlates of lapses of sustained attention.

Authors:  Nash Unsworth; Matthew K Robison
Journal:  Cogn Affect Behav Neurosci       Date:  2016-08       Impact factor: 3.282

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