Neural signatures of vigilance decrements predict behavioural errors before they occur

There are now many environments in which humans need to monitor moving displays and only rarely act, such as train control and driving autonomous vehicles; lapses of attention in these circumstances can have tragic consequences. Problematically, we know that it is difficult to sustain attention under these monitoring or vigilance conditions and performance drops: when target events are rare, we tend to miss them, or are slower to respond. This "rare target" effect becomes more marked with longer tasks, known as a vigilance decrement. Despite the importance, we still have limited understanding of how the brain processes information during monitoring, particularly with dynamic stimuli, and how this processing changes when attention lapses. Here, we designed a multiple-object monitoring (MOM) paradigm that required sustained attention to dynamic stimuli, and used multivariate analyses of magnetoencephalography (MEG) data to examine how the neural representation of the information in the display varied with target frequency and time on the task. Behavioural performance decreased over time for the rare target (monitoring) condition, but not for the frequent target (active) condition. This change was mirrored in the neural results: under monitoring conditions, there was weaker coding of the critical distance between objects during time periods when vigilance decrements in performance occurred. There was also weaker informational connectivity between peri-occipital and peri-frontal brain areas in rare versus frequent target conditions. We developed a new analysis which used the strength of information decoding to predict whether the participant was going to miss the target on a given trial. We could predict behavioural errors more than a second before they occurred. This provides a first step in developing methods to predict and pre-empt behavioural errors due to lapses in attention and provides new insight into how vigilance decrements are reflected in information coding in the brain.

[1]  Hilde T. Juvodden,et al.  Mal-Adaptation of Event-Related EEG Responses Preceding Performance Errors , 2010, Front. Hum. Neurosci..

[2]  Hamid Karimi-Rouzbahani,et al.  Three-stage processing of category and variation information by entangled interactive mechanisms of peri-occipital and peri-frontal cortices , 2018, Scientific Reports.

[3]  Susan G. Wardle,et al.  Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data , 2016, Journal of Cognitive Neuroscience.

[4]  I. Robertson,et al.  The absent mind: further investigations of sustained attention to response , 1999, Neuropsychologia.

[5]  Jeffrey N. Rouder,et al.  Default Bayes factors for ANOVA designs , 2012 .

[6]  S. Eickhoff,et al.  Sustaining attention to simple tasks: a meta-analytic review of the neural mechanisms of vigilant attention. , 2013, Psychological bulletin.

[7]  Jeremy M. Wolfe,et al.  26.5 brief comms NEW , 2005 .

[8]  Rufin VanRullen,et al.  The power of the feed-forward sweep , 2008, Advances in cognitive psychology.

[9]  T. Jung,et al.  Electroencephalographic and peripheral temperature dynamics during a prolonged psychomotor vigilance task. , 2017, Accident; analysis and prevention.

[10]  J. Sawusch,et al.  Covert Auditory Attention Generates Activation in the Rostral/Dorsal Anterior Cingulate Cortex , 2002, Journal of Cognitive Neuroscience.

[11]  B. Biswal Resting-State Functional Connectivity , 2015 .

[12]  M. Raichle The brain's default mode network. , 2015, Annual review of neuroscience.

[13]  Samuel A. Nastase,et al.  Attention Selectively Reshapes the Geometry of Distributed Semantic Representation , 2016, bioRxiv.

[14]  Monica D. Rosenberg,et al.  Sustaining visual attention in the face of distraction: a novel gradual-onset continuous performance task , 2011, Attention, Perception, & Psychophysics.

[15]  J. Duncan The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour , 2010, Trends in Cognitive Sciences.

[16]  Aaron Kucyi,et al.  Just a thought: How mind-wandering is represented in dynamic brain connectivity , 2017, NeuroImage.

[17]  Anina N. Rich,et al.  Meaningful patterns of information in the brain revealed through analysis of errors , 2019, bioRxiv.

[18]  Preston P. Thakral,et al.  The role of parietal cortex during sustained visual spatial attention , 2009, Brain Research.

[19]  Sylvain Baillet,et al.  Magnetoencephalography for brain electrophysiology and imaging , 2017, Nature Neuroscience.

[20]  Marc N. Coutanche,et al.  Beyond Functional Connectivity: Investigating Networks of Multivariate Representations , 2018, Trends in Cognitive Sciences.

[21]  Albert Gjedde,et al.  Cortical Sites of Sustained and Divided Attention in Normal Elderly Humans , 1997, NeuroImage.

[22]  R. E. Yoss,et al.  Pupil size and spontaneous pupillary waves associated with alertness, drowsiness, and sleep , 1970, Neurology.

[23]  J. Duncan,et al.  Adaptive Coding of Task-Relevant Information in Human Frontoparietal Cortex , 2011, The Journal of Neuroscience.

[24]  C. Frith,et al.  Performance-related activity in medial rostral prefrontal cortex (area 10) during low-demand tasks. , 2006, Journal of experimental psychology. Human perception and performance.

[25]  Jonathan D. Cohen,et al.  Closed-loop training of attention with real-time brain imaging , 2015, Nature Neuroscience.

[26]  E. Formisano,et al.  Sustained attention and serotonin: a pharmaco‐fMRI study , 2008, Human psychopharmacology.

[27]  G. de Marco,et al.  Alertness in young healthy subjects: An fMRI study of brain region interactivity enhanced by a warning signal , 2010, Brain and Cognition.

[28]  Gerald Matthews,et al.  Use of EEG Workload Indices for Diagnostic Monitoring of Vigilance Decrement , 2014, Hum. Factors.

[29]  O. Jensen,et al.  Prestimulus alpha and mu activity predicts failure to inhibit motor responses , 2009, Human brain mapping.

[30]  Nasour Bagheri,et al.  Hard-wired feed-forward visual mechanisms of the brain compensate for affine variations in object recognition , 2017, Neuroscience.

[31]  Joel S. Warm,et al.  Vigilance Requires Hard Mental Work and Is Stressful , 2008, Hum. Factors.

[32]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[33]  Joel S. Warm,et al.  The Effects of Signal Salience and Caffeine on Performance, Workload, and Stress in an Abbreviated Vigilance Task , 2000, Hum. Factors.

[34]  F. Dehais,et al.  Inattentional deafness to auditory alarms: Inter-individual differences, electrophysiological signature and single trial classification , 2019, Behavioural Brain Research.

[35]  Anina N. Rich,et al.  Flexible Coding of Task Rules in Frontoparietal Cortex: An Adaptive System for Flexible Cognitive Control , 2015, Journal of Cognitive Neuroscience.

[36]  Brennan R. Payne,et al.  A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving , 2019, Front. Hum. Neurosci..

[37]  J. Duncan,et al.  Common regions of the human frontal lobe recruited by diverse cognitive demands , 2000, Trends in Neurosciences.

[38]  Thomas A. Carlson,et al.  Representational dynamics of object recognition: Feedforward and feedback information flows , 2016, NeuroImage.

[39]  Armin Thron,et al.  An fMRI approach to particularize the frontoparietal network for visuomotor action monitoring: Detection of incongruence between test subjects’ actions and resulting perceptions , 2007, NeuroImage.

[40]  Shinichi Kita,et al.  The effects of local prevalence and explicit expectations on search termination times , 2012, Attention, perception & psychophysics.

[41]  I. Peñuelas,et al.  Sustained Attention in a Counting Task: Normal Performance and Functional Neuroanatomy , 2001, NeuroImage.

[42]  V. Schmithorst,et al.  Changes in neuronal activation with increasing attention demand in healthy volunteers: An fMRI study , 2001, Synapse.

[43]  Nikolaus Kriegeskorte,et al.  Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .

[44]  N. Mackworth The Breakdown of Vigilance during Prolonged Visual Search 1 , 1948 .

[45]  John J. Foxe,et al.  Uncovering the Neural Signature of Lapsing Attention: Electrophysiological Signals Predict Errors up to 20 s before They Occur , 2009, The Journal of Neuroscience.

[46]  Mark S. Young,et al.  Malleable Attentional Resources Theory: A New Explanation for the Effects of Mental Underload on Performance , 2002, Hum. Factors.

[47]  Tomoki Fukai,et al.  Dendritic processing of spontaneous neuronal sequences for single-trial learning , 2018, Scientific Reports.

[48]  Lars T. Westlye,et al.  Attentional load modulates large-scale functional brain connectivity beyond the core attention networks , 2015, NeuroImage.

[49]  Michael Esterman,et al.  Sustaining visual attention in the face of distraction: a novel gradual-onset continuous performance task , 2013, Attention, perception & psychophysics.

[50]  Kristina M. Visscher,et al.  The neural bases of momentary lapses in attention , 2006, Nature Neuroscience.

[51]  Anina N. Rich,et al.  Why do we miss rare targets? Exploring the boundaries of the low prevalence effect. , 2008, Journal of vision.

[52]  W. Helton,et al.  Signal salience and the mindlessness theory of vigilance. , 2008, Acta psychologica.

[53]  Jin Yu,et al.  Functional connectivity of resting brain , 2000 .

[54]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[55]  Denis G. Pelli,et al.  ECVP '07 Abstracts , 2007, Perception.

[56]  Michele T. Diaz,et al.  Maintenance and Representation of Mind Wandering during Resting-State fMRI , 2017, Scientific Reports.

[57]  C. Frith,et al.  A fronto-parietal network for rapid visual information processing: a PET study of sustained attention and working memory , 1996, Neuropsychologia.

[58]  Kenneth Hugdahl,et al.  Prediction of human errors by maladaptive changes in event-related brain networks , 2008, Proceedings of the National Academy of Sciences.

[59]  Jeffrey S. Bedwell,et al.  Continuous Performance Test , 2012 .

[60]  Anina N. Rich,et al.  Attention enhances multi-voxel representation of novel objects in frontal, parietal and visual cortices , 2015, NeuroImage.

[61]  Spatiotemporal Analysis , 2014, Encyclopedia of Social Network Analysis and Mining.

[62]  M. Lee,et al.  Bayesian statistical inference in psychology: comment on Trafimow (2003). , 2005, Psychological review.

[63]  Naomi M. Kenner,et al.  Low target prevalence is a stubborn source of errors in visual search tasks. , 2007, Journal of experimental psychology. General.

[64]  R. Barry,et al.  Electroencephalography theta/beta ratio covaries with mind wandering and functional connectivity in the executive control network , 2019, Annals of the New York Academy of Sciences.

[65]  J. Smallwood,et al.  The restless mind. , 2006, Psychological bulletin.

[66]  Anna M. Bianchi,et al.  Exploring Cortical Attentional System by Using fMRI during a Continuous Perfomance Test , 2009, Comput. Intell. Neurosci..

[67]  Sepideh Sadaghiani,et al.  Ongoing dynamics in large-scale functional connectivity predict perception , 2015, Proceedings of the National Academy of Sciences.

[68]  Zoltan Dienes,et al.  Using Bayes to get the most out of non-significant results , 2014, Front. Psychol..

[69]  Mohammad Bagher Menhaj,et al.  Spatiotemporal analysis of category and target-related information processing in the brain during object detection , 2018, Behavioural Brain Research.

[70]  Nancy Kanwisher,et al.  Broad domain generality in focal regions of frontal and parietal cortex , 2013, Proceedings of the National Academy of Sciences.

[71]  Martin N. Hebart,et al.  The same analysis approach: Practical protection against the pitfalls of novel neuroimaging analysis methods , 2017, NeuroImage.

[72]  A. Zellner,et al.  Posterior odds ratios for selected regression hypotheses , 1980 .

[73]  I. Radermacher,et al.  Functional anatomy of intrinsic alertness: evidencefor a fronto-parietal-thalamic-brainstem network in theright hemisphere , 1999, Neuropsychologia.

[74]  Thomas A. Carlson,et al.  Spatial and feature-selective attention have distinct effects on population-level tuning , 2019, bioRxiv.

[75]  H E ROSVOLD,et al.  A continuous performance test of brain damage. , 1956, Journal of consulting psychology.

[76]  E. Macaluso,et al.  Occipital-parietal interactions during shifts of exogenous visuospatial attention: trial-dependent changes of effective connectivity. , 2004, Magnetic Resonance Imaging.

[77]  W. Helton,et al.  Feature absence–presence and two theories of lapses of sustained attention , 2011, Psychological research.

[78]  M. Greicius,et al.  Resting-state functional connectivity reflects structural connectivity in the default mode network. , 2009, Cerebral cortex.