Out of the Loop, in Your Bubble: Mind Wandering Is Independent From Automation Reliability, but Influences Task Engagement

Gouraud, Jonas; Delorme, Arnaud; Berberian, Bruno

doi:10.3389/fnhum.2018.00383

Cited by 13 publications

(15 citation statements)

References 74 publications

(87 reference statements)

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“…Attention related to the orienting reflex, e.g., associated with sudden noise or a bright stimulus, also elicits pupillary dilatation (Liao et al., 2016; Marois et al., 2018; Steiner & Barry, 2011; Stelmack & Siddle, 1982). Conversely, smaller pupil sizes are seen with mind-wandering and introspection, and decreasing pupillary diameters reflect distraction and poor task performance (Franklin et al., 2013; Hopstaken et al., 2016; Huijser, Van Vugt & Taatgen, 2018; Smallwood et al., 2011; Unsworth & Robison, 2016; Brink Van Den, Murphy & Nieuwenhuis, 2016; Gouraud, Delorme & Berberian, 2018a; Unsworth & Robison, 2018a; Gouraud, Delorme & Berberian, 2018b). Pupillary changes can thus uncover the level of attention and the amount of mental effort with high temporal resolution (Kang, Huffer & Wheatley, 2014; Kang & Wheatley, 2017; Wierda et al., 2012; Willems, Herdzin & Martens, 2015).…”

Section: Resultsmentioning

confidence: 99%

Cortical modulation of pupillary function: systematic review

Peinkhofer

Knudsen

Moretti

et al. 2019

PeerJ

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Background The pupillary light reflex is the main mechanism that regulates the pupillary diameter; it is controlled by the autonomic system and mediated by subcortical pathways. In addition, cognitive and emotional processes influence pupillary function due to input from cortical innervation, but the exact circuits remain poorly understood. We performed a systematic review to evaluate the mechanisms behind pupillary changes associated with cognitive efforts and processing of emotions and to investigate the cerebral areas involved in cortical modulation of the pupillary light reflex. Methodology We searched multiple databases until November 2018 for studies on cortical modulation of pupillary function in humans and non-human primates. Of 8,809 papers screened, 258 studies were included. Results Most investigators focused on pupillary dilatation and/or constriction as an index of cognitive and emotional processing, evaluating how changes in pupillary diameter reflect levels of attention and arousal. Only few tried to correlate specific cerebral areas to pupillary changes, using either cortical activation models (employing micro-stimulation of cortical structures in non-human primates) or cortical lesion models (e.g., investigating patients with stroke and damage to salient cortical and/or subcortical areas). Results suggest the involvement of several cortical regions, including the insular cortex (Brodmann areas 13 and 16), the frontal eye field (Brodmann area 8) and the prefrontal cortex (Brodmann areas 11 and 25), and of subcortical structures such as the locus coeruleus and the superior colliculus. Conclusions Pupillary dilatation occurs with many kinds of mental or emotional processes, following sympathetic activation or parasympathetic inhibition. Conversely, pupillary constriction may occur with anticipation of a bright stimulus (even in its absence) and relies on a parasympathetic activation. All these reactions are controlled by subcortical and cortical structures that are directly or indirectly connected to the brainstem pupillary innervation system.

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Section: Resultsmentioning

confidence: 99%

Cortical modulation of pupillary function: systematic review

Peinkhofer

Knudsen

Moretti

et al. 2019

PeerJ

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“…MPFC (Harrivel et al, 2013;Durantin et al, 2015) DLPFC (Harrivel et al, 2013) DLPFC (Durantin et al, 2014;Fairclough et al, 2019) Left PFC (Kalia et al, 2018) occipital lobe (Kojima and Suzuki, 2010) EEG α power over occipital sites (Gouraud et al, 2018) (α and (β power (auditory stimuli) (Braboszcz and Delorme, 2011) (θ power (auditory stimuli) (Braboszcz and Delorme, 2011) N1 (Kam et al, 2011) N4 (O'Connell et al, 2009) P1 (Kam et al, 2011) P2 (Braboszcz and Delorme, 2011) P3 (Schooler et al, 2011) frontal θ power (Gärtner et al, 2014) P3 (Dierolf et al, 2017) frontal (θ power and parietal (α power (Ewing et al, 2016;Fairclough and Ewing, 2017) Event Related Coherence between midfrontal and right-frontal electrodes (Carrillo-De-La-Pena and García-Larrea, 2007) (α band power (Mathewson et al, 2009) P1 (Pourtois et al, 2006;Mathewson et al, 2009) P2 (Mathewson et al, 2009) N170 (Pourtois et al, 2006) P3 (Pourtois et al, 2006;Mathewson et al, 2009) N1 (Callan et al, 2018;Dehais et al, 2019a,b) P3 (Puschmann et al, 2013;Scannella et al, 2013;Giraudet et al, 2015b;Dehais et al, 2019a,b) (α power in IFG (Dehais et al, 2...…”

Section: Adaptation Of the User Interfacementioning

confidence: 99%

A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance

et al. 2020

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The assessment and prediction of cognitive performance is a key issue for any discipline concerned with human operators in the context of safety-critical behavior. Most of the research has focused on the measurement of mental workload but this construct remains difficult to operationalize despite decades of research on the topic. Recent advances in Neuroergonomics have expanded our understanding of neurocognitive processes across different operational domains. We provide a framework to disentangle those neural mechanisms that underpin the relationship between task demand, arousal, mental workload and human performance. This approach advocates targeting those specific mental states that precede a reduction of performance efficacy. A number of undesirable neurocognitive states (mind wandering, effort withdrawal, perseveration, inattentional phenomena) are identified and mapped within a twodimensional conceptual space encompassing task engagement and arousal. We argue that monitoring the prefrontal cortex and its deactivation can index a generic shift from a nominal operational state to an impaired one where performance is likely to degrade. Neurophysiological, physiological and behavioral markers that specifically account for these states are identified. We then propose a typology of neuroadaptive countermeasures to mitigate these undesirable mental states.

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“…Therefore, over-engagement can be seen as the fact of engaging all the resources for processing only one sub-task or one sensory canal (e.g., vision; a.k.a. attentional tunneling), while disengagement can be seen as the fact of reallocating the resources to another-usually internal-task [48][49][50]. Since both over-engagement and disengagement lead to performance degradation, it seems reasonable to estimate resource engagement and, more particularly, to detect resource depletion.…”

Section: Situation Awareness Resource Engagement and Associated Mental Statesmentioning

confidence: 99%

How Can Physiological Computing Benefit Human-Robot Interaction?

et al. 2020

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As systems grow more automatized, the human operator is all too often overlooked. Although human-robot interaction (HRI) can be quite demanding in terms of cognitive resources, the mental states (MS) of the operators are not yet taken into account by existing systems. As humans are no providential agents, this lack can lead to hazardous situations. The growing number of neurophysiology and machine learning tools now allows for efficient operators’ MS monitoring. Sending feedback on MS in a closed-loop solution is therefore at hand. Involving a consistent automated planning technique to handle such a process could be a significant asset. This perspective article was meant to provide the reader with a synthesis of the significant literature with a view to implementing systems that adapt to the operator’s MS to improve human-robot operations’ safety and performance. First of all, the need for this approach is detailed regarding remote operation, an example of HRI. Then, several MS identified as crucial for this type of HRI are defined, along with relevant electrophysiological markers. A focus is made on prime degraded MS linked to time-on-task and task demands, as well as collateral MS linked to system outputs (i.e., feedback and alarms). Lastly, the principle of symbiotic HRI is detailed and one solution is proposed to include the operator state vector into the system using a mixed-initiative decisional framework to drive such an interaction.

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Out of the Loop, in Your Bubble: Mind Wandering Is Independent From Automation Reliability, but Influences Task Engagement

Cited by 13 publications

References 74 publications

Cortical modulation of pupillary function: systematic review

Cortical modulation of pupillary function: systematic review

A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance

How Can Physiological Computing Benefit Human-Robot Interaction?

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