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Dynamic of mind wandering within automated environments

Jonas Gouraud1*, Arnaud Delorme2, 3 and Bruno Berberian1
  • 1 Office National d'Études et de Recherches Aérospatiales, Salon-de-Provence, Cognitive Engineering and Applied Neuroscience unit, France
  • 2 UMR5549 Centre de Recherche Cerveau et Cognition (CerCo), France
  • 3 Swartz Center for Computational Neurosciences, University of California, United States

Increasing safety in critical systems is paramount. To achieve this goal, engineers integrate always higher levels of automation within those systems – e.g. glass-cockpit aircrafts, power plants, autonomous cars. However, human-machine interaction failures have been observed when operators are moved to a supervising role. This phenomenon has been called the “out-of-the-loop” (OOTL) performance problem (Endsley & Kiris, 1995). If the OOTL performance problem represents a key challenge for system designers, it remains difficult to characterize and quantify after decades of research (Baxter, Rooksby, Wang, & Khajeh-Hosseini, 2012). While searching for elements explaining observed performance drops, researchers pointed vigilance decrement as a key component of OOTL situations (Amalberti, 1999). Among possible causes, mind wandering (MW) has yet received little attention. MW is the human mind propensity to generate thoughts unrelated to the her and now (Smallwood & Schooler, 2006). Despite evidences showing that operators are able to exert some control over their MW (Seli, Risko, & Smilek, 2016), they will experience it even when no convenient times are available, leading to poor performances (Casner & Schooler, 2015). The decoupling hypothesis has been proposed to explain the link between poor performance and MW and is supported by most of the MW literature, despite recent criticism (Head & Helton, 2016). As it diverts operators’ attention from their primary task, it could play an important role in OOTL situations. We designed two experiments to uncover the dynamic of MW within automated environments varying in reliability. We focused on measuring MW frequency and intensity, and quantifying interactions with operators’ vision of the system – trust and perceived workload. Environment For both experiments, we used the LIPS (Laboratoire d’Interactions Pilote-Système), an environment developed within our organization. An unmanned air vehicle (UAV) depicted as a plane seen from above stayed at the center of a 2D radar 22-inch screen and moved following waypoints arranged in a semi-straight line with clusters of obstacles along the way. Two modes were proposed. The first one was the “Manual” mode and required participants to manually avoid obstacles by choosing the moment and side for the avoidance maneuver. The second mode was “Automated” and asked participants to monitor the autopilot avoiding obstacles and recover any mistake of the system. In both conditions, attentional probes were displayed every 2-minutes on average. Participants had to indicate if their state of mind were “Focus” (on task), “Around task” (thoughts related to the instructions or performance), “Mind wandering” or “External noise”. We also used an eye-tracker to record MW influence over oculometric signal. Experiment 1: MW propensity in highly automated environments Our first experiment focused on comparing MW dynamic within manual and automated environments. We measured MW frequency and pupillometry. Each participant performed two 45-minutes sessions corresponding to the two modes (“Manual” and “Automated”), each preceded by a 10-minute training. When facing automated mode, participants encountered one mistake during training and one during session. Two main results have been shown: (1) MW increased after some time has elapsed in the automated mode (Figure 1), (2) there was a difference in pupil diameter between MW and “Focus” episodes (Figure 2). The first result was the increase of MW frequency only in the automated condition after some time. Since both conditions lasted the same amount of time, time-related phenomena (drowsiness, habituation, tiredness) alone cannot explain this result. A first hypothesis is that without errors during the first 30-minutes of the task, participants may become complacent – i.e. feel uncritically reliant on the system. On the other hand, recent studies demonstrated that agency – one’s feeling of control regarding observed effects – was inversely correlated with automation level, while also leading to a disengagement from the task. Both phenomena could also be complementary in leading participants to decrease resources allocated to the supervision task. The second result was a decrease of pupil diameter during MW compared to focus moments. This is in line with the perceptual decoupling associated with MW, and is supported by recent results (Konishi, Brown, Battaglini, & Smallwood, 2017). Experiment 2: autopilot reliability and MW We designed a second experiment to investigate the perceptual decoupling (Schooler et al., 2011) created by MW with varying reliability. We modified the questionnaire to measure perceived mental demand and trust regarding the system’s ability to avoid obstacles. We proposed two conditions: “Risky” proposed an autopilot with a 40% error rate, while the system during the “Safe” condition had only 3% mistakes. Three main results have been shown: (1) MW propensity was not linked with trust towards system reliability, (2) MW induced a decoupling from the task in automated environment and (3) this decoupling also occurred with “Around” attentional state Firstly, MW propensity was not correlated with trust, while trust were not influenced by attentional states. This result does not support complacency as the reason for the MW increase observed in the first experiment. It could be that MW depend on the very nature of the interaction – either automated or manual – but not on the reliability, thus strengthening the loss of agency hypothesis (Obhi & Hall, 2011). The second result is the decrease of mental demand ratings (Figure 3) and pupil diameter from “Focus” to MW, while blink frequency increased between the same states (Table 1). These evidences support the decoupling hypothesis. Finally, the third result is the highlight of the perceptual decoupling to the “Around” attentional state. Contrary to the criticism of Head and Helton (2016), thinking about their own performance might also induce a gap between operators and their environment. Conclusion MW seems to increase dramatically when operating an automated system, compared to handle the same system manually. Reliability did not influence MW frequency, leaving the possibility of an impact of agency. More concerning, converging evidences supported the perceptual decoupling hypothesis induced by MW, which also extended to thoughts about performance and instructions. These results present MW as a threat for operators supervising critical automated systems Nevertheless, research is still needed to assess the exact factors influencing MW frequency and its impact over safety within safety-critical environments.

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Acknowledgements

We thank the Direction Générale de l’Armement (DGA) for their financial support of the first author. This work has been supported by a grant from ANR (Young researcher program—ANR-15-CE26-0010-01).

References

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Keywords: Out of the loop, mind wandering, Automation, performance, vigilance, psychophysiological measures, complacency, agency, neuroergonomics

Conference: 2nd International Neuroergonomics Conference, Philadelphia, PA, United States, 27 Jun - 29 Jun, 2018.

Presentation Type: Oral Presentation

Topic: Neuroergonomics

Citation: Gouraud J, Delorme A and Berberian B (2019). Dynamic of mind wandering within automated environments. Conference Abstract: 2nd International Neuroergonomics Conference. doi: 10.3389/conf.fnhum.2018.227.00042

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Received: 08 Mar 2018; Published Online: 27 Sep 2019.

* Correspondence: Mr. Jonas Gouraud, Office National d'Études et de Recherches Aérospatiales, Salon-de-Provence, Cognitive Engineering and Applied Neuroscience unit, Salon-de-Provence, France, contact@jonasgouraud.com