Automatic detection of mind wandering in a simulated driving task with behavioral measures

Mind wandering (MW) is extremely common during driving and is often accompanied by performance losses. This study investigated the use of driving behavior measurements to automatically detect mind wandering state in the driving task. In the experiment, participants (N = 40) performed a car-following task in a driving simulator and reported, upon hearing a tone, whether they were experiencing mind wandering or not. Supervised machine learning techniques were applied to classify MW-absent versus MW-present state, using both driver-independent and driver-dependent modeling methods. In the driver-independent modeling, we separately built models for participants with high or low MW and participants with medium MW. The optimal models can not offer a significant improvement than other models. So building effective driver-independent models with the leave-one-participant-out cross-validation method is challenging. In the driver-dependent modeling, we built models for each participant with medium MW. The best models of some participants were effective. The results indicate the development of mind wandering detecting system should take into account both inter-individual and intra-individual difference. This study provides a step toward minimizing the negative impacts of mindless driving and should benefit other fields of psychological research.

[1]  T. Kumada,et al.  Relationship between workload and mind-wandering in simulated driving , 2017, PloS one.

[2]  Tanya R. Jonker,et al.  Can research participants comment authoritatively on the validity of their self-reports of mind wandering and task engagement? , 2015, Journal of experimental psychology. Human perception and performance.

[3]  Joanna Drummond,et al.  In the Zone: Towards Detecting Student Zoning Out Using Supervised Machine Learning , 2010, Intelligent Tutoring Systems.

[4]  Sidney K. D'Mello,et al.  Automated Physiological-Based Detection of Mind Wandering during Learning , 2014, Intelligent Tutoring Systems.

[5]  Lai-Wan Chan,et al.  ICA by PCA Approach: Relating Higher-Order Statistics to Second-Order Moments , 2006, ICA.

[6]  Arnaud Delorme,et al.  Characterization of mind wandering using fNIRS , 2015, Front. Syst. Neurosci..

[7]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[8]  T. Hendler,et al.  Towards a Neuroscience of Mind-Wandering , 2011, Front. Hum. Neurosci..

[9]  Sidney K. D'Mello,et al.  Toward Fully Automated Person-Independent Detection of Mind Wandering , 2014, UMAP.

[10]  Sidney K. D'Mello,et al.  Automatic gaze-based user-independent detection of mind wandering during computerized reading , 2016, User Modeling and User-Adapted Interaction.

[11]  Jason S. McCarley,et al.  Mind Wandering Behind the Wheel , 2011, Hum. Factors.

[12]  Claire Braboszcz,et al.  Lost in thoughts: Neural markers of low alertness during mind wandering , 2011, NeuroImage.

[13]  Jonathan Smallwood,et al.  Catching the mind in flight: Using behavioral indices to detect mindless reading in real time , 2011, Psychonomic bulletin & review.

[14]  Jacob Cohen A Coefficient of Agreement for Nominal Scales , 1960 .

[15]  Simone Melzi,et al.  Ranking to Learn: - Feature Ranking and Selection via Eigenvector Centrality , 2016, NFMCP@PKDD/ECML.

[16]  A. Fort,et al.  Inattention behind the wheel: how factual internal thoughts impact attentional control while driving , 2014 .

[17]  Marco Cristani,et al.  Infinite Feature Selection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[18]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[19]  E. Klinger Structure and functions of fantasy , 1971 .

[20]  J. Lupiáñez,et al.  Alerting, Orienting and Executive Control: The effects of sleep deprivation , 2009 .

[21]  A. Fort,et al.  Mind wandering and driving: responsibility case-control study , 2012, BMJ : British Medical Journal.

[22]  Jingtao Wang,et al.  AttentiveLearner: Improving Mobile MOOC Learning via Implicit Heart Rate Tracking , 2015, AIED.

[23]  M. Kane,et al.  Does mind wandering reflect executive function or executive failure? Comment on Smallwood and Schooler (2006) and Watkins (2008). , 2010, Psychological bulletin.

[24]  K. Christoff,et al.  Experience sampling during fMRI reveals default network and executive system contributions to mind wandering , 2009, Proceedings of the National Academy of Sciences.

[25]  M. McHugh Interrater reliability: the kappa statistic , 2012, Biochemia medica.

[26]  Sidney K. D'Mello,et al.  Attending to Attention: Detecting and Combating Mind Wandering during Computerized Reading , 2016, CHI Extended Abstracts.

[27]  S. D’Mello,et al.  An automated behavioral measure of mind wandering during computerized reading , 2017, Behavior Research Methods.

[28]  Sophie Forster,et al.  Distraction and Mind-Wandering Under Load , 2013, Front. Psychol..

[29]  Thomas M. Spalek,et al.  Driving With the Wandering Mind , 2014, Hum. Factors.

[30]  Lubomir M. Hadjiiski,et al.  Effect of finite sample size on feature selection and classification: a simulation study. , 2010, Medical physics.

[31]  Sidney K. D'Mello,et al.  Automatic Gaze-Based Detection of Mind Wandering with Metacognitive Awareness , 2015, UMAP.