Measuring task engagement as an input to physiological computing

Task engagement is a psychological dimension that describes effortful commitment to task goals. This is a multidimensional concept that combines cognition, motivation and emotion. This dimension may be important for the development of physiological computing systems that use real-time psychophysiology to monitor user state, particularly those systems seeking to optimise performance (e.g. adaptive automation, games, automatic tutoring). Two laboratory-based experiments were conducted to investigate measures of task engagement, based on EEG, pupilometry and blood pressure. The first study exposed participants to increased levels of memory load whereas the second used performance feedback to either engage (success feedback) or disengage (failure feedback) participants. EEG variables, such as frontal theta and asymmetry, were sensitive to disengagement due to cognitive load (experiment 1) whilst changes in systolic blood pressure were sensitive to feedback of task success. Implications for the development of physiological computing systems are discussed.

[1]  Stephen H. Fairclough,et al.  Fundamentals of physiological computing , 2009, Interact. Comput..

[2]  Rebecca A. Grier,et al.  Fundamental dimensions of subjective state in performance settings: task engagement, distress, and worry. , 2002, Emotion.

[3]  Winslow Burleson,et al.  Affective agents: Sustaining motivation to learn through failure and state of "stuck" , 2004 .

[4]  Cynthia Breazeal,et al.  Stoop to Conquer: Posture and Affect Interact to Influence Computer Users' Persistence , 2007, ACII.

[5]  A. Pope,et al.  Biocybernetic system evaluates indices of operator engagement in automated task , 1995, Biological Psychology.

[6]  Alex Pentland,et al.  Human Computing and Machine Understanding of Human Behavior: A Survey , 2007, Artifical Intelligence for Human Computing.

[7]  Mark W. Scerbo,et al.  A brain-based system for adaptive automation , 2003 .

[8]  Michael E. Smith,et al.  Monitoring Working Memory Load during Computer-Based Tasks with EEG Pattern Recognition Methods , 1998, Hum. Factors.

[9]  Jennifer Healey,et al.  Toward Machine Emotional Intelligence: Analysis of Affective Physiological State , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  R. Davidson,et al.  Anterior electrophysiological asymmetries, emotion, and depression: conceptual and methodological conundrums. , 1998, Psychophysiology.

[11]  John Dixon,et al.  The Design of Future Things , 2010 .

[12]  F. Freeman,et al.  Evaluation of a Psychophysiologically Controlled Adaptive Automation System, Using Performance on a Tracking Task , 2000, Applied psychophysiology and biofeedback.

[13]  J. Beatty.  Task-evoked pupillary responses, processing load, and the structure of processing resources. , 1982, Psychological bulletin.

[14]  Stephen H. Fairclough,et al.  A research agenda for physiological computing , 2004, Interact. Comput..

[15]  Michael Richter,et al.  Incentive effects on cardiovascular reactivity in active coping with unclear task difficulty. , 2006, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[16]  Glenn F. Wilson,et al.  Performance Enhancement with Real-Time Physiologically Controlled Adaptive Aiding , 2000 .

[17]  Paolo Bonato,et al.  Neuroadaptive technologies: Applying neuroergonomics to the design of advanced interfaces , 2003 .

[18]  Jeffrey M. Bradshaw,et al.  Ten Challenges for Making Automation a "Team Player" in Joint Human-Agent Activity , 2004, IEEE Intell. Syst..

[19]  John J. B. Allen,et al.  Frontal EEG asymmetry and the behavioral activation and inhibition systems. , 2003, Psychophysiology.

[20]  R. Davidson,et al.  Anterior brain electrical asymmetries in response to reward and punishment. , 1992, Electroencephalography and clinical neurophysiology.

[21]  J. C. Dill,et al.  Blood pressure responses and incentive appraisals as a function of perceived ability and objective task demand. , 1993, Psychophysiology.

[22]  Coniferous softwood.  GENERAL TERMS , 2003 .

[23]  John J. B. Allen,et al.  Behavioral activation sensitivity and resting frontal EEG asymmetry: covariation of putative indicators related to risk for mood disorders. , 1997, Journal of abnormal psychology.

[24]  Michael E. Smith,et al.  Neurophysiological measures of cognitive workload during human-computer interaction , 2003 .

[25]  L. Rothkrantz,et al.  Toward an affect-sensitive multimodal human-computer interaction , 2003, Proc. IEEE.

[26]  J. Beatty,et al.  Phasic not tonic pupillary responses vary with auditory vigilance performance. , 1982, Psychophysiology.

[27]  Alan J. Dix,et al.  Affective Videogames and Modes of Affective Gaming: Assist Me, Challenge Me, Emote Me (ACE) , 2005, DiGRA Conference.

[28]  J. Cacioppo,et al.  Inferring psychological significance from physiological signals. , 1990, The American psychologist.

[29]  John J. B. Allen,et al.  Frontal EEG asymmetry as a moderator and mediator of emotion , 2004, Biological Psychology.

[30]  R. Wright,et al.  Cardiovascular effects of experimentally induced efficacy (ability) appraisals at low and high levels of avoidant task demand. , 1995, Psychophysiology.

[31]  R. Davidson.  What does the prefrontal cortex “do” in affect: perspectives on frontal EEG asymmetry research , 2004, Biological Psychology.

[32]  Ashish Kapoor,et al.  Automatic prediction of frustration , 2007, Int. J. Hum. Comput. Stud..

[33]  Anita Miller,et al.  Task-dependent changes in frontal brain asymmetry: effects of incentive cues, outcome expectancies, and motor responses. , 2001, Psychophysiology.

[34]  Nilanjan Sarkar,et al.  Anxiety detecting robotic system – towards implicit human-robot collaboration , 2004, Robotica.

[35]  Cynthia Breazeal,et al.  Affective Learning — A Manifesto , 2004 .

[36]  Lawrence J. Prinzel,et al.  Research on Hazardous States of Awareness and Physiological Factors in Aerospace Operations , 2002 .

[37]  Jeffrey B. Henriques,et al.  Frontal Brain Asymmetry and Reward Responsiveness , 2005, Psychological science.

[38]  S. Fairclough,et al.  Prediction of subjective states from psychophysiology: A multivariate approach , 2006, Biological Psychology.

[39]  Regan L. Mandryk,et al.  A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies , 2007, Int. J. Hum. Comput. Stud..

[40]  Jonathan R Wolpaw,et al.  Brain–computer interface systems: progress and prospects , 2007, Expert review of medical devices.

[41]  Michael Richter,et al.  Incentive value, unclear task difficulty, and cardiovascular reactivity in active coping. , 2007, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[42]  R. Davidson,et al.  Prefrontal Brain Asymmetry: A Biological Substrate of the Behavioral Approach and Inhibition Systems , 1997 .

[43]  Glenn F. Wilson,et al.  Performance Enhancement in an Uninhabited Air Vehicle Task Using Psychophysiologically Determined Adaptive Aiding , 2007, Hum. Factors.

[44]  Glenn F. Wilson,et al.  Operator Functional State Classification Using Multiple Psychophysiological Features in an Air Traffic Control Task , 2003, Hum. Factors.