Emotional Investment in Naturalistic Data Collection

We present results from two experiments intended to allow naturalistic data collection of the physiological effects of cognitive load. Considering the example of command and control environments, we identify shortcomings of previous studies which use either laboratory-based scenarios, lacking realism, or real-world scenarios, lacking repeatability. We identify the hybrid approach of remote-control which allows experimental subjects to remain in a laboratory setting, performing a real-world task in a completely controlled environment. We show that emotional investment is vital for evoking natural responses and that physiological indications of cognitive load manifest themselves more readily in our hybrid experimental setup. Finally, we present a set of experimental design recommendations for naturalistic data collection.

[1]  Peter Robinson,et al.  Computation of emotions in man and machines , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[2]  Christine L. Lisetti Affective Intelligent Car Interfaces with Emotion Recognition , 2005 .

[3]  Christine L. Lisetti,et al.  Affective User Modeling for Adaptive Intelligent User Interfaces , 2007, HCI.

[4]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .

[5]  M. A. Recarte,et al.  Mental workload while driving: effects on visual search, discrimination, and decision making. , 2003, Journal of experimental psychology. Applied.

[6]  R. Coppola,et al.  Specific versus Nonspecific Brain Activity in a Parametric N-Back Task , 2000, NeuroImage.

[7]  R W Backs,et al.  Metabolic and cardiorespiratory measures of mental effort: the effects of level of difficulty in a working memory task. , 1994, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[8]  J Aasman,et al.  Operator Effort and the Measurement of Heart-Rate Variability , 1987, Human factors.

[9]  Ying Wang,et al.  The Impact of Systematic Variation of Cognitive Demand on Drivers' Visual Attention across Multiple Age Groups , 2010 .

[10]  J. Veltman,et al.  Physiological indices of workload in a simulated flight task , 1996, Biological Psychology.

[11]  J Healey,et al.  Quantifying driver stress: developing a system for collecting and processing bio-metric signals in natural situations. , 1999, Biomedical sciences instrumentation.

[12]  Jennifer Healey,et al.  Detecting stress during real-world driving tasks using physiological sensors , 2005, IEEE Transactions on Intelligent Transportation Systems.