Classical methods for eliciting emotional responses, including the use of emotionally-charged pictures and films, have been used to study the influence of affective states on human decision-making and other cognitive processes. Advanced multisensory display systems, such as Virtual Reality (VR) headsets, offer a degree of immersion that may support more reliable elicitation of emotional experiences than less-immersive displays, and can provide a powerful yet relatively safe platform for inducing negative emotions such as fear and anger. However, it is not well understood how the presentation medium influences the degree to which emotions are elicited. In this study, emotionally-charged stimuli were introduced via two display configurations – on a desktop computer and on a VR system –and were evaluated based on performance in a decision task. Results show that the use of VR can be a more effective method for emotion elicitation when study decision-making under the influence of emotions.

This study involved an investigation of patterns of arousal in a driver’s sympathetic nervous system toward a system that can detect high cognitive workload, high emotional load, and acute stress (surprise or panic) experiences in drivers. Humans are exposed to cognitive and physical stressors in the driving environment that can be exacerbated when drivers are operating under the cognitive effects of incidental emotions or secondary task loads. Each type of stressor can negatively affect the control of complex operational systems, such as a vehicle, in different ways. The body’s sympathetic autonomous nervous system (the “fight or flight” system) triggers several physiological changes in the body, such as perspiratory and cardiac dynamics, in order to prepare the human to defend themselves or run away. Physiological sensor systems can therefore be used to collect data on these physiological changes (such as heart rate variability (HRV; Taelman, Vandeput, Spaopon, & Van Huffel, 2009) and skin conductance response (SCR; Boucsein, 2012; Shi, Ruiz, Taib, Choi, & Chen, 2007; Villarejo, Zapirain, & Zorrilla, 2012). Data processing techniques can be applied to identify patterns in physiological response across multiple variables and time windows that are characteristics of potentially problematic driver mental states. As a first step toward developing an online system for detecting potentially-problematic driver mental states, this study sought to identify patterns in HRV and SCR data associated with each of these states, and to investigate the predictive power of such an algorithm for explaining aspects of driving behavior. Eighty-eight people (with equal representation from each sex and each of two age groups) participated in a driving simulator study. The simulation scenarios consisted of a simulated rural highway with multiple predetermined segments where secondary task loads were applied. After a physiological baselining procedure and adequate practice with the driving and secondary tasks, participants completed scenarios under each of the six driving conditions: a “Normal” drive with a simple roadway and no secondary tasks, four “Loaded” drives that included driving-related loads (e.g., construction zones) and some additional secondary tasks designed to impose analytical cognitive, emotional, and motoric (e.g., texting task) loads. A final scenario included varied combinations of these loads and a unique surprising “Failure” event (triggered acceleration in the vehicle that mimicked unintended acceleration problems). HRV and SCR were measured throughout the experiment using Zephyr Bioharness3 and Shimmer3 sensor systems, respectively, and numerous third-party software packages were used to process the data and identify noteworthy patterns. This presentation will report the analytical results of three dependent variables: HRV, SCR-amplitude (i.e., the magnitude of characteristic increases in arousal during sympathetic response), and SCR-frequency (i.e., the frequency of skin conductance responses identified within a predetermined time window). HRV (measured over time windows of a few seconds, as well as of several minutes) and SCR-Frequency were significantly sensitive to changes in loading, and some patterns in dynamics among multiple measures can be used to differentiate the designed mental states, although further testing is underway to refine and validate these findings. In particular, SCR- amplitude may be an effective way to distinguish acute stress events, as this measure shows a significant and substantial change within 2.5 seconds after realization of the unintended acceleration event. In addition to discussing these results, the current and future efforts in the development of an online driver “stressor detector” and mitigation system will be introduced in the presentation as well.

Driving a vehicle requires performing a combination of cognitive, visual, and manual tasks, which depend on the sensory and cognitive resources allocated to the task. Physiological measures, such as heart rate measures, can be used to objectively detect when driving task demands approach and exceed a driver’s available resources, at which point there are also performance effects. As humans gain task experience and automatize aspects of it, fewer resources are demanded, which is reflected in physiological measures. However, physiological response also changes with age. This study sought to explore physiological response to different imposed workloads as a function of age and task experience. Results indicate that older drivers showed significantly increased low frequency and decreased high frequency spectral components of heart rate variability under secondary task loads. These findings raise new questions of how age should be considered when using physiological indicators to infer cognitive loading.

Assessments of emotional states are plagued with challenges in both the quantitative and qualitative realms. There is a lack of a definitive guide to making objective assessments based on physiological responses and qualitative methods lean heavily on subjective interpretation using self-assessments, both of which are hampered by individual differences. Self-assessment tools have largely used words or phrases to describe feelings however, iconic or visual tools are also gaining popularity. A significant number of such tools are based on the dimensional models of emotion and may not always suit the needs of studies on the role of emotions on cognitive abilities such as decision making. The tool described in this paper is developed based on two key requirements: 1) use in studying emotion and decision making, and 2) can easily be integrated into a context with a demanding primary task, such as driving, to be used concurrently with the task. A survey was conducted to identify emojis that are most commonly associated with basic emotions. Emojis were weighted and ranked for inclusion in the tool. The tool developed is called the Iconic Communication of Emotions (ICE).

In studying naturalistic human decision-making, it is important to understand how emotional states shape decision-making processes and outcomes. Emotion regulation techniques can improve the quality of decisions, but there are several challenges to evaluating these techniques in a controlled research context. Determining the effectiveness of emotion regulation techniques requires methodology that can: 1) reliably elicit desired emotions in decision-makers; 2) include decision tasks with response measures that are sensitive to emotional loading; and 3) support repeated exposures/trials with relatively-consistent emotional loading and response sensitivity. The current study investigates one common method, the Balloon Analog Risk Task (BART), for its consistency and reliability in measuring the risk-propensity of decision-makers, and specifically how the method’s effectiveness might change over the course of repeated exposures. With the PANASX subjective assessment serving for comparison, results suggest the BART assessment method, when applied over repeated exposures, is reduced in its sensitivity to emotional stimuli and exhibits decision task-related learning effects which influence the observed trends in response data in complex ways. This work is valuable for researchers in decision-making and to guide design for humans with consideration for their affective states.