Using Galvanic Skin Response (GSR) to Measure Trust and Cognitive Load in the Text-Chat Environment

Khawaji, Ahmad; Zhou, Jianlong; Chen, Fang; Marcus, Nadine

doi:10.1145/2702613.2732766

Cited by 43 publications

(24 citation statements)

References 16 publications

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“…These metrics have been used as indicators of stress and cognitive load by an ample body of previous research [37][38][39]. GSR (also known as electrodermal activity) can be interpreted as an indicator of physiological arousal and has long been a widely used metric in studies seeking to characterize mental workload [37][38][39][40][41][42]. EEG on the other hand has previously been used to measure cognitive load in the context of human-computer interactions [11,43,44].…”

Section: Biometric Datamentioning

confidence: 99%

Impact of delayed response on wearable cognitive assistance

et al. 2021

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Wearable cognitive assistants (WCA) are anticipated to become a widely-used application class, in conjunction with emerging network infrastructures like 5G that incorporate edge computing capabilities. While prototypical studies of such applications exist today, the relationship between infrastructure service provisioning and its implication for WCA usability is largely unexplored despite the relevance that these applications have for future networks. This paper presents an experimental study assessing how WCA users react to varying end-to-end delays induced by the application pipeline or infrastructure. Participants interacted directly with an instrumented task-guidance WCA as delays were introduced into the system in a controllable fashion. System and task state were tracked in real time, and biometric data from wearable sensors on the participants were recorded. Our results show that periods of extended system delay cause users to correspondingly (and substantially) slow down in their guided task execution, an effect that persists for a time after the system returns to a more responsive state. Furthermore, the slow-down in task execution is correlated with a personality trait, neuroticism, associated with intolerance for time delays. We show that our results implicate impaired cognitive planning, as contrasted with resource depletion or emotional arousal, as the reason for slowed user task executions under system delay. The findings have several implications for the design and operation of WCA applications as well as computational and communication infrastructure, and additionally for the development of performance analysis tools for WCA.

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Section: Biometric Datamentioning

confidence: 99%

Impact of delayed response on wearable cognitive assistance

et al. 2021

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“…For instance, thirty-seven of the fifty-one included studies employed single psychophysiological signals for assessing trust, as explained in Figure 6. For example, fourteen studies (SM18, SM19, SM9, SM10, SM12, SM14, SM24, SM22, SM51, SM49, SM47, SM42, SM39, and SM1) used only EEG signals, twelve studies (SM28, SM29, SM30, SM21, SM23, SM34, SM35, SM36, SM37, SM40, SM41, and SM45) used only fMRI signals, one study (SM7) used only EDA signals, two studies (SM32 and SM17) used only eye tracker signals, one study (SM13) used only audio signals, one study (SM46) used only ECG signals, and one study (SM1) used only fNIRS signals.…”

Section: Rq1: Most Frequently Used and Combined Psychophysiological Smentioning

confidence: 99%

Measuring Trust with Psychophysiological Signals: A Systematic Mapping Study of Approaches Used

Ajenaghughrure

Sousa

Lamas

2020

MTI

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Trust plays an essential role in all human relationships. However, measuring trust remains a challenge for researchers exploring psychophysiological signals. Therefore, this article aims to systematically map the approaches used in studies assessing trust with psychophysiological signals. In particular, we examine the numbers and frequency of combined psychophysiological signals, the primary outcomes of previous studies, and the types and most commonly used data analysis techniques for analyzing psychophysiological data to infer a trust state. For this purpose, we employ a systematic mapping review method, through which we analyze 51 carefully selected articles (studies focused on trust using psychophysiology). Two significant findings are as follows: (1) Psychophysiological signals from EEG(electroencephalogram) and ECG(electrocardiogram) for monitoring peripheral and central nervous systems are the most frequently used to measure trust, while audio and EOG(electro-oculography) psychophysiological signals are the least commonly used. Moreover, the maximum number of psychophysiological signals ever combined so far is three (2). Most of which are peripheral nervous system monitoring psychophysiological signals that are low in spatial resolution. (3) Regarding outcomes: there is only one tool proposed for assessing trust in an interpersonal context, excluding trust in a technology context. Moreover, there are no stable and accurate ensemble models that have been developed to assess trust; all prior attempts led to unstable but fairly accurate models or did not satisfy the conditions for combining several algorithms (ensemble). In conclusion, the extent to which trust can be assessed using psychophysiological measures during user interactions (real-time) remains unknown, as there several issues, such as the lack of a stable and accurate ensemble trust classifier model, among others, that require urgent research attention. Although this topic is relatively new, much work has been done. However, more remains to be done to provide clarity on this topic.

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“…Researchers have examined GSR in correlation with human trust level. Khawaji et al found that average GSR values, and average GSR peak values, are significantly affected by both trust and cognitive load in the text-chat environment [19]. However, the use of GSR for estimating trust has not been explored and was noted as an area worth studying [39].…”

Section: Background and Related Workmentioning

confidence: 99%

A Classification Model for Sensing Human Trust in Machines Using EEG and GSR

Akash

Jain

et al. 2018

ACM Trans. Interact. Intell. Syst.

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Today, intelligent machines interact and collaborate with humans in a way that demands a greater level of trust between human and machine. A first step towards building intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real-time. In this paper, two approaches for developing classifier-based empirical trust sensor models are presented that specifically use electroencephalography (EEG) and galvanic skin response (GSR) measurements. Human subject data collected from 45 participants is used for feature extraction, feature selection, classifier training, and model validation. The first approach considers a general set of psychophysiological features across all participants as the input variables and trains a classifier-based model for each participant, resulting in a trust sensor model based on the general feature set (i.e., a "general trust sensor model"). The second approach considers a customized feature set for each individual and trains a classifier-based model using that feature set, resulting in improved mean accuracy but at the expense of an increase in training time. This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor. Implications of the work, in the context of trust management algorithm design for intelligent machines, are also discussed.

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Using Galvanic Skin Response (GSR) to Measure Trust and Cognitive Load in the Text-Chat Environment

Cited by 43 publications

References 16 publications

Impact of delayed response on wearable cognitive assistance

Impact of delayed response on wearable cognitive assistance

Measuring Trust with Psychophysiological Signals: A Systematic Mapping Study of Approaches Used

A Classification Model for Sensing Human Trust in Machines Using EEG and GSR

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