Monitoring audience engagement using electrodermal activity during an inaugural lecture

Abstract

Is an audience captured by a speech or lecture? At what times especially? Do different groups in an audience experience the same speech in different ways? Insight into attentional engagement of individuals can be valuable but difficult to quantify using self-report. Physiological synchrony, the degree to which physiological measurements such as electrodermal activity (EDA) of multiple people uniformly change, has been shown to covary with attentional engagement in lab settings. In this study, we moved out of the lab and monitored EDA of 30 individuals attending a real-life inaugural lecture. These individuals were labeled as belonging to either the personal or professional group, based on their relation with the speaker. We expected these groups to differ in their attentional engagement. We computed physiological synchrony between the participants and investigated how well this metric distinguished between the professional and personal groups, how well it marked predefined engaging events in the lecture, and its relation with levels of engagement as self-reported afterwards. Where possible, we compared physiological synchrony results to results based on individuals' EDA. We found that physiological synchrony in EDA can distinguish between the two groups. Individuals' EDA can also distinguish between the groups, if the occurrence and timing is known of an event that is expected to elicit different levels of engagement for the two groups. We further found that both synchrony and individuals' EDA measures mark predefined engaging events with above-chance accuracies. Neither was reliably related to self-reported levels of attentional engagement, highlighting the complementary value of EDA. Our work shows the sensitivity of EDA measures in real-life conditions, where low-level sensory effects, movement and speech cannot be the explanatory factor. Ultimate applications may be in educational and entertainment domains, exploring potential differences in attentional engagement patterns between experts and novices, or different target groups in entertainment.

Fig 1. Impression of the experiment.

Left: the setting in which the inaugural lecture took place. Top right: A Movisens EdaMove 4 sensor being applied to a participant. Bottom right: One of the slides of the inaugural lecture in which pictures of the speaker’s brother were integrated. The identifiable individuals have given written informed consent (as outlined in PLOS consent form) to publish this figure.

Fig 2. ISC-EDA ordered form low to high compared to a chance distribution obtained after 1000 repetitions of circular shuffling.

Circles depict participants with a personal relationship to the speaker, diamonds depict participants with a professional relationship to the speaker. All ISC-EDA are significantly higher than chance.

Fig 3. ISC-EDA for participants with a personal or professional relationship with the speaker computing with participants belonging to the same (within-group) or different group (between-group).

Blue lines depict participants with higher within-group than between-group ISC-EDA, red dashed lines depict individuals with higher between-group than within-group ISC-EDA.

Fig 4. ISC-EDA and phasic EDA for participants with a personal or professional relationship with the speaker in response to the first time the speaker’s brother was shown on slide.

The top figures show the average response traces, the bottom figures show the individual response amplitudes.

Fig 5. ISC-EDA (A) and phasic EDA (B) averaged across participants over time.

Grey shading around the lines correspond to the standard deviation across participants. Vertical colored and numbered bars show the predefined events.