Javascript must be enabled for the correct page display

Strengthening the link between science and arts: How dance-like movements alter the neural correlates of human connectedness?

Regus, Livia (2020) Strengthening the link between science and arts: How dance-like movements alter the neural correlates of human connectedness? Bachelor's Thesis, Artificial Intelligence.

[img]
Preview
Text
AI_BA_2020_LIVIAREGUS.pdf

Download (3MB) | Preview
[img] Text
Toestemming.pdf
Restricted to Registered users only

Download (97kB)

Abstract

When involved in social processes, people interact with each other in subtler ways they are aware of. The heart rate, breathing rhythm and brain activity can synchronize during joint actions. These are thought to constitute the physiological markers of human connectedness. This study aimed to investigate how inter-brain synchrony within dyads is affected by joint movement in the social as compared to nonsocial conditions. The participants danced in the absence or presence of touch, gazing or synchronous movement with their data being recorded via EEG hyperscanning. The results suggest a trend towards there being more synchrony between brains when participants are facing compared to when they are not facing each other in the theta frequency (7-9 Hz) in the frontopolar (Fp2) and centroparietal (FC2) regions. The same is observed in the beta frequency (14-24 Hz) in the frontopolar cortex (Fp1). Additionally, more inter-brain synchrony was observed during synchronous compared to asynchronous movement in the theta and alpha (9-14 Hz) frequencies. Moving together as if the participants had one body pointed to an increase in inter-brain synchrony in the prefrontal (Fp2) and occipital (PO4) areas in theta frequency; and in the frontal (F7) cortex in beta frequency. Most of the results did not survive the multiple comparison problem correction. Subsequent research with bigger sample sizes is necessary to confirm touch, gaze and synchronous movement make the humans feel more connected.

Item Type: Thesis (Bachelor's Thesis)
Supervisor name: Vugt, M.K. van
Degree programme: Artificial Intelligence
Thesis type: Bachelor's Thesis
Language: English
Date Deposited: 27 Jul 2020 13:53
Last Modified: 27 Jul 2020 13:53
URI: https://fse.studenttheses.ub.rug.nl/id/eprint/22887

Actions (login required)

View Item View Item