Modulating distraction by adapting the perceptual load: implementation of a biocybernetic loop to support performance and prevent distraction

Description

Research from cognitive science and computerized displays of simple stimuli has shown how perceptual load is a critical factor for modulating distraction. Perceptual load is the amount of information involved in processing task stimuli. According to Lavie (1995), our attentional resources are limited and mainly directed towards task-relevant goals, but we might be more prone to distractors if we have cognitive spare resources. Previous research showed that human faces have bigger distracting power than non-face objects. This project aims to assess the distracting potential distracting effect of human avatars in a social VR scenario. We aim to transfer of traditional paradigms that assess attention and distraction to immersive VR. Lastly, we adapt the target-distractor recognizability to evaluate if a physiologically-adaptive system that optimizes for perceptual load can support task performance. The research consists of three main stages: (1) validation of the psychophysiological inference underpinning the physiological loop (2) implementation of a working VR prototype, and (3) an evaluation of the adaptive environment.

You will

• Perform a literature review
• Modify an existing VR environment
• Implement an online biocybernetic loop using EEG and/or EDA
• Collect and analyze electroencephalography (EEG), electrodermal activity (EDA), and electrocardiography (ECG) data
• Summarize your findings in a thesis and present them to an audience
• (Optional) co-writing a research paper

You need

• Strong communication skills in English
• Good knowledge of Unity and/or C#
• Good knowledge of Python libraries for scientific computing (e.g. Scipy, Neurokit, MNE).

References

• Perceptual Load of faces
• Perceptual load and task engagement
• Evaluating perceptual load in VR