Attention reorienting is crucial to human survival in a constantly changing environment. In order to react and respond to novel and potentially threatening stimuli in the environment, we have to first reorient our attention to the stimuli themselves. While numerous studies in the past have attempted to uncover the principles of how our brain processes new stimuli and reorients our attention, they typically employed standardized paradigms such as an oddball or a cueing paradigm that do not represent how humans actually reorient attention in the real world. This dissertation seeks to directly address this issue by investigating the brain dynamics underlying attention reorienting in an immersive and naturalistic environment. We employ a virtual reality (VR)-based target detection paradigm that closely mimics how human would reorient their attention in real-world situations.
During the experiments, subjects are instructed to reorient their attention between a primary visual task (driving simulation) and a secondary visual task (target detection) while their electroencephalography (EEG), eyetracking and behavioral inputs are being recorded. Each set of experiments and subsequent data analysis methods are tailored to answer different questions based on the three specific aims of this dissertation (1) how do eye movements affect attention reorienting signals? (2) how do we integrate the information obtained from the neural and ocular signals to decode reorienting? and (3) what is the relationship between attention reorienting and the arousal system?
We found that while eye movements result in greater temporal variation of neural signals associated with attention reorienting, namely the P300 signal, time-locking the event-related potentials (ERPs) to image onset or saccade intersection still results in the best overall performance in classifying target vs. distractor stimuli. Similarly to eye movements, we also found that allowing for head movements results in greater temporal variations of both the neural (P300) and pupil-linked attention reorienting signals. However, by combining the EEG, pupil dilation and dwell time signals, a multi-modal hybrid classifier we developed using the hierarchical discriminant component analysis (HDCA) was able to capture and integrate the neural and ocular attention reorienting signals with similar performance both in the condition with and without head movements. In addition, the hybrid classifier outperformed single-modality classifiers (EEG-only, pupil dilation-only and dwell time-only) in all comparisons.
Lastly, we reported a close-knit relationship between pupil-linked arousal and network-level EEG dynamics underlying attention reorienting. We observed improvements in overall performance as pupil-linked arousal increased. We also observed increased oscillatory activity across multiple frequency bands in regions associated with the dorsal and ventral attention networks as pupil-linked arousal increased. Additionally, we found a decrease in functional connectivity across nodes in the salience network and the ventral attention network as pupil-linked arousal increased. The findings of this dissertation have the potential to serve as the basis for the development of the next generation of non-invasive brain-computer interfaces (BCIs) that can function in real-world environments. Furthermore, these findings may also serve to help physicians and neuroscientists better understand the neurophysiology underlying attention-related disorders including attention-deficit disorder (ADD) or attention-deficit/hyperactivity disorder (ADHD).
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/82b8-nv27 |
| Date | January 2023 |
| Creators | Lapborisuth, Pawan |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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