Biology subjects

Touryan, J.

Publications and source records attributed to Touryan, J..

5 recordsLinked to original sources

bioRxiv2021

Identification of Target Objects from Gaze Behavior during a Virtual Navigation Task

Eye tracking has been an essential tool within the vision science community for many years. However, the majority of studies involving eye-tracking technology employ a relatively passive approach through the use of static imagery, prescribed motion, or video stimuli. This is in contrast to our everyday interaction with the natural world where we navigate our environment while actively seeking and using task-relevant visual information. For this reason, vision researchers are beginning to use virtual environment platforms, which offer interactive, realistic visual environments while maintaining a substantial level of experimental control. Here, we recorded eye movement behavior while participants freely navigated through a complex virtual environment. Within this environment, participants completed a visual search task where they were asked to find and count occurrence of specific targets among numerous distractor items. We assigned each participant into one of four target groups: Humvees, motorcycles, aircraft, or furniture. Our results show a significant relationship between gaze behavior and target objects across subject groups. Specifically, we see an increased number of fixations and increase dwell time on target relative to distractor objects. In addition, we included a divided attention task to investigate how search patterns changed with the addition of a secondary task. With increased cognitive load, subjects slowed their speed, decreased gaze on objects, and increased the number of objects scanned in the environment. Overall, our results confirm previous findings from more controlled laboratory settings and demonstrate that complex virtual environments can be used for active visual search experimentation, maintaining a high level of precision in the quantification of gaze information and visual attention. This study contributes to our understanding of how individuals search for information in a naturalistic virtual environment. Likewise, our paradigm provides an intriguing look into the heterogeneity of individual behaviors when completing an un-timed visual search task while actively navigating.

neuroscience↗

bioRxiv2021

Neural responses to natural visual motion are spatially selective across the visual field, with selectivity differing across brain areas and task

It is well established that neural responses to visual stimuli are enhanced at select locations in the visual field. While spatial selectivity and the effects of spatial attention are well-understood for discrete tasks (e.g., visual cueing paradigms), little is known about neural response during a naturalistic visual experience that involves complex dynamic visual stimuli, for instance, driving. In this study, we assess the strength of neural responses across the visual space during a kart race video game. Specifically, we measure the correlation strength of scalp evoked potentials with optical flow magnitude at individual locations on the screen. We find the strongest neural responses for task-relevant locations in visual space, selectively extending to areas beyond the focus of overt attention: while the drivers gaze is directed upon the heading direction at the center of the screen, we observe robust neural evoked responses also to peripheral areas such as the road and surrounding buildings. Importantly, this spatial selectivity of neural responses differs across scalp locations. Moreover, during active gameplay, the strength of the spatially-selective neural responses are enhanced compared to passive viewing. Spatially selective neural gains have previously been interpreted as an attentional gain mechanism. In this view, the present data suggest that different brain areas focus attention on different task-relevant portions of the visual field, reaching beyond the focus of overt attention.

neuroscience↗

bioRxiv2021

During natural viewing, neural processing of visual targets continues throughout saccades

Relatively little is known about visual processing during free-viewing visual search in realistic dynamic environments. Free-viewing is characterized by frequent saccades. During saccades, visual processing is thought to be inhibited, yet we know that the pre-saccadic visual content can modulate post-saccadic processing. To better understand these processes in a realistic setting, we study here saccades and neural responses elicited by the appearance of visual targets in a realistic virtual environment. While subjects were being driven through a 3D virtual town they were asked to discriminate between targets that appear on the road. We found that the presence of a target enhances early occipital as well as late frontocentral saccade-related responses. The earlier potential, shortly after 125ms post-saccade onset, was enhanced for targets that appeared in peripheral vision as compared to central vision, suggesting that fast peripheral processing initiated before saccade onset. The later potential, at 195ms post-saccade onset, was strongly modulated by the visibility of the target with a spatial distribution reminiscent of the classic P300 response. Together these results suggest that, during natural viewing, neural processing of the pre-saccadic visual stimulus continues throughout the saccade, apparently unencumbered by saccadic inhibition.

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bioRxiv2019

Decoding P300 Variability using Convolutional Neural Networks

Deep convolutional neural networks (CNN) have previously been shown to be useful tools for signal decoding and analysis in a variety of complex domains, such as image processing and speech recognition. By learning from large amounts of data, the representations encoded by these deep networks are often invariant to moderate changes in the underlying feature spaces. Recently, we proposed a CNN architecture that could be applied to electroencephalogram (EEG) decoding and analysis. In this article, we train our CNN model using data from prior experiments in order to later decode the P300 evoked response from an unseen, hold-out experiment. We analyze the CNN output as a function of the underlying variability in the P300 response and demonstrate that the CNN output is sensitive to the experiment-induced changes in the neural response. We then assess the utility of our approach as a means of improving the overall signal-to-noise ratio in the EEG record. Finally, we show an example of how CNN-based decoding can be applied to the analysis of complex data.

neuroscience↗

bioRxiv2018

Automated EEG mega-analysis I: Spectral and amplitude characteristics across studies

Significant achievements have been made in the fMRI field by pooling statistical results from multiple studies (meta-analysis). More recently, fMRI standardization efforts have focused on enabling the joint analysis of raw fMRI data across studies (mega-analysis), with the hope of achieving more detailed insights. However, it has not been clear if such analyses in the EEG field are possible or equally fruitful. Here we present the results of a large-scale EEG mega-analysis using 18 studies from six sites representing several different experimental paradigms. We demonstrate that when meta-data are consistent across studies, both channel-level and source-level EEG mega-analysis are possible and can provide insights unavailable in single studies. The analysis uses a fully-automated processing pipeline to reduce line noise, interpolate noisy channels, perform robust referencing, remove eye-activity, and further identify outlier signals. We define several robust measures based on channel amplitude and dispersion to assess the comparability of data across studies and observe the effect of various processing steps on these measures. Using ICA-based dipolar sources, we also observe consistent differences in overall frequency baseline amplitudes across brain areas. For example, we observe higher alpha in posterior vs anterior regions and higher beta in temporal regions. We also detect consistent differences in the slope of the aperiodic portion of the EEG spectrum across brain areas. In a companion paper, we apply mega-analysis to assess commonalities in event-related EEG features across studies. The continuous raw and preprocessed data used in this analysis are available through the DataCatalog at https://cancta.net.

neuroscience↗