Abstract |
A current line of research is focusing on the processes of spatial learning. Previous studies have indicated that students have trouble learning concepts which have a spatial component to them. Topics such as molecular geometry within the field of chemistry are notably difficult. Recent research has suggested that stereoscopic presentation of these objects may promote better learning. To understand the processes which contribute to spatial learning a neurophysiological approach is necessary. Therefore, in order to investigate the effectiveness of stereoscopic presentation and the neural correlates of it, an Electroencephalograph (EEG) was used. Participants were fitted with an EEG cap and had electrical scalp activity recorded using Brain Vision software. Participants were presented with images in both 3-D and 2-D of the organic molecule Hemoglobin. During the tasks, the molecule rotated across three different planes, changed colors, and changed focal distance. The data collected was then analyzed using EEGLAB for MATLAB. Individual components associated with visual processing (visual cortex), executive processing (pre-frontal cortex), and memory (hippocampus) were identified using an Independent Components Analysis (ICA) and dipole modeling. The brainwave activity in these components were recorded and the components were correlated with one another.
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Modified Abstract |
Previous studies have indicated that students have trouble learning concepts which have a spatial component to them. Recent research has suggested that stereoscopic presentation of these objects may promote better learning. To understand the processes which contribute to spatial learning a neurophysiological approach is necessary. Therefore, in order to investigate the effectiveness of stereoscopic presentation and the neural correlates of it, an Electroencephalograph (EEG) was used. Participants were presented with images in both 3-D and 2-D of an organic molecule. During the tasks, the molecule rotated across three different planes, changed colors, and changed focal distance. The data collected was then analyzed using EEGLAB for MATLAB. The brainwave activity in these components were recorded and the components were correlated with one another.
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