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Mental rotation is the ability to rotate mental representations of two-dimensional and three-dimensional objects as it is related to the visual representation of such rotation within the human mind. [1] There is a relationship between areas of the brain associated with perception and mental rotation.
The cognitive tests used to measure spatial visualization ability including mental rotation tasks like the Mental Rotations Test or mental cutting tasks like the Mental Cutting Test; and cognitive tests like the VZ-1 (Form Board), VZ-2 (Paper Folding), and VZ-3 (Surface Development) tests from the Kit of Factor-Reference cognitive tests produced by Educational Testing Service.
3D Slicer Extensible, free open source multi-purpose software for visualization and analysis. Amira 3D visualization and analysis software; Analysis of Functional NeuroImages (AFNI) Analyze developed by the Biomedical Imaging Resource (BIR) at Mayo Clinic. Brain Image Analysis Package; CamBA; Caret Van Essen Lab, Washington University in St. Louis
Analysis of Functional NeuroImages (AFNI) is an open-source environment for processing and displaying functional MRI data—a technique for mapping human brain activity. AFNI is an agglomeration of programs that can be used interactively or flexibly assembled for batch processing using shell script .
The acknowledgement and application of different cognitive and learning styles, including visual, kinesthetic, musical, mathematical, and verbal thinking styles, are a common part of many current teacher training courses. [6] Those who think in pictures have generally claimed to be best at visual learning. [7]
Images from the scanner may be pre-processed to remove noise or correct for sampling errors. A study usually scans a subject several times. To account for the motion of the head between scans, the images are typically adjusted so voxels in each image correspond (approximately) to the same site in the brain.
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Visual object recognition refers to the ability to identify the objects in view based on visual input. One important signature of visual object recognition is "object invariance", or the ability to identify objects across changes in the detailed context in which objects are viewed, including changes in illumination, object pose, and background context.