This paper first reviews the fairly established ways of collecting sign language data. It then discusses the new technologies available and their impact on sign language research, both in terms of how data is collected and what new kinds of data are emerging as a result of technology. New data collection methods and new kinds of data are illustrated with three specific case studies.

The frontoparietal networks underlying grasping movements have been extensively studied, especially using fMRI. Accordingly, whereas much is known about their cortical locus much less is known about the temporal dynamics of visuomotor transformations. Here, we show that multivariate EEG analysis allows for detailed insights into the time course of visual and visuomotor computations of precision grasps. Male and female human participants first previewed one of several objects and, upon its reappearance, reached to grasp it with the thumb and index finger along one of its two symmetry axes. Object shape classifiers reached transient accuracies of 70% at ϳ105 ms, especially based on scalp sites over visual cortex, dropping to lower levels thereafter. Grasp orientation classifiers relied on a system of occipital-to-frontal electrodes. Their accuracy rose concurrently with shape classification but ramped up more gradually, and the slope of the classification curve predicted individual reaction times. Further, cross-temporal generalization revealed that dynamic shape representation involved early and late neural generators that reactivated one another. In contrast, grasp computations involved a chain of generators attaining a sustained state about 100 ms before movement onset. Our results reveal the progression of visual and visuomotor representations over the course of planning and executing grasp movements.

Two experiments evaluated the ability of younger and older adults to visually discriminate 3-D shape as a function of surface coherence. The coherence was manipulated by embedding the 3-D surfaces in volumetric noise (e.g., for a 55 % coherent surface, 55 % of the stimulus points fell on a 3-D surface, while 45 % of the points occupied random locations within the same volume of space). The 3-D surfaces were defined by static binocular disparity, dynamic binocular disparity, and motion. The results of both experiments demonstrated significant effects of age: Older adults required more coherence (tolerated volumetric noise less) for reliable shape discrimination than did younger adults. Motion-defined and static-binoculardisparity-defined surfaces resulted in similar coherence thresholds. However, performance for dynamic-binoculardisparity-defined surfaces was superior (i.e., the observers' surface coherence thresholds were lowest for these stimuli). The results of both experiments showed that younger and older adults possess considerable tolerance to the disrupting effects of volumetric noise; the observers could reliably discriminate 3-D surface shape even when 45 % of the stimulus points (or more) constituted noise.

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