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Yigal Agam, Robert Sekuler; Geometric structure and chunking in reproduction of motion sequences. Journal of Vision 2008;8(1):11. doi: 10.1167/8.1.11.
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© ARVO (1962-2015); The Authors (2016-present)
Learning by imitation is fundamental to human behavior, but not all observed actions are equally easy to imitate. To understand why some actions are more difficult to imitate than others, we examined how higher-order relationships among the components of a stimulus model influenced the fidelity with which an action could be observed and then reproduced. With static contours, perception and short-term memory are strongly influenced by contour geometry, particularly by the presence and distribution of curvature extrema. To determine whether analogous relationships among subcomponents of a seen action would be important in encoding the action for subsequent reproduction, we manipulated actions' spatio-temporal geometry. In three experiments, we measured imitation fidelity for sequences of randomly directed, linked motions of a disc. The geometry of the disc's motion path strongly affected the accuracy of subsequent imitation: When the disc moved along a trajectory whose components were fairly consistent in their directions, imitation was strikingly better than when with irregular, jagged trajectories. A second experiment showed that this effect depended not upon co-variation in stimulus models' spatial extent, but rather on the relationship between successive movement directions. In a final, learning experiment, subjects had multiple opportunities to view and reproduce each model. The effect of the model's geometry persisted throughout the learning process, suggesting that it does not depend upon variables such as familiarity or expectancy but is somehow inherent to the pattern generated by the disc's motion. Our findings suggest that when analyzing seen actions, the brain privileges regular, consistent curvatures, grouping components that form a coherent shape into a unified “chunk.” Inconsistencies among the directional components of a motion sequence cause the sequence to be chunked into additional components, which increases the load on working memory, undermining the fidelity with which the sequence can be imitated.
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