Statistical learning of movement

The environment is dynamic, but objects move in predictable and characteristic ways, whether they are a dancer in motion, or a bee buzzing around in flight. Sequences of movement are comprised of simpler motion trajectory elements chained together. But how do we know where one trajectory element ends and another begins, much like we parse words from continuous streams of speech? As a novel test of statistical learning, we explored the ability to parse continuous movement sequences into simpler element trajectories. Across four experiments, we showed that people can robustly parse such sequences from a continuous stream of trajectories under increasingly stringent tests of segmentation ability and statistical learning. Observers viewed a single dot as it moved along simple sequences of paths, and were later able to discriminate these sequences from novel and partial ones shown at test. Observers demonstrated this ability when there were potentially helpful trajectory-segmentation cues such as a common origin for all movements (Experiment 1); when the dot’s motions were entirely continuous and unconstrained (Experiment 2); when sequences were tested against partial sequences as a more stringent test of statistical learning (Experiment 3); and finally, even when the element trajectories were in fact pairs of trajectories, so that abrupt directional changes in the dot’s motion could no longer signal inter-trajectory boundaries (Experiment 4). These results suggest that observers can automatically extract regularities in movement — an ability that may underpin our capacity to learn more complex biological motions, as in sport or dance.