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Li Guo, Jason Fischer; Attentional dynamics during physical prediction. Journal of Vision 2019;19(10):268. doi: https://doi.org/10.1167/19.10.268.
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The ability to anticipate the physical behavior of objects is crucial in daily life. Recent work has shown that in many scenarios, people can generate rapid and accurate predictions of how the physical dynamics of object interactions will unfold. What elements of a scene do people attend to when making such judgments? Here, we used an eye tracking paradigm to characterize how observers moved their attention through computer-generated 3D scenes when predicting their physical dynamics. Participants (n=23) played a “plinko” game which required them to predict the path that a puck would take as it slid down a board and traversed a number of barriers, ultimately landing in one of four bins. Participants decided which bin the puck would land in, and we ran computer simulations of the puck’s behavior in each scene to characterize the possible paths that it could take. In line with previous work, we found that observers’ predictions were consistent with a noisy Newtonian model – their successes and failures closely matched those of the computer simulation under a small amount of perceptual uncertainty. Moreover, participants’ looking behavior revealed that they precisely tracked the anticipated path of the puck, traversing the scene with their attention and anticipating collision points with high precision. The best-fitting model of observers’ looking behavior was one in which they tracked the full path of the puck but also spent a disproportionate amount of time analyzing points at which it would collide with the barriers. Critically, this looking strategy was consistent across scenes of varying complexity, while accuracy and reaction times varied and were correlated with the number of barriers present. Our findings reveal how observers deploy their attention when predicting physical dynamics, and the results highlight the key elements of a scene that observers analyze to make such predictions.
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