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Hanbin Go, Britt Anderson, James Danckert; Mental Model Updating and Eye Movement Metrics. Journal of Vision 2019;19(10):85a. doi: https://doi.org/10.1167/19.10.85a.
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We investigated whether mental model updating can be inferred from eye movement metrics. Prior studies (O’Reilly et al., 2013) suggest that an unexpected or “surprising” event facilitates the updating of one’s mental model. To probe this further we designed a saccadic planning task to capture individuals’ mental model updating, while recording saccadic reaction time and dwell time. Saccadic reaction time was decomposed into latency and movement components. Participants learned distributions of target dots presented on a perimeter of an (invisible) circle. We periodically introduced unannounced changes to the distribution of target dots, which participants had to infer from trial history. For each distribution we computed how the eye movement metrics differed between high and low probability events. Saccadic latency and saccadic duration were faster for high probability events, however there was no difference in dwell time for the two probability outcomes. When presented with a new distribution, there was no difference in the saccadic duration, but there was an increase in saccadic latency and dwell time. These results suggest that, having learned a distribution, individuals plan for high probability events, and spend more time looking at targets that are surprising (i.e., unexpected low probability occurrences). Our results provide additional evidence in support of eye movement metrics for inferring when mental models have been learned and are being updated.
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