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Alicia Hymel, Daniel Levin; Detecting temporal misorderings requires more effort than detecting the misordered actions. Journal of Vision 2012;12(9):260. doi: 10.1167/12.9.260.
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© ARVO (1962-2015); The Authors (2016-present)
Previous research has emphasized the importance of automatic prediction and error detection in event perception (e.g., Zacks 2007). However, we have previously demonstrated that individuals have difficulty detecting out-of-order actions embedded within familiar events, calling into question the use of moment-to-moment predictions in understanding these events. In this experiment, we tested whether detecting misorderings is especially dependent on effortful processing by investigating the effects of an interfering task on both the ability to detect misordered actions and to detect the presence of a previously viewed action. Participants watched videos (consisting of a series of eight to fourteen different shots, with each shot lasting an average of 820 ms) of actors performing everyday activities that either did or did not contain a misordered action (for example, a shot of an object being used before a shot of it being picked up). Half of the participants were instructed to look for misorderings. The other half viewed a clip of an action immediately before each video, and were instructed to press a key when they saw the action appear in the subsequent video. For trials with misordered events, this action was always out of order. Additionally, all participants were instructed to engage in a verbal counting task for half the trials. When instructed to look for misorderings, the interference task significantly lowered detection of misordered events without increasing false alarm rates. The interference task, however, did not lessen the detection of clips within misordered videos. Interestingly, even though participants were able to successfully identify the target clip, which was misordered in half the trials, incidental detection of the misordering itself was very rare. These results suggest that sequence prediction and error detection requires substantial cognitive effort.
Meeting abstract presented at VSS 2012
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