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Sage Boettcher, Freek van Ede, Anna Nobre; Is the use of visual predictions dependent on expected target difficulty?. Journal of Vision 2018;18(10):1145. doi: https://doi.org/10.1167/18.10.1145.
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© ARVO (1962-2015); The Authors (2016-present)
The use of predictions in adaptive perception is becoming increasingly recognized. Here we asked whether predictions regarding the identity of upcoming visual targets are used differently when these targets are expected to be more difficult to perceive. Participants monitored a stream of objects with the task of detecting two pre-specified target objects. Different cues predicted either of two different targets, of which one was always significantly more difficult to perceive as a result of a shorter target-mask SOA. Behaviorally, we find a significant interaction between the target difficulty and the presence of a cue in both reaction times and accuracy. Analysis of the event related potentials locked to the onset of the cue (item 1) preceding a target (item 2) indicates an effect of informative cues relative to neutral cues, and also suggests a modest late positivity associated with the difficult relative to the easy cue. ERPs locked to the target show a strong interaction between the two variables – cue presence and target difficulty. Finally, we used multivariate pattern analysis (MVPA) to investigate whether the identity of the predicted target could be decoded during the cue period, and whether this also depends on expected difficulty. Preliminary analysis indeed confirms above chance decoding of the predicted target within the cue period, albeit with no clear effects of expected target difficulty. This indicates that subjects may activate a 'template' of the upcoming target during the cue period. Together, our results show that cues may be used differently when an upcoming target is more difficult to perceive, but that the exact nature of this difference may manifest differently in behavior, preparatory neural activity, and post-target neural activity.
Meeting abstract presented at VSS 2018
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