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Ru Qi Yu, Jiaying Zhao; Statistical learning generates implicit conjunctive predictions. Journal of Vision 2018;18(10):9. doi: https://doi.org/10.1167/18.10.9.
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© ARVO (1962-2015); The Authors (2016-present)
The visual system readily detects statistical relationships where the presence of an object predicts a specific outcome. What is less known is how the visual system generates predictions when multiple objects predicting different outcomes are present simultaneously. Here we examine the rules with which predictions are made in the presence of two objects that are associated with two distinct outcomes. In a visual search paradigm, participants first viewed one color dot and then searched for a target (a rotated T) in an array during the exposure phase. Each color predicted a specific location of the target. For example, after a blue dot the target would appear only in the top half of the array; and after a red dot the target would appear only in the left half of the array. The question is: Where was the target expected to appear when both the blue dot and the red dot were present? A conjunctive prediction would mean that the target was expected to appear in the top left quadrant of the array, whereas a disjunctive prediction would mean that the target was expected to appear in the top half or the left half of the array. Importantly at the test phase when both dots were present, the target was equally likely to appear in any half of the array. We found that participants were reliably faster to find the target when it appeared in the conjunctive quadrant. This was true even if participants were not consciously aware of the association between the color dots and target locations during debriefing. This effect was equally strong whether participants implicitly learned the association or were explicitly told about the association. The results suggest that in the presence of multiple predictors, statistical learning generates implicit expectations about the outcomes in a conjunctive fashion.
Meeting abstract presented at VSS 2018
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