Transfer of statistical learning between tasks.

Recent studies have shown that observers can learn to suppress locations in the visual field with a high distractor probability.Here, we investigated whether this learned suppression resulting from a spatial distractor imbalance transfers to a completely different search task that does not contain any distractors.Observers performed the additional singleton task and learned to suppress the location that was likely to contain a color singleton distractor.Within a block, the additional singleton task would randomly switch to a T-among-L task where observers searched in parallel (Experiment 1) or serially (Experiment 2) for a T among Ls.The upcoming search was either unpredictable (Experiment 1/2A) or cued (Experiment 1/2B).The results show that there was transfer of learning from one to the other task as the learned suppression stayed in place after the switch regardless of whether the T-among-L task was performed via parallel or serial search.Moreover, cueing that the task would switch had no effect on performance.The current findings indicate that implicit learned biases are rather inflexible and remain in place even when the task and the required search strategy are dramatically different and even when participants can anticipate that a change in the search required is imminent.This transfer of the suppression to a different task is consistent with the notion that suppression is proactively applied.Because the location is already suppressed proactively, that is, before display onset, regardless which display and task is presented, the suppressed location competes less for attention than all other locations. Public Significance StatementWe are able to extract regularities that are present in our environment allowing us to learn to suppress those locations that often contain distractors.The current study shows that the suppression that was learned in one search task stays in place if we switch to another search task, even when this task requires a completely different type of search.The findings are remarkable as they are inconsistent with generally agreed claim that implicit statistical learning is highly task specific with little to no transfer across tasks.