Statistical Learning of Across-Trial Regularities During Serial Search

Previous studies have shown that attention becomes biased toward those locations that frequently contain a target and is biased away from locations that have a high probability to contain a distractor. A recent study showed that participants also learned regularities that exist across trials: Participants were faster to find the singleton when its location was predicted by the location of the target singleton on the previous trial. Note, however, that this across-trial statistical learning was only demonstrated for parallel search involving pop-out singleton targets. The current study investigated whether there is also learning of across-trial regularities when search is serial, using a T-among-Ls task. In Experiment 1, using search displays with a gray T-target among gray Ls, we found that participants did not learn the existing across-trial regularities. In Experiment 2 we used the same display and same regularities except that during the first half of the experiment the targets were colored red, allowing feature search. Critically, now participants did learn the across-trial regularities during pop-out feature search and the learned biases persisted when search was serial again. Participants were not aware of these regularities suggesting that learning was automatic and implicit. We propose that across-trial target-target associations learned during feature search shape a flexible priority map whereby the selection of the predicting location results in up-weighting of the predicted location on the next trial. This flexible priority map remained active even when search task changed dramatically from parallel to serial search. Public Significance Statement The present study investigates the boundary conditions of implicit learning across trials. We show that during slow and serial search participants are not able to learn across-trial statistical regularities, most likely because there is too much noise for learning to occur. When we created conditions that reduced noise and facilitated the learning of across-trial statistical regularities, we show that the learned target-association biases in the feature search could persist when there was much noise again during serial search.

Automated summary

The study investigates implicit learning across trials, showing that participants are unable to learn across-trial statistical regularities during slow and serial search due to excessive noise. However, when conditions are created to reduce noise and facilitate learning, the target-association biases learned during feature search persist even when there is much noise during serial search.