Persistent Guidance of Attention in Visual Statistical Learning

When repeatedly selected features have predictive value, an observer can learn to prioritize them. However, relatively little is known about the mechanisms underlying this persistent statistical learning. In two experiments, we investigated the boundary conditions of statistical learning. Each task included a training phase where targets appeared more frequently in one of two target colors, followed by a test phase where targets appeared equally in both colors. A posttest survey probed awareness of target color probability differences. Experiment 1 tested whether statistical learning requires the predictive feature to be inherently bound to the target. Participants searched for a horizontal or vertical line among diagonal distractors and reported its length (long or short). In the bound condition, targets and distractors were colored, whereas targets were presented in white font and surrounded by colored boxes in the unbound condition. Experiment 2 tested whether reducing task difficulty by simplifying the judgment (horizontal or vertical) would eliminate statistical learning. The results suggested that statistical learning is robust to manipulations of binding, but is attenuated when task difficulty is reduced. Finally, we found evidence that explicit awareness may contribute to statistical learning, but its effects are small and require large sample sizes for adequate detection.