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- Visual statistical learning requires attentionPublication . Duncan, Dock H.; Van Moorselaar, Dirk; Theeuwes, JanABSTRACT: Statistical learning is a person’s ability to automatically learn environmental regularities through passive exposure. Since the earliest studies of statistical learning in infants, it has been debated exactly how “passive” this learning can be (i.e., whether attention is needed for learning to occur). In Experiment 1 of the current study, participants performed a serial feature search task where they searched for a target shape among heterogenous nontarget shapes. Unbeknownst to the participants, one of these nontarget shapes was presented much more often in location. Even though the regularity concerned a nonsalient, nontarget item that did not receive any attentional priority during search, participants still learned its regularity (responding faster when it was presented at this high-probability location). While this may suggest that not much, if any, attention is needed for learning to occur, follow-up experiments showed that if an attentional strategy (i.e., color subset search or exogenous cueing) effectively prevents attention from being directed to this critical regularity, incidental learning is no longer observed. We conclude that some degree of attention to a regularity is needed for visual statistical learning to occur.
- Attentional suppression is in place before display onsetPublication . Huang, C.; Donk, Mieke; Theeuwes, JanRecent studies have shown that observers can learn to suppress a location that is most likely to contain a distractor. The current study investigates whether the statistically learned suppression is already in place, before, or implemented exactly at the moment participants expect the display to appear. Participants performed a visual search task in which a distractor was presented more frequently at the high-probability location (HPL) in a search display. Occasionally, the search display was replaced by a probe display in which participants needed to detect a probe ofset. The temporal relationship between the probe display and the search display was manipulated by varying the stimulus onset asynchronies (SOAs) in the probe task. In this way, the attentional distribution in space was probed before, exactly at, or after the moment when the search display was expected to be presented. The results showed a statistically learned suppression at the HPL, as evidenced by faster and more accurate search when a distractor was presented at this location. Crucially, irrespective of the SOA, probe detection was always slower at the HPL than at the low-probability locations, indicating that the spatial suppression induced by statistical learning is proactively implemented not just at the moment the display is expected, but prior to display onset. We conclude that statistical learning afects the weights within the priority map relatively early in time, well before the availability of the search display