Abstract
Humans routinely encounter spatial regularities in their environment that can be exploited to improve visual search performance. A demonstration of such search facilitation in the lab is known as contextual cueing, an effect that results from implicit learning of repeated spatial arrangements. Despite countless studies, there remains uncertainty about the processes involved in the acquisition and expression of learned contextual information. Previous studies have found that taxing visual spatial working memory concurrently with the search task can impact the later development of a cueing effect. However, the time after the search task may also be important for the consolidation of learned spatial regularities. We hypothesized that a secondary task presented immediately after completion of the target search would retroactively disrupt the consolidation process, resulting in a diminished contextual cueing effect. To test this, we conducted an exploratory experiment and a pre-registered replication in which participants completed a variation of the contextual cueing procedure (Chun & Jiang, 1998). Subjects searched for a T among Ls in displays with repeating and random spatial arrangements of stimuli. There were 4 critical conditions: 1) standard repeated arrangement trials, 2) repeated arrangement trials during which the search task was followed by a secondary task of judging simple math problems, 3) repeated arrangement trials during which the search task was followed by a blank period of the same duration as the secondary task, and 4) random arrangement trials where the distractor locations did not predict the location of the target. Results showed that contextual cueing was diminished when the search task was followed by a secondary task. Such findings demonstrate that learning of visual regularities is susceptible to interference by a secondary task that occurs during the consolidation period. In addition, results show that a non-working memory, non-spatial, non-concurrent secondary task can disrupt spatial implicit learning.
Acknowledgement: NSF BCS-1632296 to ABL