Surprisingly inflexible: Statistically learned suppression of distractors generalizes across contexts

Abstract

The present study investigates the flexibility of statistically learned distractor suppression between different contexts. Participants performed the additional singleton task searching for a unique shape, while ignoring a uniquely colored distractor. Crucially, we created two contexts within the experiments, and each context was assigned its own high-probability distractor location, so that the location where the distractor was most likely to appear depended on the context. Experiment 1 signified context through the color of the background. In Experiment 2, we aimed to more strongly differentiate between the contexts using an auditory or visual cue to indicate the upcoming context. In Experiment 3, context determined the appropriate response ensuring that participants engaged the context in order to be able to perform the task. Across all experiments, participants learned to suppress both high-probability locations, even if they were not aware of these spatial regularities. However, these suppression effects occurred independent of context, as the pattern of suppression reflected a de-prioritization of both high-probability locations which did not change with the context. We employed Bayesian analyses to statistically quantify the absence of context-dependent suppression effects. We conclude that statistically learned distractor suppression is robust and generalizes across contexts.

What to expect where and when: how statistical learning drives visual selection

“…However, learning becomes context specific when processing of the context is necessary to perform the task [89], or when the two tasks differ in attentional demands [91]. Although less well characterized, the current evidence also suggests that spatial suppression effects are not context dependent [76], not even when the contexts are made task-relevant [92]. However, context-dependent learning effects in those studies may have been obscured because the contexts were randomly intermixed.…”

Statistical learning of across-trial regularities during serial search.

“…The learned spatial biases could persist more than one day even if when the spatial distribution was even (Jiang et al, 2013; Sauter et al, 2019). There is also evidence that implicitly learned suppression of high-probability distractor locations generalizes across different contexts (Britton & Anderson, 2020; de Waard et al, 2021). Compared with these previous studies, our Experiment 2 went a step further by changing the nature of the search task entirely (even though the search displays were almost identical).…”

Expected Distractor Context Biases the Attentional Template for Target Shapes

“…We think this is unlikely, because cueing distractor features on a trial-by-trial basis often leads to no benefits, or even costs (e.g., M. W. Becker et al, 2016; Cunningham & Egeth, 2016). Furthermore, while feature-based inhibition based on statistical learning and blocked distractors is possible (Stilwell et al, 2019; van Moorselaar et al, 2021; Van Moorselaar et al, 2020; Vatterott & Vecera, 2012), it is likely not context-dependent (see Britton & Anderson, 2019; de Waard et al, 2022 for space-based suppression). Successful feature-based distractor inhibition in our experiments would require learning specific distractor features associated with every target in both contexts.…”

“…, it is likely not context-dependent (seeBritton & Anderson, 2019;de Waard et al, 2022 for space-based suppression). Successful featurebased distractor inhibition in our experiments would require learning specific distractor features preprint…”