Expecting the irrelevant: the role of attentional resources in spatial and temporal distribution of attention to expected distractors
Year: 2025
Type: article
Abstract: The Preparation Effect (PE) refers to the allocation of attention to expected task-irrelevant stimuli (i.e., distractors) when the target and distractors are in separate displays. In two experiments, we investigated the deployment and the time course of attention in an expected-distractor paradigm as a function of the learned attentional set. Participants performed a memory-based change detection task that contained distractors in a separate display in one block but no distractors in the other block. During the retention interval, a small probe dot would appear unpredictably on a small number of trials, and the task was to detect the dot as quickly as possible. Only the participants who started with the distractor-absent block responded to the dot faster in the distractor-present block than in the distractor-absent block, thus showing the PE. Moreover, the PE was comparable regardless of whether the dot in the distractor-present block appeared at an expected distractor location or an expected empty location (Experiment 1), or whether the dot occurred before, during, or after the expected distractor onset (Experiment 2). In contrast, for the participants who performed the distractor-present block first, a reversed PE was found when the onset of the dot was 400 ms before the onset of the expected distractors. These results indicate that participants normally adopt a "process-all" approach with attention diffusely distributed within a relatively long temporal window. However, the enhanced attention is contingent on the availability of attentional resources. When attentional resources are insufficient, attentional control can be evoked to override the default "process-all" approach.
Source: Vision Research
Authors: Makayla Szu-Yu Chen, Kyle R. Cave, Zhe Chen
Institutions: University of Canterbury, University of Massachusetts Amherst
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,940
OpenAlex ID: W4417184162