Rare targets are less susceptible to attention capture once detection has begun.
Rare or low probability targets are detected more slowly and/ or less accurately than higher probability counterparts. Various proposals have implicated perceptual and response-based processes in this deficit. Recent evidence, however, suggests that it is attentional in nature, with low probability targets requiring more attentional resources than high probability ones to detect. This difference in attentional requirements, in turn, suggests the possibility that low and high probability targets may have different susceptibilities to attention capture, which is also known to be resource-dependent. Supporting this hypothesis, we found that, once attentional resources have begun to be engaged by detection processes, low, but not high, probability targets have a reduced susceptibility to capture. Our findings speak to several issues. First, they indicate that the likelihood of attention capture occurring when a given task-relevant stimulus is being processed is dependent, to some extent, on how said stimulus is represented within mental task sets. Second, they provide added support for the idea that the behavioural deficit associated with low probability targets is attention-based. Finally, the current data point to reduced top-down biasing of target templates as a likely mechanism underlying the attentional locus of the deficit in question.
Department of Psychology, National University of Singapore, 9 Arts Link, Singapore, 117570, Singapore. psyhonn@nus.edu.sg.
Nicholas
N
Hon
Department of Psychology, National University of Singapore, 9 Arts Link, Singapore, 117570, Singapore.
Gavin
G
Ng
Department of Psychology, National University of Singapore, 9 Arts Link, Singapore, 117570, Singapore.
Gerald
G
Chan
D000328:Adult; D001288:Attention / Q000502:physiology*; D006801:Humans; D010364:Pattern Recognition, Visual / Q000502:physiology*; D011336:Probability; D011597:Psychomotor Performance / Q000502:physiology*; D055815:Young Adult
Attention; Attention capture