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Investigating attentional control sets: Evidence for the compilation of multi-feature control sets.

Simon MerzFrank BeegeLars-Michael SchöpperCharles SpenceChristian Frings
Published in: Attention, perception & psychophysics (2022)
Top-down control over stimulus-driven attentional capture, as postulated by the contingent capture hypothesis, has been a topic of lively scientific debate for a number of years now. According to the latter hypothesis, a stimulus has to match the feature of a top-down established control set in order to be selected automatically. Today, research on the topic of contingent capture has focused mostly on the manipulation of only a single feature separating the target from the distractors (the selection feature). The research presented here examined the compilation of top-down attentional control sets having multiple selection features. We report three experiments in which the feature overlap between the distractor and the top-down sets was manipulated on different perceptual features (e.g., colour, orientation and location). Distractors could match three, two or one of the features of the top-down sets. In line with our hypotheses, the strength of the distractor interference effects decreased linearly as the feature overlap between the distractor and the participants' top-down sets decreased. These results therefore suggest a decline in the efficiency with which distractors involuntarily capture attention as the target-similarity decreases. The data support the idea of multi-feature attentional control sets and are discussed in light of prominent contemporary theories of visual attention.
Keyphrases
  • working memory
  • machine learning
  • deep learning
  • neural network
  • data analysis