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Jeffrey Lin, Scott Murray, Geoffrey Boynton; Capturing attention without perceptual awareness. Journal of Vision 2009;9(8):216. https://doi.org/10.1167/9.8.216.
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© ARVO (1962-2015); The Authors (2016-present)
Visual images that convey threatening information can automatically capture attention. One example is an object looming in the direction of the observer—presumably because such a stimulus signals an impending collision. However, it is not known if conscious processes are required to drive this attentional prioritization. Instead, detecting threatening stimuli may rely on separate, unique neural processes that are independent of perception.
To test the hypothesis that threatening stimuli can be detected without conscious perception, we generated an image sequence on a flat monitor of a looming ball that moved either on a collision path with the subject's head or on a path nearly missing the head. Critically, observers were unable to distinguish a collision path from a near-miss path in a two alternative forced-choice control experiment. Following each looming stimulus, subjects were asked to search for and determine the orientation of an oval stimulus among a series of distracting circular stimuli. Search times increased with increasing number of distractors when the target oval appeared either at a location away from the looming stimulus, or at the location of the near-miss looming stimulus. However, when the target oval appeared at the same location as a looming stimulus on a collision path, search times were nearly independent of the number of distractors. This shows that the looming stimulus attracted attention only when it was on a collision path with the observer, even though the colliding and near-miss paths were perceptually indistinguishable.
This dissociation between behavior and perception suggests that conscious perception may not be necessary to trigger behaviorally relevant responses such as detection and evaluation of an impending colliding object. These results demonstrate that the visual system can automatically categorize threatening versus non-threatening images at a level of precision beyond our perceptual capabilities.
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