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Mark Becker, Samuel Hemsteger, Taosheng Liu; What Constitutes "Value" in Value-driven Attentional Capture . Journal of Vision 2016;16(12):87. doi: 10.1167/16.12.87.
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© 2017 Association for Research in Vision and Ophthalmology.
Rewarding attention to a particular color results in a subsequent strong capture of attention by that color. However, the effect of associating a particular color with a loss is unclear. Punishing attention to a color might lead to suppression of attention to that color. Alternatively, both rewarded and punished colors might capture attention, if any stimulus associated with a large consequence, regardless of the valence, captures attention. Finally, it is possible that only rewards will drive the capture of attention. To test these possibilities we first trained participants to search for a target that could be either of two colors. One color was associated with a modest financial gain, the other was associated with a modest financial loss. In a subsequent search task, we found strong attentional capture by the rewarded color and weaker capture by the punished color. These results seem to show that both rewarded and punished features capture attention. However, during training the penalized color was always a target. Thus, its history of frequently being a target, rather than its consequence, might have been responsible for attentional capture. In a second experiment, the target could be any of three colors during training. One color was associated with gain, one with loss, and the third had no payouts. In a subsequent search task, the rewarded color showed strong capture and both the punished and no payout color had equivalent levels of weak, but significant, capture. These results suggest that being a frequent target during the training phase causes subsequent attentional capture. Associating a color with a gain further increased capture above and beyond this effect, but associating a color with a loss did not increase or decrease capture beyond the frequent target effect.
Meeting abstract presented at VSS 2016
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