Abstract
Previously (VSS 2015) we presented data from two experiments showing a cost in dual-target search for two unfamiliar faces, in which accuracy was lower than searching for the faces separately. Guidance of attention towards target-similar items in dual-target search was very limited at most. In these experiments we manipulated the visual similarity of distractor faces to target faces using morphing. Here we replicate this pattern of findings in a third experiment using more discriminable faces, and a different methodology, multidimensional scaling, was used for classifying target-distractor similarity. Data from all three experiments are evaluated using our novel capacity measure that represents the utility, and therefore precision, of the template for each of the two faces. In addition to the dual-target cost in accuracy and response time, participants in all experiments focussed search on one of the targets in dual-target search at the expense of the other. In general, probability of fixation to distractors suggests unguided search for targets, with search becoming more exhaustive for the non-preferred target in dual-target search. However, there is some evidence for attentional guidance, with targets being fixated earlier than they would be under exhaustive search, although this guidance only occurred when target representations were strong and particularly when target-distractor similarity was low. Consistently across the experiments, results suggest capacity for target representation is limited in search for two unfamiliar faces, with one target face being prioritised. However, when faces were searched individually first, before the dual-target search, capacity for the non-preferred target in dual-target search increased, indicating that increased familiarity with the individual faces improves target representation precision in dual-target search. By examining changes in capacity for target representation, our novel capacity measure can provide insights into target representations in dual-target search, and also into the process of face learning.
Meeting abstract presented at VSS 2016