TY - JOUR
T1 - Explaining the effects of distractor statistics in visual search
AU - Calder-Travis, Joshua
AU - Ma, Wei Ji
N1 - Funding Information:
The authors thank the reviewers for helpful comments and points, including the point that crowding may be a key factor in understanding the difference between our and previous results, and the point that in certain distractor environments, it may be possible to distinguish the Bayesian and heuristic models. They also thank Andra Mihali and Heiko Schütt for helpful discussions and advice over the course of the project. The authors are very grateful to Luigi Acerbi for extensive advice on dealing with model-fitting difficulties. Wei Ji Ma was supported by Grant R01EY020958 from the National Institutes of Health. The authors gratefully acknowledge the use of the University of Oxford Advanced Research Computing (ARC) facility in carrying out this work. http://dx.doi.org/10.5281/zenodo.22558.
Publisher Copyright:
© 2020. All Rights Reserved.
PY - 2020
Y1 - 2020
N2 - Visual search, the task of detecting or locating target items among distractor items in a visual scene, is an important function for animals and humans. Different theoretical accounts make differing predictions for the effects of distractor statistics. Here we use a task in which we parametrically vary distractor items, allowing for a simultaneously fine-grained and comprehensive study of distractor statistics. We found effects of target-distractor similarity, distractor variability, and an interaction between the two, although the effect of the interaction on performance differed from the one expected. To explain these findings, we constructed computational process models that make trial-by-trial predictions for behavior based on the stimulus presented. These models, including a Bayesian observer model, provided excellent accounts of both the qualitative and quantitative effects of distractor statistics, as well as of the effect of changing the statistics of the environment (in the form of distractors being drawn from a different distribution). We conclude with a broader discussion of the role of computational process models in the understanding of visual search.
AB - Visual search, the task of detecting or locating target items among distractor items in a visual scene, is an important function for animals and humans. Different theoretical accounts make differing predictions for the effects of distractor statistics. Here we use a task in which we parametrically vary distractor items, allowing for a simultaneously fine-grained and comprehensive study of distractor statistics. We found effects of target-distractor similarity, distractor variability, and an interaction between the two, although the effect of the interaction on performance differed from the one expected. To explain these findings, we constructed computational process models that make trial-by-trial predictions for behavior based on the stimulus presented. These models, including a Bayesian observer model, provided excellent accounts of both the qualitative and quantitative effects of distractor statistics, as well as of the effect of changing the statistics of the environment (in the form of distractors being drawn from a different distribution). We conclude with a broader discussion of the role of computational process models in the understanding of visual search.
UR - http://www.scopus.com/inward/record.url?scp=85098741950&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098741950&partnerID=8YFLogxK
U2 - 10.1167/jov.20.13.11
DO - 10.1167/jov.20.13.11
M3 - Article
C2 - 33331851
AN - SCOPUS:85098741950
VL - 20
SP - 1
EP - 26
JO - Journal of Vision
JF - Journal of Vision
SN - 1534-7362
IS - 13
ER -