Decision-Variable Correlation: An Extension of SDT
Decision-Variable Correlation: An Extension of SDT is a scholarly work, published in 2018 in ''Journal of Vision''. The main subjects of the publication include Automatic target recognition, contrast, correlation, artificial intelligence, psychology, identification, task, variable, mathematics, pattern recognition, and computer science. The authors demonstrate the framework for the well-known task of detecting a Gaussian target in white noise and make several theoretical and experimental discoveries: (1) subjects' DVCs are approximately equal to the square root of their efficiency relative to ideal (in agreement with the prediction of a popular class of models), (2) between-subject and within-subject (double-pass) DVCs increase with target contrast and are greater for target-present than target-absent trials (rejecting many models), (3) model parameters can be estimated by maximizing DVCs between the model and subject, (4) a model with a center-surround template and a specific (modest) level of position uncertainty predicts the trial-by-trial performance of subjects as well (or better) than presenting the same stimulus again to the subjects (i.e., the double-pass DVCs, which are as high as 0.7).