A computational technique for improving estimates of discriminability and bias across multiple dimensions of choice

Brown and White (2009) proposed measures of discriminability and bias that accommodate additional dimensions of choice--and hence, bias--in conditional discriminations such as matching-to-sample and the yes-no signal detection task. Their proposed measures increase the statistical independence of discriminability and bias estimates, thus improving their accuracy. Because Brown and White's (2009) equations partition response data more than do standard equations, however, their measures have a slightly lower ceiling. Consequently, measurements can be less accurate when there are few trials and discriminability and bias are extreme. We introduce a computational estimation technique that overcomes this limitation. It estimates Brown and White's (2009) discriminability and bias measurements from an array of related measures that have a higher ceiling. Simulations show that resulting estimates of discriminability and bias are either comparable to or more accurate than measurements calculated from traditional equations or Brown and White's (2009) direct measures, even with few trials. A worked example of our technique may be downloaded from brm.psychonomic-journals.org/content/supplemental.