Measuring discriminability when there are multiple sources of bias

Performance measures such as log d and d′?aim to measure stimulus discriminability independently of response bias in conditional discrimination tasks, including the yes/no signal-detection procedure. However, they assume only one dimension of bias (e.g., response color) and do not account for bias on additional dimensions (e.g., response side). Such bias reduces log d, thus violating the statistical independence of discriminability and bias measurements. We modified log d to account for side bias and reanalyzed previous side-biased data. With strong side bias, the modified log d differed enough from the standard log d to potentially alter the conclusions of an experiment. Simulations showed that the modified log d produces discriminability estimates that are more accurate and bias-independent than the standard log d calculation.