A Bayesian Approach to Discriminating Between Biased Responding and Sequential Dependencies in Binary Choice Data

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Sequential dependencies occur when prior decisions affect subsequent decisions, and they have been observed in both memory and perception tasks. For binary response tasks, an inherent problem in measuring sequential dependencies is the ability to distinguish between sequential dependencies and response bias. The problem arises because the sequential dependency estimate within the frequentist architecture does not contain information regarding the number of observations upon which it is based. One solution to the problem is to use a Bayesian approach that takes the uncertainty of the sequential dependency estimate into account. We describe 2 Bayesian measurement models of sequential dependencies in binary response tasks and test them using simulated data with known degrees of response bias and sequential dependencies. Both models were able to distinguish between fluctuations in sequential dependencies and response bias. We then use the model to measure the contributions of sequential dependencies and response bias to the decisions made in recognition memory and perceptual categorization.

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