Influence of Reinforcement and Its Omission on Trial-by-Trial Changes of Response Bias in Perceptual Decision-Making

Discrimination performance in perceptual choice tasks is known to reflect both sensory discriminability and non-sensory response bias. In the framework of signal detection theory (SDT), these aspects of discrimination performance are quantified through separate measures, sensitivity ( d’ ) for sensory discriminability and decision criterion ( c ) for response bias. However, it is unknown how response bias (i.e., criterion) changes at the single-trial level as a consequence of reinforcement history. We subjected rats to a two-stimulus two-response conditional discrimination task with auditory stimuli and induced response bias through unequal reinforcement probabilities for the two responses. We compared three SDT-based criterion learning models in their ability to fit experimentally observed fluctuations of response bias on a trial-by-trial level. These models shift the criterion by a fixed step (1) after each reinforced response, or (2) after each non-reinforced response, or (3) after both. We find that all three models fail to capture essential aspects of the data. Prompted by the observation that steady-state criterion values conformed well to a behavioral model of signal detection based on the generalized matching law, we constructed a trial-based version of this model and find that it provides a superior account of response bias fluctuations under changing reinforcement contingencies.