Predicting the timing of wrong decisions with LATER

Abstract

Response time, or latency, is increasingly being used to provide information about neural decision processes. LATER (Linear Approach to Threshold with Ergodic Rate) is a quasi-Bayesian model of decision-making, with the additional feature that it introduces a degree of gratuitous randomisation into the decision process. It has had some success in predicting latencies under various conditions, but has not specifically been applied to an equally important aspect of decision-making, namely errors: a complete model of decision-making should not only account for latency distributions of correct decisions but also of wrong ones. We therefore used a decision task that generates large numbers of errors: subjects are told to look at suddenly appearing targets of one colour, but not another. We found that subjects' faster responses are as likely to be correct as wrong, but eventually the latency distributions diverge, with errors becoming infrequent. It seems that colour information, arriving after a delay, results both in cancellation of the developing response to the mere existence of the target and in delayed initiation of the correct response. A simple model, using LATER units in a similar way to one that has previously successfully modelled countermanding, accurately predicts latency distributions and proportions of all responses, whether correct or incorrect, demonstrating that the LATER model can indeed account for errors as well as correct responses.