Comparative Modelling of Learning in a Decision Making Task

In this paper we compare the behaviour of three competing accounts of decision making under uncertainty (a Bayesian account, an associationist account, and a hypothesis testing account) with subject performance in a medical diagnosis task. The task requires that subjects first learn a set of symptom/disease associations. Later, subjects are required to form diagnoses based on limited symptom information. The competing theoretical accounts are embodied in three computational models, each with a single parameter governing the learning rate. Subjects’ diagnostic accuracy was used to calibrate the learning rates of the models. The resulting parameterfree models were then used to predict subjects’ behaviour in a subsequent related diagnosis task. The fit between the Associationist model’s predictions and subject behaviour was poor. The fit was slightly better in the case of the Bayesian model, but the hypothesis testing account proved to provide the most adequate account of the data.