Learning From Missing Feedback: Exemplar Versus Model-Based Methods

In many real-life settings, feedback is only available for cases that decision makers accept and so may be biased toward positive events. How do people learn to distinguish good from bad alternatives from such selective feedback, and can they correct for this bias? We describe the computational problems of classification learning from biased samples and examine how exemplar and model-based methods can deal with this challenge: Model-based methods can adjust their representation of the task based on what information is available while exemplar models can impute fictive negative outcomes in missing cases to avoid positivistic biases. Importantly, these methods imply distinct assumptions about the task and reactions to missing feedback, which can be assessed empirically. In three experiments, we test whether participants rely on imputation or use a Bayesian model of the task to correct for selection bias. We find that many participants were best described by an exemplar model, most with imputation, but an almost equal proportion was best described by a Bayesian model. People best described by different models reacted somewhat differently to missing feedback. We also observe substantial stability in whether individuals were best described by model-based or exemplar models across tasks, though participants were more likely to use exemplar models when there was greater uncertainty about the task structure. Overall, our findings show that people deal with missing feedback in an adaptive manner by adopting diverse approaches that are partially stable and partially reflect assumptions made about the experimental context.