Proceedings of the Annual Meeting of the Cognitive Science Society

Discrete choice models (e.g. logistic regression) are popular models in the economics literature that describe choices between twoor more discrete alternatives. These models have been successfully used to model value-based decisions, e.g. decisions in moraldilemmas, although temporal components of a decision, such as reaction times and changes of mind are not included. In cognitivesciences, another class of decision models, namely sequential-sampling models, has gained popularity in modelling choice accuracy,reaction time and decision uncertainty (e.g. confidence judgments). Here, we model decisions in moral dilemmas using a variant ofa hierarchical drift-diffusion model, factor drift diffusion, that combines the value-based approach with that of evidence accumulationmechanism by sequential-sampling. Specifically, we model the evidence accumulation process as resulting from a subjective weightingof abstract moral dimensions (factors). We train our model on a data set of 6500 moral decisions by 500 respondents on a popularweb platform (MoralMachine.mit.edu) and separately infer different sources of uncertainty in moral decisions. We show that the modelsuccessfully predicts reaction times and choices in moral dilemmas, while also leading to unexpected results