From single decisions to long-term choice patterns: Extending the dynamics of decision-making

Decision-making is usually studied on a trial by trial basis and each decision is assumed to represent an isolated choice process. These assumptions are also reflected in sequential sampling models which conceive of the decision-process as an accumulation of information about the attractiveness of the options at hand. Real-life decisions however are usually embedded in a rich context of previous choices at different time scales. A fundamental yet neglected question is therefore how the dynamics of choice processes unfold on a long-term time scale across several decisions. Here, we present a neural-inspired attractor model that integrates the short-term mechanism of accumulation models with the long-term dynamics of coupled neural systems. The model represents a class of models that incorporate inherent long-term dynamics. We use the model to predict long-term patterns, namely oscillatory switching, perseveration and dependence of perseveration on the delay between decisions. Furthermore, we predict RT effects for specific trials. We validate the predictions in two new studies and a reanalysis of existing data from a novel decision game in which participants have to perform delay discounting decisions. Applying the validated reasoning to a well-established choice questionnaire, we illustrate and discuss that taking long-term choice patterns into account may be necessary to accurately analyse and model decision processes.