Towards a taxonomy of tasks for human sequential decision-making

People face a vast range of different cognitive tasks in their lives. Classic problem-solving theories do not fully capture the impact of the task structure on sequential decision-making. Here, we argue that it is important to consider task features that determine the search space structure of a task as it presents itself to the problem solver because the search space determines which algorithms are appropriate to solve the problem optimally. Knowledge of these optimal algorithms provides a base for cognitive modelling of sequential decision-making. We propose a new task taxonomy by identifying ten features in four categories related to search space properties. We describe how these constrain the space of optimal algorithms. Furthermore, we categorise and compare several sequential decision-making tasks used in studies on human behaviour with our taxonomy and illustrate how the proposed task features can inform the creation of new tasks and computational modelling of behaviour.