Planning is a latent cognitive process that cannot be observed directly. This makes it difficult to study how people plan. To address this problem, we propose a new paradigm for studying planning that provides experimenters with a timecourse of participant attention to information in the task environment. This paradigm employs the information-acquisition mechanism of the Mouselab paradigm, in which participants click on options to reveal the outcome of choosing those options. However, in contrast to the original Mouselab paradigm, our paradigm is a sequential decision process, in which participants must plan multiple steps ahead to achieve high scores. We release Mouselab-MDP open-source as a plugin for the JsPsych online Psychology experiment library. The plugin displays a Markov decision process as a directed graph, which the participant navigates to maximize reward. To trace the the process of planning, the rewards associated with states or actions are initially occluded; the participant has to click on a transition to reveal its reward. This information gathering behavior makes explicit the states the participant considers. We illustrate the utility of the Mouselab-MDP paradigm with a proof-of-concept experiment in which we trace the temporal dynamics of planning in a simple environment. Our data shed new light on people’s approximate planning strategies and on how people prune decision trees. We hope that the release of Mouselab-MDP will facilitate future research on human planning strategies. In particular, we hope that the fine-grained time course data that the paradigm generates will be instrumental in specifying algorithms, tracking learning trajectories, and characterizing individual differences in human planning.
A Model of Path-Dependence in Decisions over Multiple Propositions
