A weighted constraint satisfaction approach to human goal-directed decision making

Abstract

AbstractWhen we choose actions aimed at achieving long-range goals, proximal information cannot be exploited in a blindly myopic way, as relevant future information must often be taken into account. However, when long-range information is irrelevant to achieving proximal subgoals, it can be desirable to focus exclusively on subgoal-relevant considerations. Here, we consider how an underlying parallel mechanism simultaneously influenced by proximal and future information may be at work when decision makers confront both types of situations. Participants were asked to find the shortest path in a simple maze where the optimal path depended on both starting-point and goal-proximal constraints. This simple task was then embedded in a more complex maze where the same two constraints, but not the final goal position, determined the optimal path to the subgoal. In both tasks, initial choice responses predominantly reflected the joint influence from relevant immediate and future constraints, yet we also found systematic deviations from optimality. We modeled initial path choice as an evidence integration process and found that participants weighted the starting-point more than the equally relevant goal in the simple task. In the complex task, there was no evidence of a separate processing stage where participants first zeroed in on the subgoal as would be expected if task decomposition occurred strictly prior to choosing a path to the subgoal. Participants again placed slightly more weight on the starting point than the subgoal as in the simple task, and also placing some weight on the irrelevant final goal. These results suggest that optimizing decision making can be viewed as adjusting the weighting of constraints toward values that favor relevant ones in a given task context, and that the dynamic re-weighting of constraints at different points in a decision process can allow an inherently parallel process to exhibit approximate emergent hierarchical structure.Author SummaryOptimal approaches to achieving long-term goals often require considering relevant future information and, at other times, chunking a problem into subproblems that can be focused on one at a time. These two situations seemingly require separate modes of thinking. While simultaneous consideration allows proximal and future information to jointly guide our actions, tackling subgoals is often thought to require first coming up with a higher-level plan, then focusing on solving each subtask separately. In this study, we examine how both abilities might be explained by a shared mechanism. We conducted behavioral experiments and used computational modeling to understand how people weight various factors in choosing goal-reaching paths. We found that their weighting of task-relevant factors allowed them to approximate optimal path choices, but they tend to place somewhat more weight on factors relevant to the immediate next action than on future considerations, and suboptimally place some weight on task-irrelevant factors. These results open up the space for considering the role of situation-dependent constraint weighting as a mechanism that allows people to integrate multiple pieces of information in decision making in a flexible, context-sensitive manner in service of optimizing performance in reaching an overall goal.