Decision heuristics in contexts exploiting intrinsic skill

Abstract Heuristics can inform human decision making in complex environments through a reduction of computational requirements (accuracy-resource trade-off) and a robustness to overparameterisation (less-is-more). However, tasks capturing the efficiency of heuristics typically ignore action proficiency in determining rewards. The requisite movement parameterisation in sensorimotor control questions whether heuristics preserve efficiency when actions are nontrivial. We developed a novel action selection-execution task requiring joint optimisation of action selection and spatio-temporal skillful execution. Optimal choices could be determined by a simple spatial heuristic, or by more complex planning. Computational models of action selection parsimoniously distinguished human participants who adopted the heuristic from those using a more complex planning strategy. Broader comparative analyses then revealed that participants using the heuristic showed combined decisional (selection) and skill (execution) advantages, consistent with a less-is-more framework. In addition, the skill advantage of the heuristic group was predominantly in the core spatial features that also shaped their decision policy, evidence that the dimensions of information guiding action selection might be yoked to salient features in skill learning. Author Summary We often must choose between actions and then execute them, e.g., a tennis player chooses between a forehand and backhand and then skilfully executes the shot. To select actions, the player might plan their action with either shot, and select whichever simulated outcome is more advantageous. However, a more efficient approach might instead be to use a “heuristic”, i.e., a simpler rule, such as, forehand always on one side of the court, and backhand on the other. In this work, we look at whether styles of planning are related to physical skill performing actions, e.g., would a more skillful tennis player be more likely to use planning or a heuristic? We use a new task that requires people to choose and execute complex actions. Regarding choices, we use computational modeling to identify which people use some degree of planning, and which people use a simpler heuristic. Then, regarding action execution, we reveal that heuristic decision makers are in fact more skilled. However, they are not superiorly skilled in all aspects of performance, showing an advantage solely in the aspect of skill most closely linked to the information (spatial) they use for their heuristic. We therefore reveal the first ever evidence that a relation exists between the complexity of our action-related decisions and how broadly we learn associated motor behaviour.