The Parking Task: the Development and Validation of a Novel Cognitive Task Measuring Adaptive Decision Making

Adaptive decision making requires balancing risks, benefits, and time constraints in dynamic environments. Prior research has relied on lottery tasks with limited ecological validity. This study introduces the Parking Task, a user-friendly, customizable video game designed to model real-life adaptive decision making, focusing on strategy selection. Across three studies, participants navigated a one-way road, selecting parking spots while minimizing time loss. Spot availability followed either a geometric distribution (closer spots less likely, farther spots costlier in time) or a Bernoulli distribution (free and occupied spots uniformly distributed). Numeracy was assessed using the Berlin Numeracy Test, Symbolic-number Mapping Task, and Subjective Numeracy Scale. Experiment 1 examined how environmental changes (stable vs. modified) and numeracy predicted adaptive decision making. Experiment 2 tested whether distribution order affected performance, assessed test–retest reliability, and the relationship between Parking Task performance and lottery-based tasks. Experiment 3 investigated how goal instructions influenced parking time and satisfaction. Results showed that higher numeracy, measured by the Berlin Numeracy Test and Symbolic-number Mapping Task, was linked to more adaptive strategy selection and greater time savings when environments shifted from geometric to Bernoulli distributions. The task demonstrated good test–retest reliability, and better Parking Task performance was related to higher payoff sensitivity and adaptive behavior in monetary lotteries. Instructional framing affected performance: participants instructed not to be late parked earlier and reported higher satisfaction compared to those instructed to save time. Overall, the findings support the Parking Task as a valid and reliable tool for studying adaptive decision making in dynamic settings.