Is it auto or manual? Acquiring and using recommendations from a decision aid
Decision aids are increasingly integrated into everyday choices. For example, Netflix might keep the binge rolling by automatically recommending another show. How the recommendation is acquired, automatically or actively sought out, can lead to different ways of using that information to make a choice. We present two experiments involving a decision aid in a dot motion task. Participants were told an algorithm would provide recommendations that were correct 70% of the time. This accuracy bisected performance for easier difficulty trials (~ 95%) and harder difficulty trials (~ 55%). In Experiment 1, participants could choose to seek out a recommendation. We manipulated the accuracy of the algorithm (70% vs. 80%) and found higher algorithm accuracy led to greater recommendation seeking for the easier trials when it was seemingly unnecessary. In Experiment 2, the recommendation automatically loaded after a period of time (1.8 seconds vs. 2.8 seconds). Reducing the time cost led individuals to examine but disagree with the recommendation more often. RT data identifies at least two distinct groups; one subset quickly agrees with the 70% recommendation akin to a better-than-chance guess, while another group effortfully tries to integrate the recommendation with the stimulus.