Detecting Structure in Activity Sequences

Abstract

Can humans discriminate whether strings of events (e.g., shooting success in basketball) were generated by a random or constrained process (e.g., hot and cold streaks)? Conventional wisdom suggests that humans are not good at this discrimination. Following from Cooper, Hammack, Lemasters, and Flach (2014), a series of Monte Carlo simulations and an empirical experiment examined the abilities of both humans and statistical tests (Wald-Wolfowitz Runs Test and 1/f) to detect specific constraints that are representative of plausible factors that might influence the performance of athletes (e.g., learning, non-stationary task constraints). Using a performance/success dependent learning constraint that was calibrated to reflect shooting percentages representative of shooting in NBA games, we found that the conventional null hypothesis tests were unable to detect this constraint as being significantly different from random. Interestingly however, the analysis of human performance showed that people were able to make this discrimination reliably better than chance. Hence, people may also be able to detect patterned/constrained processes in a real-world setting (e.g., streaks in basketball performance), thus supporting the belief in the hot hand.