Although we are generally good at observing a busy scene and determining whether it contains one agent pursuing another, we are not immune to making errors and may identify a pursuit when there is none. Further, we may have difficulty articulating exactly what information allowed us to determine whether there was a pursuit. To gain a better measure of when people correctly or erroneously detect pursuit, we designed a novel pursuit detection task. To compare performance given different strategies, we developed a cognitive model that can perform this task. The results of our pursuit detection experiment indicate that, indeed, people typically identify pursuit events correctly, but they make infrequent yet systematic errors for particular scenes. When the model implements specific strategies, simulation results are well correlated with empirical results. Moreover, the model makes the same errors as human participants. We show how the empirical results can be accounted for in terms of decision criteria indicated by high performing model strategies.

Authors:

Maria Kon: U.S. Naval Research Laboratory; Sangeet Khemlani: U.S. Naval Research Laboratory; Andrew Lovett: U.S. Naval Research Laboratory