Signal detection by human observers: A cutoff reinforcement learning model of categorization decisions under uncertainty

Previous experimental examinations of binary categorization decisions have documented robust behavioral regularities that cannot be predicted by signal detection theory (D. M. Green & J. AI Swets, 1966/1988). The present article reviews the known regularities and demonstrates that they can be accounted for by a minimal modification of signal detection theory: the replacement of the "ideal observer" cutoff placement rule with a cutoff reinforcement learning rule. This modification is derived from a cognitive game theoretic analysis (A. E. Roth & I. Erev, 1995). The modified model reproduces all 19 experimental regularities that have been considered. In all cases, it outperforms the original explanations. Some of these previous explanations are based on important concepts such as conservatism, probability matching, and "the gambler's fallacy" that receive new meanings given the current results. Implications for decision-making research and for applications of traditional signal detection theory are discussed.