Rate My Professor Daniel Little

Little's research examines complex decision making, investigating how knowledge influences the perception and interpretation of new information, how knowledge develops through experience and learning, and the integration of multiple sources of evidence that may conflict or interact. He develops computational models to understand human thought, memory, attention, and decision making, bridging brain and behavior in attentionally demanding environments. Key publications include the book Systems Factorial Technology: A Theory Driven Methodology for the Identification of Perceptual and Cognitive Mechanisms (Academic Press, 2017, co-authored with Altieri, Fific, and Yang); Small is beautiful: In defense of the small-N design (Psychonomic Bulletin & Review, 2018, with Smith); Metastudies for robust tests of theory (Proceedings of the National Academy of Sciences, 2018, with Baribault et al.); Short-term memory scanning viewed as exemplar-based categorization (Psychological Review, 2011, with Nosofsky, Donkin, and Fific); Logical-rule models of classification response times: A synthesis of mental-architecture, random-walk, and decision-bound approaches (Psychological Review, 2010, with Fific and Nosofsky); and recent works such as Function estimation: Quantifying individual differences in hand-drawn functions (Memory & Cognition, 2024, with Shiffrin and Laham) and Unifying approaches to understanding capacity in change detection (Psychological Review, 2024, with Fong et al.). His scholarship has over 3,600 citations, demonstrating impact in the field through advancements in methodological approaches and theoretical models for cognitive processes.