Office: 2001 McGill College, 711
Department of Psychology
2001 McGill College, 7th floor
Why do we sometimes rely on slow, deliberative, and effortful choices, while at other times we rely on fast, habitual, and reflexive choice? On one hand, making the best possible decision is effortful and time-consuming, but on the other hand, the benefits resulting from deliberative behavior may be small relative to its cost. My research investigates why we sometimes rely on slow and effortful choices, while at other times we rely on fast and reflexive choice. For example, how does an individual’s reliance upon reflective versus reflexive choice vary situationally based on factors like availability of cognitive resources, stress, time pressure, or perceived costs and benefits? Why might individuals differ, dispositionally, in their reliance upon reflective versus reflexive choices? To answer these questions, we use a combination of computational, behavioral, and psychophysiological, and neuroimaging techniques.
Otto, A. R., Fleming, S.M., & Glimcher, P.W. (2016). Unexpected but Incidental Positive Outcomes Predict Real-World Gambling. Psychological Science, 27(3), 299-311.
Otto, A. R., Skatova, A., Madlon-Kay, S., & Daw, N. D. (2015). Cognitive Control Predicts Use of Model-based Reinforcement Learning. Journal of Cognitive Neuroscience, 27(2), 319–333.
Otto, A.R., Knox, W.B., Markman, A.B., & Love, B.C. (2014). Behavioral and physiological signatures of reflective exploratory choice. Cognitive, Affective, & Behavioral Neuroscience, 14(4), 1167–1183.
Otto, A. R., Raio, C. M., Chiang, A., Phelps, E. A., & Daw, N. D. (2013). Working-memory capacity protects model-based learning from stress. Proceedings of the National Academy of Sciences, 110(52), 20941–20946.
Otto, A.R., Gershman, S.J., Markman, A.B., & Daw, N.D. (2013). The curse of planning: dissecting multiple Reinforcement Learning systems by taxing the central executive. Psychological Science, 24(5), 751-761.