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Li, Xiao; Xu, Hanchen; Zhang, Jinming; Chang, Hua-hua – Journal of Educational and Behavioral Statistics, 2023
The adaptive learning problem concerns how to create an individualized learning plan (also referred to as a learning policy) that chooses the most appropriate learning materials based on a learner's latent traits. In this article, we study an important yet less-addressed adaptive learning problem--one that assumes continuous latent traits.…
Descriptors: Learning Processes, Models, Algorithms, Individualized Instruction
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Schulze, Christin; van Ravenzwaaij, Don; Newell, Ben R. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2017
Learning to choose adaptively when faced with uncertain and variable outcomes is a central challenge for decision makers. This study examines repeated choice in dynamic probability learning tasks in which outcome probabilities changed either as a function of the choices participants made or independently of those choices. This presence/absence of…
Descriptors: Decision Making, Rewards, Persistence, Probability
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Karsina, Allen; Thompson, Rachel H.; Rodriguez, Nicole M.; Vanselow, Nicholas R. – Analysis of Verbal Behavior, 2012
We evaluated the effects of differential reinforcement and accurate verbal rules with feedback on the preference for choice and the verbal reports of 6 adults. Participants earned points on a probabilistic schedule by completing the terminal links of a concurrent-chains arrangement in a computer-based game of chance. In free-choice terminal links,…
Descriptors: Correlation, Numbers, Probability, Reinforcement
Stuttgen, Maik C.; Yildiz, Ali; Gunturkun, Onur – Journal of the Experimental Analysis of Behavior, 2011
Pigeons responded in a perceptual categorization task with six different stimuli (shades of gray), three of which were to be classified as "light" or "dark", respectively. Reinforcement probability for correct responses was varied from 0.2 to 0.6 across blocks of sessions and was unequal for correct light and dark responses. Introduction of a new…
Descriptors: Infants, Reinforcement, Probability, Animals
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Stagner, Jessica P.; Laude, Jennifer R.; Zentall, Thomas R. – Learning and Motivation, 2011
When pigeons are given a choice between two alternatives, one leading to a stimulus 20% of the time that always signals reinforcement (S+) or another stimulus 80% of the time that signals no reinforcement (S-), and the other alternative leading to one of two stimuli each signaling reinforcement 50% of the time, they show a strong preference for…
Descriptors: Animals, Reinforcement, Probability, Stimuli
Mazur, James E.; Biondi, Dawn R. – Journal of the Experimental Analysis of Behavior, 2011
Parallel experiments with rats and pigeons examined reasons for previous findings that in choices with probabilistic delayed reinforcers, rats' choices were affected by the time between trials whereas pigeons' choices were not. In both experiments, the animals chose between a standard alternative and an adjusting alternative. A choice of the…
Descriptors: Animals, Intervals, Psychological Patterns, Probability
Alferink, Larry A.; Critchfield, Thomas S.; Hitt, Jennifer L.; Higgins, William J. – Journal of Applied Behavior Analysis, 2009
Based on a small sample of highly successful teams, past studies suggested that shot selection (two- vs. three-point field goals) in basketball corresponds to predictions of the generalized matching law. We examined the generality of this finding by evaluating shot selection of college (Study 1) and professional (Study 3) players. The matching law…
Descriptors: Team Sports, College Athletics, Athletes, Probability
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Huh, Namjung; Jo, Suhyun; Kim, Hoseok; Sul, Jung Hoon; Jung, Min Whan – Learning & Memory, 2009
Reinforcement learning theories postulate that actions are chosen to maximize a long-term sum of positive outcomes based on value functions, which are subjective estimates of future rewards. In simple reinforcement learning algorithms, value functions are updated only by trial-and-error, whereas they are updated according to the decision-maker's…
Descriptors: Learning Theories, Animals, Rewards, Probability
Chivers, Laura L.; Higgins, Stephen T.; Heil, Sarah H.; Proskin, Rebecca W.; Thomas, Colleen S. – Journal of Applied Behavior Analysis, 2008
Fifty-eight smokers received abstinence-contingent monetary payments for 1 (n = 15) or 14 (n = 43) days. Those who received contingent payments for 14 days also received 0, 1, or 8 experimenter-delivered cigarette puffs on 5 evenings. The relative reinforcing effects of smoking were assessed in a 3-hr session on the final study day, when…
Descriptors: Smoking, Contingency Management, Reinforcement, Program Effectiveness
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Frank, Michael J.; Claus, Eric D. – Psychological Review, 2006
The authors explore the division of labor between the basal ganglia-dopamine (BG-DA) system and the orbitofrontal cortex (OFC) in decision making. They show that a primitive neural network model of the BG-DA system slowly learns to make decisions on the basis of the relative probability of rewards but is not as sensitive to (a) recency or (b) the…
Descriptors: Brain, Decision Making, Probability, Reinforcement