Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 1 |
Since 2016 (last 10 years) | 1 |
Since 2006 (last 20 years) | 14 |
Descriptor
Models | 19 |
Probability | 19 |
Reinforcement | 19 |
Animals | 9 |
Experiments | 8 |
Stimuli | 6 |
Feedback (Response) | 5 |
Intervals | 5 |
Prediction | 5 |
Decision Making | 4 |
Learning Processes | 4 |
More ▼ |
Source
Journal of the Experimental… | 8 |
Psychological Review | 4 |
Learning & Memory | 2 |
Behavior Analyst | 1 |
Cognitive Science | 1 |
Journal of Educational and… | 1 |
Journal of Experimental… | 1 |
Author
Davison, Michael | 2 |
Barron, Greg | 1 |
Blei, David M. | 1 |
Cerutti, D. T. | 1 |
Chang, Hua-hua | 1 |
Christensen, Darren R. | 1 |
Claus, Eric D. | 1 |
Cleaveland, J. Mark | 1 |
Elliffe, Douglas | 1 |
Erev, Ido | 1 |
Frank, Michael J. | 1 |
More ▼ |
Publication Type
Journal Articles | 18 |
Reports - Research | 11 |
Reports - Descriptive | 3 |
Reports - Evaluative | 3 |
Opinion Papers | 1 |
Speeches/Meeting Papers | 1 |
Education Level
Higher Education | 1 |
Audience
Location
Israel | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
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
Janssen, Christian P.; Gray, Wayne D. – Cognitive Science, 2012
Reinforcement learning approaches to cognitive modeling represent task acquisition as learning to choose the sequence of steps that accomplishes the task while maximizing a reward. However, an apparently unrecognized problem for modelers is choosing when, what, and how much to reward; that is, when (the moment: end of trial, subtask, or some other…
Descriptors: Rewards, Reinforcement, Models, Memory
Neuringer, Allen – Behavior Analyst, 2012
The target paper by Barba (2012) raises issues that were the focus of the author's first two publications on operant variability. The author will describe the main findings in those papers and then discuss Barba's specific arguments. Barba has argued against the operant nature of variability. (Contains 2 figures.)
Descriptors: Reinforcement, Conditioning, Operant Conditioning, Feedback (Response)
Shteingart, Hanan; Neiman, Tal; Loewenstein, Yonatan – Journal of Experimental Psychology: General, 2013
We quantified the effect of first experience on behavior in operant learning and studied its underlying computational principles. To that goal, we analyzed more than 200,000 choices in a repeated-choice experiment. We found that the outcome of the first experience has a substantial and lasting effect on participants' subsequent behavior, which we…
Descriptors: Operant Conditioning, Behavior, Models, 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
Misak, Paul; Cleaveland, J. Mark – Journal of the Experimental Analysis of Behavior, 2011
In this article, we describe a test of the active time model for concurrent variable interval (VI) choice. The active time model (ATM) suggests that the time since the most recent response is one of the variables controlling choice in concurrent VI VI schedules of reinforcement. In our experiment, pigeons were trained in a multiple concurrent…
Descriptors: Models, Behavioral Science Research, Feedback (Response), Experiments
Christensen, Darren R.; Grace, Randolph C. – Journal of the Experimental Analysis of Behavior, 2009
Eight pigeons were trained in a concurrent-chains procedure in which the terminal-link immediacy ratio followed an ascending or descending series. Across sessions, one terminal-link delay changed from 2 s to 32 s to 2 s or from 32 s to 2 s to 32 s, while the other was always 8 s. For all pigeons, response allocation tracked changes in delay and…
Descriptors: Prediction, Models, Experiments, Reinforcement
White, K. Geoffrey; Wixted, John T. – Journal of the Experimental Analysis of Behavior, 2010
Delayed matching to sample is typically a two-alternative forced-choice procedure with two sample stimuli. In this task the effects of varying the probability of reinforcers for correct choices and the resulting receiver operating characteristic are symmetrical. A version of the task where a sample is present on some trials and absent on others is…
Descriptors: Response Style (Tests), Psychology, Probability, Gender Differences
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
Rodriguez, Paul F. – Learning & Memory, 2009
Memory systems are known to be influenced by feedback and error processing, but it is not well known what aspects of outcome contingencies are related to different memory systems. Here we use the Rescorla-Wagner model to estimate prediction errors in an fMRI study of stimulus-outcome association learning. The conditional probabilities of outcomes…
Descriptors: Feedback (Response), Prediction, Memory, Probability
Gershman, Samuel J.; Blei, David M.; Niv, Yael – Psychological Review, 2010
A. Redish et al. (2007) proposed a reinforcement learning model of context-dependent learning and extinction in conditioning experiments, using the idea of "state classification" to categorize new observations into states. In the current article, the authors propose an interpretation of this idea in terms of normative statistical inference. They…
Descriptors: Conditioning, Statistical Inference, Inferences, Bayesian Statistics
Jozefowiez, J.; Staddon, J. E. R.; Cerutti, D. T. – Psychological Review, 2009
The authors propose a simple behavioral economic model (BEM) describing how reinforcement and interval timing interact. The model assumes a Weber-law-compliant logarithmic representation of time. Associated with each represented time value are the payoffs that have been obtained for each possible response. At a given real time, the response with…
Descriptors: Intervals, Metacognition, Reinforcement, Time
Elliffe, Douglas; Davison, Michael; Landon, Jason – Journal of the Experimental Analysis of Behavior, 2008
One assumption of the matching approach to choice is that different independent variables control choice independently of each other. We tested this assumption for reinforcer rate and magnitude in an extensive parametric experiment. Five pigeons responded for food reinforcement on switching-key concurrent variable-interval variable-interval…
Descriptors: Criteria, Statistical Analysis, Reinforcement, Models
Erev, Ido; Barron, Greg – Psychological Review, 2005
Analysis of binary choice behavior in iterated tasks with immediate feedback reveals robust deviations from maximization that can be described as indications of 3 effects: (a) a payoff variability effect, in which high payoff variability seems to move choice behavior toward random choice; (b) underweighting of rare events, in which alternatives…
Descriptors: Behavior Patterns, Task Analysis, Feedback, Reinforcement
McDowell, J. J. – Journal of the Experimental Analysis of Behavior, 2004
Darwinian selection by consequences was instantiated in a computational model that consisted of a repertoire of behaviors undergoing selection, reproduction, and mutation over many generations. The model in effect created a digital organism that emitted behavior continuously. The behavior of this digital organism was studied in three series of…
Descriptors: Reinforcement, Models, Intervals, Behavior
Previous Page | Next Page ยป
Pages: 1 | 2