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  1. What do reinforcers strengthen? The unit of selection.John W. Donahoe - 1994 - Behavioral and Brain Sciences 17 (1):138-139.
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  • The return of the reinforcement theorists.C. D. L. Wynne - 1994 - Behavioral and Brain Sciences 17 (1):156-156.
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  • Has learning been shown to be attractor modification within reinforcement modelling?Robert A. M. Gregson - 1994 - Behavioral and Brain Sciences 17 (1):140-141.
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  • Reinforcement without representation.Stephen José Hanson - 1994 - Behavioral and Brain Sciences 17 (1):141-142.
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  • Integration and specificity of retrieval in a memory-based model of reinforcement.Marvin D. Krank - 1994 - Behavioral and Brain Sciences 17 (1):142-143.
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  • When, What, and How Much to Reward in Reinforcement Learning-Based Models of Cognition.Christian P. Janssen & Wayne D. Gray - 2012 - Cognitive Science 36 (2):333-358.
    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 interval of task performance), what (the objective function: e.g., performance time or performance accuracy), and how much (the magnitude: with binary, categorical, or continuous values). In this article, (...)
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  • Précis of simple heuristics that make us Smart.Peter M. Todd & Gerd Gigerenzer - 2000 - Behavioral and Brain Sciences 23 (5):727-741.
    How can anyone be rational in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury? Traditional models of unbounded rationality and optimization in cognitive science, economics, and animal behavior have tended to view decision-makers as possessing supernatural powers of reason, limitless knowledge, and endless time. But understanding decisions in the real world requires a more psychologically plausible notion of bounded rationality. In Simple heuristics that make us smart (Gigerenzer et al. 1999), we (...)
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  • Extension to multiple schedules: Some surprising (and accurate) predictions.John A. Nevin - 1994 - Behavioral and Brain Sciences 17 (1):145-146.
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  • From overt behavior to hypothetical behavior to memory: Inference in the wrong direction.Howard Rachlin - 1994 - Behavioral and Brain Sciences 17 (1):147-148.
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  • Animal-centered models of reinforcement.William Timberlake - 1994 - Behavioral and Brain Sciences 17 (1):153-154.
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  • Rats, responses and reinforcers: Using a little psychology on our subjects.Peter R. Killeen - 1994 - Behavioral and Brain Sciences 17 (1):157-172.
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  • Awareness and reinforcement.Charles P. Shimp - 1994 - Behavioral and Brain Sciences 17 (1):149-150.
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  • Practical effects of response specification.Richard L. Shull - 1994 - Behavioral and Brain Sciences 17 (1):150-150.
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  • A mathematical theory of reinforcement: An unexpected place to find support for analogical memory coding.Donald M. Wilkie & Lisa M. Saksida - 1994 - Behavioral and Brain Sciences 17 (1):155-156.
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  • Memories and functional response units.Kennon A. Lattal & Josele Abreu-Rodrigues - 1994 - Behavioral and Brain Sciences 17 (1):143-144.
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  • Killeen's theory provides an answer – and a question.Mary Ann Metzger & Terje Sagvolden - 1994 - Behavioral and Brain Sciences 17 (1):144-145.
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  • What defines a legitimate issue for Skinnerian psychology: Philosophy or technology?Hank Davis - 1994 - Behavioral and Brain Sciences 17 (1):137-138.
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  • Moving beyond schedules and rate: A new trajectory?Gregory Galbicka - 1994 - Behavioral and Brain Sciences 17 (1):139-140.
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  • Validation of behavioural equations: Can neurobiology help?C. M. Bradshaw - 1994 - Behavioral and Brain Sciences 17 (1):136-137.
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  • Memory and the integration of response sequences.Phil Reed - 1994 - Behavioral and Brain Sciences 17 (1):148-149.
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  • How general is a general theory of reinforcement?Stephen F. Walker - 1994 - Behavioral and Brain Sciences 17 (1):154-155.
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  • The response problem.Robert C. Bolles - 1994 - Behavioral and Brain Sciences 17 (1):135-136.
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  • Mathematical principles of reinforcement.Peter R. Killeen - 1994 - Behavioral and Brain Sciences 17 (1):105-135.
    Effective conditioning requires a correlation between the experimenter's definition of a response and an organism's, but an animal's perception of its behavior differs from ours. These experiments explore various definitions of the response, using the slopes of learning curves to infer which comes closest to the organism's definition. The resulting exponentially weighted moving average provides a model of memory that is used to ground a quantitative theory of reinforcement. The theory assumes that: incentives excite behavior and focus the excitement on (...)
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  • Problems and pitfalls for Killeen's mathematical principles of reinforcement.Joseph J. Pear - 1994 - Behavioral and Brain Sciences 17 (1):146-147.
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  • The scale of nature: Fitted parameters and dimensional correctness.D. W. Stephens - 1994 - Behavioral and Brain Sciences 17 (1):150-152.
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  • Short-term memory in human operant conditioning.Frode Svartdal - 1994 - Behavioral and Brain Sciences 17 (1):152-153.
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  • Fifty years on: The new “principles of behavior”?J. H. Wearden - 1994 - Behavioral and Brain Sciences 17 (1):155-155.
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