Model of conditioning incorporating the Rescorla-Wagner associative axiom, a dynamic attention process, and a catastrophe rule.

Frey, Peter W.; Sears, Ronald J.

doi:10.1037/0033-295x.85.4.321

Cited by 159 publications

(110 citation statements)

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“…The similar acquisition rate in the current study might lead one to conclude that 0.1 mg/kg nicotine is as salient as the higher nicotine doses (for discussions of effects of conditional stimulus salience on acquisition of Pavlovian conditioning, see Frey and Sears, 1978, Pavlov, 1927, Rescorla, 1988and Rescorla and Wagner, 1972. A non-salience explanation for the similar acquisition rate of the 3 groups (0.1, 0.2, and 0.4 mg/kg) involves the rich schedule of sucrose deliveries in nicotine sessions.…”

Section: Discussionmentioning

confidence: 99%

Behavioral and neuropharmacological characterization of nicotine as a conditional stimulus

Murray

Bevins

2007

European Journal of Pharmacology

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Section: Discussionmentioning

confidence: 99%

Behavioral and neuropharmacological characterization of nicotine as a conditional stimulus

Murray

Bevins

2007

European Journal of Pharmacology

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“…This explicit rendering of model output into expected behavioral responding allows for more directly testable predictions. There are many possible response rules (e.g., Church & Kirkpatrick, 2001;Frey & Sears, 1978;Moore et al, 1986), but, for our purposes, a simple formalism will suffice. We assume that there is a reflexive mapping from US prediction to CR in the form of a thresholded leaky integrator.…”

Section: Model Specificationmentioning

confidence: 99%

Evaluating the TD model of classical conditioning

2012

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The temporal-difference (TD) algorithm from reinforcement learning provides a simple method for incrementally learning predictions of upcoming events. Applied to classical conditioning, TD models suppose that animals learn a real-time prediction of the unconditioned stimulus (US) on the basis of all available conditioned stimuli (CSs). In the TD model, similar to other error-correction models, learning is driven by prediction errors-the difference between the change in US prediction and the actual US. With the TD model, however, learning occurs continuously from moment to moment and is not artificially constrained to occur in trials. Accordingly, a key feature of any TD model is the assumption about the representation of a CS on a moment-to-moment basis. Here, we evaluate the performance of the TD model with a heretofore unexplored range of classical conditioning tasks. To do so, we consider three stimulus representations that vary in their degree of temporal generalization and evaluate how the representation influences the performance of the TD model on these conditioning tasks.Keywords Associative learning . Classical conditioning . Timing . Reinforcement learning Classical conditioning is the process of learning to predict the future. The temporal-difference (TD) algorithm is an incremental method for learning predictions about impending outcomes that has been used widely, under the label of reinforcement learning, in artificial intelligence and robotics for real-time learning (Sutton & Barto, 1998). In this article, we evaluate a computational model of classical conditioning based on this TD algorithm. As applied to classical conditioning, the TD model supposes that animals use the conditioned stimulus (CS) to predict in real time the upcoming unconditioned stimuli (US) (Sutton & Barto, 1990). The TD model of conditioning has become the leading explanation for conditioning in neuroscience, due to the correspondence between the phasic firing of dopamine neurons and the reward-prediction error that drives learning in the model (Schultz, Dayan, & Montague, 1997; for reviews, see Ludvig, Bellemare, & Pearson, 2011;Maia, 2009;Niv, 2009;Schultz, 2006).The TD model can be viewed as an extension of the Rescorla-Wagner (RW) learning model, with two additional twists (Rescorla & Wagner, 1972). First, the TD model makes real-time predictions at each moment in a trial, thereby allowing the model to potentially deal with intratrial effects, such as the effects of stimulus timing on learning and the timing of responses within a trial. Second, the TD algorithm uses a slightly different learning rule with important implications. As will be detailed below, at each time step, the TD algorithm compares the current prediction about future US occurrences with the US predictions generated on the last time step. This temporal difference in US prediction is compared with any actual US received; if the latter two quantities differ, a prediction error is generated. This prediction error is then used to alter the associative ...

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“…If we extend the baseline of the learning curve ( Figure 31.1, left) prior to the onset of the learning trials, two stable states are apparent; according to Frey and Sears (1978) hysteresis exists between learning and extinction curves cannot be explained otherwise. Different inflections in learning curves can be explained as a cusp bifurcation manifold (Guastello et aI., 2005a) as shown in Figure 31.1 (right).…”

Section: Learningmentioning

confidence: 99%

Nonlinear Dynamical Systems Applications to Psychology and Management

Guastello

2011

The Sage Handbook of Complexity and Management

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This chapter surveys the recent developments in the application of nonlinear dynamical systems (NDS) theory to theoretical and practical problems encountered in psychology that are also relevant to management. For the benefit of non-psychologists, it is important to note that the scope of psychology is expansive. Introductory textbooks are typically organized around the following themes: brain physiology and behavior, psychophysics, sensation, perception, learning, memory, cognition, intelligence and mental measurement, development, social psychology, motivation and emotion, personality of normal range people, abnormal psychology, psychotherapy and counseling, and industrial-organizational psychology. At the other end of the professional spectrum, the largest professional organization for psychologists, the American Psychological Association, contains more than 50 topical interest groups in addition to its general membership core. The literature on NDS psychology reaches all the major areas of psychology and is

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Model of conditioning incorporating the Rescorla-Wagner associative axiom, a dynamic attention process, and a catastrophe rule.

Cited by 159 publications

References 43 publications

Behavioral and neuropharmacological characterization of nicotine as a conditional stimulus

Behavioral and neuropharmacological characterization of nicotine as a conditional stimulus

Evaluating the TD model of classical conditioning

Nonlinear Dynamical Systems Applications to Psychology and Management

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