Behavior Analysis and Revaluation

Donahoe, John W.; Burgos, José E.

doi:10.1901/jeab.2000.74-331

Cited by 31 publications

(15 citation statements)

References 80 publications

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“…[8], [27], [28], [29], [30], [31]). Therefore, lever pressing by both of the 10S10E groups during the extinction test likely was governed not just by 10S10E-reinforced learning, but also by 10S-reinforced learning.…”

Section: Resultsmentioning

confidence: 99%

Ethanol Seeking by Long Evans Rats Is Not Always a Goal-Directed Behavior

Mangieri

Cofresí²,

Gonzales³

2012

PLoS ONE

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BackgroundTwo parallel and interacting processes are said to underlie animal behavior, whereby learning and performance of a behavior is at first via conscious and deliberate (goal-directed) processes, but after initial acquisition, the behavior can become automatic and stimulus-elicited (habitual). With respect to instrumental behaviors, animal learning studies suggest that the duration of training and the action-outcome contingency are two factors involved in the emergence of habitual seeking of “natural” reinforcers (e.g., sweet solutions, food or sucrose pellets). To rigorously test whether behaviors reinforced by abused substances such as ethanol, in particular, similarly become habitual was the primary aim of this study.Methodology/Principal FindingsMale Long Evans rats underwent extended or limited operant lever press training with 10% sucrose/10% ethanol (10S10E) reinforcement (variable interval (VI) or (VR) ratio schedule of reinforcement), or with 10% sucrose (10S) reinforcement (VI schedule only). Once training and pretesting were complete, the impact of outcome devaluation on operant behavior was evaluated after lithium chloride injections were paired with the reinforcer, or unpaired 24 hours later. After limited, but not extended instrumental training, lever pressing by groups trained under VR with 10S10E and under VI with 10S was sensitive to outcome devaluation. In contrast, responding by both the extended and limited training 10S10E VI groups was not sensitive to ethanol devaluation during the test for habitual behavior.Conclusions/SignificanceOperant behavior by rats trained to self-administer an ethanol-sucrose solution showed variable sensitivity to a change in the value of ethanol, with relative insensitivity developing sooner in animals that received time-variable ethanol reinforcement during training sessions. One important implication, with respect to substance abuse in humans, is that initial learning about the relationship between instrumental actions and the opportunity to consume ethanol-containing drinks can influence the time course for the development or expression of habitual ethanol seeking behavior.

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Section: Resultsmentioning

confidence: 99%

Ethanol Seeking by Long Evans Rats Is Not Always a Goal-Directed Behavior

Mangieri

Cofresí²,

Gonzales³

2012

PLoS ONE

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“…It has provided a wealth of useful information about the speed with which discriminations are learned, the ability of learned performances to survive delays between the discriminative stimuli and the opportunity to respond, transfer of performance across disparate stimuli, and the associative relations to which subjects are sensitive and that underlie these behavioral effects. Differentialoutcome data have contributed substantially to theoretical analyses of discrimination learning, in particular, and instrumental learning, in general (e.g., Colwill, 1994;Colwill & Rescorla, 1986;Urcuioli & DeMarse, 1996), and to the understanding of diverse issues, such as the nature of working memory (e.g., Honig & Dodd, 1986;Overmier, Savage, & Sweeney, 1999;Urcuioli & Zentall, 1992), the origins of equivalence classes (e.g., Astley & Wasserman, 1999;de Rose, McIlvane, Dube, Galpin, & Stoddard, 1988;Edwards, Jagielo, Zentall, & Hogan, 1982), the neural bases of associative learning (e.g., Blundell, Hall, & Killcross, 2001;Savage, 2001;Savage & Parsons, 1997; see also Donahoe & Burgos, 2000), and behavioral remediation (e.g., Estévez, Fuentes, Overmier, & González, 2003;Hochhalter & Joseph, 2001;Malanga & Poling, 1992;Overmier et al, 1999).…”

mentioning

confidence: 99%

Behavioral and associative effects of differential outcomes in discrimination learning

Urcuioli

2005

Learning & Behavior

131

137

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The role of the reinforcer in instrumental discriminations has often been viewed as that of facilitating associative learning between a reinforced response and the discriminative stimulus that occasions it. The differential-outcome paradigm introduced by Trapold (1970), however, has provided compelling evidence that reinforcers are also part of what is learned in discrimination tasks. Specifically, when the availability of different reinforcing outcomes is signaled by different discriminative stimuli, the conditioned anticipation of those outcomes can provide another source of stimulus control over responding. This article reviews how such control develops and how it can be revealed, its impact on behavior, and different possible mechanisms that could mediate the behavioral effects. The main conclusion is that differential-outcome effects are almost entirely explicable in terms of the cue properties of outcome expectancies-namely, that conditioned expectancies acquire discriminative control just like any other discriminative or conditional stimulus in instrumental learning.

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“…More recently, Burgos and Murillo-Rodríguez [73] used a neural-network model to simulate two context-dependent phenomena in Pavlovian conditioning: context specificity and renewal. Prior to that, the computational framework was used to simulate a wide range of conditioning phenomena such as reacquisition savings [74], reinforcement reevaluation [75], superstition [76], and latent inhibition [77]. Although these neural-network models were inspired by biological data of Pavlovian fear conditioning, there was no correspondence between the models and exact neural structures, and the amygdala circuit was not modeled explicitly.…”

Section: Connectionist Modelsmentioning

confidence: 99%

Computational Models of the Amygdala in Acquisition and Extinction of Conditioned Fear

Li¹

2017

The Amygdala - Where Emotions Shape Perception, Learning and Memories

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The amygdala plays a central role in both acquisition and expression of conditioned fear associations and dysregulation of the amygdala leads to fear and anxiety disorders such as posttraumatic stress disorder (PTSD). Computational modeling has served as an important tool to understand the cellular and circuit mechanisms of fear acquisition and extinction. This review provides a critical appraisal of existing computational modeling studies of the amygdala and extended circuitry in acquisition and extinction of learned fear associations. It gives a broad overview of the computational techniques applied to amygdala modeling with an emphasis on how computational models could shed light on the neural mechanisms of fear learning, inform experimental design, and lead to specific, experimentally testable hypotheses. It covers different types of published models including rule-based models, connectionist type models, phenomenological spiking neuronal models, and detailed biophysical conductance-based models. Specific attention is given to the evolution of amygdala models from simple rule-based and connectionist type models to more sophisticated and biologically realistic models. Future direction on computational modeling of the amygdala and associated networks in emotional learning is also discussed.

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Behavior Analysis and Revaluation

Cited by 31 publications

References 80 publications

Ethanol Seeking by Long Evans Rats Is Not Always a Goal-Directed Behavior

Ethanol Seeking by Long Evans Rats Is Not Always a Goal-Directed Behavior

Behavioral and associative effects of differential outcomes in discrimination learning

Computational Models of the Amygdala in Acquisition and Extinction of Conditioned Fear

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