2018
DOI: 10.1007/s00213-017-4825-0
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Dissecting drug effects in preclinical models of impulsive choice: emphasis on glutamatergic compounds

Abstract: Future studies should utilize paradigms that allow one to observe alterations in responding at each delay (e.g., concurrent-chains schedules). Concerning statistical analyses, using parameter estimates derived from nonlinear functions or incorporating the generalized matching law can allow one to determine if drugs affect sensitivity to delayed reinforcement or impair discrimination of the large and small magnitude reinforcers. Using these approaches can help further our understanding of the neurochemical unde… Show more

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Cited by 11 publications
(9 citation statements)
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References 196 publications
(478 reference statements)
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“…Perhaps, these inconsistent findings reflect idiosyncratic differences across studies (e.g., using different delays/probabilities, using different reinforcer magnitudes [1 vs. 3 or 1 vs. 4], using a different number of training sessions, etc.). Considering numerous factors can influence responding for large, delayed/probabilistic reinforcers (see Yates, 2018 for a discussion), future studies are needed to systematically determine when ascending and descending schedules modulate impulsive/risky choice.…”
Section: Discussionmentioning
confidence: 99%
“…Perhaps, these inconsistent findings reflect idiosyncratic differences across studies (e.g., using different delays/probabilities, using different reinforcer magnitudes [1 vs. 3 or 1 vs. 4], using a different number of training sessions, etc.). Considering numerous factors can influence responding for large, delayed/probabilistic reinforcers (see Yates, 2018 for a discussion), future studies are needed to systematically determine when ascending and descending schedules modulate impulsive/risky choice.…”
Section: Discussionmentioning
confidence: 99%
“…Significant interactions can be probed with follow-up ANOVAs and/or paired-samples t -tests. Although ANOVAs are commonly used, this analysis has multiple limitations that have been described in detail elsewhere (Yates, 2018; Young, Clark, Goffus, & Hoane, 2009). This review will briefly cover a couple of the most relevant issues of using ANOVAs to determine drug effects in risky choice tasks.…”
Section: Analytic Considerations In Risky Choice Tasksmentioning
confidence: 99%
“…In both the Yates et al (2015) and Yates et al (2016) studies, the A and h parameter estimates were derived via nonlinear regression and then subjected to a secondary analysis (e.g., ANOVA, Friedman test). Using two-stage approaches to analyzing data can be problematic (see Jonsson, Wade, & Karlsson, 2000; Yates, 2018; Young, 2017 for discussions) as they have the same limitations as discussed above (e.g., listwise deletion of an entire subject’s dataset when partially missing data are included, increased Type I error). Also, in cases in which an animal shows extreme sensitivity to probabilistic reinforcement (e.g., stops responding for the large magnitude reinforcer as soon as the probability of earning that alternative decreases), the h parameter obtained for that subject can significantly increase within-groups variability.…”
Section: Analytic Considerations In Risky Choice Tasksmentioning
confidence: 99%
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“…The advantage of this procedure is that it allows one to determine how pharmacological manipulations alter DD within a single session. Specifically, a hyperbolic function can be used to describe discounting, which is defined by the equation V = A /(1+ k D), where V is the subjective value of the reinforcer, A is the intercept of the function and indicates how much an animal responds for the LR when its delivery is immediate (for simplicity, we will use the term “sensitivity to reinforcer magnitude” [SRM]), k is the slope of the function and reflects sensitivity to delayed reinforcement (SDR; note: the term impulsive choice is often used interchangeably with SDR, but these terms are not always isomorphic; [2]), and D is delay.…”
mentioning
confidence: 99%