Limits to preference and the sensitivity of choice to rate and amount of food

Self Cite

Most studies of operant choice have focused on presenting subjects with a fixed pair of schedules across many experimental sessions. Using these methods, studies of concurrent variable- interval variable-ratio schedules helped to evaluate theories of choice. More recently, a growing literature has focused on dynamic choice behavior. Those dynamic choice studies have analyzed behavior on a number of different time scales using concurrent variable-interval schedules. Following the dynamic choice approach, the present experiment examined performance on concurrent variable-interval variable-ratio schedules in a rapidly changing environment. Our objectives were to compare performance on concurrent variable-interval variable-ratio schedules with extant data on concurrent variable-interval variable-interval schedules using a dynamic choice procedure and to extend earlier work on concurrent variable-interval variable-ratio schedules. We analyzed performances at different time scales, finding strong similarities between concurrent variable-interval variable-interval and concurrent variable-interval variable- ratio performance within dynamic choice procedures. Time-based measures revealed almost identical performance in the two procedures compared with response-based measures, supporting the view that choice is best understood as time allocation. Performance at the smaller time scale of visits accorded with the tendency seen in earlier research toward developing a pattern of strong preference for and long visits to the richer alternative paired with brief "samples" at the leaner alternative ("fix and sample").

Section: Discussionmentioning

confidence: 94%

Concurrent variable‐interval variable‐ratio schedules in a dynamic choice environment

Bell

Baum

2017

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“…These mathematical models have often been analyzed using simple methods, without a focus on statistical rigor. Typically, for example, parameter estimates are found by least-squares optimization (e.g., Aparicio, Baum, Hughes, & Pitts, 2016;Baum & Davison, 2014;Davison & McCarthy, 1988), and sometimes hypotheses are simply tested by observing that parameter estimates are close (or far) from a theoretically expected value (e.g., Baum, 1979Baum, , 1983Mullins, Agunwamba, & Donohoe, 1982).…”

Section: Introductionmentioning

confidence: 99%

Bayesian methods applied to the generalized matching law

Villarreal

Velázquez

Baroja

et al. 2019

We demonstrate the usefulness of Bayesian methods in developing, evaluating, and using psychological models in the experimental analysis of behavior. We do this through a case study, involving new experimental data that measure the response count and time allocation behavior in pigeons under concurrent random‐ratio random‐interval schedules of reinforcement. To analyze these data, we implement a series of behavioral models, based on the generalized matching law, as graphical models, and use computational methods to perform fully Bayesian inference. We demonstrate how Bayesian methods, implemented in this way, make inferences about parameters representing psychological variables, how they test the descriptive adequacy of models as accounts of behavior, and how they compare multiple competing models. We also demonstrate how the Bayesian graphical modeling approach allows for more complicated modeling structures, including hierarchical, common cause, and latent mixture structures, to formalize more complicated behavioral models. As part of the case study, we demonstrate how the statistical properties of Bayesian methods allow them to provide more direct and intuitive tests of theories and hypotheses, and how they support the creative and exploratory development of new theories and models.

“…Indeed, numerous studies indicate that response allocation in the initial links by pigeons is exquisitely controlled by the reinforcement parameters arranged in the terminal links. Although concurrent-chains procedures have been used with rodents (e.g., Aparicio et al, 2013Aparicio et al, , 2016Aparicio et al, , 2019Ito & Asaki, 1982;Johnson et al, 2013;Orduña et al, 2013;Yates et al, 2018Yates et al, , 2020, this literature is much less extensive than with pigeons. Furthermore, there is lab lore suggesting that choice in rodents does not come under control of within-session changes in reinforcement parameters as readily as choice does in pigeons (e.g., Kyonka, personal communication; we have not been as successful with rats as we have been with pigeons).…”

mentioning

confidence: 99%

A method for studying reinforcement factors controlling impulsive choice for use in behavioral neuroscience

Hughes

Langford

Heukelom

et al. 2022

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Although procedures originating within the experimental analysis of behavior commonly are used in behavioral neuroscience to produce behavioral endpoints, they are used less often to analyze the behavioral processes involved, particularly at the level of individual organisms (see Soto, 2020). Concurrent‐chains procedures have been used extensively to study choice and to quantify relations between various dimensions of reinforcement and preference. Unfortunately, parametric analysis of those relations using traditional steady‐state, single‐subject experimental designs can be time‐consuming, often rendering these procedures impractical for use in behavioral neuroscience. The purpose of this paper is to describe how concurrent‐chains procedures can be adapted to allow for parametric examination of effects of the reinforcement dimensions involved in impulsive choice (magnitude and delay) within experimental sessions in rats. Data are presented indicating that this procedure can produce relatively consistent within‐session estimates of sensitivity to reinforcement in individual subjects, and that these estimates can be modified by neurobiological manipulation (drug administration). These data suggest that this type of procedure offers a promising approach to the study of neurobiological mechanisms of complex behavior in individual organisms, which could facilitate a more fruitful relationship between behavior analysis and behavioral neuroscience.