Sensitivity to delay is affected by magnitude of reinforcement in rats

Orduña, Vladimir; Valencia-Torres, Lourdes; Cruz, G. J. Chacón; Bouzas, Arturo

doi:10.1016/j.beproc.2013.04.011

Cited by 23 publications

(42 citation statements)

References 44 publications

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“…Many past studies of operant conditioning have emphasized Table 2 Rankings of the relative contribution of each behavioral measure to the spatial organization of self-organizing maps as determined through SOM Discrimination Indices Rank SOM Individuals (n = 9 rats) SOM Group (n = 12 rats) Note: Italicized measures correspond to those analyzed by Orduña et al (2013). Initial link -2 = lever presses made to the lever associated with terminal links (TL) with a 2 s delay; Initial link -28 = lever presses made to the lever associated with terminal links with a 28 s delay; (a, b) = parameters within the exponential function a * exp(bx); switch bias = the number of switches in one direction/the number of switches in the opposite direction; return bias = the number of returns to the short-delay lever after a feeder entry/the number of returns to the long-delay lever after a feeder entry; DUR = duration; CV = coefficient of variation; RPDE = recurrence period density entropy; ACD = autocorrelation difference, a measure of the difference between the measured autocorrelation function and an autocorrelation function for an aperiodic time series containing the same intervals; SOM Individuals rankings were based on SOM Discrimination Indices calculated from SOMs trained with data from individual rats reflecting how strongly each measure discriminated behavioral patterns associated with smaller or larger reinforcers; SOM Group rankings were based on SOM Discrimination Indices calculated from SOMs trained with data from all rats, again reflecting how strongly a measure differentiated patterns associated with smaller or larger reinforcers.…”

Section: Discussionmentioning

confidence: 99%

“…Sessions with FI schedules in terminal links were associated with faster rates of responding on both levers (producing shorter IRTs), and a greater numbers of switches between levers (leading to more bouts) relative to sessions with FT schedules in terminal links. Orduña et al (2013) found no statistically significant effect of terminal link schedules on response rates within initial links when they analyzed performance in the last five sessions of training, suggesting that the effects of terminal link schedules on responding may be less evident later in training.…”

Section: Individual Variations In Response Patternsmentioning

confidence: 96%

“…In the current analysis, the degree of regularity in lever pressing and nose poking actions of rats was quantified by analyzing the time series of responses produced during training, as well as more molar measures of response rate. Details of the subjects and experimental procedure are provided by Orduña et al (2013), and will only be briefly summarized here.…”

Section: Behavioral Analysismentioning

confidence: 99%

“…Each condition was presented for 20 sessions; the conditions were conducted in different orders across subjects. Orduña et al (2013) plotted and statistically analyzed rats' response rates in the initial links during the final five sessions of training as a function of different reinforcer magnitudes and terminal link delay conditions, whereas in the present study the regularity of response patterns across all training sessions was quantified and analyzed with SOMs. Furthermore, systematic variations in the responses of individual rats were analyzed and compared to those evident at the group level.…”

Section: General Experimental Proceduresmentioning

confidence: 99%

“…Additionally, analyses of spatial organization within SOMs trained with data from all rats made it possible to evaluate whether the same systematic variations evident in the SOMs from individual rats were also evident in SOMs created from multiple rats. Note: Italicized measures correspond to those analyzed by Orduña et al (2013). Initial link -2 = lever presses made to the lever associated with terminal links (TL) with a 2 s delay; Initial link -28 = lever presses made to the lever associated with terminal links with a 28 s delay; (a, b) = parameters within the exponential function a * exp(bx); switch bias = the number of switches in one direction/the number of switches in the opposite direction; return bias = the number of returns to the short-delay lever after a feeder entry/the number of returns to the long-delay lever after a feeder entry; DUR = duration; CV = coefficient of variation; RPDE = recurrence period density entropy; ACD = autocorrelation difference, a measure of the difference between the measured autocorrelation function and an autocorrelation function for an aperiodic time series containing the same intervals…”

Section: Analyzing Variations In Response Patternsmentioning

confidence: 99%

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Regularities in responding during performance of a complex choice task

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2015

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Systematic variations in the rate and temporal patterns of responding under a multiple concurrent-chains schedule were quantified using recurrence metrics and selforganizing maps to assess whether individual rats showed consistent or idiosyncratic patterns. The results indicated that (1) the temporal regularity of response patterns varied as a function of number of training sessions, time on task, magnitude of reinforcement, and reinforcement contingencies; (2) individuals showed heterogeneous, stereotyped patterns of responding, despite similarities in matching behavior; (3) the specific trajectories of behavioral variation shown by individuals were less evident in group-level analyses; and (4) reinforcement contingencies within terminal links strongly modulated response patterns within initial links. Temporal regularity in responding was most evident for responses that led to minimally delayed reinforcers of larger magnitude. Models of response production and selection that take into account the time between individual responses, probabilities of transitions between response options, periodicity within response sequences, and individual differences in response dynamics can clarify the mechanisms that drive behavioral adjustments during operant conditioning.Keywords Behavior dynamics . Individual variations . Matching law . Neural networks . Reinforcement schedules A basic assumption of several theories of operant conditioning is that group-level measures of performance are sufficient for understanding the mechanisms of learning within individuals (Pear, 2001;Rachlin, 2000;Staddon & Cerutti, 2003;Williams, 1990). Although there is good evidence that this is often not the case (Gallistel, Fairhurst, & Balsam, 2004;Gallistel et al., 2007), what kinds of behavioral descriptions might best address such complications is unknown. Possibly, modeling behavior at the level of the individual would suffice. But, if the mechanisms engaged during learning vary significantly across individuals, then fitting a single uniform learning model to individual performances might be just as inappropriate as assuming that group-level models adequately describe behavior (e.g., see Worthy, Hawthorne, & Otto, 2013). Gaining a clearer understanding of which aspects of learned behavior are prevalent within and across individuals, and the extent to which those properties are predictable, is thus critical to choosing measures of behavior that most clearly reveal the nature of the mechanisms that drive long-term changes in behavior. The purpose of the present study was to quantitatively assess the regularity 1 of responses produced by individual subjects performing in a relatively complex operant conditioning task to determine which measures of behavior best characterized changes in subjects' response patterns as a function of training conditions.Early in the history of animal learning research, it was discovered that training animals within constrained environments often led subjects to perform stereotyped actions. For instance, Gut...

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Section: Discussionmentioning

confidence: 99%

Section: Individual Variations In Response Patternsmentioning

confidence: 96%

Section: Behavioral Analysismentioning

confidence: 99%

Section: General Experimental Proceduresmentioning

confidence: 99%

Section: Analyzing Variations In Response Patternsmentioning

confidence: 99%

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Orduña

2015

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Delay‐specific stimuli and genotype interact to determine temporal discounting in a rapid‐acquisition procedure

Pope

Newland

Hutsell

2015

J Exper Analysis Behavior

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The importance of delay discounting to many socially important behavior problems has stimulated investigations of biological and environmental mechanisms responsible for variations in the form of the discount function. The extant experimental research, however, has yielded disparate results, raising important questions regarding Gene X Environment interactions. The present study determined the influence of stimuli that uniquely signal delays to reinforcement on delay discounting in two inbred mouse strains using a rapid-acquisition procedure. BALB/c and C57BL/6 mice responded under a six-component, concurrent-chained schedule in which the terminal-link delays preceding the larger-reinforcer were presented randomly across components of an individual session. Across conditions, components were presented either with or without delay-specific auditory stimuli, i.e., as multiple or mixed schedules. A generalized matching-based model was used to incorporate the impact of current and previous component reinforcer-delay ratios on current component response allocation. Sensitivity to reinforcer magnitude and delay were higher for BALB/c mice, but within-component preference reached final levels faster for C57Bl/6 mice. For BALB/c mice, acquisition of preference across blocks of a component was faster under the multiple than the mixed schedule, but final levels of sensitivity to reinforcement were unaffected by schedule. The speed of acquisition of preference was not different across schedules for C57Bl/6 mice, but sensitivity to reinforcement was higher under the multiple than the mixed schedule. Overall, differences in the acquisition and final form of the discount function were determined by a Gene X Environment interaction, but the presence of delay-specific stimuli attenuated genotype-dependent differences in magnitude and delay sensitivity.

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Reinforcement value and fixed‐interval performance

Buriticá

Santos

2017

J Exper Analysis Behavior

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The concept of reinforcement value summarizes the effect of different variables, such as reinforcement delay, reinforcement magnitude, and deprivation level, on behavior. In the present set of experiments, we evaluated the effect of reinforcement devaluation on performance under FI schedules. The literature on timing and reinforcement value suggests that devaluation generates longer expected times to reinforcement than the same intervals trained under control conditions. We devalued reinforcement with delay in Experiments 1A, 1B, and 2, and diminished deprivation in Experiments 3A and 3B. Devaluation reduced response rates, increased the number of one-response intervals, and lengthened postreinforcement pauses, but had inconsistent effects on other timing measures such as quarter life and breakpoint. The results of delayed reinforcement and diminished deprivation manipulations are well summarized as reinforcement devaluation effects. These results suggest that devaluation may reduce stimulus control. In addition, we argue that the process by which delayed reinforcement affects behavior might also explain some effects observed in other devaluation procedures through the concept of reinforcement value.

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Sensitivity to delay is affected by magnitude of reinforcement in rats

Cited by 23 publications

References 44 publications

Regularities in responding during performance of a complex choice task

Regularities in responding during performance of a complex choice task

Delay‐specific stimuli and genotype interact to determine temporal discounting in a rapid‐acquisition procedure

Reinforcement value and fixed‐interval performance

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