Rescaling of temporal expectations during extinction.

Drew, Michael R.; Walsh, Carolyn J.; Balsam, Peter D.

doi:10.1037/xan0000127

Cited by 7 publications

(14 citation statements)

References 41 publications

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“…They show a marked delay on extinction when the CS duration was shortened, but not when it was lengthened. Compare these curves with the actual data analysed by Drew et al (2017) and displayed in the rightmost panel of figure 5. The simulations conflict in part with the same analysis in Drew et al (2017), which showed no delay on extinction, only facilitation in the case of extending CS duration.…”

Section: Simulationsmentioning

confidence: 95%

“…As pointed out by Drew et al (2017), the value obtained for the rate of extinction may be affected by which portion of the CS was measured. To analyse this, Drew et al (2017) measured response frequency only during the first 6-sec (half the duration of the CS in acquisition) of each CS duration in extinction. We have followed the same procedure and the results can be seen on the middle panel of figure 5.…”

Section: Simulationsmentioning

confidence: 99%

“…Guilhardi and Church (2006) performed a similar experiment (experiment 2) and observed that when stimulus duration is changed from acquisition to extinction, the pattern of responding during extinction gradually shifts to the new duration over extinction trials. Following the same procedure, Drew et al (2017) also used partial reinforcement to slow down the rate of acquisition, and thus observe if response patterns really do shift gradually to the new duration. They confirmed that when CS duration was increased from acquisition to extinction, the within-trial response peak shifted gradually to the right over the course of extinction.…”

Section: Time Change In Extinctionmentioning

confidence: 99%

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A Rescorla-Wagner Drift-Diffusion Model of Conditioning and Timing

Luzardo

Alonso

Mondragón

2017

Preprint

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Computational models of classical conditioning have made significant contributions to the theoretic understanding of associative learning, yet they still struggle when the temporal aspects of conditioning are taken into account. Interval timing models have contributed a rich variety of time representations and provided accurate predictions for the timing of responses, but they usually have little to say about associative learning. In this article we present a unified model of conditioning and timing that is based on the influential RescorlaWagner conditioning model and the more recently developed Timing Drift-Diffusion model. We test the model by simulating 10 experimental phenomena and show that it can provide an adequate account for 8, and a partial account for the other 2. We argue that the model can account for more phenomena in the chosen set than these other similar in scope models: CSC-TD, MS-TD, Learning to Time and Modular Theory. A comparison and analysis of the mechanisms in these models is provided, with a focus on the types of time representation and associative learning rule used. Author summaryHow does the time of events affect the way we learn about associations between these events? Computational models have made great contributions to our understanding of associative learning, but they usually do not perform very well when time is taken into account. Models of timing have reached high levels of accuracy in describing timed behaviour, but they usually do not have much to say about associations. A unified approach would involve combining associative learning and timing models into a single framework. This article takes just this approach. It combines the influential Rescorla-Wagner associative model with a timing model based on the Drift-Diffusion process, and shows how the resultant model can account for a number of learning and timing phenomena. The article also compares the new model to others that are similar in scope.PLOS Computational Biology | https://doi

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Section: Simulationsmentioning

confidence: 95%

Section: Simulationsmentioning

confidence: 99%

Section: Time Change In Extinctionmentioning

confidence: 99%

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A Rescorla-Wagner Drift-Diffusion Model of Conditioning and Timing

Luzardo

Alonso

Mondragón

2017

Preprint

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“…The policy that determines how rapidly they respond during a CS takes the reinforced trials so far accumulated as an input (Fig. The strategy pigeons and rodents implement during extinction takes into account the probability that a trial during the training epoch was reinforced (Bouton, Woods, & Todd, 2014;Drew, Walsh, & Balsam, 2017;Drew, Yang, Ohyama, & Balsam, 2004;Harris, 2019;Harris & Andrew, 2017). The policy that determines how many responses they will generate before not responding at all for five successive trials does not take reinforcements so far accumulated into account (Fig.…”

Section: Theoretical Implicationsmentioning

confidence: 99%

“…5, top right and middle left). The strategy pigeons and rodents implement during extinction takes into account the probability that a trial during the training epoch was reinforced (Bouton, Woods, & Todd, 2014;Drew, Walsh, & Balsam, 2017;Drew, Yang, Ohyama, & Balsam, 2004;Harris, 2019;Harris & Andrew, 2017). The policy that determines behavior during extinction construes trials as reinforced or not; it is, however, indifferent to how often reinforcement occurred within a reinforced trial (Harris, Kwok, & Gottlieb, 2019) and it is indifferent to the C/T interval, the variable that has a scalar effect on trials and reinforcements to acquisition (Gibbon et al, 1980).…”

Section: Theoretical Implicationsmentioning

confidence: 99%

Number and time in acquisition, extinction and recovery

Gallistel

Papachristos

2019

J Exper Analysis Behavior

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We measured rate of acquisition, trials to extinction, cumulative responses in extinction, and the spontaneous recovery of anticipatory hopper poking in a Pavlovian protocol with mouse subjects. We varied by factors of 4 number of sessions, trials per session, intersession interval, and span of training (number of days over which training extended). We find that different variables affect each measure: Rate of acquisition [1/(trials to acquisition)] is faster when there are fewer trials per session. Terminal rate of responding is faster when there are more total training trials. Trials to extinction and amount of responding during extinction are unaffected by these variables. The number of training trials has no effect on recovery in a 4-trial probe session 21 days after extinction. However, recovery is greater when the span of training is greater, regardless of how many sessions there are within that span. Our results and those of others suggest that the numbers and durations and spacings of longer-duration "episodes" in a conditioning protocol (sessions and the spans in days of training and extinction) are important variables and that different variables affect different aspects of subjects' behavior. We discuss the theoretical and clinical implications of these and related findings and conclusions-for theories of conditioning and for neuroscience.The work reported here comprised a portion of a dissertation submitted to Rutgers University in partial fulfillment of the requirements for the doctoral degree. EBP designed and did the experiments, did the initial data analyses, and wrote a draft, with some advice from CRG. CRG did several further analyses and wrote the final draft.

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