The basis of behavioral momentum in the nonlinearity of strength

Killeen, Peter R.; Nevin, John A.

doi:10.1002/jeab.304

Cited by 19 publications

(24 citation statements)

References 101 publications

(144 reference statements)

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“…It may also be the case that operant extinction is governed predominantly by discriminative stimulus-reinforcer relations under some circumstances (i.e., multiple schedules) and reinforcerdiscrimination based processes under others (i.e., single schedules). To our knowledge, no contemporary theory of extinction explores fully the potential for joint control of behavior by these two processes, though such a theory might hold the promise of conceptually reconciling the differences in resistance to extinction between single and multiple schedules (see Killeen & Nevin, 2018, for an alternative approach to understanding extinction performance that also may address this issue). Future empirical and theoretical work is necessary to determine more precisely whether and how these behavior processes interact to give rise to the behavior that is observed during extinction.…”

Section: Discussionmentioning

confidence: 99%

Behavioral momentum and resistance to extinction across repeated extinction tests

Craig

Sweeney

Shahan

2019

J Exper Analysis Behavior

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The present experiments assessed whether resistance to extinction of pigeons' key pecking decreased across repeated extinction tests. An additional impetus for this research was to determine how the quantitative framework provided by behavioral momentum theory might be used to describe any such changes across tests. Pigeons pecked keys in two-component multiple schedules (one component associated with a higher reinforcer rate and the other with a lower rate) in which baseline and extinction conditions alternated. In Experiment 1, baseline and extinction conditions alternated every session, and, in Experiment 2, these conditions lasted for 10 and 7 sessions, respectively. Resistance to extinction decreased across successive extinction conditions in both experiments. Fits of the behavioralmomentum based model of extinction to the data returned uncertain results in Experiment 1 but implicated both generalization decrement and response-reinforcer contingency termination as the possible mechanisms responsible for behavior change in Experiment 2. Thus, these data suggest that experimental manipulations that affect discrimination of changes in reinforcement contingencies may influence resistance to extinction by modulating the disruptive impacts of removing reinforcers from the experimental context and of suspending response-reinforcer contingencies.

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Section: Discussionmentioning

confidence: 99%

Behavioral momentum and resistance to extinction across repeated extinction tests

Craig

Sweeney

Shahan

2019

J Exper Analysis Behavior

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“…At this point, it appears to us that the current version of the Behavioral Momentum Theory of resurgence need not be seriously considered further. Nevertheless, we note that a more recent formulation of the general framework of behavioral momentum (Killeen & Nevin, 2018) based on Killeen's (1994) Mathematical Principles of Reinforcement is couched in terms of response competition, and as those authors note, shares some conceptual similarities to the choice-based approach of RaC. As of yet, this new approach has not been extended to resurgence or other relapse phenomena.…”

Section: Ghosts Of Models Past and Futurementioning

confidence: 94%

Resurgence as Choice in Context: Treatment duration and on/off alternative reinforcement

Shahan

Browning

Nall

2019

J Exper Analysis Behavior

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Resurgence as Choice (RaC) is a quantitative theory suggesting that an increase in an extinguished target behavior with subsequent extinction of an alternative behavior (i.e., resurgence) is governed by the same processes as choice more generally. We present data from an experiment with rats examining a range of treatment durations with alternative reinforcement plus extinction and demonstrate that increases in treatment duration produce small but reliable decreases in resurgence. Although RaC predicted the relation between target responding and treatment duration, the model failed in other respects. First, contrary to predictions, the present experiment also replicated previous findings that exposure to cycling on/off alternative reinforcement reduces resurgence. Second, RaC did a poor job simultaneously accounting for target and alternative behaviors across conditions. We present a revised model incorporating a role for more local signaling effects of reinforcer deliveries or their absence on response allocation. Such signaling effects are suggested to impact response allocation above and beyond the values of the target and alternative behaviors as longer-term repositories of experience. The new model provides an excellent account of the data and can be viewed as an integration of RaC and a quantitative approximation of some aspects of Context Theory.

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“…Moreover, our models may obtain confirmation or be ruled out by extant or future data not only about Pavlovian conditioning, but also from the operant conditioning literature. In the second case, one invokes the possibility that associative models can also describe operant behavior (Killeen and Nevin, 2018). In the first case, the generality of the construction of learning models as dynamical models and the psychological interpretation put forward in section 4.2 make us believe that they can both be extended to other Pavlovian conditioning paradigms, such as eyeblink conditioning, where the average learning pattern may differ from the RW curve.…”

Section: Discussionmentioning

confidence: 99%

Behavior Stability and Individual Differences in Pavlovian Extended Conditioning

2020

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How stable and general is behavior once maximum learning is reached? To answer this question and understand post-acquisition behavior and its related individual differences, we propose a psychological principle that naturally extends associative models of Pavlovian conditioning to a dynamical oscillatory model where subjects have a greater memory capacity than usually postulated, but with greater forecast uncertainty. This results in a greater resistance to learning in the first few sessions followed by an over-optimal response peak and a sequence of progressively damped response oscillations. We detected the first peak and trough of the new learning curve in our data, but their dispersion was too large to also check the presence of oscillations with smaller amplitude. We ran an unusually long experiment with 32 rats over 3,960 trials, where we excluded habituation and other well-known phenomena as sources of variability in the subjects' performance. Using the data of this and another Pavlovian experiment by Harris et al. (2015), as an illustration of the principle we tested the theory against the basic associative single-cue Rescorla-Wagner (RW) model. We found evidence that the RW model is the best non-linear regression to data only for a minority of the subjects, while its dynamical extension can explain the almost totality of data with strong to very strong evidence. Finally, an analysis of short-scale fluctuations of individual responses showed that they are described by random white noise, in contrast with the colored-noise findings in human performance.

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The basis of behavioral momentum in the nonlinearity of strength

Cited by 19 publications

References 101 publications

Behavioral momentum and resistance to extinction across repeated extinction tests

Behavioral momentum and resistance to extinction across repeated extinction tests

Resurgence as Choice in Context: Treatment duration and on/off alternative reinforcement

Behavior Stability and Individual Differences in Pavlovian Extended Conditioning

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