scholarly journals Associative Learning from Replayed Experience

2017 ◽  
Author(s):  
Elliot A. Ludvig ◽  
Mahdieh S. Mirian ◽  
E. James Kehoe ◽  
Richard S. Sutton
Keyword(s):  
Associative Learning ◽  
Spontaneous Recovery ◽  
Explanatory Power ◽  
Learning Rule ◽  
Associative Model ◽  
Retrospective Revaluation ◽  
Spacing Effects ◽  
Trial Spacing ◽  
Associative Models ◽  
Present Simulation

AbstractWe develop an extension of the Rescorla-Wagner model of associative learning. In addition to learning from the current trial, the new model supposes that animals store and replay previous trials, learning from the replayed trials using the same learning rule. This simple idea provides a unified explanation for diverse phenomena that have proved challenging to earlier associative models, including spontaneous recovery, latent inhibition, retrospective revaluation, and trial spacing effects. For example, spontaneous recovery is explained by supposing that the animal replays its previous trials during the interval between extinction and test. These include earlier acquisition trials as well as recent extinction trials, and thus there is a gradual re-acquisition of the conditioned response. We present simulation results for the simplest version of this replay idea, where the trial memory is assumed empty at the beginning of an experiment, all experienced trials are stored and none removed, and sampling from the memory is performed at random. Even this minimal replay model is able to explain the challenging phenomena, illustrating the explanatory power of an associative model enhanced by learning from remembered as well as real experiences.

Trial Spacing in Instrumental Running

1966 ◽  
Vol 18 (2) ◽  
pp. 623-630 ◽  
Author(s):  
James L. Fozard
Keyword(s):  
Spatial Variability ◽  
Statistical Learning ◽  
Learning Theory ◽  
Spontaneous Regression ◽  
Spontaneous Recovery ◽  
Extinction Performance ◽  
Acquisition Performance ◽  
Spacing Effects ◽  
Trial Spacing ◽  
Daily Block

Trial spacing was studied over 120 acquisition trials and 20 each of extinction, reacquisition, and re-extinction, using five intertrial intervals from 40 sec. to 120 min. in between four daily trials. Throughout acquisition, performance of the 120-min. group increased throughout the course of a daily block, while the others first increased, then decreased. Spontaneous regression persisted throughout acquisition. Extinction performance was not systematically related to trial spacing, and no evidence for spontaneous recovery was found. Changes in measured spatial variability in the alley indexed learning but did not differentiate trial spacing effects. The results generally failed to confirm qualitative hypotheses about trial spacing derived from statistical learning theory.

Priming and trial spacing in extinction: Effects on extinction performance, spontaneous recovery, and reinstatement in appetitive conditioning

2006 ◽  
Vol 59 (5) ◽  
pp. 809-829 ◽  
Author(s):  
Erik W. Moody ◽  
Ceyhun Sunsay ◽  
Mark E. Bouton
Keyword(s):  
Short Term Memory ◽  
Spontaneous Recovery ◽  
Appetitive Conditioning ◽  
Extinction Performance ◽  
Learning Mechanisms ◽  
Short Term ◽  
Spacing Effects ◽  
Trial Spacing ◽  
Short Itis

Previous research in this laboratory suggests that priming of the conditional stimulus (CS) in short-term memory may play a role in the trial-spacing effects in appetitive conditioning. For example, a nonreinforced presentation of a CS 60 s before a reinforced trial with the same CS produced slower acquisition than a CS presentation that occurred 240 s before the reinforced trial. The results were consistent with the self-generated priming mechanism proposed by Wagner (e.g., Wagner 1978, 1981). The present experiments extended the earlier work by examining the effects of trial spacing in extinction rather than acquisition. After conditioning with a mixture of intertrial intervals (ITIs), rats received extinction with ITIs of 60 or 240 s, longer or shorter values, or different ways of “chunking” extinction trials in time. Although trial spacing produced effects on extinction performance that were consistent with our previous research on acquisition, there were few long-term differences in spontaneous recovery or in reinstatement. Short ITIs in extinction appear to affect extinction performance more than they affect extinction learning. Mechanisms of trial spacing in conditioning and extinction are discussed.

Judging the Importance of Constant and Variable Candidate Causes: A Test of the Power PC Theory

1998 ◽  
Vol 51 (1) ◽  
pp. 65-84 ◽  
Author(s):  
Frédéric Vallée-Tourangeau ◽  
Robin A. Murphy ◽  
Susan Drew ◽  
A.G. Baker
Keyword(s):  
Associative Learning ◽  
Causal Power ◽  
Base Rate ◽  
The Other ◽  
Associative Model ◽  
Contrast Model ◽  
Causal Induction ◽  
Power Theory ◽  
Factorial Combination ◽  
Target Effect

In two causal induction experiments subjects rated the importance of pairs of candidate causes in the production of a target effect; one candidate was present on every trial (constant cause), whereas the other was present on only some trials (variable cause). The design of both experiments consisted of a factorial combination of two values of the variable cause's covariation with the effect and three levels of the base rate of the effect. Judgements of the constant cause were inversely proportional to the level of covariation of the variable cause but were proportional to the base rate of the effect. The judgements were consistent with the predictions derived from the Rescorla-Wagner (1972) model of associative learning and with the predictions of the causal power theory of the probabilistic contrast model (Cheng, 1997) or “power PC theory”. However, judgements of the importance of the variable candidate cause were proportional to the base rate of the effect, a phenomenon that is in some cases anticipated by the power PC theory. An alternative associative model, Pearce's (1987) similarity-based generalization model, predicts the influence of the base rate of the effect on the estimates of both the constant and the variable cause.

scholarly journals Associative Models for Storing and Retrieving Concept Lattices

2010 ◽  
Vol 2010 ◽  
pp. 1-27 ◽  
Author(s):  
María Elena Acevedo ◽  
Cornelio Yáñez-Márquez ◽  
Marco Antonio Acevedo
Keyword(s):  
Associative Memory ◽  
Concept Lattice ◽  
Human Being ◽  
Associative Memories ◽  
Associative Model ◽  
Bidirectional Associative Memory ◽  
Perfect Recall ◽  
Concept Lattices ◽  
Alpha Beta ◽  
Associative Models

Alpha-beta bidirectional associative memories are implemented for storing concept lattices. We use Lindig's algorithm to construct a concept lattice of a particular context; this structure is stored into an associative memory just as a human being does, namely, associating patterns. Bidirectionality and perfect recall of Alpha-Beta associative model make it a great tool to store a concept lattice. In the learning phase, objects and attributes obtained from Lindig's algorithm are associated by Alpha-Beta bidirectional associative memory; in this phase the data is stored. In the recalling phase, the associative model allows to retrieve objects from attributes or vice versa. Our model assures the recalling of every learnt concept.

scholarly journals Probabilistic associative learning suffices for learning the temporal structure of multiple sequences

2019 ◽  
Author(s):  
Ramon H. Martinez ◽  
Anders Lansner ◽  
Pawel Herman
Keyword(s):  
Associative Learning ◽  
Internal Control ◽  
Computational Study ◽  
Brain Activity ◽  
Temporal Structure ◽  
Learning Rule ◽  
Multi Scale ◽  
Temporal Aspects ◽  
And Behavior ◽  
Multiple Sequences

AbstractMany brain phenomena both at the cognitive and behavior level exhibit remarkable sequential characteristics. While the mechanisms behind the sequential nature of the underlying brain activity are likely multifarious and multi-scale, in this work we attempt to characterize to what degree some of this properties can be explained as a consequence of simple associative learning. To this end, we employ a parsimonious firing-rate attractor network equipped with the Hebbian-like Bayesian Confidence Propagating Neural Network (BCPNN) learning rule relying on synaptic traces with asymmetric temporal characteristics. The proposed network model is able to encode and reproduce temporal aspects of the input, and offers internal control of the recall dynamics by gain modulation. We provide an analytical characterisation of the relationship between the structure of the weight matrix, the dynamical network parameters and the temporal aspects of sequence recall. We also present a computational study of the performance of the system under the effects of noise for an extensive region of the parameter space. Finally, we show how the inclusion of modularity in our network structure facilitates the learning and recall of multiple overlapping sequences even in a noisy regime.

scholarly journals Trial spacing effects in pavlovian conditioning: A role for local context

1995 ◽  
Vol 23 (3) ◽  
pp. 340-348 ◽  
Author(s):  
Robert C. Barnet ◽  
Nicholas J. Grahame ◽  
Ralph R. Miller
Keyword(s):  
Pavlovian Conditioning ◽  
Local Context ◽  
Spacing Effects ◽  
Trial Spacing

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

2017 ◽  
Author(s):  
André Luzardo ◽  
Eduardo Alonso ◽  
Esther Mondragón
Keyword(s):  
Associative Learning ◽  
Diffusion Model ◽  
Computational Models ◽  
Learning Rule ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Timing Model ◽  
Conditioning Model ◽  
Temporal Aspects ◽  
Timing Models

AbstractComputational 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 Rescorla-Wagner 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.

Backward and forward Blocking in Human Electrodermal Conditioning: Blocking Requires an Assumption of Outcome Additivity

2002 ◽  
Vol 55 (4b) ◽  
pp. 311-329 ◽  
Author(s):  
Chris J. Mitchell ◽  
Peter F. Lovibond
Keyword(s):  
Error Correction ◽  
Pavlovian Conditioning ◽  
Learning Rule ◽  
Blocking Effect ◽  
Detection Mechanism ◽  
Single Shock ◽  
Contingency Detection ◽  
Data Blocking ◽  
Associative Models ◽  
The Impact

Blocking was observed in two human Pavlovian conditioning studies in which colour cues signalled shock. Both forward (Experiment 1) and backward (Experiment 2) blocking was demonstrated, but only when prior verbal and written instructions suggested that if two signals of shock (A+ and B+) were presented together, a double shock would result (AB++). In this case, participants could assume that the outcome magnitude was additive. Participants given non-additivity instructions (A+ and B+ combined would result in the same outcome, a single shock) failed to show blocking. Modifications required for associative models of learning, and normative statistical accounts of causal induction, to account for the impact of additivity instructions on the blocking effect, are discussed. It is argued that the blocking shown in the present experiments resulted from the operation, not of an error-correction learning rule, nor of a simple contingency detection mechanism, but of a more complex inferential process based on propositional knowledge. Consistent with the present data, blocking is a logical outcome of an A+/AB+ design only if participants can assume that outcomes will be additive.

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