Differentiating models of associative learning: Reorientation, superconditioning, and the role of inhibition.

Dupuis, Brian; Dawson, Michael R. W.

doi:10.1037/a0032174

Cited by 4 publications

(18 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The concept of non-zero starting associative strengths is not in itself novel (e.g. Gluck & Bower, 1988); for example, non-zero initial strengths have been used to represent pre-training (Miller & Shettleworth, 2007, Dupuis & Dawson, 2013. However, using them as a representation of human uncertainty is novel.…”

Section: Modifying the Rescorla-wagner Modelmentioning

confidence: 99%

Representing uncertainty in the Rescorla-Wagner model: Blocking, the redundancy effect, and outcome base rate

Spicer

Wills

Jones

et al. 2021

OJEPN

View full text Add to dashboard Cite

It is generally assumed that the Rescorla and Wagner (1972) model adequately accommodates the full results of simple cue competition experiments in humans (e.g. Dickinson et al., 1984), while the Bush and Mosteller (1951) model cannot. We present simulations that demonstrate this assumption is wrong in at least some circumstances. The Rescorla-Wagner model, as usually applied, fits the full results of a simple forward cue-competition experiment no better than the Bush-Mosteller model. Additionally, we present a novel finding, where letting the associative strength of all cues start at an intermediate value (rather than zero), allows this modified model to provide a better account of the experimental data than the (equivalently modified) Bush-Mosteller model. This modification also allows the Rescorla-Wagner model to account for a redundancy effect experiment (Uengoer et al., 2013); something that the unmodified model is not able to do. Furthermore, the modified Rescorla-Wagner model can accommodate the effect of varying the proportion of trials on which the outcome occurs (i.e. the base rate) on the redundancy effect (Jones et al., 2019). Interestingly, the initial associative strength of cues varies in line with the outcome base rate. We propose that this modification provides a simple way of mathematically representing uncertainty about the causal status of novel cues within the confines of the Rescorla-Wagner model. The theoretical implications of this modification are discussed. We also briefly introduce free and open resources to support formal modelling in associative learning. Keywords: associative learning, prediction error, uncertainty, modelling, blocking, redundancy effect, open science.

show abstract

Section: Modifying the Rescorla-wagner Modelmentioning

confidence: 99%

Representing uncertainty in the Rescorla-Wagner model: Blocking, the redundancy effect, and outcome base rate

Spicer

Wills

Jones

et al. 2021

OJEPN

View full text Add to dashboard Cite

show abstract

“…As Dupuis and Dawson (in press, Appendix) show, multiplying Equation 1 by a location's choice probability (P L ) causes the model, under certain parameter values, to give wildly fluctuating associative strengths and choice probabilities outside the range 0 to 1. In an attempt to remedy this problem, we (Miller & Shettleworth, 2008) altered the model's choice function (Eq.…”

Section: The Ms Modelmentioning

confidence: 99%

“…Miller and Shettleworth (2007) used an associative model of instrumental choice to explain a confusing pattern of results in the geometry learning literature. Dupuis and Dawson (in press) identified a structural flaw in the Miller-Shettleworth (MS) model and suggested replacing it with an operant perceptron model which can correctly reproduce some experimental results that the MS model does not. Here we demonstrate that the error in the MS model can be easily corrected without altering any of the model's predictions by making it stochastic rather than deterministic.…”

mentioning

confidence: 99%

“…In response to a comment by Dawson, Kelly, Spetch, and Dupuis (2008) we altered the model slightly (Miller & Shettleworth, 2008) without affecting any of its predictions. However, Dupuis and Dawson (in press) have identified a more fundamental flaw in the structure of the MS model that causes it to give nonsensical results under certain parameter regimes. Here, following the suggestions of Dupuis and Dawson, we correct this problem and demonstrate that the revised model gives qualitatively identical results to the original in all cases.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Associative models of instrumental learning: A response to Dupuis and Dawson.

Miller

Shettleworth²

2013

Journal of Experimental Psychology: Animal Behavior Processes

View full text Add to dashboard Cite

Miller and Shettleworth (2007) used an associative model of instrumental choice to explain a confusing pattern of results in the geometry learning literature. Dupuis and Dawson (in press) identified a structural flaw in the Miller-Shettleworth (MS) model and suggested replacing it with an operant perceptron model which can correctly reproduce some experimental results that the MS model does not. Here we demonstrate that the error in the MS model can be easily corrected without altering any of the model's predictions by making it stochastic rather than deterministic. In addition, we show that the raw outputs of the perceptron model cannot be interpreted as discriminative choices in an instrumental task without first being normalized. We show that this additional step renders the results of the perceptron model identical to those of the MS model in exactly those cases in which it has been claimed to correctly predict results that the latter cannot.

show abstract

“…However, perceptrons are still important to particular research domains, such as animal learning. When we interpret perceptron outputs as probabilities, perceptrons provide important new insights to the animal learning literature [ 8 – 11 ]. Furthermore, there exists a formal equivalence between models of perceptron learning and mathematical accounts of classical conditioning [ 12 – 14 ].…”

Section: Introductionmentioning

confidence: 99%

Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability

Dawson

Gupta

2017

PLoS ONE

Self Cite

View full text Add to dashboard Cite

Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent’s environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit) learn to match probability when presented different cues in isolation. The current paper extends this research by showing that perceptrons can match probabilities when presented simultaneous cues, with each cue signaling different reward likelihoods. In our first simulation, we presented up to four different cues simultaneously; the likelihood of reward signaled by the presence of one cue was independent of the likelihood of reward signaled by other cues. Perceptrons learned to match reward probabilities by treating each cue as an independent source of information about the likelihood of reward. In a second simulation, we violated the independence between cues by making some reward probabilities depend upon cue interactions. We did so by basing reward probabilities on a logical combination (AND or XOR) of two of the four possible cues. We also varied the size of the reward associated with the logical combination. We discovered that this latter manipulation was a much better predictor of perceptron performance than was the logical structure of the interaction between cues. This indicates that when perceptrons learn to match probabilities, they do so by assuming that each signal of a reward is independent of any other; the best predictor of perceptron performance is a quantitative measure of the independence of these input signals, and not the logical structure of the problem being learned.

show abstract

Differentiating models of associative learning: Reorientation, superconditioning, and the role of inhibition.

Cited by 4 publications

References 23 publications

Representing uncertainty in the Rescorla-Wagner model: Blocking, the redundancy effect, and outcome base rate

Representing uncertainty in the Rescorla-Wagner model: Blocking, the redundancy effect, and outcome base rate

Associative models of instrumental learning: A response to Dupuis and Dawson.

Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability

Contact Info

Product

Resources

About