SSCC TD: A Serial and Simultaneous Configural-Cue Compound Stimuli Representation for Temporal Difference Learning

Mondragón, Esther; Gray, Jonathan; Alonso, Eduardo; Bonardi, Charlotte; Jennings, Dómhnall J.

doi:10.1371/journal.pone.0102469

Cited by 12 publications

(20 citation statements)

References 71 publications

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“…In contrast, an adapted real-time trial-based model of conditioning was able to account for the pattern of results we present (Sutton and Barto, 1987;Mondragón et al, 2014). It appears that associative trial-based accounts of learning that are adapted to operate in real time might be best placed to offer a coherent account of the role of temporal CS factors on learning.…”

Section: Discussioncontrasting

confidence: 55%

Overshadowing by fixed- and variable-duration stimuli

Bonardi

Mondragón²,

Brilot

et al. 2015

Quarterly Journal of Experimental Psychology

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Two experiments investigated the effect of the temporal distribution form of a stimulus on its ability to produce an overshadowing effect. The overshadowing stimuli were either of the same duration on every trial, or of a variable duration drawn from an exponential distribution with the same mean duration as that of the fixed stimulus. Both experiments provided evidence that a variable duration stimulus was less effective than a fixed duration cue at overshadowing conditioning to a target CS; moreover, this effect was independent of whether the overshadowed CS was fixed or variable. The findings presented here are consistent with the idea that the strength of the association between CS and US is, in part, determined by the temporal distribution form of the CS. These results are discussed in terms of time-accumulation and trial-based theories of conditioning and timing.

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Section: Discussioncontrasting

confidence: 55%

Overshadowing by fixed- and variable-duration stimuli

Bonardi

Mondragón²,

Brilot

et al. 2015

Quarterly Journal of Experimental Psychology

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“…Mondragón et al, (2014); Sutton & Barto, 1990;Vogel Brandon & Wagner, 2003). Models of this type can anticipate the pattern of results reported here, and may represent the best chance for associative theories to accommodate time-based characteristics of the conditioning process.…”

Section: Discussionmentioning

confidence: 99%

“…The models of conditioning and timing best equipped to explain these findings come from the class of hybrid models based on predictionerror learning in real time, such as the SSCC TD model proposed by Mondragón et al (2014). …”

Section: Discussionmentioning

confidence: 99%

Blocking by Fixed and Variable Stimuli: Effects of Stimulus Distribution on Blocking

Jennings

Bonardi

2017

Quarterly Journal of Experimental Psychology

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An experiment with rats compared the ability of fixed and variable duration cues to produce blocking. Rats in Group B (Blocking) were trained that both fixed-(F) and variable-(V) duration cues would be followed by food delivery. In a subsequent training stage F and V continued to be reinforced, but F was accompanied by X, and V by Y. In the test phase responding to X and Y was examined. Control Group O (Overshadowing) received identical treatment, except that F and V were nonreinforced in the first training stage. In Group B there was evidence for blocking, but only of X which had been conditioned in compound with the fixed-duration F; there was no evidence for blocking of Y, which had been conditioned in compound with the variable duration V. It is suggested that this result may occur because fixed cues reach a higher, more stable asymptote of associative strength than their variable equivalents.

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“…It may be thought of as a real-time version of RW. When used with stimulus representations such as the Complete Serial Compound (CSC, Moore et al, 1998), Microstimuli (MS, Ludvig et al, 2008,0) and the Simultaneous and Serial Configural-cue Compound (SSCC, Mondragón et al, 2014) it is capable of reproducing some timing phenomena like the gradual increase in anticipatory responding that occurs before a signalled reinforcer, and the lower response rates observed during longer CSs. However, only MS-TD has a time representation capable of approximating the most fundamental property of timing, timescale invariance.…”

Section: Introductionmentioning

confidence: 99%

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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SSCC TD: A Serial and Simultaneous Configural-Cue Compound Stimuli Representation for Temporal Difference Learning

Cited by 12 publications

References 71 publications

Overshadowing by fixed- and variable-duration stimuli

Overshadowing by fixed- and variable-duration stimuli

Blocking by Fixed and Variable Stimuli: Effects of Stimulus Distribution on Blocking

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

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