ArticlePLoS computational biology2017
A Rescorla-Wagner drift-diffusion model of conditioning and timing.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Modeling decision dynamics disentangles working memory, cognitive control and reinforcement learning and reveals clinical differences.PLoS computational biology · 2026Article
- Developmental and genetic modulation of evidence integration dynamics in zebrafish sensorimotor decision-making.bioRxiv : the preprint server for biology · 2026Article
- Reconciling time and prediction error theories of associative learning.Nature communications · 2025Article
- From eye-blinks to state construction: Diagnostic benchmarks for online representation learning.Adaptive behavior · 2023Article
- Reinforcement learning with associative or discriminative generalization across states and actions: fMRI at 3 T and 7 T.Human brain mapping · 2022Article
- How do real animals account for the passage of time during associative learning?Behavioral neuroscience · 2022Article
- Benefiting from trial spacing without the cost of prolonged training: Frequency, not duration, of trials with absent stimuli enhances perceived contingency.Journal of experimental psychology. General · 2022Article
- Temporal scaling and computing time in neural circuits: Should we stop watching the clock and look for its gears?Frontiers in behavioral neuroscience · 2022Article
- Review: Subjective Time Perception, Dopamine Signaling, and Parkinsonian Slowness.Frontiers in neurology · 2022Review
- Article
- Time-scale-invariant information-theoretic contingencies in discrimination learning.Journal of experimental psychology. Animal learning and cognition · 2019Article
- Adapting the flow of time with dopamine.Journal of neurophysiology · 2019Article
- Improving the reliability of model-based decision-making estimates in the two-stage decision task with reaction-times and drift-diffusion modeling.PLoS computational biology · 2019Article
- Evidence accumulation in a Laplace domain decision space.Computational brain & behavior · 2018Article
- A Population-Based Model of the Temporal Memory in the Hippocampus.Frontiers in neuroscience · 2018Article
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3 authors.
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Abstract
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 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.
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