Bayesian parameter estimation and Shannon's theory of information provide tools for analysing and understanding data from behavioural and neurobiological experiments on interval timing-and from experiments on Pavlovian and operant conditioning, because timing plays a fundamental role in associative learning. In this tutorial, we explain basic concepts behind these tools and show how to apply them to estimating, on a trial-by-trial, reinforcement-by-reinforcement and response-by-response basis, important parameters of timing behaviour and of the neurobiological manifestations of timing in the brain. These tools enable quantification of relevant variables in the trade-off between acting as an ideal observer should act and acting as an ideal agent should act, which is also known as the trade-off between exploration (information gathering) and exploitation (information utilization) in reinforcement learning. They enable comparing the strength of the evidence for a measurable association to the strength of the behavioural evidence that the association has been perceived. A GitHub site and an OSF site give public access to well-documented Matlab and Python code and to raw data to which these tools have been applied.
CITATION STYLE
Gallistel, C. R., & Latham, P. E. (2022). Bringing Bayes and Shannon to the Study of Behavioural and Neurobiological Timing and Associative Learning. Timing and Time Perception, 11(1–4), 29–89. https://doi.org/10.1163/22134468-bja10069
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