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The role of Prediction Error in Probabilistic Associative Learning



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Cevora, Jiri 


This thesis focuses on probabilistic associative learning. One of the classic effects in this field is the stimulus associability effect for which I derive a statistically optimal inference model and a corresponding approximation that addresses a number of problems with the original account of Mackintosh.

My proposed account of associability - a variable learning rate depending on a relative informativeness of stimuli - also accounts of the classic blocking effect \cite{kamin1969predictability} without the need for Prediction Error [PE] computation. Given that blocking was the main impetus for placing PE at the centre of learning theories, I critically re-evaluate other evidence for PE in learning, particularly the recent neuroimaging evidence. I conclude that the brain data are not as clear cut as often presumed.

The main shortcoming of the evidence implicating PE in learning is that probabilistic associative learning is mostly described as a transition from one state of belief to another, yet those beliefs are typically observed only after multiple learning episodes and in a very coarse manner. To address this problem, I develop an experimental paradigm and accompanying statistical methods that allow one to infer the beliefs at any given point in time.

However, even with the rich data provided by this new paradigm, the blocking effect still cannot provide conclusive evidence for the role of PE in learning. I solve this problem by deriving a novel conceptualisation of learning as a flow in probability space. This allows me to derive two novel effects that can unambiguously distinguish learning that is driven by PE from learning not driven by PE. I call these effectsgeneralized blocking and false blocking, given their inspiration by the original paradigm of Kamin (1969). These two effects can be generalized to the entirety of probability space, rather than just the two specific points provided by the paradigms used by Mackintosh and Kamin, and therefore offer greater sensitivity to differences in learning mechanisms. In particular, I demonstrate that these effects are necessary consequences of PE-driven learning, but not learning based on the relative informativeness of stimuli.

Lastly I develop an online experiment to acquire data on the new paradigm from a large number (approximately 2000) of participants recruited via social media. The results of model fitting, together with statistical tests of generalized blocking and false blocking, provide strong evidence against a PE-driven account of learning, instead favouring the relative informativeness account derived at the start of the thesis.





Henson, Richard


prediction error, relative informativeness, associative learning, probabilistic learning


Doctor of Philosophy (PhD)

Awarding Institution

University of Cambridge
Medical Research Council PhD studenship