Action discovery and intrinsic motivation: A biologically constrained formalisation

Kevin Gurney*, Nathan Lepora, Ashvin Shah, Ansgar Koene, Peter Redgrave

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

12 Citations (Scopus)


We introduce a biologically motivated, formal framework or ontology for dealing with many aspects of action discovery which we argue is an example of intrinsically motivated behaviour (as such, this chapter is a companion to that by Redgrave et al. in this volume). We argue that action discovery requires an interplay between separate internal forward models of prediction and inverse models mapping outcomes to actions. The process of learning actions is driven by transient changes in the animal's policy (repetition bias) which is, in turn, a result of unpredicted, phasic sensory information (surprise). The notion of salience as value is introduced and broken down into contributions from novelty (or surprise), immediate reward acquisition, or general task/goal attainment. Many other aspects of biological action discovery emerge naturally in our framework which aims to guide future modelling efforts in this domain.

Original languageEnglish
Title of host publicationIntrinsically Motivated Learning in Natural and Artificial Systems
PublisherSpringer Berlin Heidelberg
Number of pages31
ISBN (Print)9783642323751, 364232374X, 9783642323744
Publication statusPublished - 1 Nov 2013


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