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.
|Title of host publication||Intrinsically Motivated Learning in Natural and Artificial Systems|
|Publisher||Springer Berlin Heidelberg|
|Number of pages||31|
|ISBN (Print)||9783642323751, 364232374X, 9783642323744|
|Publication status||Published - 1 Nov 2013|