Thursday, April 26, 2007

Models of Action Selection

Discovered today that a special issue of the Royal Society Proceedings (B) has been published, with the subject of Models of Action Selection (link here). It comprises of 14 articles, on subjects as wide ranging as animal behaviour (one on the concept of optimal animal behaviour caught my eye in particular), social decision making (politics etc), and of course models of human decision making. One which jumped out at me was a paper by Botvinick on what looks like an implementation of Fuster's hierarchical memory theory, which has been the subject of a few of my posts (e.g. here and here). This one is a must-read, for me at least... The special issue blurb follows:

Action selection, at its simplest, is the problem that every human and animal faces at each instant of "what to do next?". To scientists this problem raises a plethora of further questions: How do we know how to do the right thing? Why is it that we sometimes make poor choices? How do we plan ahead for complex tasks and remember what we are trying to do as we go along? Are there central decision-making mechanisms in the brain or do actions somehow 'select themselves' through the interaction of many concurrent brain processes? What happens when different parts the brain want to do different things? How do the actions selected by individuals shape and change the social groups in which they live?

This theme issue addresses these questions by focusing on a particular strategy for finding scientific explanations - computer modelling. The contributions employ state-of-the-art modelling techniques ranging from large networks of simulated brain cells, through to models of individuals (people or animals) viewed as agents operating in simulated worlds. The research has broad applications, from understanding brain disorders such as Parkinson's disease and attention deficit/hyperactivity disorder, to explaining how we choose which political parties we vote for, and how they adapt to
increase their appeal to us.

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