Abstract

The ability to group perceptual objects into functionally relevant categories is vital to our comprehension of the world. Such categorisation aids in how we search for objects in familiar scenes and how we identify an object and its likely uses despite never having seen that specific object before. The systems that mediate this process are only now coming to be understood through considerable research efforts combining neurological, psychological and behavioural studies. What is much less well understood are the differences between the categories, how they are formed and how they are used by experts and non-experts in a complex task that can take decades to master. In a quite different direction to previous studies, this work infers the different categorical structures that might be used by amateurs and professionals in the oriental game of Go. This is achieved by using a newly developed combination of artificial neural networks (Self-organising Maps) and perceptual inference to show that categories of strategic scenes can be learned while playing games using a model of ‘conditional perceptual learning’. Applying this technique to two databases of games, one of amateurs and one of professionals, shows that a structural hierarchy of scene information develops that can be readily incorporated into traditional psychological models of decisions and readily implemented in computational systems. The results are discussed in terms of the heuristics and biases literature, emphasising where the significant similarities and differences lie between this work and previous work