Towards the evaluation of cognitive models using anytime intelligence tests

Cognitive models are usually evaluated based on their fit to empirical data. Artificial intelligence (AI) systems on the other hand are mainly evaluated based on their performance. Within the field of artificial general intelligence (AGI) research, a new type of performance measure for AGI systems has recently been proposed that tries to cover both humans and artificial systems: Anytime Intelligence Tests (AIT; Hernández-Orallo & Dowe, 2010). This paper explores the viability of the AIT formalism for the evaluation of cognitive models based on data from the ICCM 2009 “Dynamic Stocks and Flows” modeling challenge