Our society depends on scientific progress, which is reliant on the development of scientific theories and models. However, the development of scientific models suffers from two related problems: the ever-growing number of experimental results and scientists’ cognitive limitations (including cognitive biases). This multidisciplinary project (psychology, computer modelling, computer science and cognitive neuroscience) addressed these problems by developing a novel methodology for generating scientific models semi-automatically. The methodology is not specific to any particular discipline and can be applied to any science where experimental data are available.
The methodology treats models as computer programs and evolves a population of models using genetic programming. The extent to which the models fit the empirical data is used as a fitness function. The best models – potentially modified by cross-over and mutation – are selected for the next generation.
To demonstrate that the methodology is sound, can be used with complex experiments and can be generalised across sciences, four related strands of research were implemented. First, ‘Building New Tools’ developed and refined the methodology, and created techniques to understand and compare the evolved models. Second, ‘Explaining Human Data’ used the methodology to explain a wide range of data on human cognition. This was done in two steps: (a) data without learning (attention and working memory); and (b) data with learning (categorisation, implicit learning and explicit learning). Third, ‘Explaining Animal Data’ developed models to account for various aspects of animal behaviour, focusing on categorisation. Finally, ‘Explaining Neuroscience Data’ showed that the methodology is in principle able to account for data combining information about cognitive processes and brain structure.
In conclusion, evolutionary computational methods can be used to evolve scientific theories, producing valuable models of similar validity as those developed by human scientists. Of particular interest were the simulations showing that the GEMS methodology developed in this project can be successfully used with the cognitive architecture CHREST to evolve models of verbal learning with human data, and models of categorisation both with human and animal data.
