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Genetically Evolving Models of Science

Project description

Automated generation of the best scientific models for a plethora of data sets

The scientific method is a rigorous, practical approach to observing and explaining phenomena in almost any field. It relies on formulating and testing hypotheses, which are models of how things work, and modifying these hypotheses based on the outcomes. Conventionally, the hypotheses or models are defined based on extensive research and understanding of the field and phenomena a priori. Imagine if this process could be ‘automated’ and the most likely hypothesis or explanation to be tested could be found. The EU-funded GEMS project is integrating experimental psychology, cognitive modelling, cognitive neuroscience and computer science to evolve a population of models for a number of different data sets using genetic programming.


The development of scientific models suffers from two related problems: ever-growing number of experimental results and scientists’ cognitive limitations (including cognitive biases). This multidisciplinary project (psychology, computer modelling, computer science and cognitive neuroscience) addresses these problems by developing a novel methodology for generating scientific models automatically. The methodology is general and can be applied to any science where experimental data are available.

The method 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. Pilot simulations have established the validity of the methodology with simple experiments.

To demonstrate that the methodology is sound, can be used with complex datasets and can be generalised across sciences, four related strands of research are planned. First, ‘Building New Tools’ develops the methodology and creates techniques to understand and compare the evolved models. Second, ‘Explaining Human Data’ uses the methodology to explain a wide range of data on human cognition. This will be done in two steps: (a) data without learning (working memory and attention); and (b) data with learning (categorisation, implicit learning and explicit learning). Third, ‘Explaining Animal Data’ develops models to account for various aspects of animal behaviour, focusing on conditioning and categorisation. Finally, ‘Explaining Neuroscience Data’ extends the methodology to account for data combining information about cognitive and brain processes.

This project explores virgin territory and thus opens up a new field of research. It combines insights from experimental psychology, cognitive modelling, cognitive neuroscience and computer science, disciplines in which the PI has strong track record.


Net EU contribution
€ 2 086 937,75
Houghton street 1
WC2A 2AE London
United Kingdom

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London Inner London — West Westminster
Activity type
Higher or Secondary Education Establishments
Other funding
€ 0,00

Beneficiaries (2)