Using computational models of object recognition to investigate representational change through development

Empirical research on mental representation is challenging because internal representations are not available to direct observation. This chapter will show how empirical results from developmental studies, and insights from computational modelling of those results, can be combined with existing research on adults. So together all these research perspectives can provide convergent evidence for how visual representations mediate object recognition. Recent experimental studies have shown that development towards adult performance levels in configural processing in object recognition is delayed through middle childhood. Whilst part-changes to animal and artefact stimuli are processed with similar to adult levels of accuracy from 7 years of age, relative size changes to stimuli result in a significant decrease in relative performance for participants aged between 7 and 10. Two sets of computational experiments were run using the JIM3 artificial neural network with adult and ‘immature’ versions to simulate these results. One set progressively decreased the number of neurons involved in the representation of view-independent metric relations within multi-geon objects. A second set of computational experiments involved decreasing the number of neurons that represent view-dependent (non-relational) object attributes in JIM3’s surface map. The simulation results which show the best qualitative match to empirical data occurred when artificial neurons representing metric-precision relations were entirely eliminated. These results therefore provide further evidence for the late development of relational processing in object recognition and suggest that children in middle childhood may recognise objects without forming structural description representations