The Predictive Processing Account of Infant Colour Vision Development on Generative Models

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Predictive processing is a theory that models how the human brain tries to predict sensory input from the environment. How generative models are being shaped in infancy is still a new, but very interesting topic. In my thesis, I focus on developmental predictive processing by investigating the conceptual and behavioural consequences of infant development on generative models. In this project, I specifically look at the development of colour vision in new-borns. I modelled two different scenarios, one in which intensities are learned based on movement and where colour perception is added afterwards, and one in which the model immediately learns intensity and colour based on movement. My research question is based on both scenarios: “What is the difference in the size of the prediction error between step-wise learning intensity and colour perception based on movement (Scenario 1), and immediately learning intensity and colour perception based on motor movement (Scenario 2)?”. Based on the fact that children start by only perceiving intensities, and later learn to discriminate between colours, my expectation was that the first scenario results in a lower total size of prediction error. I compared both scenarios based on the total size of the prediction error computed by the Kullback-Leibler divergence. K-means clustering resulted in two divisions. In the first division, both prediction errors were similar, so no scenario performed better. For the second division, learning intensity and colour directly from motor movement resulted in a lower prediction error. Keywords: predictive processing, generative models, child development, colour vision
Faculteit der Sociale Wetenschappen