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A Neural Network Test of the Expert Attractor Hypothesis: Chaos Theory Accounts for Individual Variance in Learning

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Research and Development in Intelligent Systems XXXIII (SGAI 2016)


By positing that complex, abstract memories can be formalised as network attractors, the present paper introduces chaos theory in the field of psychological learning and, in particular, in the field of expertise acquisition. The expert attractor hypothesis is that the cortical re-organisation of biological networks via neural plasticity leads to a stable state that implements the memory template underpinning expert performance. An artificial neural network model of chess players’ strategic thinking, termed Templates for Expert Knowledge Simulation, was used to simulate, in 500 individuals, the learning of 128 positions which belong to 8 different chess strategies. The behavioural performance of the system as a novice, as an expert, and its variance in learning, are all in line with psychological findings. Crucially, the distribution of weights, the learning curves, and the evolution of the distribution of weights support the attractor hypothesis. Following a discussion on the psychological implications of the simulations, the next step towards unravelling the chaotic features of the human mind are evoked.

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Chassy, P. (2016). A Neural Network Test of the Expert Attractor Hypothesis: Chaos Theory Accounts for Individual Variance in Learning. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXIII. SGAI 2016. Springer, Cham.

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