Understanding the neural code for conscious symbolic thought: A challenge for human cognitive neuroscience", Prof. Stanislas Dehane (Collège de France)
According to the global neural workspace hypothesis, the mechanisms of conscious access are similar in human and non-human species. Wherein, then, lies the singularity of the human brain? In this talk, I will propose that the contents of consciousness became markedly richer in humans as our brains acquired a capacity for compositional thought using discrete symbols. Recent comparative data from my lab show that humans possess unique abilities for symbolic learning and a mathematical “language of thought”. Even the mere perception of a square or a zig-zag involves a short mental program that captures the observed data in an internal language of geometry. Behavioral and brain-imaging experiments indicate that the perception of geometric shapes is poorly captured by current convolutional neural network models of the ventral visual pathway, but involves a symbolic geometrical description within the dorsal parieto-prefrontal network. I will argue that existing connectionist models do not suffice to account for even elementary human perceptual data, and that neural codes for symbols and syntax remain to be discovered.
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