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Extracting space dimension information from the auditory modality sensori-motor flow using a bio-inspired model of the cochlea

First task robots have to realise is sensing and acting in the environment. Can a robot learn the way it is able to sense and act in the world without any hardwired notions? Is it able to learn it from the only data he has access to, that is high-dimension sensory inputs and motor outputs? This paper presents experimental results obtained on a simulated human listener using a bio-inspired model of the cochlea and real records from human related transfer functions (HRTF). These results show that a naive system that interacts with its environment without knowing the laws governing these interactions can discover information about dimensionality of space. Moreover, the laws determining the sensations of the system as a function of the state of the system and the environment, called the “sensorimotor law”, are not simplified as usually in simulations. They are bio-realistic as they are determined by the HRTF recorded on human beings.