Learning sensorimotor maps through curiosity

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Imagine you are a child first learning to speak or to play a musical instrument. Your brain will need to learn which movement leads to which sound. One of the puzzles of movement neuroscience is how the motor system solves this challenge: how we link what we do (movement) with what we see or hear (perception), that is, how we learn a sensorimotor map. The long term aim of my research program is to provide a new way of thinking about this process as driven by information seeking: curiosity. Learning sensorimotor maps can be studied using an experimental paradigm that I introduced previously in which participants make arm movements that are mapped to sounds. Since arm movements normally do not result in sounds, this paradigm allows us to monitor participants in the first stages of learning where they form a novel sensorimotor map. Now we can ask what drives the learning we observe. Work to date using this paradigm has revealed that learning can occur when subjects are allowed to make free movements, for which they are given the corresponding feedback sounds. Subsequent tests show that subjects acquired the map. This suggests that learning is driven by a principle that does not rely on targets. The focus for this DG cycle is the hypothesis that map learning is driven by the search for information, that is, curiosity. Prior work in motor neuroscience has investigated random exploration, whereas in the field of machine learning algorithms have been developed that explore their environment in a principled way, based on an internal estimation of the accuracy of their own predictions. This notion of curiosity has not been applied to the study of human motor learning which is what we will do here. Aims: 1) We artificially manipulate the predictability of the mapping in order to test whether information seeking forms the basis of learning. 2) We will adapt algorithms from machine learning to develop a new, curiosity-based model of how humans learn sensorimotor maps. We will test whether this model can predict human behavior better than other models that do not incorporate curiosity but rather random exploration, which, if the case, will show that curiosity drives human map learning. 3) We will test the hypothesis that brain areas in the human motor network are involved in curiosity-based learning. We will use magnetic resonance spectroscopy (MRS), a technique that allows measuring the concentration of excitatory and inhibitory neurochemicals in the brain, to collect data from human subjects who perform our learning task in the scanner. We will track the expected information gain of each movement, thus forming a precise pattern of expected activity. This pattern can be compared against the observed data from various brain areas, thus identifying the neural basis of curiosity-based learning. Taken together, these studies will provide much needed insight into the basis of exploratory phases of motor learning.