Human and non-human primates (NHPs) are exposed in their environment to a continuous stream of sequential information. To structure this flow, the cognitive system is able to identify patterns of invariant events in order to organize sequences of planned actions in response. This phenomenon is commonly referred to as statistical learning. However, due to the limited capacity of working memory, when a regularity takes the form of a long sequence of information, this sequence must be segmented into bundles of information – called chunks – to be compressed and executed or stored in memory more quickly and efficiently. This talk will focus on the precise dynamics of sequence learning and chunking, including the formation of chunks during the learning of visuo-motor sequences, their evolution during intensive practice, and the impact of the size of these sequences on chunking. In addition, we sought to understand the role of Hebbian associative learning, common to NHPs and humans, and the role of human-specific skills, such as language, in the learning of these sequences. I will therefore compare data collected in NHPs, Guinea baboons (Papio papio), with data collected in humans. Eventually, these data not only inform about chunking mechanisms in sequence learning but also challenge current computational models of statistical learning.
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