The brain is able to manipulate itself, adapting its internal world in a changing environment. In particular it continuously manipulates its representations to achieve goals. This competence is supported by many neurocognitive high-order processes that interact during the performance of a goal-directed behaviour (e.g. attention processes, executive functions, motivational systems). Overall, adult humans often face a new problem trough a change of the `perceptual point of view' (i.e. a representational change), rather then an extended research of the correct action to perform. On the other hand, infants and children contemporary develop motor competence and task-directed perceptual representations (e.g. categorical perception). Here I approach the main research question `how the brain manipulates its representations to solve a task that requires cognitive flexibility?'. Moreover, I started to approach the second related research question `how the brain acquires suitable representations to solve a categorisation task?'. First, adopting a synergistic theoretical and computational approach, I identified the systems and basic computational principles that allow the brain to learn, to generate and to manipulate its internal representations in order to achieve a goal. Second, I built a set of computational models and I tested them against experimental human data extracted from already published experimental works. This translational approach corroborates my theoretical proposals and, vice versa, provides scientific and clinical knowledge on the investigated processes. In particular, my models represent a novel computational tool for the investigation of flexible cognition and categorical perception in case of clinical populations (e.g. autistic people). Moreover, they represent a starting point to propose a new theory of conscious cognition showing both scientific implications (e.g new models of consciousness) and technological implications (e.g consciousness-inspired robots).

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