Learning where to look with movement-based intrinsic motivations: a bio-inspired model

Most sophisticated mammals, in particular primates, interact with the world to acquire knowledge and skills later exploitable to obtain biologically relevant resources. These interactions are driven by intrinsic motivations. Recent research on brain is revealing the system of neural structures, pivoting on superior colliculus, underlying trial-and-error learning processes guided by movement-detection, one important element of one specific type of intrinsic motivation mechanism. Here we present a preliminary computational model of such system guiding the acquisition of overt attentional skills. The model is formed by bottom-up attentional components, exploiting the intrinsic properties of the scene, and top-down attentional components, learning under the guidance of movement-based intrinsic motivation. The model is tested with a simple task, inspired by the 'gazecontingency paradigm' proposed in cognitive psychology, where looking some portions of the environment can directly change it. The tests of the model show how its integrated components can learn skills causing relevant changes in the environment while ignoring changes non-contingent to own action. The model also allows the presentation of a wider research agenda directed to build biologically plausible models of the interaction between overt attention control and intrinsic motivations.