Solving motion coherence with biologically plausible agents

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dc.contributor.advisorGerven, Marcel, van
dc.contributor.advisorKachergis, G.E.
dc.contributor.advisorBosch, Sander
dc.contributor.authorPanagiotou, Filippos
dc.date.issued2017-08-30
dc.description.abstractArtificial Neural Networks (ANNs) have been increasingly used in attempts to mechanis- tically explain the inner workings of human cognition. Motion coherence is a a well-known paradigm whose computation requires closing the Perception-Action cycle. Here, artificial agents with short-term memory and simulated vision learn how to solve the task through reinforcement learning. The agents are able to solve the task in a way comparable to human participants. The results are consistent with behavioral and psychological measures, and bear electrophysiological similarities. The results suggest that the proposed framework is a robust model choice for solving tasks that require closing the Perception-Action cycle.en_US
dc.embargo.lift2043-08-30
dc.embargo.typeTijdelijk embargoen_US
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/7687
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationResearchmaster Cognitive Neuroscienceen_US
dc.thesis.studyprogrammeResearchmaster Cognitive Neuroscienceen_US
dc.thesis.typeResearchmasteren_US
dc.titleSolving motion coherence with biologically plausible agentsen_US
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