Objetivo Is the human mind a symbolic computational device? This issue was at the core Chomsky’s critique of Skinner in the 1960s, and motivated the debates regarding Parallel Distributed Processing models developed in the 1980s. The recent successes of “deep” networks make this issue topical for psychology and neuroscience, and it raises the question of whether symbols are needed for artificial intelligence more generally.One of the innovations of the current project is to identify simple empirical phenomena that will serve a critical test-bed for both symbolic and non-symbolic neural networks. In order to make substantial progress on this issue a series of empirical and computational investigations are organised as follows. First, studies focus on tasks that, according to proponents of symbolic systems, require symbols for the sake of generalisation. Accordingly, if non-symbolic networks succeed, it would undermine one of the main motivations for symbolic systems. Second, studies focus on generalisation in tasks in which human performance is well characterised. Accordingly, the research will provide important constraints for theories of cognition across a range of domains, including vision, memory, and reasoning. Third, studies develop new learning algorithms designed to make symbolic systems biologically plausible. One of the reasons why symbolic networks are often dismissed is the claim that they are not as biologically plausible as non-symbolic models. This last ambition is the most high-risk but also potentially the most important: Introducing new computational principles may fundamentally advance our understanding of how the brain learns and computes, and furthermore, these principles may increase the computational powers of networks in ways that are important for engineering and artificial intelligence. Ámbito científico natural sciencesbiological sciencesneurobiologysocial sciencespsychologynatural sciencescomputer and information sciencesartificial intelligencecomputational intelligence Palabras clave symbolic non-symbolic neural networks connectionism object recognition word identification Programa(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Tema(s) ERC-2016-ADG - ERC Advanced Grant Convocatoria de propuestas ERC-2016-ADG Consulte otros proyectos de esta convocatoria Régimen de financiación ERC-ADG - Advanced Grant Institución de acogida UNIVERSITY OF BRISTOL Aportación neta de la UEn € 2 495 578,00 Dirección BEACON HOUSE QUEENS ROAD BS8 1QU Bristol Reino Unido Ver en el mapa Región South West (England) Gloucestershire, Wiltshire and Bristol/Bath area Bristol, City of Tipo de actividad Higher or Secondary Education Establishments Enlaces Contactar con la organización Opens in new window Sitio web Opens in new window Participación en los programas de I+D de la UE Opens in new window Red de colaboración de HORIZON Opens in new window Coste total € 2 495 578,00 Beneficiarios (1) Ordenar alfabéticamente Ordenar por aportación neta de la UE Ampliar todo Contraer todo UNIVERSITY OF BRISTOL Reino Unido Aportación neta de la UEn € 2 495 578,00 Dirección BEACON HOUSE QUEENS ROAD BS8 1QU Bristol Ver en el mapa Región South West (England) Gloucestershire, Wiltshire and Bristol/Bath area Bristol, City of Tipo de actividad Higher or Secondary Education Establishments Enlaces Contactar con la organización Opens in new window Sitio web Opens in new window Participación en los programas de I+D de la UE Opens in new window Red de colaboración de HORIZON Opens in new window Coste total € 2 495 578,00