Objetivo In evaluative learning, associative learning refers to the effect of repeated exposure to an object and an evaluation that appear together (e.g. “Snakes-Unpleasantness”), whereas propositional learning refers to learning the actual relationship between the object and the evaluation (e.g. “Snakes cause unpleasantness”). A common core assumption in contemporary attitude research is that associative learning affects mainly automatic evaluations (unintentional and sometimes unconscious), whereas propositional learning affects mainly controlled evaluations (deliberated and conscious). However, contemporary attitude theory acknowledges some relationships across the implicit/explicit boundary. The proposed research will extend the investigation of these assumed relationships by examining another possible interrelation across the implicit/explicit boundary: the effect of non-associative information on automatic evaluations. The research will examine how learning specific object-evaluation relationships (e.g. “X causes unpleasantness” vs. “X prevents unpleasantness”) affect implicit measures and automatic evaluation. The research includes unprecedented large-scale (overall N > 30,000) comparative studies that will examine current assumptions about implicit/explicit distinctions in formation and measurement, in addition to the effect of propositional learning on automatic evaluation. It will use a large number of associative and propositional attitude induction methods, four difference implicit measures, and attitude-relevant automatic behaviors. These large-scale tests are possible because of the researcher’s involvement in an American research website that collects data from thousands of participants every week. At the same time, the research will test these questions in smaller-scale lab-studies with a subset of the same learning procedures and attitude measurements, but with attitude-relevant social interactions created in an immersive virtual reality environment. Ámbito científico natural sciencescomputer and information sciencessoftwaresoftware applicationsvirtual reality Programa(s) FP7-PEOPLE - Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) Tema(s) FP7-PEOPLE-2009-RG - Marie Curie Action: "Reintegration Grants" Convocatoria de propuestas FP7-PEOPLE-2009-RG Consulte otros proyectos de esta convocatoria Régimen de financiación MC-IRG - International Re-integration Grants (IRG) Coordinador BEN-GURION UNIVERSITY OF THE NEGEV Aportación de la UE € 100 000,00 Dirección . 84105 Beer Sheva Israel Ver en el mapa Tipo de actividad Higher or Secondary Education Establishments Contacto administrativo Daphna Tripto (Ms.) Enlaces Contactar con la organización Opens in new window Sitio web Opens in new window Coste total Sin datos
