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Effects of Different Learning Experiences on Automatic Evaluative Processes

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... ver más

31/03/2014

BGU

100K€

Presupuesto del proyecto: 100K€

Líder del proyecto

BENGURION UNIVERSITY OF THE NEGEV No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2014-03-31 No tenemos la información de la convocatoria

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BGU

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Líder del proyecto

BENGURION UNIVERSITY OF THE NEGEV No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 100K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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