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Description: In an attempt to provide a computational account of the relationship between implicit attitudes and value-guided learning and decision making, the present project probes whether IAT scores predict differential learning rates (readiness to update value estimates) and inverse temperatures (willingness to base choice behavior on updated value estimates) across attitude-consistent vs. attitude-inconsistent interactions in a reinforcement learning paradigm.

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