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Model-free and model-based learning processes in the updating of explicit and implicit evaluations
Evaluating stimuli along a positive–negative dimension is a fundamental computation performed by the human mind. In recent decades, research has documented both dissociations and associations between explicit (self-reported) and implicit (indirectly measured) forms of evaluations. Together, these two forms of evaluation are central to organizing social cognition and drive behavior in intergroup relations, consumer choice, psychopathology, and close relationships. However, it is unclear whether explicit–implicit dissociations arise from relatively more superficial differences in measurement techniques or from deeper differences in the processes by which explicit and implicit evaluations are acquired and represented. The current project (total N = 2,354) relies on the computationally well-specified distinction between model-based and model-free reinforcement learning to investigate the unique and shared aspects of explicit and implicit evaluations. Study 1 used a revaluation procedure to reveal that whereas explicit evaluations of novel targets are updated via both model-free and model-based processes, implicit evaluations depend on the former but are impervious to the latter. Studies 2–3 demonstrated the robustness of this effect to (a) the number of stimulus exposures in the revaluation phase and (b) the deterministic vs. probabilistic nature of initial reinforcement. These findings provide a novel framework, going beyond traditional dual-process and single-process accounts, to highlight the context-sensitivity and long-term recalcitrance of implicit evaluations as well as variations in their relationship with their explicit counterparts. These results also suggest novel avenues for designing theoretically guided interventions to produce change in implicit evaluations.