期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2019
卷号：116
期号：13
页码：6035-6044
DOI：10.1073/pnas.1820238116
出版社：The National Academy of Sciences of the United States of America
摘要：Evaluating stimuli along a good–bad dimension is a fundamental computation performed by the human mind. In recent decades, research has documented dissociations and associations between explicit (i.e., self-reported) and implicit (i.e., indirectly measured) forms of evaluations. However, it is unclear whether such 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 present 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 model-free and model-based processes, implicit evaluations depend on the former but are impervious to the latter. Studies 2 and 3 demonstrated the robustness of this effect to ( i ) the number of stimulus exposures in the revaluation phase and ( ii ) the deterministic vs. probabilistic nature of initial reinforcement. These findings provide a 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 avenues for designing theoretically guided interventions to produce change in implicit evaluations.
关键词：Implicit Association Test ; implicit evaluations ; implicit social cognition ; model-free vs;model-based learning ; reinforcement learning
