Model-free and model-based learning processes in the updating of explicit and implicit evaluations

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 uncle...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2019-03, Vol.116 (13), p.6035-6044
Main Authors: Kurdi, Benedek, Gershman, Samuel J., Banaji, Mahzarin R.
Format: Article
Language:English
Subjects:
Adult
Choice Behavior
Human performance
Humans
Judgment
Learning
Measurement techniques
Models, Psychological
PNAS Plus
Reinforcement
Reinforcement (Psychology)
Sensitivity analysis
Social Sciences
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Summary: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.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1820238116