ReviewTrends in cognitive sciences2026
The next generation of fear generalization.
Review in Trends in cognitive sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Generalizing from past experience is a fundamental feature of cognition. Although generalization has been of long-standing interest to diverse domains in psychological science, the past decade has seen a rapid acceleration of fear generalization research in particular. The field of human fear generalization is at a pivotal juncture where it is now possible to synthesize extensive behavioral work, competing theoretical models, animal and human neuroscience, and clinical and individual-differences research to address enduring gaps. In this review, we provide an updated account of where the field of human fear generalization currently resides and offer concrete avenues for new research emphasizing tighter integration between animal and human neuroscience, prioritizing cross-disciplinary perspectives, and updating conceptual frameworks to align with rapidly advancing methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.