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Twin study finds extinction learning rate is heritable and linked to anxiety severity

A pre-registered bioRxiv preprint replicating findings in 925 twin pairs identifies extinction learning rate — but not safety learning rate — as both heritable and associated with anxiety severity, pointing to a potential computational endophenotype.

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A pre-registered replication study, posted as a preprint on bioRxiv, examined the heritability of reinforcement learning parameters and their relationship to anxiety in a sample of 925 twin pairs — approximately ten times larger than the prior discovery cohort.

The study focused on two computational constructs: extinction learning (the rate at which an organism updates its assessment that a previously dangerous stimulus is now safe) and safety learning (learning that a novel stimulus is not a threat). Both have been proposed as heritable endophenotypes of anxiety disorders, reflecting impairments documented across multiple anxiety presentations.

Extinction learning rate was significantly associated with anxiety severity in the replication sample (Bayes factor BFr0 = 1189.67), lending strong evidential weight to earlier findings. Safety learning rate did not replicate as an anxiety-associated parameter. The heritability analyses provide quantitative estimates of the genetic contribution to these computational phenotypes, supporting their potential utility in genetic studies of anxiety.

The work is relevant to researchers in psychiatric genetics and computational psychiatry who are working to bridge GWAS-derived risk loci with measurable behavioural and cognitive mechanisms. Because this is a preprint that has not yet completed peer review, findings should be interpreted with appropriate caution pending independent scrutiny.

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  1. Primary sourcePreprint bioRxiv · 2026-05-28
    The heritability of reinforcement learning parameters and their association with anxiety

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heritability anxiety reinforcement-learning endophenotype twin-study computational-psychiatry extinction-learning
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Genetic Current is the news section of Evagene, an academic, research, and educational pedigree-modelling platform. Stories are AI-drafted summaries of items from trusted public sources, written for researchers, clinicians, educators, students, genealogists, and patients with an interest in genetics. Summaries are for educational and research purposes only and are not medical advice.

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