ReviewTrends in neurosciences2024
Latent-state and model-based learning in PTSD.
Review in Trends in neurosciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
10 citing papers in PubMed.
- Semantic encoding of trauma memories in the hippocampus among individuals with PTSD.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Trial
- Model-Free and Model-Based Learning in Human Fear Conditioning.Psychophysiology · 2026Article
- Cortical neuron classes and recursive curvature collapse: a neurobiological model of conscious dynamics.Theory in biosciences = Theorie in den Biowissenschaften · 2026Article
- The allostatic triage model of psychopathology (ATP Model): How reallocation of brain energetic resources under stress elicits psychiatric symptoms.Neuroscience and biobehavioral reviews · 2025Review
- Physical activity moderates the relationship between PTSD symptom severity and approach-avoidance conflict decision-making.Journal of anxiety disorders · 2025Article
- Episodic Memories Bias Contextual Threat Inferences after Aerobic and Mind-Body Exercise.Mental health and physical activity · 2025Article
- Physical Activity Is Positively Associated with Model-Based Decision-Making in Pursuit of Reward in Trauma-Exposed Adults.Medicine and science in sports and exercise · 2025Article
- Decoding threat neurocircuitry representations during traumatic memory recall in PTSD.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2025Article
- The Ubiquity of Time in Latent-cause Inference.Journal of cognitive neuroscience · 2024Article
- Integrating and fragmenting memories under stress and alcohol.Neurobiology of stress · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Post-traumatic stress disorder (PTSD) is characterized by altered emotional and behavioral responding following a traumatic event. In this article, we review the concepts of latent-state and model-based learning (i.e., learning and inferring abstract task representations) and discuss their relevance for clinical and neuroscience models of PTSD. Recent data demonstrate evidence for brain and behavioral biases in these learning processes in PTSD. These new data potentially recast excessive fear towards trauma cues as a problem in learning and updating abstract task representations, as opposed to traditional conceptualizations focused on stimulus-specific learning. Biases in latent-state and model-based learning may also be a common mechanism targeted in common therapies for PTSD. We highlight key knowledge gaps that need to be addressed to further elaborate how latent-state learning and its associated neurocircuitry mechanisms function in PTSD and how to optimize treatments to target these processes.
Indexed as
Identifiers
What OpenQuestion holds
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.