ArticlePLoS computational biology2018
Human Pavlovian fear conditioning conforms to probabilistic learning.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Serotonin depletion impairs both Pavlovian and instrumental reversal learning in healthy humans.Molecular psychiatry · 2021Trial
- Psychophysiological Outcome Responses in Human Pavlovian Fear Conditioning: A Prediction Error Analysis.Psychophysiology · 2026Article
- Hidden State Inference or Continuous Belief Updating during a Dynamic Visuomotor Skill.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Computational Modelling Reveals Slower Safety Learning and Threat Extinction are Associated With Higher Anxiety Severity in Remote Fear Conditioning.Computational psychiatry (Cambridge, Mass.) · 2026Article
- From facing fears to experimental frontiers: immersive cinematic virtual reality environments as a catalyst for innovation in behavioral neuroscience.Frontiers in psychiatry · 2026Article
- Threat expectancies in a VR fear conditioning paradigm follow non-linear extinction patterns but are not influenced by intolerance of uncertainty.Scientific reports · 2025Article
- Article
- Measuring Human Pavlovian Reward Conditioning and Memory Retention After Consolidation.Psychophysiology · 2025Article
- Pavlovian safety learning: An integrative theoretical review.Psychonomic bulletin & review · 2025Review
- Representation of Anticipated Rewards and Punishments in the Human Brain.Annual review of psychology · 2025Review
- Dimensionality and optimal combination of autonomic fear-conditioning measures in humans.Behavior research methods · 2024Article
- Animal affect, welfare and the Bayesian brain.Animal welfare (South Mimms, England) · 2024Review
- Interactive cognitive maps support flexible behavior under threat.Cell reports · 2023Article
- Computational perspectives on human fear and anxiety.Neuroscience and biobehavioral reviews · 2023Review
- Characterizing cardiac autonomic dynamics of fear learning in humans.Psychophysiology · 2022Article
- Oscillatory brain activity links experience to expectancy during associative learning.Psychophysiology · 2022Article
- Computational modeling of threat learning reveals links with anxiety and neuroanatomy in humans.eLife · 2022Article
- Impaired learning to dissociate advantageous and disadvantageous risky choices in adolescents.Scientific reports · 2022Article
- Rating expectations can slow aversive reversal learning.Psychophysiology · 2022Article
- Reliability of web-based affective auditory stimulus presentation.Behavior research methods · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
Learning to predict threat from environmental cues is a fundamental skill in changing environments. This aversive learning process is exemplified by Pavlovian threat conditioning. Despite a plethora of studies on the neural mechanisms supporting the formation of associations between neutral and aversive events, our computational understanding of this process is fragmented. Importantly, different computational models give rise to different and partly opposing predictions for the trial-by-trial dynamics of learning, for example expressed in the activity of the autonomic nervous system (ANS). Here, we investigate human ANS responses to conditioned stimuli during Pavlovian fear conditioning. To obtain precise, trial-by-trial, single-subject estimates of ANS responses, we build on a statistical framework for psychophysiological modelling. We then consider previously proposed non-probabilistic models, a simple probabilistic model, and non-learning models, as well as different observation functions to link learning models with ANS activity. Across three experiments, and both for skin conductance (SCR) and pupil size responses (PSR), a probabilistic learning model best explains ANS responses. Notably, SCR and PSR reflect different quantities of the same model: SCR track a mixture of expected outcome and uncertainty, while PSR track expected outcome alone. In summary, by combining psychophysiological modelling with computational learning theory, we provide systematic evidence that the formation and maintenance of Pavlovian threat predictions in humans may rely on probabilistic inference and includes estimation of uncertainty. This could inform theories of neural implementation of aversive learning.
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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.