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.
Highlights
Learning to predict threat from environmental cues is a skill found in many species across the animal kingdom
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
We sought to arbitrate between different associative learning models in humans by comparing their trial-by-trial predictions to the measured trajectory of autonomic nervous system (ANS) responses, and examine which learning quantities are reflected on different ANS output
Summary
Learning to predict threat from environmental cues is a skill found in many species across the animal kingdom. A laboratory example is Pavlovian threat conditioning ( termed fear conditioning [1]) in which the contingent presentation of predictive cues (conditioned stimuli, CS) and aversive events (unconditioned stimuli, US) engages a process of associative learning [2]. Associative learning theory offers a range of computational models, which make specific behavioural predictions. Many of these models have historically been developed to capture behavioural and neural phenomena in reward learning [11], and it is not known to what extent threat learning follows the same algorithms. We considered models that learn transition probabilities (i.e. parameters) in a known environmental structure, rather than learning the structure itself [10] or its underlying latent causes [12]
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