You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: Human Pavlovian fear conditioning conforms to probabilistic learning}
Article:
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.
Introduction:
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( also 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]. In mammals, including humans, establishing CS/US associations requires synaptic plasticity in basolateral and central amygdala[2–5] and thus relies on a neural circuit distinct from that involved in learning reward associations[6]. Despite progress in elucidating systems-level mechanisms that pre-process and relay CS and US information to the amygdala[7, 8], a computational understanding of aversive learning remains incomplete, and available neural data do not fully fit standard learning models[4, 9, 10]. Associative learning theory offers a range of computational models, which make specific behavioural predictions. However, 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. Here, we sought to arbitrate between different associative learning models in humans by comparing their trial-by-trial predictions to the measured trajectory of ANS responses, and examine which learning quantities are reflected on different ANS output. 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]. Previous models proposed to explain Pavlovian threat learning are derived from classical reinforcement learning( RL) theory and build on Rescorla-Wagner( RW)[13] and Pearce-Hall( PH)[14] rules. Other studies suggested that a combination of these models best captures aversive learning[15, 16]( hybrid model, HM). All of these models provide point estimates of future outcomes, by modifying current predictions with a prediction error, weighted by a learning rate that is either fixed( RW) or determined by a fixed proportion of previous prediction errors( PH and HM). While such models can, in theory, be implemented in simple neural architectures[17], at least in the reward domain they have been challenged because they cannot explain some experimental observations such as latent inhibition[18]. However, latent inhibition is easily accommodated by models in which the learning rate is variable and determined by an estimate of the prediction uncertainty[18]. This uncertainty can be implemented in explicit probabilistic computations, for example in hierarchical models[19], or by Kalman filters in the RL framework, which could be classified as implicitly probabilistic and where uncertainty is encoded in a summary statistic, the Kalman gain[18]. Here, we sought to compare previously proposed algorithms of threat learning with a probabilistic account. Notably, probabilistic RL algorithms[18] and general-purpose, hierarchical probabilistic models[19] contain parameters that are difficult to constrain from biological or psychological principles and thus have to be estimated from measured data. We reasoned that this may be difficult given the relatively low signal-to-noise ratio in ANS estimates. However, if threat learning is adaptive and approximates statistical optimality[20], these parameters could be usefully constrained. This is why we relied on a parameter-free probabilistic model constructed from statistical principles, rather than from neural or cognitive considerations. Specifically, from the perspective of an agent that is fully informed about the task structure, and assumes stationary transition probabilities and trial independence, US occurrence follows a Bernoulli process with a US probability parameter. The model then computes the likelihood over this parameter based on the evidence that has been observed so far. The binomial likelihood takes the functional form of a beta distribution. Hence, we implemented a sequentially updated beta-binomial model[21]. The most common way to behaviourally assess human threat learning is by measuring ANS responses, such as skin conductance responses( SCR, mostly sympathetic)[22] or pupil size responses( PSR, sympathetic and parasympathetic)[23]. These are mediated by neural circuits including the central amygdala and thus directly linked to circuits involved in formation of CS/US associations[24, 25]. Here, we optimized signal-to-noise ratio with a statistical framework for psychophysiological modelling( PsPM)[26] that exploits the entire time-series of autonomic measurements rather than a low number of arbitrarily selected data features( e. g. peak-to-trough measurements). We have previously shown that this method allows estimating the ANS input causing SCR[27] and PSR[28] with higher precision than standard approaches. Crucially, the observation function that maps an associative learning model onto measured behaviour is not known. Unlike choice data, it has been suggested that ANS outputs may not reflect outcome expectations[15, 16]. Furthermore, given their distinct innervation it is possible that different ANS outputs may reflect different aspects of the same learning mechanism. Hence, we additionally sought to clarify this observation function for both measures. We hypothesised that PSR and SCR follow the same underlying learning algorithm but might link to this algorithm by different observation functions. Specifically, SCR are known to habituate during learning, and intraneural stimulations suggested that this habituation does not occur in the effector organ[29], such that either the learning model or the observation function needs to account for this phenomenon.
Discussion:
While humans may show overt behaviour during Pavlovian fear conditioning, such as freezing[50], formal attempts to study such phenomena in humans remain limited[51]. This is why we relied on autonomic readouts, the most common way of quantifying fear in humans[2]. In order to analyse ANS activity, we employed a statistical framework for modelling outputs of the peripheral nervous system, such as the SCR and PSR[28, 52]. In this framework, estimates of ANS impulse amplitude are obtained using independently validated canonical functions that map ANS firing bursts to peripheral output, with a priori defined constraints on ANS burst timing. Presumably, these estimates more directly reflect central neural states than the noisy peripheral responses themselves. Indeed, ANS amplitude estimates derived from this approach have been shown to better distinguish CS+ and CS- than other approaches, not only for SCR[30, 52] and PSR[31] but also for fear-conditioned bradycardia[53], respiratory amplitude responses[54] and fear-potentiated startle[55]. Whether these latter readouts reflect similar quantities as SCR and PSR, and are controlled by the same learning algorithm, remains a topic for future investigation. Our results extend previous fear conditioning studies which were restricted to Rescorla-Wagner and hybrid Rescorla-Wagner-Pearce-Hall models[15, 16, 56]. Importantly, these studies demonstrated that changes in trial-by-trial SCR can be better explained by an associability term of a RW-PH hybrid model than the US prediction itself[15, 16]. When considering non probabilistic RL models only, we could replicate these results, showing that our approach is indeed sensitive and able to capture the same learning mechanisms as previously reported. By using more precise trial-by-trial ANS response estimates and including a probabilistic model, we highlight a different underlying learning mechanism, but conceptually confirm that SCR do not reflect US predictions. In our winning model, the observation function is a combination of the expected US( prior mean) and estimated uncertainty, which depends on the total number of observations during the experiment. Interestingly, there is qualitative evidence that PSR may be informative of past prediction errors[57], something that we did not confirm here. More elaborate experimental designs with specific trial sequences may be required to shed light on this question. Our probabilistic model is normative under stationary outcome probabilities and allows incorporating prior information in the form of beta distributions. This formalism is mathematically elegant but even under an in-built assumption of stationarity it may appear too specific as a candidate for a neural model. Furthermore, there is weak evidence towards a decaying CS+/CS- difference over time, something that requires future experiments to be determined and that none of the models considered here could predict. While our probabilistic model makes the strong assumption of a logarithmic decay of SCR over time, this prediction was not quantitatively corroborated in the current data set. We note that Bayesian models with more generic distributional assumptions such as a Kalman temporal difference filter[18] or an hierarchical Gaussian filter[19] make very similar predictions under the experimental circumstances employed here, such that more sophisticated procedures will be required to disentangle different Bayesian models. Future work may thus elucidate plausible neural implementations that predict the progression of ANS responses highlighted here, which may be distinguished by hemodynamic or electrophysiological activity to formalise the neural implementation of aversive learning.
Using environmental cues to predict threat is a crucial skill , encountered in many species . A laboratory model to study associating predictive cues with aversive events is Pavlovian fear conditioning . A computational understanding of this process at a systems-level is lacking . Here , we investigate which computational learning model best predicts activity of the human autonomic nervous system during fear conditioning . We show , across three data sets and two autonomic readouts , that a probabilistic learning model explains the data decisively better than previously proposed models . We suggest that humans learn to predict threat by maintaining and constantly updating a probabilistic model of the environment through Bayes’ rule .