An active inference perspective for the amygdala complex
Authors: Ronald Sladky, Dominic Kargl, Wulf Haubensak, Claus Lamm
Journal: Trends in Cognitive Sciences (2023)

We outline how a predictive processing framework based on active inference can help overcome the limitations of fragmented feed-forward processing theories and more comprehensively explain the role of the amygdala in anxiety, fear, and danger detection.This framework integrates theoretical predictions with empirical findings, suggesting that the central amygdala subnucleus acts as a Bayesian regulator of an interoceptive self-model, sending top-down predictions to the basolateral amygdala for efficient perception.Fear conditioning is discussed as an example of proactive homeostatic regulation, where exteroceptive cues are used to anticipate negative interoceptive experiences.By extension, other amygdala functions (e.g., its role in approach/avoidance behavior or attention) can be explained using the same computational principles within a hierarchical active inference model. The amygdala is a heterogeneous network of subcortical nuclei with central importance in cognitive and clinical neuroscience. Various experimental designs in human psychology and animal model research have mapped multiple conceptual frameworks (e.g., valence/salience and decision making) to ever more refined amygdala circuitry. However, these predominantly bottom up-driven accounts often rely on interpretations tailored to a specific phenomenon, thus preventing comprehensive and integrative theories. We argue here that an active inference model of amygdala function could unify these fractionated approaches into an overarching framework for clearer empirical predictions and mechanistic interpretations. This framework embeds top-down predictive models, informed by prior knowledge and belief updating, within a dynamical system distributed across amygdala circuits in which self-regulation is implemented by continuously tracking environmental and homeostatic demands. The amygdala is a heterogeneous network of subcortical nuclei with central importance in cognitive and clinical neuroscience. Various experimental designs in human psychology and animal model research have mapped multiple conceptual frameworks (e.g., valence/salience and decision making) to ever more refined amygdala circuitry. However, these predominantly bottom up-driven accounts often rely on interpretations tailored to a specific phenomenon, thus preventing comprehensive and integrative theories. We argue here that an active inference model of amygdala function could unify these fractionated approaches into an overarching framework for clearer empirical predictions and mechanistic interpretations. This framework embeds top-down predictive models, informed by prior knowledge and belief updating, within a dynamical system distributed across amygdala circuits in which self-regulation is implemented by continuously tracking environmental and homeostatic demands. The amygdala and its subnuclei are quintessential in social, cognitive, affective, and clinical neuroscience [1.Adolphs R. What does the amygdala contribute to social cognition?.Ann. N. Y. Acad. Sci. 2010; 1191: 42-61Crossref PubMed Scopus (613) Google Scholar, 2.Bickart K.C. et al.The amygdala as a hub in brain networks that support social life.Neuropsychologia. 2014; 63: 235-248Crossref PubMed Scopus (246) Google Scholar, 3.Rosenberger L.A. et al.The human basolateral amygdala is indispensable for social experiential learning.Curr. Biol. 2019; 29: 3532-3537Abstract Full Text Full Text PDF PubMed Scopus (22) Google Scholar, 4.Terburg D. et al.The basolateral amygdala is essential for rapid escape: a human and rodent study.Cell. 2018; 175: 723-735Abstract Full Text Full Text PDF PubMed Scopus (89) Google Scholar]. With more human neuroimaging and circuit neuroscience charting its functional heterogeneity [5.Sladky R. et al.Unsmoothed functional MRI of the human amygdala and bed nucleus of the stria terminalis during processing of emotional faces.Neuroimage. 2018; 168: 383-391Crossref Scopus (21) Google Scholar, 6.Sladky R. et al.Basolateral and central amygdala orchestrate how we learn whom to trust.Commun. Biol. 2021; 4: 1-9Crossref Scopus (3) Google Scholar, 7.Torrisi S. et al.Extended amygdala connectivity changes during sustained shock anticipation.Transl. Psychiatry. 2018; 8: 1-12Crossref PubMed Scopus (0) Google Scholar, 8.Tillman R.M. et al.Intrinsic functional connectivity of the central extended amygdala.Hum. Brain Mapp. 2018; 39: 1291-1312Crossref PubMed Scopus (51) Google Scholar], our understanding of the amygdala has become more detailed, yet more fractionated. Moreover, some key findings, such as its role in fear conditioning, do not easily replicate across paradigms and model organisms [9.Visser R.M. et al.Robust BOLD responses to faces but not to conditioned threat: challenging the amygdala's reputation in human fear and extinction learning.J. Neurosci. 2021; 41: 10278-10292Crossref PubMed Scopus (15) Google Scholar]. We argue here that the problem lies not in the data but in the absence of an overarching theory that would allow for a unifying and less situational ad hoc interpretation of amygdala function(s). The amygdala comprises several smaller subnuclei [10.Janak P.H. Tye K.M. From circuits to behaviour in the amygdala.Nature. 2015; 517: 284-292Crossref PubMed Scopus (1205) Google Scholar]. The basolateral amygdala (BLA) is considered to be mainly stimulus specific in sensory processing and can be further divided into two parts: the lateral amygdala (LA) is sensitive to unimodal sensations [11.Uwano T. et al.Neuronal responsiveness to various sensory stimuli, and associative learning in the rat amygdala.Neuroscience. 1995; 68: 339-361Abstract Full Text PDF PubMed Scopus (143) Google Scholar] with connections to sensory cortices and thalamus, whereas the basal amygdala (BA) encodes multimodal and contextual features [12.Yaniv D. et al.A gradient of plasticity in the amygdala revealed by cortical and subcortical stimulation, in vivo.Neuroscience. 2001; 106: 613-620Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar] and behavioral responses [13.Kyriazi P. et al.Multi-dimensional coding by basolateral amygdala neurons.Neuron. 2018; 99: 1315-1328Abstract Full Text Full Text PDF PubMed Scopus (64) Google Scholar] with connections to hippocampus and entorhinal cortex [14.Davis P. 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Bull. 2016; 142: 1275-1314Crossref PubMed Scopus (109) Google Scholar] and, together with the distal bed nucleus of the stria terminalis (BST), might encode abstract situational features, such as sustained threat (Table 1, Key table). Although cell populations with marked differences in cell types, neurotransmitter binding sites, gene expression, and functions [18.Babaev O. et al.Inhibition in the amygdala anxiety circuitry.Exp. Mol. Med. 2018; 50: 1-16Crossref PubMed Scopus (168) Google Scholar,19.Hur J. et al.Anxiety and the neurobiology of temporally uncertain threat anticipation.J. Neurosci. 2020; 40: 7949-7964Crossref PubMed Scopus (48) Google Scholar] inhabit the same small space, functional differentiation can be found with noninvasive human fMRI, such as between BLA and CeA [6.Sladky R. et al.Basolateral and central amygdala orchestrate how we learn whom to trust.Commun. Biol. 2021; 4: 1-9Crossref Scopus (3) Google Scholar] or between CeA and BST [7.Torrisi S. et al.Extended amygdala connectivity changes during sustained shock anticipation.Transl. Psychiatry. 2018; 8: 1-12Crossref PubMed Scopus (0) Google Scholar], given high fMRI sensitivity (e.g., ultra-high field fMRI) and/or well-powered tasks providing sufficient specificity.Table 1Key table. Feed-forward versus active inference model for selected topicsAnatomical diagramFeed-forward processingActive inferenceLA is mainly unimodal and connected to sensory cortices and thalamus, as well as multimodal BA. BLA connects to CeA by direct and indirect pathways (via insula and basal forebrain)Theory that BLA and CeA integrate stimuli and gate behaviorCeA is informed by interoceptive self-model and passes valence predictions to BLA, which are used to tune sensory predictionsTopic 1: danger detection and BLASensory features are integrated by BLA and signaled to CeA. How does BLA filter relevant features?Sensations are actively filtered (suppressed or enhanced) using BLA predictions to thalamus and cortical areas, allowing for active sensingTopic 2: fear, anxiety, and CeASensations trigger fear and anxiety in CeA. What feature makes a particular sensation fear-inducing?CeA function is a form of a interoceptive self-regulatory process. Fear and anxiety are deviations of homeo-/allostatic set-pointsTopic 3: fear conditioningNeutral stimulus is associated with feared stimulus. How is affective value assigned to previously neutral stimuli?(Harmless) sensations that predict deviations from homeo-/allostatic priors can be used for proactive adaptive behaviorTopic 4: approach and avoidance behaviorCeA causes behavioral and somatic changes. How are actions gated and regulated?Actions are parallel self-regulatory processes on multiple levels (BLA, attention; CeA, stereotypical behavior; cortex, flexible behavior) Open table in a new tab Commonly, the amygdala is presented as following feed-forward functional principles: after stimulus processing in BLA, CeA operates as a relay and output region for motivated behavior [20.Davis M. Whalen P.J. The amygdala: vigilance and emotion.Mol. Psychiatry. 2001; 6: 13-34Crossref PubMed Scopus (2347) Google Scholar,21.LeDoux J.E. Emotion circuits in the brain.Annu. Rev. Neurosci. 2000; 23: 155-184Crossref PubMed Scopus (6385) Google Scholar]. Modern views appreciate the complexity of executive control and information integration in CeA [22.Fadok J.P. et al.New perspectives on central amygdala function.Curr. Opin. Neurobiol. 2018; 49: 141-147Crossref PubMed Scopus (148) Google Scholar], but still face substantial conceptual and empirical limitations. Foremost, at what stage of sensory integration are mere data transformed into information and meaning for the organism (e.g., danger, safety, or reward)? How can different amygdala research topics (Table 1), typically studied in isolation, be unified under one biological/computational principle? Empirical amygdala research has typically aimed at relating microcircuit dynamics to basic primitives of affectivity (e.g., positive/negative valence; see Glossary) or behavior (e.g., approach/avoidance or active/passive responding) [13.Kyriazi P. et al.Multi-dimensional coding by basolateral amygdala neurons.Neuron. 2018; 99: 1315-1328Abstract Full Text Full Text PDF PubMed Scopus (64) Google Scholar,23.Fadok J.P. et al.A competitive inhibitory circuit for selection of active and passive fear responses.Nature. 2017; 542: 96-100Crossref PubMed Scopus (267) Google Scholar, 24.Gozzi A. et al.A neural switch for active and passive fear.Neuron. 2010; 67: 656-666Abstract Full Text Full Text PDF PubMed Scopus (150) Google Scholar, 25.Pliota P. et al.Stress peptides sensitize fear circuitry to promote passive coping.Mol. Psychiatry. 2020; 25: 428-441Crossref PubMed Scopus (11) Google Scholar], but with limited experimental success [9.Visser R.M. et al.Robust BOLD responses to faces but not to conditioned threat: challenging the amygdala's reputation in human fear and extinction learning.J. Neurosci. 2021; 41: 10278-10292Crossref PubMed Scopus (15) Google Scholar], which we attribute to several limitations of the traditional feed-forward perspective. First, even in carefully planned experiments, humans and animals exhibit striking variability in their behavior, pointing toward considerable intersubjective differences and state dependencies of their responses. This can be handled by treating variability as uninformative noise, which essentially means discarding part of the observed data; alternatively, additional confounding variables might obscure mechanistic interpretations and the apparent neutrality of behavioristic research. Then, the model no longer describes a naturally observable relationship between stimulus and response: it is contaminated by artifactual assumptions. Second, mounting evidence from resting-state functional brain connectivity reveals that intrinsic brain activity independent of external perturbations from an experimental condition has a fundamental role in brain function [26.Coste C.P. et al.Ongoing brain activity fluctuations directly account for intertrial and indirectly for intersubject variability in Stroop task performance.Cereb. Cortex. 2011; 21: 2612-2619Crossref PubMed Scopus (0) Google Scholar,27.Sadaghiani S. et al.Ongoing dynamics in large-scale functional connectivity predict perception.Proc. Natl. Acad. Sci. U. S. A. 2015; 112: 8463-8468Crossref PubMed Scopus (163) Google Scholar]. Third, on the level of subjective, first-person experience, the assumption that relevant cognition is only triggered by an external stimulus violates face validity. All these necessary corollary steps imply that, for the sake of experimental operationalization, important aspects of brain functions are usually not accounted for, in neither theory nor practice. Stimulus-driven, feed-forward models could be replaced by action-oriented models for adaptive self-regulation, driven by hierarchical predictive top-down models in which higher levels of the computational hierarchy predict states of the lower level (Box 1). If input diverges from top-down predictions, a prediction error signal is passed back to the higher level. In this way, only novel and surprising information is relayed [28.Clark A. Whatever next? Predictive brains, situated agents, and the future of cognitive science.Behav. Brain Sci. 2013; 36: 181-204Crossref PubMed Scopus (2893) Google Scholar], resulting in computationally efficient predictive coding [29.Ali A. et al.Predictive coding is a consequence of energy efficiency in recurrent neural networks.Patterns. 2022; 3100639Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar,30.Rao R.P.N. Ballard D.H. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects.Nat. Neurosci. 1999; 2: 79-87Crossref PubMed Scopus (3074) Google Scholar] allowing for active inference [31.Friston K.J. Stephan K.E. Free-energy and the brain.Synthese. 2007; 159: 417-458Crossref PubMed Scopus (409) Google Scholar, 32.Friston K. The free-energy principle: a rough guide to the brain?.Trends Cogn. Sci. 2009; 13: 293-301Abstract Full Text Full Text PDF PubMed Scopus (1084) Google Scholar, 33.Parr T. et al.Active Inference: The Free Energy Principle in Mind, Brain, and Behavior. MIT Press, 2022Crossref Google Scholar] and Bayesian regulation (Box 2). Prediction errors known from reinforcement learning of rewards [34.Hollerman J.R. Schultz W. Dopamine neurons report an error in the temporal prediction of reward during learning.Nat. Neurosci. 1998; 1: 304-309Crossref PubMed Google Scholar,35.Mirenowicz J. Schultz W. Preferential activation of midbrain dopamine neurons by appetitive rather than aversive stimuli.Nature. 1996; 379: 449-451Crossref PubMed Scopus (661) Google Scholar] can be generalized to perception and action [32.Friston K. The free-energy principle: a rough guide to the brain?.Trends Cogn. Sci. 2009; 13: 293-301Abstract Full Text Full Text PDF PubMed Scopus (1084) Google Scholar,36.Friston K. et al.Active inference and epistemic value.Cogn. Neurosci. 2015; 6: 187-214Crossref PubMed Scopus (432) Google Scholar] as well as emotions by applying concepts such as interoceptive self-models [37.Seth A.K. Friston K.J. Active interoceptive inference and the emotional brain.Philos. Trans. R. Soc. B. 2016; 37120160007Crossref Scopus (448) Google Scholar], constructed emotions [38.Barrett L.F. The theory of constructed emotion: an active inference account of interoception and categorization.Soc. Cogn. Affect. Neurosci. 2017; 12: 1-23Crossref PubMed Scopus (142) Google Scholar], and changes in variational free energy [39.Joffily M. Coricelli G. Emotional valence and the free-energy principle.PLoS Comput. Biol. 2013; 9e1003094Crossref Scopus (171) Google Scholar] (Box 3). Expanding on these ideas, we propose that the amygdala subnuclei form a distributed network for active inference, which we illustrate using four different amygdala research topics (Table 1).Box 1Free energy, active inference, predictive processing, and the Bayesian brainFoundational to our active inference model is the more general free energy principle [31.Friston K.J. Stephan K.E. Free-energy and the brain.Synthese. 2007; 159: 417-458Crossref PubMed Scopus (409) Google Scholar, 32.Friston K. The free-energy principle: a rough guide to the brain?.Trends Cogn. Sci. 2009; 13: 293-301Abstract Full Text Full Text PDF PubMed Scopus (1084) Google Scholar, 33.Parr T. et al.Active Inference: The Free Energy Principle in Mind, Brain, and Behavior. MIT Press, 2022Crossref Google Scholar], which assumes that all nervous functions follow the principle of minimizing the (variational) free energy, Fμαy, of internal states μ, actions α, and observations y. To infer on the statistical regularities of the world, an agent creates generative models (m; i.e., models, thoughts, or fantasies), which are tested against the available observations (y; i.e., input, data, sensations, or evidence). The neurocomputational implementation is hierarchical, that is, higher levels send predictions down the hierarchy, while the lower levels send prediction errors up (predictive processing). This continuous bidirectional process refines the internal generative model of the brain and reduces discrepancies between expectations and reality (prediction error minimization). Higher levels of the hierarchy encode more abstract and temporally slower features of the environment (temporal depth). Going beyond passively updating its model (predictive coding), the brain can also act upon the world to make it conform to its predictions (i.e., it can actively change the sensory inputs it receives by interacting with the environment).Minimizing free energy minimizes Bayesian surprise Fμαy≥−lnpym,α, resulting in prediction error reduction, making active inference a special formulation of predictive processing. The inverse of surprise is Bayesian model evidence, which can be used for model optimization and comparison, providing a computationally tractable and biologically plausible method for implementing the mechanistic building blocks of a Bayesian brain. Given that the free energy term can be reformulated to Fμαy=Complexity–Accuracy [31.Friston K.J. Stephan K.E. Free-energy and the brain.Synthese. 2007; 159: 417-458Crossref PubMed Scopus (409) Google Scholar, 32.Friston K. The free-energy principle: a rough guide to the brain?.Trends Cogn. Sci. 2009; 13: 293-301Abstract Full Text Full Text PDF PubMed Scopus (1084) Google Scholar, 33.Parr T. et al.Active Inference: The Free Energy Principle in Mind, Brain, and Behavior. MIT Press, 2022Crossref Google Scholar], it can be minimized in two ways. First, creating sparser, more efficient, and generalizable models reduces Complexity. Second, Accuracy is related to minimizing prediction error and is achieved by developing an accurate model (m) that reliably predicts the sensations (y). Actions (α) affect accuracy in two ways: (i) sampling the world in a particular way (e.g., by modulation of top-down attention or by moving the eyes toward or away from a stimulus) changes what is sensed by selectively enhancing or suppressing stimulus features, allowing the organism to distinguish signal from noise; and (ii) an agent can act on the world to make a prediction come true (e.g., the agent prefers to see what is going on in a dark forest and turns on the flashlight). The actions themselves are constantly guided by epistemic goals for perceptual inference (to improve on the models for making accurate sensory predictions) and instrumental goals for adaptive control (to verify the models by predictable sensation) [68.Seth A.K. Tsakiris M. Being a beast machine: the somatic basis of selfhood.Trends Cogn. Sci. 2018; 22: 969-981Abstract Full Text Full Text PDF PubMed Scopus (143) Google Scholar] (see Box 2 in the main text).Box 2Active inference for perception and self-regulationActive inference, as described in Box 1 in the main text, is a specific implementation of hierarchical Bayesian inference in the brain, which can be understood as finding an approximate optimal solution for belief updating (integrating sensory evidence with prior beliefs) and action selection or choice policy based on these integrated beliefs. Computationally, this can be achieved by precision-weighted belief updating [49.Mathys C.D. et al.Uncertainty in perception and the hierarchical Gaussian filter. Front. Hum, Neurosci2014: 8Google Scholar], which posits that the weight on prediction error depends on the ratio of the precision (or variance, π=σ−2) of the priors [e.g., px∼Nμxπx−1] and likelihood function [e.g., pyx∼Nμyπy−1]. The posterior distribution pxy∼Nμx∣yπx∣y−1 can then be calculated by updating the precision πx∣y=πx+πy and the mean μx∣y=μx+πyπx∣yy−μx [i.e., posterior belief = prediction + precision weight (i.e., learning rate) × prediction error] (see Figure 1 in the main text).On a neural level, precision weighting is thought to be implemented by neuromodulatory mechanisms [32.Friston K. The free-energy principle: a rough guide to the brain?.Trends Cogn. Sci. 2009; 13: 293-301Abstract Full Text Full Text PDF PubMed Scopus (1084) Google Scholar], for example, dopamine and acetylcholine are tuning synaptic gain and, consequently, adjust the learning rate. Updating is typically called inference when it occurs on short timescales (e.g., for a single observation), and learning when it occurs on longer timescales (e.g., model parameter updates between trials). For optimal perception, the learning rate should be highest if prior precision is low (e.g., in novel situations) and the prediction error originates from precise sensory evidence (e.g., seeing an object very clearly). However, the learning rate should be lowest if prior precision is high (e.g., knowing one's living room) and sensory evidence has low precision (e.g., unreliable visual perception in the dark). The mechanisms of precision weighting guide two distinct aspects of 'active' in active inference:First, perception depends on actions to sample the environment for relevant features. Early eye-tracking studies had already showed how participants process images differently depending on the task [112.Yarbus A.L. Eye Movements and Vision. Springer, 1967Crossref Google Scholar]. The famous basketball and gorilla attention experiment [113.Simons D.J. Chabris C.F. Gorillas in our midst: sustained inattentional blindness for dynamic events.Perception. 1999; 28: 1059-1074Crossref PubMed Google Scholar] showed that focused attention leads to attentional blindness toward highly unusual events (low a priori probability for the task context). Even though these mechanisms are useful for optimal inference, they have a price of introducing well-known biases, such as sensory illusions that exploit priors (seeing what you are used to seeing), confirmation bias (seeing what you want to see given prior knowledge), and overgeneralizations (not seeing what is there). Our dependence on priors can explain why false beliefs about the world can still feel as though they were real [114.Dijkstra N. Fleming S.M. Subjective signal strength distinguishes reality from imagination.Nat. Commun. 2023; 14: 1627Crossref PubMed Scopus (7) Google Scholar], which has important implications for biased perception in anxiety disorders (see Figure 1 in the main text).Second, the same mechanisms can be used for regulation. If the prior precision is high, the model parameters are not simply updated to match the sensory data, which would be the case in an epistemically neutral form of predictive coding. These less flexible, more precise preference priors correspond to the metabolic, allostatic, autopoietic needs of an organism, their body state, and the environment that are more or less preferable (e.g., being in a safe space vs. being exposed to predation). This means that priors not only depend on objective knowledge (how the world is), but also on subjective preferences (how the world should be)Box 3Emotions: from descriptive to more naturalisticThe concept of basic emotions is useful as a social construction [38.Barrett L.F. The theory of constructed emotion: an active inference account of interoception and categorization.Soc. Cogn. Affect. Neurosci. 2017; 12: 1-23Crossref PubMed Scopus (142) Google Scholar] but less useful as a research theory due to their limited empirical success. The active inference perspective provides an alternative action-/goal-/outcome-oriented embodied perspective linked to the mechanisms of a predictive model (e.g., for interoception [37.Seth A.K. Friston K.J. Active interoceptive inference and the emotional brain.Philos. Trans. R. Soc. B. 2016; 37120160007Crossref Scopus (448) Google Scholar], especially in the form of adaptive self-regulation [68.Seth A.K. Tsakiris M. Being a beast machine: the somatic basis of selfhood.Trends Cogn. Sci. 2018; 22: 969-981Abstract Full Text Full Text PDF PubMed Scopus (143) Google Scholar]). This perspective enables us to reformulate several psychological concepts on affectivity.Salience results from top-down predictions based on perceptual and affective relevance, mediated by precision weighting. Valence results from estimating current and expected future self-regulation success. Formally, divergence between preference priors and sensory data is proportional to changes in free energy, which can inspire testable, computational models of emotions [103.Hesp C. et al.Deeply felt affect: the emergence of valence in deep active inference.Neural Comput. 2021; 33: 398-446Crossref Scopus (71) Google Scholar]. For example, it has been suggested that an (expected) increase in free energy results in disappointment, fear, and unhappiness, whereas a (expected) decrease results in relief, hope, and happiness [39.Joffily M. Coricelli G. Emotional valence and the free-energy principle.PLoS Comput. Biol. 2013; 9e1003094Crossref Scopus (171) Google Scholar]. Altogether, basic emotions, reward, and value are emergent consequences of these general neurocomputational principles. Ideally, an organism seeks out sensory experiences that are predicted by preference priors and avoids those that result in substantial prediction error (e.g., feeling too cold or hot, or being exposed to predation). Consequently, (subjectively) rewarding stimuli are preferably detected [115.Banerjee A. et al.Value-guided remapping of sensory cortex by lateral orbitofrontal cortex.Nature. 2020; 585: 245-250Crossref PubMed Scopus (70) Google Scholar,116.Schaffner J. et al.Sensory perception relies on fitness-maximizing codes.Nat. Hum. Behav. 2023; 7: 1135-1151Crossref PubMed Scopus (0) Google Scholar] and automatic threat responses for harm avoidance are elicited by extremely simple stimuli [117.De Franceschi G. et al.Vision guides selection of freeze or flight defense strategies in mice.Curr. Biol. 2016; 26: 2150-2154Abstract Full Text Full Text PDF PubMed Scopus (156) Google Scholar]. This means that expected states are also preferred states, which shifts the focus of reward learning toward expected sensations, away from an additional value function. There is evidence that genetically defined CeA neurons can encode sensations of distinct modality and valence at a population level (Figure 3 in [69.Kim J. et al.Basolateral to central amygdala neural circuits for appetitive behaviors.Neuron. 2017; 93: 1464-1479Abstract Full Text Full Text PDF PubMed Scopus (256) Google Scholar]), but encoding of these priors at the single cell level is currently unclear.Thevalue of sensations drives approach and avoidance behavior [118.Sharpe M.J. et al.Dopamine transients are sufficient and necessary for acquisition of model-based associations.Nat. Neurosci. 2017; 20: 735-742Crossref PubMed Scopus (142) Google Scholar] and, while reward-sensitive CeA neurons encode subjective value [89.Warlow S.M. et al.The central amygdala recruits mesocorticolimbic circuitry for pursuit of reward or pain.Nat. Commun. 2020; 11: 2716Crossref PubMed Scopus (31) Google Scholar], they do not simply signal pleasure. Instead, as rodents do not engage in optogenetic self-stimulation of these neurons [100.Warlow S.M. Berridge K.C. Incentive motivation: 'wanting' roles of central amygdala circuitry.Behav. Brain Res. 2021; 411113376Crossref Scopus (29) Google Scholar], they require concurrent exteroceptive input, which supports our perspective that the amygdala links interoceptive self-regulation and exteroceptive inference.Arousal could be mediated by precision weighting via neuromodulation, controlled by a hierarchy of preferences (e.g., slight hunger experiences are less critical compared with discomfort from anxiety, pain, and death). This would be a way to implement asymmetric p