Autism is a neurodevelopmental disorder characterized by problems with social-communication, restricted interests and repetitive behavior. A recent and thought-provoking article presented a normative explanation for the perceptual symptoms of autism in terms of a failure of Bayesian inference (Pellicano and Burr, 2012). In response, we suggested that when Bayesian inference is grounded in its neural instantiation—namely, predictive coding—many features of autistic perception can be attributed to aberrant precision (or beliefs about precision) within the context of hierarchical message passing in the brain (Friston et al., 2013). Here, we unpack the aberrant precision account of autism. Specifically, we consider how empirical findings—that speak directly or indirectly to neurobiological mechanisms—are consistent with the aberrant encoding of precision in autism; in particular, an imbalance of the precision ascribed to sensory evidence relative to prior beliefs.

Insistence on sameness and intolerance of change are part of the diagnostic criteria for Autism Spectrum Disorder (ASD) but there is little research addressing how people with ASD represent and respond to environmental change. Here, we find that behavioural and pupillometric measurements show adults with ASD are less surprised than neurotypical adults when expectations are violated, with reduced surprise predicting greater symptom severity. A hierarchical Bayesian model of learning suggests that in ASD a tendency to over-learn about volatility in the face of environmental change drives a corresponding reduction in learning about probabilistically aberrant events – putatively rendering them less surprising. Participant-specific modelled estimates of surprise about environmental conditions are linked to pupil size in the ASD group, suggesting heightened phasic noradrenergic responsivity in line with neural gain impairments. This study offers novel insight into the behavioural, algorithmic and physiological mechanisms that underlie responses to environmental volatility in ASD.

Autism spectrum disorder currently lacks an explanation that bridges cognitive, computational, and neural domains. In the past 5 years, progress has been sought in this area by drawing on Bayesian probability theory to describe both social and nonsocial aspects of autism in terms of systematic differences in the processing of sensory information in the brain. The present article begins by synthesizing the existing literature in this regard, including an introduction to the topic for unfamiliar readers. The key proposal is that autism is characterized by a greater weighting of sensory information in updating probabilistic representations of the environment. Here, we unpack further how the hierarchical setting of Bayesian inference in the brain (i.e., predictive processing) adds significant depth to this approach. In particular, autism may relate to finer mechanisms involved in the context-sensitive adjustment of sensory weightings, such as in how neural representations of environmental volatility inform perception. Crucially, in light of recent sensorimotor treatments of predictive processing (i.e., active inference), hypotheses regarding atypical sensory weighting in autism have direct implications for the regulation of action and behavior. Given that core features of autism relate to how the individual interacts with and samples the world around them (e.g., reduced social responding, repetitive behaviors, motor impairments, and atypical visual sampling), the extension of Bayesian theories of autism to action will be critical for yielding insights into this condition. (PsycINFO Database Record

The habenula is a small, evolutionarily conserved brain structure that plays a central role in aversive processing and is hypothesised to be hyperactive in depression, contributing to the generation of symptoms such as anhedonia. However, habenula responses during aversive processing have yet to be reported in individuals with major depressive disorder (MDD). Unmedicated and currently depressed MDD patients (N=25, aged 18–52 years) and healthy volunteers (N=25, aged 19–52 years) completed a passive (Pavlovian) conditioning task with appetitive (monetary gain) and aversive (monetary loss and electric shock) outcomes during high-resolution functional magnetic resonance imaging; data were analysed using computational modelling. Arterial spin labelling was used to index resting-state perfusion and high-resolution anatomical images were used to assess habenula volume. In healthy volunteers, habenula activation increased as conditioned stimuli (CSs) became more strongly associated with electric shocks. This pattern was significantly different in MDD subjects, for whom habenula activation decreased significantly with increasing association between CSs and electric shocks. Individual differences in habenula volume were negatively associated with symptoms of anhedonia across both groups. MDD subjects exhibited abnormal negative task-related (phasic) habenula responses during primary aversive conditioning. The direction of this effect is opposite to that predicted by contemporary theoretical accounts of depression based on findings in animal models. We speculate that the negative habenula responses we observed may result in the loss of the capacity to actively avoid negative cues in MDD, which could lead to excessive negative focus.

Recently there has been renewed interest in the habenula; a pair of small, highly evolutionarily conserved epithalamic nuclei adjacent to the medial dorsal (MD) nucleus of the thalamus. The habenula has been implicated in a range of behaviours including sleep, stress and pain, and studies in non-human primates have suggested a potentially important role in reinforcement processing, putatively via its effects on monoaminergic neurotransmission. Over the last decade, an increasing number of neuroimaging studies have reported functional responses in the human habenula using functional magnetic resonance imaging (fMRI). However, standard fMRI analysis approaches face several challenges in isolating signal from this structure because of its relatively small size, around 30 mm3 in volume. In this paper we offer a set of guidelines for locating and manually tracing the habenula in humans using high-resolution T1-weighted structural images. We also offer recommendations for appropriate pre-processing and analysis of high-resolution functional magnetic resonance imaging (fMRI) data such that signal from the habenula can be accurately resolved from that in surrounding structures.

Learning what to approach, and what to avoid, involves assigning value to environmental cues that predict positive and negative events. Studies in animals indicate that the lateral habenula encodes the previously learned negative motivational value of stimuli. However, involvement of the habenula in dynamic trialby-trial aversive learning has not been assessed, and the functional role of this structure in humans remains poorly characterized, in part, due to its small size. Using high-resolution functional neuroimaging and computational modeling of reinforcement learning, we demonstrate positive habenula responses to the dynamically changing values of cues signaling painful electric shocks, which predict behavioral suppression of responses to those cues across individuals. By contrast, negative habenula responses to monetary reward cue values predict behavioral invigoration. Our findings show that the habenula plays a key role in an online aversive learning system and in generating associated motivated behavior in humans.high-resolution fMRI | conditioned behavior | pallidum L earning which stimuli predict positive and negative outcomes, and thus should be approached or avoided, respectively, is central to an organism's ability to survive. Midbrain dopamine neurons respond to both unpredicted rewarding stimuli and to cues previously paired with rewards (1), consistent with behavioral approach toward those cues. As a counterpoint to these reward-related signals, neurons in the lateral habenula (LHb) of nonhuman primates respond to previously learned stimuli predicting the delivery of punishments and the omission of rewards, whereas they are inhibited by stimuli that signal upcoming rewards (2). These studies in nonhuman primates have concentrated on well-learned stimuli, and so have forsaken the opportunity to study the details of dynamic adaptation in the habenula. However, in many real-world scenarios, organisms learn about the motivational value of novel cues in their environment gradually, one exposure at a time, which raises the question as to whether the habenula plays a role in encoding the dynamically changing motivational value of cues that predict negative events.Dynamic learning from aversive events permits the rapid experience-dependent updating of behavior, for example, the automatic suppression of approach, which is a characteristic of aversive conditioning (3). The LHb receives inputs from the globus pallidus (4), and its excitation inhibits midbrain dopamine neurons via the rostromedial tegmental nucleus (2). This position as a hub between corticolimbic networks and midbrain monoaminergic nuclei provides a means through which positively or negatively valenced stimuli can modulate motor output, leading to the hypothesis that the habenula plays a critical role in motivated behavior (5).Studies using temporally precise optogenetic stimulation of the LHb in rodents provide convincing evidence that the habenula drives behavioral suppression (6). This structure has been suggested as a novel target for de...

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Repetition of the same stimulus leads to a reduction in neural activity known as repetition suppression (RS). In functional magnetic resonance imaging (fMRI), RS is found for multiple object categories. One proposal is that RS reflects locally based "within-region" changes, such as neural fatigue. Thus, if a given region shows RS across changes in stimulus size or view, then it is inferred to hold size-or view-invariant representations. An alternative hypothesis characterizes RS as a consequence of "top-down" between-region modulation. Differentiating between these accounts is central to the correct interpretation of fMRI RS data. It is also unknown whether the same mechanisms underlie RS to identical stimuli and RS across changes in stimulus size or view. Using fMRI, we investigated RS within a body-sensitive network in human visual cortex comprising the extrastriate body area (EBA) and the fusiform body area (FBA). Both regions showed RS to identical images of the same body that was unaffected by changes in body size or view. Dynamic causal modeling demonstrated that changes in backward, top-down (FBA-to-EBA) effective connectivity play a critical role in RS. Furthermore, only repetition of the identical image showed additional changes in forward connectivity (EBA-to-FBA). These results suggest that RS is driven by changes in top-down modulation, whereas the contribution of "feedforward" changes in connectivity is dependent on the precise nature of the repetition. Our results challenge previous interpretations regarding the underlying nature of neural representations made using fMRI RS paradigms.