Rebecca P. Lawson

An aberrant precision account of autism.

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.

On hyperpriors and hypopriors: comment on Pellicano and Burr

Pellicano and Burr [1] present a compelling explanation for the perceptual symptoms of autism in terms of a failure of Bayesian inference. In this letter, we nuance a few observations relating to the nature of their normative explanation. This leads to the interesting suggestion that autism may be a disorder of metacognition.

The habenula encodes negative motivational value associated with primary punishment in humans

Significance Organisms must learn adaptively about environmental cue–outcome associations to survive. Studies in nonhuman primates suggest that a small phylogenetically conserved brain structure, the habenula, encodes the values of cues previously paired with aversive outcomes. However, such a role for the habenula has never been demonstrated in humans. We establish that the habenula encodes associations with aversive outcomes in humans, specifically that it tracks the dynamically changing negative values of cues paired with painful electric shocks, consistent with a role in learning. Importantly, habenula responses predicted the extent to which individuals withdrew from or approached negative and positive cues, respectively. These results suggest that the habenula plays a central role in driving aversively motivated learning and behavior in humans. 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 trial-by-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.

Changes in "top-down" connectivity underlie repetition suppression in the ventral visual pathway.

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.

Zero degrees of empathy

When I first read Simon Baron-Cohen’s Zero Degrees of Empathy, with my popular science hat on, I really enjoyed this brief (132 pages, excluding appendices and references!) and thought-provoking introduction to the science of empathy. It’s well-written, engaging, readable, and ambitiously broad, combining historical examples with psychiatric case studies and cutting-edge neuroscience research. Baron-Cohen touches base with all the classics of psychology; Zimbardo, Damasio, Phineas Gage, Bowlby, Harlow, and Zimmerman all get a mention. Upon rereading Zero Degrees of Empathy with my cognitive neuroscientist hat on, however, I came to engage more critically with the content, albeit without losing this initial appreciation of its strengths. Baron-Cohen’s aim is not trivial: He wishes to ‘‘understand human cruelty’’. He’s not the first psychologist to seek better explanations for the dark side of the human condition*Stanley Milgram springs to mind. Milgram’s experiments investigating obedience and authority are mentioned in Chapter 1, along with a good helping of stomach-churning examples of human cruelty, exemplifying the ways in which we’re capable of objectifying or dehumanising other people. Famously, Milgram was motivated to conduct his experiments by the events of the Nazi Holocaust; for his part, Baron-Cohen recounts how ‘‘the Nazis turned Jews into bars of soap’’ and ‘‘lampshades’’. The starting position is simple enough: ‘‘empathy [erosion] is the final common pathway to cruelty’’. This provides strong explanatory power not afforded by the unscientific notion of ‘‘evil’’. The rest of the book concerns what empathy is and the routes to its erosion. The definition of empathy offered is multifaceted; it is the ability to keep in mind someone else’s mind (a ‘‘dual-focus’’ of attention), which affords the ability to recognise (cognitive empathy) and respond (affective empathy) appropriately to the emotions, thoughts, and feelings of others. ‘‘We all lie somewhere on an empathy spectrum,’’ says Baron-Cohen. At one tail of the normal distribution lies ‘‘zero degrees of empathy’’, where people become capable of dehumanising others, leading to acts of cruelty. A significant portion of the book is devoted to introducing the reader to what Cognitive Neuropsychiatry, 2013 Vol. 18, No. 3, 252 256, http://dx.doi.org/10.1080/13546805.2012.741789

Defining the habenula in human neuroimaging studies

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.

Disrupted habenula function in major depression.

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.

Adults with autism overestimate the volatility of the sensory environment.

Insistence on sameness and intolerance of change are among the diagnostic criteria for autism spectrum disorder (ASD), but little research has addressed how people with ASD represent and respond to environmental change. Here, behavioral and pupillometric measurements indicated that adults with ASD are less surprised than neurotypical adults when their expectations are violated, and decreased surprise is predictive of greater symptom severity. A hierarchical Bayesian model of learning suggested that in ASD, a tendency to overlearn about volatility in the face of environmental change drives a corresponding reduction in learning about probabilistically aberrant events, thus putatively rendering these events less surprising. Participant-specific modeled estimates of surprise about environmental conditions were linked to pupil size in the ASD group, thus suggesting heightened noradrenergic responsivity in line with compromised neural gain. This study offers insights into the behavioral, algorithmic and physiological mechanisms underlying responses to environmental volatility in ASD.

Bayesian Approaches to Autism: Towards Volatility, Action, and Behavior

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.

Perception and Processing of Faces in the Human Brain Is Tuned to Typical Feature Locations

Faces are salient social stimuli whose features attract a stereotypical pattern of fixations. The implications of this gaze behavior for perception and brain activity are largely unknown. Here, we characterize and quantify a retinotopic bias implied by typical gaze behavior toward faces, which leads to eyes and mouth appearing most often in the upper and lower visual field, respectively. We found that the adult human visual system is tuned to these contingencies. In two recognition experiments, recognition performance for isolated face parts was better when they were presented at typical, rather than reversed, visual field locations. The recognition cost of reversed locations was equal to ∼60% of that for whole face inversion in the same sample. Similarly, an fMRI experiment showed that patterns of activity evoked by eye and mouth stimuli in the right inferior occipital gyrus could be separated with significantly higher accuracy when these features were presented at typical, rather than reversed, visual field locations. Our findings demonstrate that human face perception is determined not only by the local position of features within a face context, but by whether features appear at the typical retinotopic location given normal gaze behavior. Such location sensitivity may reflect fine-tuning of category-specific visual processing to retinal input statistics. Our findings further suggest that retinotopic heterogeneity might play a role for face inversion effects and for the understanding of conditions affecting gaze behavior toward faces, such as autism spectrum disorders and congenital prosopagnosia. SIGNIFICANCE STATEMENT Faces attract our attention and trigger stereotypical patterns of visual fixations, concentrating on inner features, like eyes and mouth. Here we show that the visual system represents face features better when they are shown at retinal positions where they typically fall during natural vision. When facial features were shown at typical (rather than reversed) visual field locations, they were discriminated better by humans and could be decoded with higher accuracy from brain activity patterns in the right occipital face area. This suggests that brain representations of face features do not cover the visual field uniformly. It may help us understand the well-known face-inversion effect and conditions affecting gaze behavior toward faces, such as prosopagnosia and autism spectrum disorders.