The Dopaminergic Midbrain Encodes the Expected Certainty about Desired Outcomes

Dopamine plays a key role in learning; however, its exact function in decision making and choice remains unclear. Recently, we proposed a generic model based on active (Bayesian) inference wherein dopamine encodes the precision of beliefs about optimal policies. Put simply, dopamine discharges reflect the confidence that a chosen policy will lead to desired outcomes. We designed a novel task to test this hypothesis, where subjects played a "limited offer" game in a functional magnetic resonance imaging experiment. Subjects had to decide how long to wait for a high offer before accepting a low offer, with the risk of losing everything if they waited too long. Bayesian model comparison showed that behavior strongly supported active inference, based on surprise minimization, over classical utility maximization schemes. Furthermore, midbrain activity, encompassing dopamine projection neurons, was accurately predicted by trial-by-trial variations in model-based estimates of precision. Our findings demonstrate that human subjects infer both optimal policies and the precision of those inferences, and thus support the notion that humans perform hierarchical probabilistic Bayesian inference. In other words, subjects have to infer both what they should do as well as how confident they are in their choices, where confidence may be encoded by dopaminergic firing

Non-Invasive Brain Stimulation Applied to Heschl's Gyrus Modulates Pitch Discrimination

The neural basis of the human brain's ability to discriminate pitch has been investigated by functional neuroimaging and the study of lesioned brains, indicating the critical importance of right and left Heschl's gyrus (HG) in pitch perception. Nonetheless, there remains some uncertainty with regard to localization and lateralization of pitch discrimination, partly because neuroimaging results do not allow us to draw inferences about the causality. To address the problem of causality in pitch discrimination functions, we used transcranial direct current stimulation to downregulate (via cathodal stimulation) and upregulate (via anodal stimulation) excitability in either left or right auditory cortex and measured the effect on performance in a pitch discrimination task in comparison with sham stimulation. Cathodal stimulation of HG on the left and on the right hemispheres adversely affected pitch discrimination in comparison to sham stimulation, with the effect on the right being significantly stronger than on the left. Anodal stimulation on either side had no effect on performance in comparison to sham. Our results indicate that both left and right HG are causally involved in pitch discrimination, although the right auditory cortex might be a stronger contributor

Scene Construction, Visual Foraging, and Active Inference

This paper describes an active inference scheme for visual searches and the perceptual synthesis entailed by scene construction. Active inference assumes that perception and action minimize variational free energy, where actions are selected to minimize the free energy expected in the future. This assumption generalizes risk-sensitive control and expected utility theory to include epistemic value; namely, the value (or salience) of information inherent in resolving uncertainty about the causes of ambiguous cues or outcomes. Here, we apply active inference to saccadic searches of a visual scene. We consider the (difficult) problem of categorizing a scene, based on the spatial relationship among visual objects where, crucially, visual cues are sampled myopically through a sequence of saccadic eye movements. This means that evidence for competing hypotheses about the scene has to be accumulated sequentially, calling upon both prediction (planning) and postdiction (memory). Our aim is to highlight some simple but fundamental aspects of the requisite functional anatomy; namely, the link between approximate Bayesian inference under mean field assumptions and functional segregation in the visual cortex. This link rests upon the (neurobiologically plausible) process theory that accompanies the normative formulation of active inference for Markov decision processes. In future work, we hope to use this scheme to model empirical saccadic searches and identify the prior beliefs that underwrite intersubject variability in the way people forage for information in visual scenes (e.g., in schizophrenia). Copyright © 2016 Mirza, Adams, Mathys, Friston

Blocking D2/D3 dopamine receptors in male participants increases volatility of beliefs when learning to trust others

The ability to learn about other people is crucial for human social functioning. Dopamine has been proposed to regulate the precision of beliefs, but direct behavioural evidence of this is lacking. In this study, we investigate how a high dose of the D2/D3 dopamine receptor antagonist sulpiride impacts learning about other people’s prosocial attitudes in a repeated Trust game. Using a Bayesian model of belief updating, we show that in a sample of 76 male participants sulpiride increases the volatility of beliefs, which leads to higher precision weights on prediction errors. This effect is driven by participants with genetically conferred higher dopamine availability (Taq1a polymorphism) and remains even after controlling for working memory performance. Higher precision weights are reflected in higher reciprocal behaviour in the repeated Trust game but not in single-round Trust games. Our data provide evidence that the D2 receptors are pivotal in regulating prediction error-driven belief updating in a social context

Attractor-like dynamics in belief updating in schizophrenia

Subjects with a diagnosis of schizophrenia (Scz) overweight unexpected evidence in probabilistic inference: such evidence becomes 'aberrantly salient'. A neurobiological explanation for this effect is that diminished synaptic gain (e.g. hypofunction of cortical N-methyl-D-aspartate receptors) in Scz destabilizes quasi-stable neuronal network states (or 'attractors'). This attractor instability account predicts that i) Scz would overweight unexpected evidence but underweight consistent evidence, ii) belief updating would be more vulnerable to stochastic fluctuations in neural activity, and iii) these effects would correlate.Hierarchical Bayesian belief updating models were tested in two independent datasets (n=80 and n=167, male and female) comprising human subjects with schizophrenia, and both clinical and non-clinical controls (some tested when unwell and on recovery) performing the 'probability estimates' version of the beads task (a probabilistic inference task). Models with a standard learning rate, or including a parameter increasing updating to 'disconfirmatory evidence', or a parameter encoding belief instability were formally compared.The 'belief instability' model (based on the principles of attractor dynamics) had most evidence in all groups in both datasets. Two of four parameters differed between Scz and non-clinical controls in each dataset: belief instability and response stochasticity. These parameters correlated in both datasets. Furthermore, the clinical controls showed similar parameter distributions to Scz when unwell, but were no different to controls once recovered.These findings are consistent with the hypothesis that attractor network instability contributes to belief updating abnormalities in Scz, and suggest that similar changes may exist during acute illness in other psychiatric conditions.SIGNIFICANCE STATEMENTSubjects with a diagnosis of schizophrenia (Scz) make large adjustments to their beliefs following unexpected evidence, but also smaller adjustments than controls following consistent evidence. This has previously been construed as a bias towards 'disconfirmatory' information, but a more mechanistic explanation may be that in Scz, neural firing patterns ('attractor states') are less stable and hence easily altered in response to both new evidence and stochastic neural firing. We model belief updating in Scz and controls in two independent datasets using a hierarchical Bayesian model, and show that all subjects are best fit by a model containing a belief instability parameter. Both this and a response stochasticity parameter are consistently altered in Scz, as the unstable attractor hypothesis predicts

Spatial Attention, Precision, and Bayesian Inference: A Study of Saccadic Response Speed

Inferring the environment's statistical structure and adapting behavior accordingly is a fundamental modus operandi of the brain. A simple form of this faculty based on spatial attentional orienting can be studied with Posner's location-cueing paradigm in which a cue indicates the target location with a known probability. The present study focuses on a more complex version of this task, where probabilistic context (percentage of cue validity) changes unpredictably over time, thereby creating a volatile environment. Saccadic response speed (RS) was recorded in 15 subjects and used to estimate subject-specific parameters of a Bayesian learning scheme modeling the subjects' trial-by-trial updates of beliefs. Different response models—specifying how computational states translate into observable behavior—were compared using Bayesian model selection. Saccadic RS was most plausibly explained as a function of the precision of the belief about the causes of sensory input. This finding is in accordance with current Bayesian theories of brain function, and specifically with the proposal that spatial attention is mediated by a precision-dependent gain modulation of sensory input. Our results provide empirical support for precision-dependent changes in beliefs about saccade target locations and motivate future neuroimaging and neuropharmacological studies of how Bayesian inference may determine spatial attentio

Spatial attention, precision, and Bayesian inference: a study of saccadic response speed

Inferring the environment's statistical structure and adapting behavior accordingly is a fundamental modus operandi of the brain. A simple form of this faculty based on spatial attentional orienting can be studied with Posner's location-cueing paradigm in which a cue indicates the target location with a known probability. The present study focuses on a more complex version of this task, where probabilistic context (percentage of cue validity) changes unpredictably over time, thereby creating a volatile environment. Saccadic response speed (RS) was recorded in 15 subjects and used to estimate subject-specific parameters of a Bayesian learning scheme modeling the subjects' trial-by-trial updates of beliefs. Different response models-specifying how computational states translate into observable behavior-were compared using Bayesian model selection. Saccadic RS was most plausibly explained as a function of the precision of the belief about the causes of sensory input. This finding is in accordance with current Bayesian theories of brain function, and specifically with the proposal that spatial attention is mediated by a precision-dependent gain modulation of sensory input. Our results provide empirical support for precision-dependent changes in beliefs about saccade target locations and motivate future neuroimaging and neuropharmacological studies of how Bayesian inference may determine spatial attention. © The Author 2013. Published by Oxford University Pres

Duodenoduodenal intussusception in a 16‐year‐old German Warmblood mare

A 16-year-old German Warmblood mare was presented with an acute history of abdominal pain that failed to respond to medical treatment. Diagnostic evaluations including transabdominal and transrectal ultrasound and palpation were suggestive of a small-intestinal intussusception. Exploratory laparotomy revealed a duodenoduodenal intussusception of the proximal and middle third of the duodenum. The horse was subjected to euthanasia due to the poor prognosis. The post-mortem examination showed the intussusception to be associated with a pseudodiverticulum orally to the intussusception and duodenal muscular hypertrophy