Anxiety attenuates learning advantages conferred by statistical stability and induces loss of volatility-attuning in brain activity.

Anxiety can alter an individual's perception of their external sensory environment. Previous studies suggest that anxiety can increase the magnitude of neural responses to unexpected (or surprising) stimuli. Additionally, surprise responses are reported to be boosted during stable compared to volatile environments. Few studies, however, have examined how learning is impacted by both threat and volatility. To investigate these effects, we used threat-of-shock to transiently increase subjective anxiety in healthy adults while they performed an auditory oddball task under stable and volatile environments and while undergoing functional Magnetic Resonance Imaging (fMRI) scanning. We then used Bayesian Model Selection (BMS) mapping to identify the brain areas where different models of anxiety displayed the highest evidence. Behaviourally, we found that threat-of-shock eliminated the accuracy advantage conferred by environmental stability over volatility. Neurally, we found that threat-of-shock led to attenuation and loss of volatility-attuning of brain activity evoked by surprising sounds across most subcortical and limbic regions including the thalamus, basal ganglia, claustrum, insula, anterior cingulate, hippocampal gyrus and the superior temporal gyrus. Taken together, our findings suggest that threat eliminates learning advantages conferred by statistical stability compared to volatility. Thus, we propose that anxiety disrupts behavioural adaptation to environmental statistics, and that multiple subcortical and limbic regions are implicated in this process.

Statistical Learning and Inference Is Impaired in the Nonclinical Continuum of Psychosis.

Our perceptions result from the brain's ability to make inferences, or predictive models, of sensory information. Recently, it has been proposed that psychotic traits may be linked to impaired predictive processes. Here, we examine the brain dynamics underlying statistical learning and inference in stable and volatile environments, in a population of healthy human individuals (N = 75; 36 males, 39 females) with a range of psychotic-like experiences. We measured prediction error responses to sound sequences with electroencephalography, gauged sensory inference explicitly by behaviorally recording sensory statistical learning errors, and used dynamic causal modeling to tap into the underlying neural circuitry. We discuss the findings that were robust to replication across the two experiments (Discovery dataset, N = 31; Validation dataset, N = 44). First, we found that during stable conditions, participants demonstrated greater precision in their predictive model, reflected in a larger prediction error response to unexpected sounds, and decreased statistical learning errors. Moreover, individuals with attenuated prediction errors in stable conditions were found to make greater incorrect predictions about sensory information. Critically, we show that greater errors in statistical learning and inference are related to increased psychotic-like experiences. These findings link neurophysiology to behavior during statistical learning and prediction formation, as well as providing further evidence for the idea of a continuum of psychosis in the healthy, nonclinical population.SIGNIFICANCE STATEMENT While perceiving the world, we make inferences by learning the statistics present in the sensory environment. It has been argued that psychosis may emerge because of a failure to learn sensory statistics, resulting in an impaired representation of the world. Recently, it has been proposed that psychosis exists on a continuum; however, there is conflicting evidence on whether sensory learning deficits align on the nonclinical end of the psychosis continuum. We found that statistical learning of sensory events is associated with the magnitude of mismatch negativity and, critically, is impaired in healthy people who report more psychotic-like experiences. We replicated these findings in an independent sample, demonstrating strengthened credibility to support the continuum of psychosis that extends into the nonclinical population.

Bayesian Model Selection Maps for Group Studies Using M/EEG Data.

Predictive coding postulates that we make (top-down) predictions about the world and that we continuously compare incoming (bottom-up) sensory information with these predictions, in order to update our models and perception so as to better reflect reality. That is, our so-called "Bayesian brains" continuously create and update generative models of the world, inferring (hidden) causes from (sensory) consequences. Neuroimaging datasets enable the detailed investigation of such modeling and updating processes, and these datasets can themselves be analyzed with Bayesian approaches. These offer methodological advantages over classical statistics. Specifically, any number of models can be compared, the models need not be nested, and the "null model" can be accepted (rather than only failing to be rejected as in frequentist inference). This methodological paper explains how to construct posterior probability maps (PPMs) for Bayesian Model Selection (BMS) at the group level using electroencephalography (EEG) or magnetoencephalography (MEG) data. The method has only recently been used for EEG data, after originally being developed and applied in the context of functional magnetic resonance imaging (fMRI) analysis. Here, we describe how this method can be adapted for EEG using the Statistical Parametric Mapping (SPM) software package for MATLAB. The method enables the comparison of an arbitrary number of hypotheses (or explanations for observed responses), at each and every voxel in the brain (source level) and/or in the scalp-time volume (scalp level), both within participants and at the group level. The method is illustrated here using mismatch negativity (MMN) data from a group of participants performing an audio-spatial oddball attention task. All data and code are provided in keeping with the Open Science movement. In doing so, we hope to enable others in the field of M/EEG to implement our methods so as to address their own questions of interest.