The very earliest stages of sensory processing have the potential to alter how we perceive and respond to our environment. These initial processing circuits can incorporate subcortical regions, such ...as the thalamus and brainstem nuclei, which mediate complex interactions with the brain's cortical processing hierarchy. These subcortical pathways, many of which we share with other animals, are not merely vestigial but appear to function as 'shortcuts' that ensure processing efficiency and preservation of vital life-preserving functions, such as harm avoidance, adaptive social interactions and efficient decision-making. Here, we propose that functional interactions between these higher-order and lower-order brain areas contribute to atypical sensory and cognitive processing that characterizes numerous neuropsychiatric disorders.
The Organization for Human Brain Mapping (OHBM) has been active in advocating for the instantiation of best practices in neuroimaging data acquisition, analysis, reporting and sharing of both data ...and analysis code to deal with issues in science related to reproducibility and replicability. Here we summarize recommendations for such practices in magnetoencephalographic (MEG) and electroencephalographic (EEG) research, recently developed by the OHBM neuroimaging community known by the abbreviated name of COBIDAS MEEG. We discuss the rationale for the guidelines and their general content, which encompass many topics under active discussion in the field. We highlight future opportunities and challenges to maximizing the sharing and exploitation of MEG and EEG data, and we also discuss how this 'living' set of guidelines will evolve to continually address new developments in neurophysiological assessment methods and multimodal integration of neurophysiological data with other data types.
The encoding of sensory information in the human brain is thought to be optimised by two principal processes: 'prediction' uses stored information to guide the interpretation of forthcoming sensory ...events, and 'attention' prioritizes these events according to their behavioural relevance. Despite the ubiquitous contributions of attention and prediction to various aspects of perception and cognition, it remains unknown how they interact to modulate information processing in the brain. A recent extension of predictive coding theory suggests that attention optimises the expected precision of predictions by modulating the synaptic gain of prediction error units. Because prediction errors code for the difference between predictions and sensory signals, this model would suggest that attention increases the selectivity for mismatch information in the neural response to a surprising stimulus. Alternative predictive coding models propose that attention increases the activity of prediction (or 'representation') neurons and would therefore suggest that attention and prediction synergistically modulate selectivity for 'feature information' in the brain. Here, we applied forward encoding models to neural activity recorded via electroencephalography (EEG) as human observers performed a simple visual task to test for the effect of attention on both mismatch and feature information in the neural response to surprising stimuli. Participants attended or ignored a periodic stream of gratings, the orientations of which could be either predictable, surprising, or unpredictable. We found that surprising stimuli evoked neural responses that were encoded according to the difference between predicted and observed stimulus features, and that attention facilitated the encoding of this type of information in the brain. These findings advance our understanding of how attention and prediction modulate information processing in the brain, as well as support the theory that attention optimises precision expectations during hierarchical inference by increasing the gain of prediction errors.
Abstract The mismatch negativity (MMN) is a brain response to violations of a rule, established by a sequence of sensory stimuli (typically in the auditory domain) Näätänen R. Attention and brain ...function. Hillsdale, NJ: Lawrence Erlbaum; 1992. The MMN reflects the brain’s ability to perform automatic comparisons between consecutive stimuli and provides an electrophysiological index of sensory learning and perceptual accuracy. Although the MMN has been studied extensively, the neurophysiological mechanisms underlying the MMN are not well understood. Several hypotheses have been put forward to explain the generation of the MMN; amongst these accounts, the “ adaptation hypothesis ” and the “ model adjustment hypothesis ” have received the most attention. This paper presents a review of studies that focus on neuronal mechanisms underlying the MMN generation, discusses the two major explanatory hypotheses, and proposes predictive coding as a general framework that attempts to unify both.
Our ability to rapidly detect threats is thought to be subserved by a subcortical pathway that quickly conveys visual information to the amygdala. This neural shortcut has been demonstrated in ...animals but has rarely been shown in the human brain. Importantly, it remains unclear whether such a pathway might influence neural activity and behavior. We conducted a multimodal neuroimaging study of 622 participants from the Human Connectome Project. We applied probabilistic tractography to diffusion-weighted images, reconstructing a subcortical pathway to the amygdala from the superior colliculus via the pulvinar. We then computationally modeled the flow of haemodynamic activity during a face-viewing task and found evidence for a functionally afferent pulvinar-amygdala pathway. Critically, individuals with greater fibre density in this pathway also had stronger dynamic coupling and enhanced fearful face recognition. Our findings provide converging evidence for the recruitment of an afferent subcortical pulvinar connection to the amygdala that facilitates fear recognition.
This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that minor issues remain unresolved (see decision letter).
Bayesian inference suggests that perception is inferred from a weighted integration of prior contextual beliefs with current sensory evidence (likelihood) about the world around us. The perceived ...precision or uncertainty associated with prior and likelihood information is used to guide perceptual decision-making, such that more weight is placed on the source of information with greater precision. This provides a framework for understanding a spectrum of clinical transdiagnostic symptoms associated with aberrant perception, as well as individual differences in the general population. While behavioral paradigms are commonly used to characterize individual differences in perception as a stable characteristic, measurement reliability in these behavioral tasks is rarely assessed. To remedy this gap, we empirically evaluate the reliability of a perceptual decision-making task that quantifies individual differences in Bayesian belief updating in terms of the relative precision weighting afforded to prior and likelihood information (i.e., sensory weight). We analyzed data from participants (n = 37) who performed this task twice. We found that the precision afforded to prior and likelihood information showed high internal consistency and good test-retest reliability (ICC = 0.73, 95% CI 0.53, 0.85) when averaged across participants, as well as at the individual level using hierarchical modeling. Our results provide support for the assumption that Bayesian belief updating operates as a stable characteristic in perceptual decision-making. We discuss the utility and applicability of reliable perceptual decision-making paradigms as a measure of individual differences in the general population, as well as a diagnostic tool in psychiatric research.
There is significant controversy over the existence and function of a direct subcortical visual pathway to the amygdala. It is thought that this pathway rapidly transmits low spatial frequency ...information to the amygdala independently of the cortex, and yet the directionality of this function has never been determined. We used magnetoencephalography to measure neural activity while human participants discriminated the gender of neutral and fearful faces filtered for low or high spatial frequencies. We applied dynamic causal modeling to demonstrate that the most likely underlying neural network consisted of a pulvinar-amygdala connection that was uninfluenced by spatial frequency or emotion, and a cortical-amygdala connection that conveyed high spatial frequencies. Crucially, data-driven neural simulations revealed a clear temporal advantage of the subcortical connection over the cortical connection in influencing amygdala activity. Thus, our findings support the existence of a rapid subcortical pathway that is nonselective in terms of the spatial frequency or emotional content of faces. We propose that that the "coarseness" of the subcortical route may be better reframed as "generalized."
The human amygdala coordinates how we respond to biologically relevant stimuli, such as threat or reward. It has been postulated that the amygdala first receives visual input via a rapid subcortical route that conveys "coarse" information, namely, low spatial frequencies. For the first time, the present paper provides direction-specific evidence from computational modeling that the subcortical route plays a generalized role in visual processing by rapidly transmitting raw, unfiltered information directly to the amygdala. This calls into question a widely held assumption across human and animal research that fear responses are produced faster by low spatial frequencies. Our proposed mechanism suggests organisms quickly generate fear responses to a wide range of visual properties, heavily implicating future research on anxiety-prevention strategies.
The mismatch negativity (MMN) is an event related potential evoked by violations of regularity. Here, we present a model of the underlying neuronal dynamics based upon the idea that auditory cortex ...continuously updates a generative model to predict its sensory inputs. The MMN is then modelled as the superposition of the electric fields evoked by neuronal activity reporting prediction errors. The process by which auditory cortex generates predictions and resolves prediction errors was simulated using generalised (Bayesian) filtering--a biologically plausible scheme for probabilistic inference on the hidden states of hierarchical dynamical models. The resulting scheme generates realistic MMN waveforms, explains the qualitative effects of deviant probability and magnitude on the MMN - in terms of latency and amplitude--and makes quantitative predictions about the interactions between deviant probability and magnitude. This work advances a formal understanding of the MMN and--more generally--illustrates the potential for developing computationally informed dynamic causal models of empirical electromagnetic responses.
Perception emerges from unconscious probabilistic inference, which guides behaviour in our ubiquitously uncertain environment. Bayesian decision theory is a prominent computational model that ...describes how people make rational decisions using noisy and ambiguous sensory observations. However, critical questions have been raised about the validity of the Bayesian framework in explaining the mental process of inference. Firstly, some natural behaviours deviate from Bayesian optimum. Secondly, the neural mechanisms that support Bayesian computations in the brain are yet to be understood. Taking Marr’s cross level approach, we review the recent progress made in addressing these challenges. We first review studies that combined behavioural paradigms and modelling approaches to explain both optimal and suboptimal behaviours. Next, we evaluate the theoretical advances and the current evidence for ecologically feasible algorithms and neural implementations in the brain, which may enable probabilistic inference. We argue that this cross-level approach is necessary for the worthwhile pursuit to uncover mechanistic accounts of human behaviour.
•Real-life behaviour can be described as Bayesian inference with imperfect execution.•Bayesian-approximation algorithms explain sources of behavioural suboptimality.•Approximation algorithms inspire neural implementations.•Biologically feasible neural coding schemes constrain algorithms and behaviour.•Mechanistic accounts emerge from top-down and bottom-up integration of Marr’s levels.
We constantly look for patterns in the environment that allow us to learn its key regularities. These regularities are fundamental in enabling us to make predictions about what is likely to happen ...next. The physiological study of regularity extraction has focused primarily on repetitive sequence-based rules within the sensory environment, or on stimulus-outcome associations in the context of reward-based decision-making. Here we ask whether we implicitly encode non-sequential stochastic regularities, and detect violations therein. We addressed this question using a novel experimental design and both behavioural and magnetoencephalographic (MEG) metrics associated with responses to pure-tone sounds with frequencies sampled from a Gaussian distribution. We observed that sounds in the tail of the distribution evoked a larger response than those that fell at the centre. This response resembled the mismatch negativity (MMN) evoked by surprising or unlikely events in traditional oddball paradigms. Crucially, responses to physically identical outliers were greater when the distribution was narrower. These results show that humans implicitly keep track of the uncertainty induced by apparently random distributions of sensory events. Source reconstruction suggested that the statistical-context-sensitive responses arose in a temporo-parietal network, areas that have been associated with attention orientation to unexpected events. Our results demonstrate a very early neurophysiological marker of the brain's ability to implicitly encode complex statistical structure in the environment. We suggest that this sensitivity provides a computational basis for our ability to make perceptual inferences in noisy environments and to make decisions in an uncertain world.
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