1,987 publications from this institution
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.
We used positron emission tomography to contrast changes in cerebral blood flow associated with willed and routine acts. In the six tasks used, volunteers had to make a series of responses to a sequence of stimuli. For the routine acts, each response was completely specified by the stimulus. For the willed acts, the response was open-ended and therefore volunteers had to make a deliberate choice. Willed acts in the two response modalities studied (speaking a word, or lifting a finger) were associated with increased blood flow in the dorsolateral prefrontal cortex (Brodmann area 46). Willed acts were also associated with decreases in blood flow, but the location of these decreases was modality dependent.
This paper introduces active listening, as a unified framework for synthesising and recognising speech. The notion of active listening inherits from active inference, which considers perception and action under one universal imperative: to maximise the evidence for our (generative) models of the world. First, we describe a generative model of spoken words that simulates (i) how discrete lexical, prosodic, and speaker attributes give rise to continuous acoustic signals; and conversely (ii) how continuous acoustic signals are recognised as words. The 'active' aspect involves (covertly) segmenting spoken sentences and borrows ideas from active vision. It casts speech segmentation as the selection of internal actions, corresponding to the placement of word boundaries. Practically, word boundaries are selected that maximise the evidence for an internal model of how individual words are generated. We establish face validity by simulating speech recognition and showing how the inferred content of a sentence depends on prior beliefs and background noise. Finally, we consider predictive validity by associating neuronal or physiological responses, such as the mismatch negativity and P300, with belief updating under active listening, which is greatest in the absence of accurate prior beliefs about what will be heard next.
When dissimilar images are presented to the two eyes, perception alternates spontaneously between each monocular view, a phenomenon called binocular rivalry. Functional brain imaging in humans was used to study the neural basis of these subjective perceptual changes. Cortical regions whose activity reflected perceptual transitions included extrastriate areas of the ventral visual pathway, and parietal and frontal regions that have been implicated in spatial attention; whereas the extrastriate areas were also engaged by nonrivalrous perceptual changes, activity in the frontoparietal cortex was specifically associated with perceptual alternation only during rivalry. These results suggest that frontoparietal areas play a central role in conscious perception, biasing the content of visual awareness toward abstract internal representations of visual scenes, rather than simply toward space.
Recent studies have established a relation between ongoing brain activity fluctuations and intertrial variability in evoked neural responses, perception, and motor performance. Here, we extended these investigations into the domain of cognitive control. Using functional neuroimaging and a sparse event-related design (with long and unpredictable intervals), we measured ongoing activity fluctuations and evoked responses in volunteers performing a Stroop task with color-word interference. Across trials, prestimulus activity of several regions predicted subsequent response speed and across subjects this effect scaled with the Stroop effect size, being significant only in subjects manifesting behavioral interference. These effects occurred only in task relevant as the dorsal anterior cingulate and dorsolateral prefrontal cortex as well as ventral visual areas sensitive to color and visual words. Crucially, in subjects showing a Stroop effect, reaction times were faster when prestimulus activity was higher in task-relevant (color) regions and slower when activity was higher in irrelevant (word form) regions. These findings suggest that intrinsic brain activity fluctuations modulate neural mechanisms underpinning selective voluntary attention and cognitive control. Rephrased in terms of predictive coding models, ongoing activity can hence be considered a proxy of the precision (gain) with which prediction error signals are transmitted upon sensory stimulation.
Estimating the precision or uncertainty associated with sensory signals is an important part of perception. Based on a previous computational model, we tested the hypothesis that increasing visual contrast increased the precision encoded in early visual areas by the gain or excitability of superficial pyramidal cells. This hypothesis was investigated using electroencephalography and dynamic causal modelling (DCM); a biologically constrained modelling of the cortical processes underlying EEG activity. Source localisation identified the electromagnetic sources of visually evoked responses and DCM was used to characterise the coupling among these sources. Bayesian model selection was used to select the most likely connectivity pattern and contrast-dependent changes in connectivity. As predicted, the model with the highest evidence entailed increased superficial pyramidal cell gain in higher-contrast trials. As predicted theoretically, contrast-dependent increases were reduced at higher levels of the hierarchy. These results demonstrate that increased signal-to-noise ratio in sensory signals produce (or are represented by) increased superficial pyramidal cell gain, and that synaptic parameters encoding statistical properties like sensory precision can be quantified using EEG and dynamic causal modelling.
In the realm of law enforcement, the "police hunch" has long been a mysterious but crucial aspect of decision-making. Drawing on the developing framework of Active Inference from cognitive science, this theoretical article examines the genesis, mechanics, and implications of the police hunch. It argues that hunches - often vital in high-stakes situations - should not be described as mere intuitions, but as intricate products of our mind's generative models. These models, shaped by observations of the social world and assimilated and enacted through active inference, seek to reduce surprise and make hunches an indispensable tool for officers, in exactly the same way that hypotheses are indispensable for scientists. However, the predictive validity of hunches is influenced by a range of factors, including experience and bias, thus warranting critical examination of their reliability. This article not only explores the formation of police hunches but also provides practical insights for officers and researchers on how to harness the power of active inference to fully understand policing decisions and subsequently explore new avenues for future research.
Unsupervised models of how the brain identifies and categorizes the causes of its sensory input can be divided into two classes: those that minimize the mutual information (i.e., redundancy) among evoked responses and those that minimize the prediction error. Although these models have the same goal, the way that goal is attained, and the functional architectures required, are fundamentally different. This review describes the differences, in the functional anatomy of sensory cortical hierarchies, implied by the two models. We then consider how neuroimaging can be used to disambiguate between them. The key distinction reduces to whether backward connections are employed by the brain to generate a prediction of sensory inputs. To ascertain whether backward influences are evident empirically requires a characterization of functional integration among brain systems. This review summarizes the approaches to measuring functional integration in terms of effective connectivity and proceeds to address the question posed by the theoretical considerations. In short, it will be shown that the conjoint manipulation of bottom-up and top-down inputs to an area can be used to test for interactions between them, in elaborating cortical responses. The conclusion, from these sorts of neuroimaging studies, points to the prevalence of top-down influences and the plausibility of generative models of sensory brain function.
The term ‘stress’ – coined in 1936 – has many definitions, but until now has lacked a theoretical foundation. Here we present an information-theoretic approach – based on the ‘free energy principle’ – defining the essence of stress; namely, uncertainty. We address three questions: What is uncertainty? What does it do to us? What are our resources to master it? Mathematically speaking, uncertainty is entropy or ‘expected surprise’. The ‘free energy principle’ rests upon the fact that self-organizing biological agents resist a tendency to disorder and must therefore minimize the entropy of their sensory states. Applied to our everyday life, this means that we feel uncertain, when we anticipate that outcomes will turn out to be something other than expected – and that we are unable to avoid surprise. As all cognitive systems strive to reduce their uncertainty about future outcomes, they face a critical constraint: Reducing uncertainty requires cerebral energy. The characteristic of the vertebrate brain to prioritize its own high energy is captured by the notion of the ‘selfish brain’. Accordingly, in times of uncertainty, the selfish brain demands extra energy from the body. If, despite all this, the brain cannot reduce uncertainty, a persistent cerebral energy crisis may develop, burdening the individual by ‘allostatic load’ that contributes to systemic and brain malfunction (impaired memory, atherogenesis, diabetes and subsequent cardio- and cerebrovascular events). Based on the basic tenet that stress originates from uncertainty, we discuss the strategies our brain uses to avoid surprise and thereby resolve uncertainty.
