1,987 publications from this institution
Perception seems so simple.I look out of the window to see houses, trees, people walking past, the sky above, the grass below.I hear birds in the trees, cars going past, the distant sound of an alarm.The world is full of objects that make their presence known to me through my senses -what could be more simple?Yet the efficacy of perceptual experience hides a host of questions for which we do not yet have the answers.Information reaching our senses is generally incomplete, ambiguous, distributed in space and time and not neatly sorted according to its source, so a key function of our perceptual systems is to discover the likely causes of our sensations.Perception as inference or hypothesis testing, formalised in the predictive coding theory, offers an attractive framework for exploring these issues.From this perspective, regularities or patterns provide perceptual systems with some traction, allowing the formation of expectations and a basis for decomposing the world into discrete objects.But in the dynamic world which we inhabit, object representations must be similarly dynamic, and need to form and dissolve, dominate and yield, in a way that facilitates veridical perception.In this talk I will discuss auditory scene analysis in the context of predictive coding using experimental data, exemplar models, and the phenomenon of perceptual multistability.
Abstract In order to study brain activity associated with “incidental” cognitive processing, regional cerebral blood flow (rCBF) was measured in six volunteers while they monitored a sequence of pseudo‐words (e.g., FLOPE) for the rare occasions when the letters were displayed in blue rather than white. In the control condition, the same pseudo‐word was presented repeatedly. In one experimental condition all 60 pseudo‐words were different, while in the other there were 18 repetitions. Although it was not necessary to “read” the pseudo‐words to perform the monitoring task, subsequent forced choice recognition memory for these stimuli was significantly greater than chance. Furthermore, there were significant differences in blood flow between the three conditions. When different pseudo‐words were presented there was significantly greater activity in brain areas concerned with shape and object identity (extrastriate cortex bilaterally), with visual word form (left inferior temporal gyrus), and with articulatory word form (Broca's area) even though none of this information about the pseudo‐words was needed for performance of the monitoring task. In the condition in which some of the words were repeated, there was significantly reduced activity in the right lingual gyrus. This area may therefore be a possible anatomical locus for repetition priming with verbal stimuli. These results indicate the importance of taking into account incidental processing when designing tasks for functional imaging experiments. © 1995 Wiley‐Liss, Inc.
Neural mass models are used to simulate cortical dynamics and to explain the electrical and magnetic fields measured using electro- and magnetoencephalography. Simulations evince a complex phase-space structure for these kinds of models; including stationary points and limit cycles and the possibility for bifurcations and transitions among different modes of activity. This complexity allows neural mass models to describe the itinerant features of brain dynamics. However, expressive, nonlinear neural mass models are often difficult to fit to empirical data without additional simplifying assumptions: e.g., that the system can be modelled as linear perturbations around a fixed point. In this study we offer a mathematical analysis of neural mass models, specifically the canonical microcircuit model, providing analytical solutions describing dynamical itinerancy. We derive a perturbation analysis up to second order of the phase flow, together with adiabatic approximations. This allows us to describe amplitude modulations as gradient flows on a potential function of intrinsic connectivity. These results provide analytic proof-of-principle for the existence of semi-stable states of cortical dynamics at the scale of a cortical column. Crucially, this work allows for model inversion of neural mass models, not only around fixed points, but over regions of phase space that encompass transitions among semi or multi-stable states of oscillatory activity. In principle, this formulation of cortical dynamics may improve our understanding of the itinerancy that underwrites measures of cortical activity (through EEG or MEG). Crucially, these theoretical results speak to model inversion in the context of multiple semi-stable brain states, such as onset of seizure activity in epilepsy or beta bursts in Parkinsons disease.
No abstract is provided for this article.
Positron emission tomography (PET) differs fundamentally from computerised tomography (CT) and magnetic resonance imaging (MRI) in that it is a method for measuring function as opposed to structure. It is the most powerful tool available for the measurement of in-vivo brain function. This review describes the basic principles of the technique and its application to the study of brain metabolism in neurological and psychiatric disorder. The development of resting-state metabolic studies by the application of specific activation paradigms, a major current focus of the technique, is discussed.
Active inference is a first principle account of how autonomous agents operate in dynamic, non-stationary environments. This problem is also considered in reinforcement learning (RL), but limited work exists on comparing the two approaches on the same discrete-state environments. In this paper, we provide: 1) an accessible overview of the discrete-state formulation of active inference, highlighting natural behaviors in active inference that are generally engineered in RL; 2) an explicit discrete-state comparison between active inference and RL on an OpenAI gym baseline. We begin by providing a condensed overview of the active inference literature, in particular viewing the various natural behaviors of active inference agents through the lens of RL. We show that by operating in a pure belief-based setting, active inference agents can carry out epistemic exploration, and account for uncertainty about their environment in a Bayes-optimal fashion. Furthermore, we show that the reliance on an explicit reward signal in RL is removed in active inference, where reward can simply be treated as another observation; even in the total absence of rewards, agent behaviors are learned through preference learning. We make these properties explicit by showing two scenarios in which active inference agents can infer behaviors in reward-free environments compared to both Q-learning and Bayesian model-based RL agents; by placing zero prior preferences over rewards and by learning the prior preferences over the observations corresponding to reward. We conclude by noting that this formalism can be applied to more complex settings if appropriate generative models can be formulated. In short, we aim to demystify the behavior of active inference agents by presenting an accessible discrete state-space and time formulation, and demonstrate these behaviors in a OpenAI gym environment, alongside RL agents.
The patterns of cerebral blood flow associated with three syndromes of schizophrenic symptoms are compared with the loci of cerebral activation in normal subjects during the performance of mental processes implicated in the three syndromes. The psychomotor poverty syndrome, which has been shown to involve a diminished ability to generate words, is associated with decreased perfusion of the dorsolateral prefrontal cortex at a locus which is activated in normal subjects during the internal generation of words. The disorganization syndrome, which has been shown to involve impaired suppression of inappropriate responses (eg in the Stroop test), is associated with increased perfusion of the right anterior cingulate gyrus at a location activated in normal subjects performing the Stroop test. The reality distortion syndrome, which evidence suggests arises from disordered internal monitoring of activity, is associated with increased perfusion in the medial temporal lobe at a locus activated in normal subjects during the internal monitoring of eye movements.
