1,987 publications from this institution
We suggest that music perception is an active act of listening, providing an irresistible epistemic offering. When listening to music we constantly generate plausible hypotheses about what could happen next, while actively attending to music resolves the ensuing uncertainty. Within the predictive coding framework, we present a novel formulation of precision filtering and attentional selection, which explains why some lower-level auditory, and even higher-level music-syntactic processes elicited by irregular events are relatively exempt from top-down predictive processes. We review findings providing unique evidence for the attentional selection of salient auditory features. This formulation suggests that ‘listening’ is a more active process than traditionally conceived in models of perception.
This paper proposes a formal reconstruction of the script construct by leveraging the active inference framework, a behavioural modelling framework that casts action, perception, emotions, and attention as processes of (Bayesian or variational) inference. We propose a first principles account of the script construct that integrates its different uses in the behavioural and social sciences. We begin by reviewing the recent literature that uses the script construct. We then examine the main mathematical and computational features of active inference. Finally, we leverage the resources of active inference to offer a formal model of scripts. Our integrative model accounts for the dual nature of scripts (as internal, psychological schema used by agents to make sense of event types and as constitutive behavioural categories that make up the social order) and also for the stronger and weaker conceptions of the construct (which do and do not relate to explicit action sequences, respectively).
Convolutional and Recurrent, deep neural networks have been successful in machine learning systems for computer vision, reinforcement learning, and other allied fields. However, the robustness of such neural networks is seldom apprised, especially after high classification accuracy has been attained. In this paper, we evaluate the robustness of three recurrent neural networks to tiny perturbations, on three widely used datasets, to argue that high accuracy does not always mean a stable and a robust (to bounded perturbations, adversarial attacks, etc.) system. Especially, normalizing the spectrum of the discrete recurrent network to bound the spectrum (using power method, Rayleigh quotient, etc.) on a unit disk produces stable, albeit highly non-robust neural networks. Furthermore, using the $\epsilon$-pseudo-spectrum, we show that training of recurrent networks, say using gradient-based methods, often result in non-normal matrices that may or may not be diagonalizable. Therefore, the open problem lies in constructing methods that optimize not only for accuracy but also for the stability and the robustness of the underlying neural network, a criterion that is distinct from the other.
This article reviews the dysconnection hypothesis of schizophrenia with an account of how dysfunctional integration among neuronal systems might arise, based upon the role of neuronal plasticity in shaping the connections and ensuing dynamics that underlie brain function. The hypothesis is that the pathophysiology of schizophrenia is expressed at the level of modulation of associative changes in synaptic efficacy; specifically plasticity in those brain systems responsible for learning, memory and emotion. This modulation is mediated by ascending neurotransmitter systems that; (i) have been implicated in schizophrenia and (ii) are known to be involved in consolidating synaptic connections during learning. This would translate, in functional terms, into a disruption of the reinforcement of adaptive behaviour that is consistent with the disintegrative aspects of schizophrenic neuropsychology.
Motivated control refers to the coordination of behaviour to achieve affectively valenced outcomes or goals. The study of motivated control traditionally assumes a distinction between control and motivational processes, which map to distinct (dorsolateral versus ventromedial) brain systems. However, the respective roles and interactions between these processes remain controversial. We offer a novel perspective that casts control and motivational processes as complementary aspects - goal propagation and prioritization, respectively - of active inference and hierarchical goal processing under deep generative models. We propose that the control hierarchy propagates prior preferences or goals, but their precision is informed by the motivational context, inferred at different levels of the motivational hierarchy. The ensuing integration of control and motivational processes underwrites action and policy selection and, ultimately, motivated behaviour, by enabling deep inference to prioritize goals in a context-sensitive way.
Abstract This paper examines the constraints that the free-energy principle (FEP) places on possible model of consciousness, particularly models of attentional control and imaginative experiences, including episodic memory and planning. We first rehearse the classical and quantum formulations of the FEP, focusing on their application to multi-component systems, in which only some components interact directly with the external environment. In particular, we discuss the role of internal boundaries that have the structure of Markov blankets, and hence function as classical information channels between components. We then show how this formal structure supports models of attentional control and imaginative experience, with a focus on (i) how imaginative experience can employ the spatio-temporal and object-recognition reference frames employed in ordinary, non-imaginative experience and (ii) how imaginative experience can be internally generated but still surprising. We conclude by discussing the implementation, phenomenology, and phylogeny of imaginative experience, and the implications of the large state and trait variability of imaginative experience in humans.
The proportional recovery rule asserts that most stroke survivors recover a fixed proportion of lost function. To the extent that this is true, recovery from stroke can be predicted accurately from baseline measures of acute post-stroke impairment alone. Reports that baseline scores explain more than 80%, and sometimes more than 90%, of the variance in the patients' recoveries, are rapidly accumulating. Here, we show that these headline effect sizes are likely inflated. The key effects in this literature are typically expressed as, or reducible to, correlation coefficients between baseline scores and recovery (outcome scores minus baseline scores). Using formal analyses and simulations, we show that these correlations will be extreme when outcomes are significantly less variable than baselines, which they often will be in practice regardless of the real relationship between outcomes and baselines. We show that these effect sizes are likely to be over-optimistic in every empirical study that we found that reported enough information for us to make the judgement, and argue that the same is likely to be true in other studies as well. The implication is that recovery after stroke may not be as proportional as recent studies suggest.
This paper introduces the concepts and procedures of “cognitive conjunction,” a new approach to designing and analyzing cognitive activation experiments. Cognitive conjunction compliments categorical approaches such as cognitive subtraction and requires a specific form of statistical inference that involves the conjunction of several hypotheses. While cognitive subtraction studies are designed such that a pair of tasks differ only by the processing component(s) of interest, cognitive conjunction studies are designed such that two or more distinct task pairs each share a common processing difference. The neural correlates of the process of interest are then associated with the common areas of activation for each task pair. There are two main advantages of cognitive conjunction relative to cognitive subtraction. The first is that it provides a greater latitude for selecting baseline tasks because it is not necessary to control for all but the component of interest. The only constraint on selecting the baseline is that the component of interest is the only process that differs in each task pair. The second advantage is that cognitive conjunction does not depend on “pure insertion”—the assumption that the addition of an extra processing component in the activation task has no effect on the implementation of processes that are also engaged by the baseline task. The differences between the design and the statistical analysis of experiments based on cognitive subtraction, cognitive conjunction, and factorial designs are illustrated with a study of phonological retrieval. Cognitive conjunction analysis indicates that irrespective of whether subjects name words, objects, letters, or colors, there is activation of the left posterior basal temporal lobe, the left frontal operculum, the left thalamus, and the midline cerebellum.
Abstract In this work, we address the neuronal encoding problem from a Bayesian perspective. Specifically, we ask whether neuronal responses in an in vitro neuronal network are consistent with ideal Bayesian observer responses under the free energy principle. In brief, we stimulated an in vitro cortical cell culture with stimulus trains that had a known statistical structure. We then asked whether recorded neuronal responses were consistent with variational message passing based upon free energy minimisation (i.e., evidence maximisation). Effectively, this required us to solve two problems: first, we had to formulate the Bayes-optimal encoding of the causes or sources of sensory stimulation, and then show that these idealised responses could account for observed electrophysiological responses. We describe a simulation of an optimal neural network (i.e., the ideal Bayesian neural code) and then consider the mapping from idealised in silico responses to recorded in vitro responses. Our objective was to find evidence for functional specialisation and segregation in the in vitro neural network that reproduced in silico learning via free energy minimisation. Finally, we combined the in vitro and in silico results to characterise learning in terms of trajectories in a variational information plane of accuracy and complexity.
This chapter provides an illustrative treatment of psychiatric morbidity that offers an alternative to the standard nosological model in psychiatry. It considers what would happen if we treated diagnostic categories not as putative causes of signs and symptoms, but as diagnostic consequences of psychopathology and pathophysiology. This reconstitution (of the standard model) opens the door to a more natural formulation of how patients present and their likely response to therapeutic interventions. The chapter describes a model that generates symptoms, signs, and diagnostic outcomes from latent psychopathological states. In turn, psychopathology is caused by pathophysiological processes that are perturbed by (etiological) causes (e.g., predisposing factors, life events, therapeutic interventions). The key advantages of this nosological formulation include: (a) the formal integration of diagnostic categories and latent psychopathological constructs; (b) the provision of a hypothesis or model space that accommodates formal evidence-based hypothesis testing or model selection; (c) the ability to predict therapeutic responses; and (d) a framework that allows one to test hypotheses about the interactions between pharmacological and psychotherapeutic interventions. This chapter shows what might be possible, through the use of idealized simulations. These simulations can be regarded as a (conceptual) prospectus that motivates a computational nosology for psychiatry.
