Biology subjects

Seth, A. K.

Publications and source records attributed to Seth, A. K..

4 recordsLinked to original sources

bioRxiv2018

Quantifying metacognitive thresholds using signal-detection theory

How sure are we about what we know? Confidence, measured via self-report, is often interpreted as a subjective probabilistic estimate on having made a correct judgement. The neurocognitive mechanisms underlying the construction of confidence and the information incorporated into these judgements are of increasing interest. Investigating these mechanisms requires principled and practically applicable measures of confidence and metacognition. Unfortunately, current measures of confidence are subject to distortions from decision biases and task performance. Motivated by a recent signal-detection theoretic behavioural measure of metacognitive sensitivity, known as meta-[d], here we present a quantitative behavioural measure of confidence that is invariant to decision bias and task performance. This measure, which we call m-distance, captures in a principled way the propensity to report decisions with high (or low) confidence. Computational simulations demonstrate the robustness of m-distance to decision bias and task performance, as well as its behaviour under conditions of high and low metacognitive sensitivity and under dual-channel and hierarchical models of metacognition. The introduction of the m-distance measure will enhance systematic quantitative studies of the behavioural expression and neurocognitive basis of subjective confidence.

neuroscience↗

bioRxiv2017

The Hallucination Machine: A Deep-Dream VR platform for Studying the Phenomenology of Visual Hallucinations

Altered states of consciousness, such as psychotic or pharmacologically-induced hallucinations, provide a unique opportunity to examine the mechanisms underlying conscious perception. However, the phenomenological properties of these states are difficult to isolate experimentally from other, more general physiological and cognitive effects of psychoactive substances or psychopathological conditions. Thus, simulating phenomenological aspects of altered states in the absence of these other more general effects provides an important experimental tool for consciousness science and psychiatry. Here we describe such a tool, the Hallucination Machine. It comprises a novel combination of two powerful technologies: deep convolutional neural networks (DCNNs) and panoramic videos of natural scenes, viewed immersively through a head-mounted display (panoramic VR). By doing this, we are able to simulate visual hallucinatory experiences in a biologically plausible and ecologically valid way. Two experiments illustrate potential applications of the Hallucination Machine. First, we show that the system induces visual phenomenology qualitatively similar to classical psychedelics. In a second experiment, we find that simulated hallucinations do not evoke the temporal distortion commonly associated with altered states. Overall, the Hallucination Machine offers a valuable new technique for simulating altered phenomenology without directly altering the underlying neurophysiology.

neuroscience↗

bioRxiv2017

Neurophysiological signatures of duration and rhythm prediction across sensory modalities

Effective behaviour and cognition requires the ability to make predictions about the temporal properties of events, such as duration. It is well known that violations of temporal structure within sequences of stimuli lead to neurophysiological effects known as the (temporal) mismatch negativity (TMMN). However, previous studies investigating this phenomenon have typically presented successive stimulus intervals (i.e., durations) within a rhythmic structure, conflating the contributions of rhythmic temporal processing with those specific to duration. In a novel behavioural paradigm which extends the classic temporal oddball design, we examined the neurophysiological correlates of prediction violation under both rhythmically (isochronous) and arrhythmically (anisochronous) presented durations, in visual and auditory modalities. Using event-related potential (ERP), multivariate pattern analysis (MVPA), and temporal generalisation analysis (TGA) analyses, we found evidence for common, and distinct neurophysiological responses related to duration predictions and their violation, across isochronous and anisochronous conditions. Further, using TGA we could directly compare processes underlying duration prediction violation across different modalities, despite differences in processing latency of audition and vision. We discovered a common set of neurophysiological responses that are elicited whenever a duration prediction is violated, regardless of presentation modality, indicating the existence of a supramodal duration prediction mechanism. Altogether, our data show that the human brain encodes predictions specifically about duration, in addition to those from rhythmic structure, and that the neural underpinnings of these predictions generalize across modalities. These findings support the idea that time perception is based on similar principles of inference as characterize predictive processing theories of perception.

neuroscience↗

bioRxiv2017

A functioning model of human time perception

Despite being a fundamental dimension of experience, how the human brain generates the perception of time remains unknown. Here, we provide a novel explanation for how human time perception might be accomplished, based on non-temporal perceptual clas-sification processes. To demonstrate this proposal, we built an artificial neural system centred on a feed-forward image classification network, functionally similar to human visual processing. In this system, input videos of natural scenes drive changes in network activation, and accumulation of salient changes in activation are used to estimate duration. Estimates produced by this system match human reports made about the same videos, replicating key qualitative biases, including differentiating between scenes of walking around a busy city or sitting in a cafe or office. Our approach provides a working model of duration perception from stimulus to estimation and presents a new direction for examining the foundations of this central aspect of human experience.

neuroscience↗