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Neural signatures of vigilance decrements predict behavioural errors before they occur.

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TLDR
In this paper, the authors designed a multiple-object monitoring paradigm to examine how the neural representation of information varied with target frequency and time performing the task and found that behavioural performance decreased over time for the rare target (monitoring) condition, but not for a frequent target (active) condition.
Abstract
There are many monitoring environments, such as railway control, in which lapses of attention can have tragic consequences. Problematically, sustained monitoring for rare targets is difficult, with more misses and longer reaction times over time. What changes in the brain underpin these ‘vigilance decrements’? We designed a multiple-object monitoring (MOM) paradigm to examine how the neural representation of information varied with target frequency and time performing the task. Behavioural performance decreased over time for the rare target (monitoring) condition, but not for a frequent target (active) condition. This was mirrored in neural decoding using magnetoencephalography: coding of critical information declined more during monitoring versus active conditions along the experiment. We developed new analyses that can predict behavioural errors from the neural data more than a second before they occurred. This facilitates pre-empting behavioural errors due to lapses in attention and provides new insight into the neural correlates of vigilance decrements.

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Journal ArticleDOI

Perceptual difficulty modulates the direction of information flow in familiar face recognition.

TL;DR: In this paper, the authors developed a novel informational connectivity method to test whether peri-frontal brain areas contribute to familiar face recognition and found that feed-forward flow dominates for the most familiar faces and top-down flow was only dominant when sensory evidence was insufficient to support face recognition.
Posted ContentDOI

Perceptual difficulty modulates the direction of information flow in familiar face recognition

TL;DR: This work developed a novel informational connectivity method and demonstrated that perceptual difficulty and the level of familiarity influence the neural representation of familiar faces and the degree to which peri-frontal neural networks contribute to familiar face recognition.
Posted ContentDOI

An empirically-driven guide on using Bayes Factors for M/EEG decoding

TL;DR: In this paper, an empirically-driven guide on implementing Bayes factors for time-series neural decoding results is provided, using real and simulated Magnetoencephalography (MEG) data.
Posted ContentDOI

Codes-Temporal codes provide additional category-related information in object category decoding a systematic comparison of informative EEG features

TL;DR: Correlational analyses showed that the features which provided the most information about object categories, could predict participants’ performance (reaction time) more accurately than the less informative features.
Journal ArticleDOI

An Empirically Driven Guide on Using Bayes Factors for M/EEG Decoding

TL;DR: In this paper , the authors provide an empirically driven guide on implementing Bayes factors for time-series neural decoding results using real and simulated magnetoencephalography (MEG) data.
References
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TL;DR: The Psychophysics Toolbox is a software package that supports visual psychophysics and its routines provide an interface between a high-level interpreted language and the video display hardware.
Journal ArticleDOI

Functional connectivity in the resting brain: A network analysis of the default mode hypothesis

TL;DR: This study constitutes, to the knowledge, the first resting-state connectivity analysis of the default mode and provides the most compelling evidence to date for the existence of a cohesive default mode network.
Journal ArticleDOI

Human error: models and management

TL;DR: The longstanding and widespread tradition of the person approach focuses on the unsafe acts—errors and procedural violations—of people at the sharp end: nurses, physicians, surgeons, anaesthetists, pharmacists, and the like.
Journal ArticleDOI

Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience

TL;DR: A new experimental and data-analytical framework called representational similarity analysis (RSA) is proposed, in which multi-channel measures of neural activity are quantitatively related to each other and to computational theory and behavior by comparing RDMs.
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