scholarly journals Metacognition facilitates the exploitation of unconscious brain states

2019 ◽  
Author(s):  
Aurelio Cortese ◽  
Hakwan Lau ◽  
Mitsuo Kawato
Keyword(s):  
Reinforcement Learning ◽  
Neural Activity ◽  
Pattern Analysis ◽  
Brain Activity ◽  
High Dimensional ◽  
Multivoxel Pattern Analysis ◽  
Brain States ◽  
Optimal Action ◽  
The Brain ◽  
Do So

AbstractCan humans be trained to make strategic use of unconscious representations in their own brains? We investigated how one can derive reward-maximizing choices from latent high-dimensional information represented stochastically in neural activity. In a novel decision-making task, reinforcement learning contingencies were defined in real-time by fMRI multivoxel pattern analysis; optimal action policies thereby depended on multidimensional brain activity that took place below the threshold of consciousness. We found that subjects could solve the task, when their reinforcement learning processes were boosted by implicit metacognition to estimate the relevant brain states. With these results we identified a frontal-striatal mechanism by which the brain can untangle tasks of great dimensionality, and can do so much more flexibly than current artificial intelligence.

scholarly journals Temporo-parietal cortex involved in modeling one’s own and others’ attention

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Arvid Guterstam ◽  
Branden J Bio ◽  
Andrew I Wilterson ◽  
Michael Graziano
Keyword(s):  
Theory Of Mind ◽  
Pattern Analysis ◽  
Brain Activity ◽  
Activity Patterns ◽  
Exogenous Attention ◽  
Attentional State ◽  
Brain Areas ◽  
Multivoxel Pattern Analysis ◽  
Directing Attention ◽  
The Brain

In a traditional view, in social cognition, attention is equated with gaze and people track other people’s attention by tracking their gaze. Here, we used fMRI to test whether the brain represents attention in a richer manner. People read stories describing an agent (either oneself or someone else) directing attention to an object in one of two ways: either internally directed (endogenous) or externally induced (exogenous). We used multivoxel pattern analysis to examine how brain areas within the theory-of-mind network encoded attention type and agent type. Brain activity patterns in the left temporo-parietal junction (TPJ) showed significant decoding of information about endogenous versus exogenous attention. The left TPJ, left superior temporal sulcus (STS), precuneus, and medial prefrontal cortex (MPFC) significantly decoded agent type (self versus other). These findings show that the brain constructs a rich model of one’s own and others’ attentional state, possibly aiding theory of mind.

scholarly journals Temporo-Parietal Cortex Involved in Modeling One’s Own and Others’ Attention

2020 ◽  
Author(s):  
Arvid Guterstam ◽  
Branden J Bio ◽  
Andrew I Wilterson ◽  
Michael SA Graziano
Keyword(s):  
Theory Of Mind ◽  
Pattern Analysis ◽  
Brain Activity ◽  
Activity Patterns ◽  
Exogenous Attention ◽  
Attentional State ◽  
Brain Areas ◽  
Multivoxel Pattern Analysis ◽  
Directing Attention ◽  
The Brain

AbstractIn a traditional view, in social cognition, attention is equated with gaze and people track attention by tracking other people’s gaze. Here we used fMRI to test whether the brain represents attention in a richer manner. People read stories describing an agent (either oneself or someone else) directing attention to an object in one of two ways: either internally directed (endogenous) or externally induced (exogenous). We used multivoxel pattern analysis to examine how brain areas within the theory-of-mind network encoded attention type and agent type. Brain activity patterns in the left temporo-parietal junction (TPJ) showed significant decoding of information about endogenous versus exogenous attention. The left TPJ, left superior temporal sulcus (STS), precuneus, and medial prefrontal cortex (MPFC) significantly decoded agent type (self versus other). These findings show that the brain constructs a rich model of one’s own and others’ attentional state, possibly aiding theory of mind.Impact statementThis study used fMRI to show that the human brain encodes other people’s attention in enough richness to distinguish whether that attention was directed exogenously (stimulus-driven) or endogenously (internally driven).

scholarly journals Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

2017 ◽  
Vol 24 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Selen Atasoy ◽  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Joel Pearson
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Building Blocks ◽  
Mental States ◽  
Brain Dynamics ◽  
Space And Time ◽  
Wide Range ◽  
The Brain

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at “rest.” Here, we introduce the concept of harmonic brain modes—fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

scholarly journals Unsupervised seizure localisation with attention-based graph neural networks

2020 ◽  
Author(s):  
Daniele Grattarola ◽  
Lorenzo Livi ◽  
Cesare Alippi ◽  
Richard Wennberg ◽  
Taufik Valiante
Keyword(s):  
Neural Networks ◽  
Brain Activity ◽  
Functional Networks ◽  
Seizure Onset ◽  
The Past ◽  
Seizure Onset Zone ◽  
Brain States ◽  
Graph Neural Networks ◽  
Intracranial Electroencephalography ◽  
The Brain

Abstract Graph neural networks (GNNs) and the attention mechanism are two of the most significant advances in artificial intelligence methods over the past few years. The former are neural networks able to process graph-structured data, while the latter learns to selectively focus on those parts of the input that are more relevant for the task at hand. In this paper, we propose a methodology for seizure localisation which combines the two approaches. Our method is composed of several blocks. First, we represent brain states in a compact way by computing functional networks from intracranial electroencephalography recordings, using metrics to quantify the coupling between the activity of different brain areas. Then, we train a GNN to correctly distinguish between functional networks associated with interictal and ictal phases. The GNN is equipped with an attention-based layer which automatically learns to identify those regions of the brain (associated with individual electrodes) that are most important for a correct classification. The localisation of these regions is fully unsupervised, meaning that it does not use any prior information regarding the seizure onset zone. We report results both for human patients and for simulators of brain activity. We show that the regions of interest identified by the GNN strongly correlate with the localisation of the seizure onset zone reported by electroencephalographers. We also show that our GNN exhibits uncertainty on those patients for which the clinical localisation was also unsuccessful, highlighting the robustness of the proposed approach.

scholarly journals Warning Signals Influence Motor Processing

2007 ◽  
Vol 97 (2) ◽  
pp. 1600-1609 ◽  
Author(s):  
Jillian H. Fecteau ◽  
Douglas P. Munoz
Keyword(s):  
Motor Neurons ◽  
Neural Activity ◽  
Warning Signal ◽  
Future Research ◽  
Target Task ◽  
Motor Processing ◽  
Fixation Marker ◽  
Speed Up ◽  
The Brain ◽  
Do So

When observers initiate responses to visual targets, they do so sooner when a preceding stimulus indicates that the target will appear shortly. This consequence of a warning signal may change neural activity in one of four ways. On the sensory side, the warning signal may speed up the rate at which the target is registered by the brain or enhance the magnitude of its signal. On the motor end, the warning signal may lower the threshold required to initiate a response or speed up the rate at which activity accumulates to reach threshold. Here, we describe which explanation is better supported. To accomplish this end, monkeys performed different versions of a cue-target task while we monitored the activity of visuomotor and motor neurons in the superior colliculus. Although the cue target task was designed to measure the properties of reflexive spatial attention, there are two events in this task that produce nonspecific warning effects: a central reorienting event (brightening of central fixation marker) that is used to direct attention away from the cue, and the presentation of the cue itself. Monopolizing on these tendencies, we show that warning effects are associated with several changes in neural activity: the target-related response is enhanced, the threshold for initiating a saccade is lowered, and the rate at which activity accumulates toward threshold rises faster. Ultimately, the accumulation of activity toward threshold predicted behavior most closely. In the discussion, we describe the implications and limitations of these data for theories of warning effects and potential avenues for future research.

scholarly journals Seeing without Knowing: Neural Signatures of Perceptual Inference in the Absence of Report

2014 ◽  
Vol 26 (5) ◽  
pp. 955-969 ◽  
Author(s):  
Annelinde R. E. Vandenbroucke ◽  
Johannes J. Fahrenfort ◽  
Ilja G. Sligte ◽  
Victor A. F. Lamme
Keyword(s):  
Pattern Analysis ◽  
Brain Activity ◽  
Decision Task ◽  
Distractor Task ◽  
Perceptual Inference ◽  
Multivoxel Pattern Analysis ◽  
Image Composition ◽  
Choice Decision ◽  
Neural Signature ◽  
Kanizsa Figure

Every day, we experience a rich and complex visual world. Our brain constantly translates meaningless fragmented input into coherent objects and scenes. However, our attentional capabilities are limited, and we can only report the few items that we happen to attend to. So what happens to items that are not cognitively accessed? Do these remain fragmentary and meaningless? Or are they processed up to a level where perceptual inferences take place about image composition? To investigate this, we recorded brain activity using fMRI while participants viewed images containing a Kanizsa figure, an illusion in which an object is perceived by means of perceptual inference. Participants were presented with the Kanizsa figure and three matched nonillusory control figures while they were engaged in an attentionally demanding distractor task. After the task, one group of participants was unable to identify the Kanizsa figure in a forced-choice decision task; hence, they were “inattentionally blind.” A second group had no trouble identifying the Kanizsa figure. Interestingly, the neural signature that was unique to the processing of the Kanizsa figure was present in both groups. Moreover, within-subject multivoxel pattern analysis showed that the neural signature of unreported Kanizsa figures could be used to classify reported Kanizsa figures and that this cross-report classification worked better for the Kanizsa condition than for the control conditions. Together, these results suggest that stimuli that are not cognitively accessed are processed up to levels of perceptual interpretation.

scholarly journals Vision-to-value transformations in artificial neural networks and human brain

2021 ◽  
Author(s):  
Trung Quang Pham ◽  
Takaaki Yoshimoto ◽  
Haruki Niwa ◽  
Haruka K Takahashi ◽  
Ryutaro Uchiyama ◽  
...  
Keyword(s):  
Neural Networks ◽  
Pattern Analysis ◽  
Brain Activity ◽  
Brain Regions ◽  
Transformation Process ◽  
Neural Mechanisms ◽  
Association Cortex ◽  
Multivoxel Pattern Analysis ◽  
Parietal Cortices ◽  
The Relationship

AbstractHumans and now computers can derive subjective valuations from sensory events although such transformation process is essentially unknown. In this study, we elucidated unknown neural mechanisms by comparing convolutional neural networks (CNNs) to their corresponding representations in humans. Specifically, we optimized CNNs to predict aesthetic valuations of paintings and examined the relationship between the CNN representations and brain activity via multivoxel pattern analysis. Primary visual cortex and higher association cortex activities were similar to computations in shallow CNN layers and deeper layers, respectively. The vision-to-value transformation is hence proved to be a hierarchical process which is consistent with the principal gradient that connects unimodal to transmodal brain regions (i.e. default mode network). The activity of the frontal and parietal cortices was approximated by goal-driven CNN. Consequently, representations of the hidden layers of CNNs can be understood and visualized by their correspondence with brain activity–facilitating parallels between artificial intelligence and neuroscience.

scholarly journals EEG microstate dynamics indicate a U-shaped path to propofol-induced loss of consciousness

2021 ◽  
Author(s):  
Fiorenzo Artoni ◽  
Julien Maillard ◽  
Juliane Britz ◽  
Martin Seeber ◽  
Christopher Lysakowski ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Brain Activity ◽  
Altered States Of Consciousness ◽  
Initial Increase ◽  
Altered States ◽  
Stream Of Consciousness ◽  
Sequence Complexity ◽  
States Of Consciousness ◽  
Brain States ◽  
The Brain

It is commonly believed that the stream of consciousness is not continuous but parsed into transient brain states manifesting themselves as discrete spatiotemporal patterns of global neuronal activity. Electroencephalographical (EEG) microstates are proposed as the neurophysiological correlates of these transiently stable brain states that last for fractions of seconds. To further understand the link between EEG microstate dynamics and consciousness, we continuously recorded high-density EEG in 23 surgical patients from their awake state to unconsciousness, induced by step-wise increasing concentrations of the intravenous anesthetic propofol. Besides the conventional parameters of microstate dynamics, we introduce a new method that estimates the complexity of microstate sequences. The brain activity under the surgical anesthesia showed a decreased sequence complexity of the stereotypical microstates, which became sparser and longer-lasting. However, we observed an initial increase in microstates' temporal dynamics and complexity with increasing depth of sedation leading to a distinctive U-shape that may be linked to the paradoxical excitation induced by moderate levels of propofol. Our results support the idea that the brain is in a metastable state under normal conditions, balancing between order and chaos in order to flexibly switch from one state to another. The temporal dynamics of EEG microstates indicate changes of this critical balance between stability and transition that lead to altered states of consciousness.

scholarly journals Macroscopic quantities of collective brain activity during wakefulness and anesthesia

2021 ◽  
Author(s):  
Adrián Ponce-Alvarez ◽  
Lynn Uhrig ◽  
Nikolas Deco ◽  
Camilo M. Signorelli ◽  
Morten L. Kringelbach ◽  
...  
Keyword(s):  
Brain Activity ◽  
Brain Regions ◽  
Brain State ◽  
Loss Of Consciousness ◽  
Information Theoretic ◽  
Maximum Entropy Models ◽  
Information Theoretic Measures ◽  
Brain States ◽  
Parietal Cortices ◽  
The Brain

AbstractThe study of states of arousal is key to understand the principles of consciousness. Yet, how different brain states emerge from the collective activity of brain regions remains unknown. Here, we studied the fMRI brain activity of monkeys during wakefulness and anesthesia-induced loss of consciousness. Using maximum entropy models, we derived collective, macroscopic properties that quantify the system’s capabilities to produce work, to contain information and to transmit it, and that indicate a phase transition from critical awake dynamics to supercritical anesthetized states. Moreover, information-theoretic measures identified those parameters that impacted the most the network dynamics. We found that changes in brain state and in state of consciousness primarily depended on changes in network couplings of insular, cingulate, and parietal cortices. Our findings suggest that the brain state transition underlying the loss of consciousness is predominantly driven by the uncoupling of specific brain regions from the rest of the network.

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