scholarly journals Temporal metacognition as the decoding of self-generated brain dynamics

2017 ◽  
Author(s):  
Tadeusz W. Kononowicz ◽  
Clémence Roger ◽  
Virginie van Wassenhove
Keyword(s):  
Dimensional Space ◽  
Internal Variable ◽  
Brain Dynamics ◽  
Time Interval ◽  
Thought Process ◽  
Time Resolved ◽  
Self Evaluation ◽  
Oscillatory Mechanisms ◽  
Human Participants ◽  
Low Dimensional

SUMMARYMetacognition, the ability to know about one’s thought process, is self-referential. Here, we combined psychophysics and time-resolved neuroimaging to explore metacognitive inference on the accuracy of a self-generated behavior. Human participants generated a time interval and evaluated the signed magnitude of their temporal production. We show that both self-generation and self-evaluation relied on the power of beta oscillations (β; 15−40 Hz) with increases in early β power predictive of increases in duration. We characterized the dynamics of β power in a low dimensional space (β state-space trajectories) as a function of timing and found that the more distinct trajectories, the more accurate metacognitive inferences were. These results suggest that β states instantiates an internal variable determining the fate of the timing network’s trajectory, possibly as release from inhibition. Altogether, our study describes oscillatory mechanisms for timing, suggesting that temporal metacognition relies on inferential processes of self-generated dynamics.

scholarly journals Temporal Metacognition as the Decoding of Self-Generated Brain Dynamics

2018 ◽  
Vol 29 (10) ◽  
pp. 4366-4380 ◽  
Author(s):  
Tadeusz W Kononowicz ◽  
Clémence Roger ◽  
Virginie van Wassenhove
Keyword(s):  
Dimensional Space ◽  
Internal Variable ◽  
Brain Dynamics ◽  
Time Interval ◽  
Thought Process ◽  
Time Resolved ◽  
Self Evaluation ◽  
Oscillatory Mechanisms ◽  
Human Participants ◽  
Low Dimensional

Abstract Metacognition, the ability to know about one’s thought process, is self-referential. Here, we combined psychophysics and time-resolved neuroimaging to explore metacognitive inference on the accuracy of a self-generated behavior. Human participants generated a time interval and evaluated the signed magnitude of their temporal production. We show that both self-generation and self-evaluation relied on the power of beta oscillations (β; 15–40 Hz) with increases in early β power predictive of increases in duration. We characterized the dynamics of β power in a low-dimensional space (β state-space trajectories) as a function of timing and found that the more distinct trajectories, the more accurate metacognitive inferences were. These results suggest that β states instantiate an internal variable determining the fate of the timing network’s trajectory, possibly as release from inhibition. Altogether, our study describes oscillatory mechanisms for timing, suggesting that temporal metacognition relies on inferential processes of self-generated dynamics.

scholarly journals Evaluation of self-generated behavior: untangling metacognitive read-out and error detection

2019 ◽  
Author(s):  
Tadeusz W. Kononowicz ◽  
Virginie van Wassenhove
Keyword(s):  
Error Detection ◽  
Performance Monitoring ◽  
Brain Activity ◽  
Time Interval ◽  
Time Resolved ◽  
Self Evaluation ◽  
Time Estimates ◽  
Oscillatory Brain Activity ◽  
Human Participants ◽  
The Brain

ABSTRACTWhen producing a duration, for instance by pressing a key for one second, the brain relies on self-generated neuronal dynamics to monitor the “flow of time”. Converging evidence has suggested that the brain can also monitor itself monitoring time. Here, we investigated which brain mechanisms support metacognitive inferences when self-generating timing behavior. Although studies have shown that participants can reliably detect temporal errors when generating a duration (Akdogan & Balci, 2017; Kononowicz et al., 2017), the neural bases underlying the evaluation and the monitoring of this self-generated temporal behavior are unknown. Theories of psychological time have also remained silent about such self-evaluation abilities. How are temporal errors inferred on the basis of purely internally driven brain dynamics without external reference for time? We contrasted the error-detection hypothesis, in which error-detection would result from the comparison of competing motor plans with the read-out hypothesis, in which errors would result from inferring the state of an internal code for motor timing. Human participants generated a time interval, and evaluated the magnitude of their timing (first and second order behavioral judgments, respectively) while being recorded with time-resolved neuroimaging. Focusing on the neural signatures following the termination of self-generated duration, we found several regions involved in performance monitoring, which displayed a linear association between the power of α (8-14 Hz) oscillations, and the duration of the produced interval. Altogether, our results support the read-out hypothesis and indicate that first-order signals may be integrated for the evaluation of self-generated behavior.SIGNIFICANCE STATEMENTWhen typing on a keyboard, the brain estimates where the finger should land, but also when. The endogenous generation of the when in time is naturally accompanied by timing errors which, quite remarkably, participants can accurately rate as being too short or too long, and also by how much. Here, we explored the brain mechanisms supporting such temporal metacognitive inferences. For this, we contrasted two working hypotheses (error-detection vs. read-out), and showed that the pattern of evoked and oscillatory brain activity parsimoniously accounted best for a read-out mechanism. Our results suggest the existence of meta-representations of time estimates.

scholarly journals Evaluation of Self-generated Behavior: Untangling Metacognitive Readout and Error Detection

2019 ◽  
Vol 31 (11) ◽  
pp. 1641-1657 ◽  
Author(s):  
Tadeusz W. Kononowicz ◽  
Virginie van Wassenhove
Keyword(s):  
Error Detection ◽  
Performance Monitoring ◽  
Time Interval ◽  
Detection Mechanism ◽  
Self Evaluation ◽  
Linear Association ◽  
External Reference ◽  
Human Participants ◽  
Motor Actions ◽  
The Brain

When producing a duration, for instance, by pressing a key for 1 sec, the brain relies on self-generated neuronal dynamics to monitor the “flow of time.” Evidence has suggested that the brain can also monitor itself monitoring time, the so-called self-evaluation. How are temporal errors inferred on the basis of purely internally driven brain dynamics with no external reference for time? Although studies have shown that participants can reliably detect temporal errors when generating a duration, the neural bases underlying the evaluation of this self-generated temporal behavior are unknown. Theories of psychological time have also remained silent about such self-evaluation abilities. We assessed the contributions of an error-detection mechanism, in which error detection results from the ability to estimate the latency of motor actions, and of a readout mechanism, in which errors would result from inferring the state of a duration representation. Error detection predicts a V-shape association between neural activity and self-evaluation at the offset of a produced interval, whereas the readout predicts a linear association. Here, human participants generated a time interval and evaluated the magnitude of their timing (first- and second-order behavioral judgments, respectively). Focusing on the MEG/EEG signatures after the termination of the self-generated duration, we found several cortical sources involved in performance monitoring displaying a linear association between the power of alpha (α = 8–14 Hz) oscillations and self-evaluation. Altogether, our results support the readout hypothesis and indicate that duration representation may be integrated for the evaluation of self-generated behavior.

scholarly journals Non-linear manifold learning in fMRI uncovers a low-dimensional space of brain dynamics

2020 ◽  
Author(s):  
Siyuan Gao ◽  
Gal Mishne ◽  
Dustin Scheinost
Keyword(s):  
Resting State ◽  
Dimensional Space ◽  
Linear Manifold ◽  
Fmri Data ◽  
Cognitive Tasks ◽  
Brain Dynamics ◽  
State Data ◽  
Non Linear ◽  
Brain States ◽  
Low Dimensional

Large-scale brain dynamics are believed to lie in a latent, low-dimensional space. Typically, the embeddings of brain scans are derived independently from different cognitive tasks or resting-state data, ignoring a potentially large-and shared-portion of this space. Here, we establish that a shared, robust, and interpretable low-dimensional space of brain dynamics can be recovered from a rich repertoire of task-based fMRI data. This occurs when relying on non-linear approaches as opposed to traditional linear methods. The embedding maintains proper temporal progression of the tasks, revealing brain states and the dynamics of network integration. We demonstrate that resting-state data embeds fully onto the same task embedding, indicating similar brain states are present in both task and resting-state data. Our findings suggest analysis of fMRI data from multiple cognitive tasks in a low-dimensional space is possible and desirable, and our proposed framework can thus provide an interpretable framework to investigate brain dynamics in the low-dimensional space.

scholarly journals A Generative-Discriminative Framework that Integrates Imaging, Genetic, and Diagnosis into Coupled Low Dimensional Space

NeuroImage ◽  
2021 ◽  
pp. 118200
Author(s):  
Sayan Ghosal ◽  
Qiang Chen ◽  
Giulio Pergola ◽  
Aaron L. Goldman ◽  
William Ulrich ◽  
...  
Keyword(s):  
Dimensional Space ◽  
Low Dimensional

scholarly journals Artifacts in Simultaneous hdEEG/fMRI Imaging: A Nonlinear Dimensionality Reduction Approach

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4454 ◽  
Author(s):  
Marek Piorecky ◽  
Vlastimil Koudelka ◽  
Jan Strobl ◽  
Martin Brunovsky ◽  
Vladimir Krajca
Keyword(s):  
Data Structure ◽  
Spatial Clustering ◽  
Dimensional Space ◽  
Head Movements ◽  
Nonlinear Dimensionality Reduction ◽  
Space Resolution ◽  
Additional Information ◽  
Nonlinear Dimension ◽  
The Time Domain ◽  
Low Dimensional

Simultaneous recordings of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) are at the forefront of technologies of interest to physicians and scientists because they combine the benefits of both modalities—better time resolution (hdEEG) and space resolution (fMRI). However, EEG measurements in the scanner contain an electromagnetic field that is induced in leads as a result of gradient switching slight head movements and vibrations, and it is corrupted by changes in the measured potential because of the Hall phenomenon. The aim of this study is to design and test a methodology for inspecting hidden EEG structures with respect to artifacts. We propose a top-down strategy to obtain additional information that is not visible in a single recording. The time-domain independent component analysis algorithm was employed to obtain independent components and spatial weights. A nonlinear dimension reduction technique t-distributed stochastic neighbor embedding was used to create low-dimensional space, which was then partitioned using the density-based spatial clustering of applications with noise (DBSCAN). The relationships between the found data structure and the used criteria were investigated. As a result, we were able to extract information from the data structure regarding electrooculographic, electrocardiographic, electromyographic and gradient artifacts. This new methodology could facilitate the identification of artifacts and their residues from simultaneous EEG in fMRI.

scholarly journals Optimizing the structure and movement of a robotic bat with biological kinematic synergies

2018 ◽  
Vol 37 (10) ◽  
pp. 1233-1252 ◽  
Author(s):  
Jonathan Hoff ◽  
Alireza Ramezani ◽  
Soon-Jo Chung ◽  
Seth Hutchinson
Keyword(s):  
Principal Component Analysis ◽  
Topological Structure ◽  
Degrees Of Freedom ◽  
Dimensional Space ◽  
Principal Component ◽  
Biologically Inspired ◽  
Wing Kinematics ◽  
Wing Motion ◽  
Kinematic Synergies ◽  
Low Dimensional

In this article, we present methods to optimize the design and flight characteristics of a biologically inspired bat-like robot. In previous, work we have designed the topological structure for the wing kinematics of this robot; here we present methods to optimize the geometry of this structure, and to compute actuator trajectories such that its wingbeat pattern closely matches biological counterparts. Our approach is motivated by recent studies on biological bat flight that have shown that the salient aspects of wing motion can be accurately represented in a low-dimensional space. Although bats have over 40 degrees of freedom (DoFs), our robot possesses several biologically meaningful morphing specializations. We use principal component analysis (PCA) to characterize the two most dominant modes of biological bat flight kinematics, and we optimize our robot’s parametric kinematics to mimic these. The method yields a robot that is reduced from five degrees of actuation (DoAs) to just three, and that actively folds its wings within a wingbeat period. As a result of mimicking synergies, the robot produces an average net lift improvesment of 89% over the same robot when its wings cannot fold.

Reduced $$k$$ k -means clustering with MCA in a low-dimensional space

2014 ◽  
Vol 30 (2) ◽  
pp. 463-475 ◽  
Author(s):  
Masaki Mitsuhiro ◽  
Hiroshi Yadohisa
Keyword(s):  
Dimensional Space ◽  
Low Dimensional

scholarly journals Season- and Trend-aware Symbolic Approximation for Accurate and Efficient Time Series Matching

2021 ◽  
Author(s):  
Lars Kegel ◽  
Claudio Hartmann ◽  
Maik Thiele ◽  
Wolfgang Lehner
Keyword(s):  
Time Series ◽  
State Of The Art ◽  
Dimensional Space ◽  
Symbolic Aggregate Approximation ◽  
Current State ◽  
Optimal Representation ◽  
Symbolic Approximation ◽  
Low Dimensional ◽  
Deterministic Behavior ◽  
Support Time

AbstractProcessing and analyzing time series datasets have become a central issue in many domains requiring data management systems to support time series as a native data type. A core access primitive of time series is matching, which requires efficient algorithms on-top of appropriate representations like the symbolic aggregate approximation (SAX) representing the current state of the art. This technique reduces a time series to a low-dimensional space by segmenting it and discretizing each segment into a small symbolic alphabet. Unfortunately, SAX ignores the deterministic behavior of time series such as cyclical repeating patterns or a trend component affecting all segments, which may lead to a sub-optimal representation accuracy. We therefore introduce a novel season- and a trend-aware symbolic approximation and demonstrate an improved representation accuracy without increasing the memory footprint. Most importantly, our techniques also enable a more efficient time series matching by providing a match up to three orders of magnitude faster than SAX.

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