scholarly journals Brain signatures of a multiscale process of sequence learning in humans

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Maxime Maheu ◽  
Stanislas Dehaene ◽  
Florent Meyniel
Keyword(s):  
Language Processing ◽  
Multiple Scales ◽  
Transition Probabilities ◽  
Brain Activity ◽  
Temporal Structure ◽  
Brain Waves ◽  
Statistical Inferences ◽  
Statistical Regularities ◽  
Simple Statistic ◽  
Computational Systems

Extracting the temporal structure of sequences of events is crucial for perception, decision-making, and language processing. Here, we investigate the mechanisms by which the brain acquires knowledge of sequences and the possibility that successive brain responses reflect the progressive extraction of sequence statistics at different timescales. We measured brain activity using magnetoencephalography in humans exposed to auditory sequences with various statistical regularities, and we modeled this activity as theoretical surprise levels using several learning models. Successive brain waves related to different types of statistical inferences. Early post-stimulus brain waves denoted a sensitivity to a simple statistic, the frequency of items estimated over a long timescale (habituation). Mid-latency and late brain waves conformed qualitatively and quantitatively to the computational properties of a more complex inference: the learning of recent transition probabilities. Our findings thus support the existence of multiple computational systems for sequence processing involving statistical inferences at multiple scales.

scholarly journals Learning the statistics of pain: computational and neural mechanisms

2021 ◽  
Author(s):  
Flavia Mancini ◽  
Suyi Zhang ◽  
Ben Seymour
Keyword(s):  
Pain Intensity ◽  
Parietal Cortex ◽  
High Intensity ◽  
Posterior Parietal Cortex ◽  
Brain Activity ◽  
Premotor Cortex ◽  
Left Hand ◽  
Statistical Inferences ◽  
Statistical Regularities ◽  
The Right

Abstract Pain invariably changes over time, and these temporal fluctuations are riddled with uncertainty about body safety. In theory, statistical regularities of pain through time contain useful information that can be learned, allowing the brain to generate expectations and inform behaviour. To investigate this, we exposed healthy participants to probabilistic sequences of low and high-intensity electrical stimuli to the left hand, containing sudden changes in stimulus frequencies. We demonstrate that humans can learn to extract these regularities, and explicitly predict the likelihood of forthcoming pain intensities in a manner consistent with optimal Bayesian models with dynamic update of beliefs. We studied brain activity using functional MRI whilst subjects performed the task, which allowed us to dissect the underlying neural correlates of these statistical inferences from their uncertainty and update. We found that the inferred frequency (posterior probability) of high intensity pain correlated with activity in bilateral sensorimotor cortex, secondary somatosensory cortex and right caudate. The uncertainty of statistical inferences of pain was encoded in the right superior parietal cortex. An intrinsic part of this hierarchical Bayesian model is the way that unexpected changes in frequency lead to shift beliefs and update the internal model. This is reflected by the KL divergence between consecutive posterior distributions and associated with brain responses in the premotor cortex, dorsolateral prefrontal cortex, and posterior parietal cortex. In conclusion, this study extends what is conventionally considered a sensory pain pathway dedicated to process pain intensity, to include the generation of Bayesian internal models of temporal statistics of pain intensity levels in sensorimotor regions, which are updated dynamically through the engagement of premotor, prefrontal and parietal regions.

scholarly journals Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

2018 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Activity ◽  
Temporal Structure ◽  
Structural Connectivity ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Network ◽  
Dynamical Analysis ◽  
Whole Brain

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.

scholarly journals Human Emotion Detection using Eeg Sensor

2020 ◽  
Vol 9 (5) ◽  
pp. 409-413
Keyword(s):  
Data Visualization ◽  
Concentration Level ◽  
Brain Activity ◽  
Emotion Detection ◽  
Attention Span ◽  
Brain Waves ◽  
Alpha Beta ◽  
The Right ◽  
The Waves ◽  
The Brain

This is a data visualization art piece using 10 seconds of mind waves recordings of the human, captured with EEG sensor.10 seconds of Alpha, Beta, Gamma & Theta brain waves while meditating are recorded, the different wave channels are categorized to state when the right brain representing artistic brain activity, isolating the ranges for each channel when the brain channels were more meditating and imaginative. Based on the waves of the brain obtained, we will be able to deduce few attributes such as attention span and mood. The moods we will be trying to assess and display here the level of happiness, sadness, anger along with attention span and meditation level (Concentration level).

Spontaneous Brain Activity Predicts Task-Evoked Activity During Animate Versus Inanimate Touch

2019 ◽  
Vol 29 (11) ◽  
pp. 4628-4645 ◽  
Author(s):  
Andrea Scalabrini ◽  
Sjoerd J H Ebisch ◽  
Zirui Huang ◽  
Simone Di Plinio ◽  
Mauro Gianni Perrucci ◽  
...  
Keyword(s):  
Anterior Cingulate Cortex ◽  
Spontaneous Activity ◽  
Brain Activity ◽  
Temporal Structure ◽  
Cingulate Cortex ◽  
Anterior Cingulate ◽  
Scale Free ◽  
Spontaneous Brain Activity ◽  
Posterior Insula ◽  
Evoked Activity

Abstract The spontaneous activity of the brain is characterized by an elaborate temporal structure with scale-free properties as indexed by the power law exponent (PLE). We test the hypothesis that spontaneous brain activity modulates task-evoked activity during interactions with animate versus inanimate stimuli. For this purpose, we developed a paradigm requiring participants to actively touch either animate (real hand) or inanimate (mannequin hand) stimuli. Behaviorally, participants perceived the animate target as closer in space, temporally more synchronous with their own self, and more personally relevant, compared with the inanimate. Neuronally, we observed a modulation of task-evoked activity by animate versus inanimate interactions in posterior insula, in medial prefrontal cortex, comprising anterior cingulate cortex, and in medial superior frontal gyrus. Among these regions, an increased functional connectivity was shown between posterior insula and perigenual anterior cingulate cortex (PACC) during animate compared with inanimate interactions and during resting state. Importantly, PLE during spontaneous brain activity in PACC correlated positively with PACC task-evoked activity during animate versus inanimate stimuli. In conclusion, we demonstrate that brain spontaneous activity in PACC can be related to the distinction between animate and inanimate stimuli and thus might be specifically tuned to align our brain with its animate environment.

scholarly journals Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies

2016 ◽  
Vol 4 ◽  
pp. 521-535 ◽  
Author(s):  
Tal Linzen ◽  
Emmanuel Dupoux ◽  
Yoav Goldberg
Keyword(s):  
Language Processing ◽  
Short Term Memory ◽  
Structural Information ◽  
Syntactic Structure ◽  
Language Modeling ◽  
Language Models ◽  
Grammatical Structure ◽  
Long Distance ◽  
Target Number ◽  
Statistical Regularities

The success of long short-term memory (LSTM) neural networks in language processing is typically attributed to their ability to capture long-distance statistical regularities. Linguistic regularities are often sensitive to syntactic structure; can such dependencies be captured by LSTMs, which do not have explicit structural representations? We begin addressing this question using number agreement in English subject-verb dependencies. We probe the architecture’s grammatical competence both using training objectives with an explicit grammatical target (number prediction, grammaticality judgments) and using language models. In the strongly supervised settings, the LSTM achieved very high overall accuracy (less than 1% errors), but errors increased when sequential and structural information conflicted. The frequency of such errors rose sharply in the language-modeling setting. We conclude that LSTMs can capture a non-trivial amount of grammatical structure given targeted supervision, but stronger architectures may be required to further reduce errors; furthermore, the language modeling signal is insufficient for capturing syntax-sensitive dependencies, and should be supplemented with more direct supervision if such dependencies need to be captured.

An ERP Study in English Relative Clause Processing by Chinese-English Bilinguals

2015 ◽  
Vol 38 (1) ◽  
Author(s):  
Huili Wang ◽  
Liwen Ma ◽  
Youyou Wang ◽  
Melissa Troyer ◽  
Qiang Li
Keyword(s):  
Language Processing ◽  
Sentence Processing ◽  
Relative Clause ◽  
Relative Clauses ◽  
Native Speakers ◽  
Brain Activity ◽  
Working Memory Capacity ◽  
Large Body ◽  
Language Background ◽  
Study Support

AbstractThe processing of relative clauses receives much concern from linguists. The finding that object relatives are easier to process than subject relatives in Chinese challenges the notion that subject relative clauses are preferred universally. A large body of literature provides theories related to sentence processing mechanisms for native speakers but leaves one area relatively untouched: how bilinguals process sentences. This study is designed to examine the case where the individuals with a Chinese L1 language background process subject-extracted subject relative clauses (SS) and subject-extracted object relative clauses (SO) by using eventrelated potentials (ERPs) to probe into the real-time language processing and presents a direct manifestation of brain activity. The findings from this study support the subject relative clause preference due to the strong influence of English relative clause markedness and bilinguals’ relative lower working memory capacity.

scholarly journals Fractal Dimension as Quantifier of EEG Activity in Driving Simulation

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1311
Author(s):  
Mª Victoria Sebastián ◽  
Mª Antonia Navascués ◽  
Antonio Otal ◽  
Carlos Ruiz ◽  
Mª Ángeles Idiazábal ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Brain Activity ◽  
Fractal Theory ◽  
Driving Simulation ◽  
Geometric Complexity ◽  
Eeg Activity ◽  
Modeling And Analysis ◽  
Brain Waves ◽  
Electroencephalographic Signals ◽  
The Brain

Dynamical systems and fractal theory methodologies have been proved useful for the modeling and analysis of experimental datasets and, in particular, for electroencephalographic signals. The computation of the fractal dimension of approximation curves in the plane enables the assignment of numerical values to bioelectric recordings in order to discriminate between different states of the observed system. The procedure does not require the stationarity of the signals nor extremely long segments of data. In previous works, we checked that this parameter is a good index for brain activity. In this paper, we consider this measurement in order to quantify the geometric complexity of the brain waves in states of rest and during vehicle driving simulation in different scenarios. This work presents evidence that the fractal dimension allows the detection of the brain bioelectric changes produced in the areas that carry out the different driving simulation tasks, increasing with their complexity.

The perception of temporal structure and auditory evoked brain activity

1995 ◽  
Vol 40 (3) ◽  
pp. 267-280 ◽  
Author(s):  
W. Skrandies ◽  
T. Rammsayer
Keyword(s):  
Brain Activity ◽  
Temporal Structure

scholarly journals Probabilistic associative learning suffices for learning the temporal structure of multiple sequences

2019 ◽  
Author(s):  
Ramon H. Martinez ◽  
Anders Lansner ◽  
Pawel Herman
Keyword(s):  
Associative Learning ◽  
Internal Control ◽  
Computational Study ◽  
Brain Activity ◽  
Temporal Structure ◽  
Learning Rule ◽  
Multi Scale ◽  
Temporal Aspects ◽  
And Behavior ◽  
Multiple Sequences

AbstractMany brain phenomena both at the cognitive and behavior level exhibit remarkable sequential characteristics. While the mechanisms behind the sequential nature of the underlying brain activity are likely multifarious and multi-scale, in this work we attempt to characterize to what degree some of this properties can be explained as a consequence of simple associative learning. To this end, we employ a parsimonious firing-rate attractor network equipped with the Hebbian-like Bayesian Confidence Propagating Neural Network (BCPNN) learning rule relying on synaptic traces with asymmetric temporal characteristics. The proposed network model is able to encode and reproduce temporal aspects of the input, and offers internal control of the recall dynamics by gain modulation. We provide an analytical characterisation of the relationship between the structure of the weight matrix, the dynamical network parameters and the temporal aspects of sequence recall. We also present a computational study of the performance of the system under the effects of noise for an extensive region of the parameter space. Finally, we show how the inclusion of modularity in our network structure facilitates the learning and recall of multiple overlapping sequences even in a noisy regime.

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