scholarly journals Neural surprise in somatosensory Bayesian learning

2020 ◽  
Author(s):  
Sam Gijsen ◽  
Miro Grundei ◽  
Robert T. Lange ◽  
Dirk Ostwald ◽  
Felix Blankenburg
Keyword(s):  
Perceptual Learning ◽  
Bayesian Learning ◽  
Model Updating ◽  
Selection Procedure ◽  
Learning Model ◽  
Learning System ◽  
Somatosensory Processing ◽  
Eeg Data ◽  
Statistical Regularities ◽  
The Brain

AbstractTracking statistical regularities of the environment is important for shaping human behavior and perception. Evidence suggests that the brain learns environmental dependencies using Bayesian principles. However, much remains unknown about the employed algorithms, for somesthesis in particular. Here, we describe the cortical dynamics of the somatosensory learning system to investigate both the form of the generative model as well as its neural surprise signatures. Specifically, we recorded EEG data from 40 participants subjected to a somatosensory roving-stimulus paradigm and performed single-trial modeling across peri-stimulus time in both sensor and source space. Our Bayesian model selection procedure indicates that evoked potentials are best described by a non-hierarchical learning model that tracks transitions between observations using leaky integration. From around 70ms post-stimulus onset, secondary somatosensory cortices are found to represent confidence-corrected surprise as a measure of model inadequacy. Primary somatosensory cortex is found to encode Bayesian surprise, reflecting model updating, from around 140ms. As such, this dissociation indicates that early surprise signals may control subsequent model update rates. In sum, our findings support the hypothesis that early somatosensory processing reflects Bayesian perceptual learning and contribute to an understanding of its precise mechanisms.Author summaryOur environment features statistical regularities, such as a drop of rain predicting imminent rainfall. Despite the importance for behavior and survival, much remains unknown about how these dependencies are learned, particularly for somatosensation. As surprise signalling about novel observations indicates a mismatch between one’s beliefs and the world, it has been hypothesized that surprise computation plays an important role in perceptual learning. By analyzing EEG data from human participants receiving sequences of tactile stimulation, we compare different formulations of surprise and investigate the employed underlying learning model. Our results indicate that the brain estimates transitions between observations. Furthermore, we identified different signatures of surprise computation and thereby provide a dissociation of the neural correlates of belief inadequacy and belief updating. Specifically, early surprise responses from around 70ms were found to signal the need for changes to the model, with encoding of its subsequent updating occurring from around 140ms. These results provide insights into how somatosensory surprise signals may contribute to the learning of environmental statistics.

scholarly journals Neural surprise in somatosensory Bayesian learning

2021 ◽  
Vol 17 (2) ◽  
pp. e1008068
Author(s):  
Sam Gijsen ◽  
Miro Grundei ◽  
Robert T. Lange ◽  
Dirk Ostwald ◽  
Felix Blankenburg
Keyword(s):  
Bayesian Learning ◽  
Model Updating ◽  
Selection Procedure ◽  
Stimulus Onset ◽  
Learning System ◽  
Somatosensory Processing ◽  
Somatosensory Cortices ◽  
Statistical Regularities ◽  
Leaky Integration ◽  
Underlying Mechanisms

Tracking statistical regularities of the environment is important for shaping human behavior and perception. Evidence suggests that the brain learns environmental dependencies using Bayesian principles. However, much remains unknown about the employed algorithms, for somesthesis in particular. Here, we describe the cortical dynamics of the somatosensory learning system to investigate both the form of the generative model as well as its neural surprise signatures. Specifically, we recorded EEG data from 40 participants subjected to a somatosensory roving-stimulus paradigm and performed single-trial modeling across peri-stimulus time in both sensor and source space. Our Bayesian model selection procedure indicates that evoked potentials are best described by a non-hierarchical learning model that tracks transitions between observations using leaky integration. From around 70ms post-stimulus onset, secondary somatosensory cortices are found to represent confidence-corrected surprise as a measure of model inadequacy. Indications of Bayesian surprise encoding, reflecting model updating, are found in primary somatosensory cortex from around 140ms. This dissociation is compatible with the idea that early surprise signals may control subsequent model update rates. In sum, our findings support the hypothesis that early somatosensory processing reflects Bayesian perceptual learning and contribute to an understanding of its underlying mechanisms.

scholarly journals Retrieving a Context Tree from EEG Data

Mathematics ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 427
Author(s):  
Aline Duarte ◽  
Ricardo Fraiman ◽  
Antonio Galves ◽  
Guilherme Ost ◽  
Claudia D. Vargas
Keyword(s):  
Stochastic Processes ◽  
Statistical Approach ◽  
Variable Length ◽  
New Class ◽  
Eeg Data ◽  
Context Tree ◽  
Statistical Regularities ◽  
The Brain ◽  
With Memory

It has been repeatedly conjectured that the brain retrieves statistical regularities from stimuli. Here, we present a new statistical approach allowing to address this conjecture. This approach is based on a new class of stochastic processes, namely, sequences of random objects driven by chains with memory of variable length.

scholarly journals Model brain based learning (BBL) and whole brain teaching (WBT) in learning

2017 ◽  
Vol 1 (2) ◽  
pp. 153
Author(s):  
Baiq Sri Handayani ◽  
A. D. Corebima
Keyword(s):  
Prefrontal Cortex ◽  
Limbic System ◽  
Learning Process ◽  
Learning Model ◽  
The Body ◽  
Learning System ◽  
Whole Brain ◽  
Whole Brain Teaching ◽  
Brain Based Learning ◽  
The Brain

<p class="Abstract">The learning process is a process of change in behavior as a form of the result of learning. The learning model is a crucial component of the success of the learning process. The learning model is growing fastly, and each model has different characteristics. Teachers are required to be able to understand each model to teach the students optimally by matching the materials and the learning model. The best of the learning model is the model that based on the brain system in learning that are the model of Brain Based Learning (BBL) and the model of Whole Brain Teaching (WBT). The purposes of this article are to obtain information related to (1) the brain’s natural learning system, (2) analyze the characteristics of the model BBL and WBT based on theory, brain sections that play a role associated with syntax, similarities, and differences, (3) explain the distinctive characteristics of both models in comparison to other models. The results of this study are: (1) the brain’s natural learning system are: (a) the nerves in each hemisphere do not work independently, (b) doing more activities can connect more brain nerves, (c) the right hemisphere controls the left side motoric sensor of the body, and vice versa; (2) the characteristics of BBL and WBT are: (a) BBL is based on the brain’s structure and function, while the model WBT is based on the instructional approach, neurolinguistic, and body language, (b) the parts of the brain that work in BBL are: cerebellum, cerebral cortex, frontal lobe, limbic system, and prefrontal cortex; whereas the parts that work WBT are: prefrontal cortex, visual cortex, motor cortex, limbic system, and amygdala, (c) the similarities between them are that they both rely on the brain’s system and they both promote gesture in learning, whereas the differences are on the view of the purposes of gestures and the learning theory that they rely on. BBL relies on cognitive theory while WBT relies on social theory; (3) the typical attribute of them compared to other models are that in BBL there are classical music and gestures in the form of easy exercises, while on the WBT model there are fast instructions and movements as instructions or code of every spoken word.</p>

EFFЕCIIVENESS OF USING NEW METHODS AND METHODS OF LEARNING THE UKRAINIAN LANGUAGE AS A FOREIGN IN MEDICAL UNIVERSITY

2019 ◽  
pp. 80-90
Author(s):  
S. V. Lychuk
Keyword(s):  
Foreign Language ◽  
Blended Learning ◽  
Foreign Students ◽  
Learning Model ◽  
Educational Process ◽  
Learning System ◽  
European Experience ◽  
Methods Of Learning ◽  
Medical University ◽  
Accessible Form

The article attempts to identify effective methods of teaching Ukrainian as a foreign language and to base them on the European experience. The most commonly used methods in the European practice of foreign language teaching are characterized: communicative, project, audio-lingual, distance, intensive and blended learning. The advantages of the blended learning system are examined. An interpretation of the term "blended learning" is proposed. The features of the organization of online learning and the structure of blended learning are described. The data of the conducted survey are presented: a) teachers conducting classes in Ukrainian as a foreign language; b) foreign students from different countries. Questionnaire was developed for the survey. The article proposes some tasks for teachers of Ukrainian as a foreign language, commenting on the specifics of different models of blended learning. Based on the results of the survey, the respondents outlined effective methods of teaching a foreign language, identified factors that influence the use of the blended learning model of future doctors when teaching Ukrainian as a foreign language. The advantages and prospects of using blended learning in the educational process of a higher medical institution are analyzed. The results of the study strongly suggest that the blended learning model opens up new possibilities for presenting educational material in a new and accessible form for students.

A fuzzy sensitivity analysis approach to estimate brain effective connectivity and its application to epileptic seizure detection

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nader Moharamzadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Effective Connectivity ◽  
Multivariate Time Series ◽  
Inference System ◽  
Eeg Data ◽  
Adaptive Neurofuzzy Inference System ◽  
Intracranial Electroencephalography ◽  
Fuzzy Network ◽  
The Brain

Abstract The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach to introduce a connectivity measure for detecting causal interactions between interactive input time series. The results of simulations indicate that this method is successful in detecting causal connectivity. After validating the performance of the proposed method on synthetic linear and nonlinear interconnected time series, it is applied to epileptic intracranial Electroencephalography (EEG) signals. The result of applying the proposed method on Freiburg epileptic intracranial EEG data recorded during seizure shows that the proposed method is capable of discriminating between the seizure and non-seizure states of the brain.

scholarly journals Binaural summation of amplitude modulation involves weak interaural suppression

2018 ◽  
Author(s):  
D.H. Baker ◽  
G. Vilidaite ◽  
E. McClarnon ◽  
E. Valkova ◽  
A. Bruno ◽  
...  
Keyword(s):  
Amplitude Modulation ◽  
Modulation Depth ◽  
Visual Signal ◽  
Data Sets ◽  
Single Object ◽  
Ecological Constraints ◽  
Binaural Summation ◽  
Eeg Data ◽  
Signal Combination ◽  
The Brain

AbstractThe brain combines sounds from the two ears, but what is the algorithm used to achieve this summation of signals? Here we combine psychophysical amplitude modulation discrimination and steady-state electroencephalography (EEG) data to investigate the architecture of binaural combination for amplitude-modulated tones. Discrimination thresholds followed a ‘dipper’ shaped function of pedestal modulation depth, and were consistently lower for binaural than monaural presentation of modulated tones. The EEG responses were greater for binaural than monaural presentation of modulated tones, and when a masker was presented to one ear, it produced only weak suppression of the response to a signal presented to the other ear. Both data sets were well-fit by a computational model originally derived for visual signal combination, but with suppression between the two channels (ears) being much weaker than in binocular vision. We suggest that the distinct ecological constraints on vision and hearing can explain this difference, if it is assumed that the brain avoids over-representing sensory signals originating from a single object. These findings position our understanding of binaural summation in a broader context of work on sensory signal combination in the brain, and delineate the similarities and differences between vision and hearing.

scholarly journals Faster processing of moving compared to flashed bars in awake macaque V1 provides a neural correlate of the flash lag illusion

2015 ◽  
Author(s):  
Manivannan Subramaniyan ◽  
Alexander S. Ecker ◽  
Saumil S. Patel ◽  
R. James Cotton ◽  
Matthias Bethge ◽  
...  
Keyword(s):  
Moving Object ◽  
Motion Processing ◽  
Neural Correlate ◽  
Compensatory Mechanisms ◽  
Motion Direction ◽  
Object A ◽  
Moving Stimuli ◽  
Natural Motion ◽  
Statistical Regularities ◽  
The Brain

AbstractWhen the brain has determined the position of a moving object, due to anatomical and processing delays, the object will have already moved to a new location. Given the statistical regularities present in natural motion, the brain may have acquired compensatory mechanisms to minimize the mismatch between the perceived and the real position of a moving object. A well-known visual illusion — the flash lag effect — points towards such a possibility. Although many psychophysical models have been suggested to explain this illusion, their predictions have not been tested at the neural level, particularly in a species of animal known to perceive the illusion. Towards this, we recorded neural responses to flashed and moving bars from primary visual cortex (V1) of awake, fixating macaque monkeys. We found that the response latency to moving bars of varying speed, motion direction and luminance was shorter than that to flashes, in a manner that is consistent with psychophysical results. At the level of V1, our results support the differential latency model positing that flashed and moving bars have different latencies. As we found a neural correlate of the illusion in passively fixating monkeys, our results also suggest that judging the instantaneous position of the moving bar at the time of flash — as required by the postdiction/motion-biasing model — may not be necessary for observing a neural correlate of the illusion. Our results also suggest that the brain may have evolved mechanisms to process moving stimuli faster and closer to real time compared with briefly appearing stationary stimuli.New and NoteworthyWe report several observations in awake macaque V1 that provide support for the differential latency model of the flash lag illusion. We find that the equal latency of flash and moving stimuli as assumed by motion integration/postdiction models does not hold in V1. We show that in macaque V1, motion processing latency depends on stimulus luminance, speed and motion direction in a manner consistent with several psychophysical properties of the flash lag illusion.

scholarly journals Innovation Design Development of Citizenship Education Model on Characters of Indonesian Communities in Digital Media Era and Technology Revolution

2019 ◽  
Vol 8 (2S9) ◽  
pp. 322-328 ◽  
Keyword(s):  
Digital Media ◽  
Citizenship Education ◽  
Research Method ◽  
Learning Model ◽  
Learning System ◽  
Development Model ◽  
Design Development ◽  
Smart Learning ◽  
Need Analysis ◽  
Addie Model

Increasing independence and competitiveness of the nation is carried out in order to advance the Indonesian civilization. One of them is through the development of a national system of science, and technology in Citizenship Education. Citizenship Education is one of the subjects that shape the character of Indonesian society. The long-term goals to be achieved in this study are as follows: the researcher found a developmental design of Citizenship Education Learning Model to improve the character of Indonesian society in the era of digital media and the Revolution of Technology. The research method used research and development which is a research method used to yield certain products and to test the validity and the effectiveness of the products. This study used a procedural development model. The procedure in this study adapted the ADDIE model (Analyze, Design, Develop, Implement, and Evaluate). The sample in this study involved State and Private Universities in Indonesia. The results of the study show the following: First, doing a need analysis, identifying problems (needs), and performing task analysis; Second, Design, this design phase, formulated SMART learning objective; Third, Development was realizing blue-print with digital media innovation; Fourth, Implementation was a real step to implement the learning system that we were making; Fifth, Evaluation was a process to see whether the learning system being built was successful, in accordance with early expectations or not. The evaluation was the final way from the design model of the ADDIE learning system. Based on the instructional development model ADDIE, it was then adopted in the developmental stage of the Citizenship Education learning model, with the modification of "MPC" (Modification of Project Citizen) which has adopted the absorption of digital media and the technological revolution.

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