scholarly journals Temporal response modelling uncovers electrophysiological correlates of trial-by-trial error-driven learning

2021 ◽  
Author(s):  
Tomas Lentz ◽  
Jessie S. Nixon ◽  
Jacolien van Rij
Keyword(s):  
Bimodal Distribution ◽  
Statistical Distributions ◽  
Temporal Response ◽  
Lexical Tones ◽  
Function Fitting ◽  
Statistical Regularities ◽  
Tentative Evidence ◽  
Response Modelling ◽  
The Brain ◽  
Better Than

Humans learn from statistical regularities in the environment. We tested if prediction and prediction error may play a role in such learning in the brain. We used Error-Driven Learning (EDL) to simulate participants’ trial-by-trial learning during exposure to a bimodal distribution of non-native lexical tones. We simulated incremental trial-by-trial learning to get estimates of the degree of expectation of upcoming stimuli over the course of the experiment. The expectation estimates were combined with Temporal Response Function fitting to generate a prediction of the trial-by-trial ERP waveform. EDL simulations captured the data significantly better than chance and better than models based on either stimulus characteristics or statistical distributions. The results provide tentative evidence that trial-by-trial learning as measured in neural activity is error-driven.

scholarly journals Multiple Sclerosis Recognition by Biorthogonal Wavelet Features and Fitness-Scaled Adaptive Genetic Algorithm

2021 ◽  
Vol 15 ◽  
Author(s):  
Shui-Hua Wang ◽  
Xianwei Jiang ◽  
Yu-Dong Zhang
Keyword(s):  
Multiple Sclerosis ◽  
Genetic Algorithm ◽  
Data Augmentation ◽  
Area Under The Curve ◽  
Adaptive Genetic Algorithm ◽  
Biorthogonal Wavelet ◽  
Wide Range ◽  
Wavelet Features ◽  
The Brain ◽  
Better Than

Aim: Multiple sclerosis (MS) is a disease, which can affect the brain and/or spinal cord, leading to a wide range of potential symptoms. This method aims to propose a novel MS recognition method.Methods: First, the bior4.4 wavelet is used to extract multiscale coefficients. Second, three types of biorthogonal wavelet features are proposed and calculated. Third, fitness-scaled adaptive genetic algorithm (FAGA)—a combination of standard genetic algorithm, adaptive mechanism, and power-rank fitness scaling—is harnessed as the optimization algorithm. Fourth, multiple-way data augmentation is utilized on the training set under the setting of 10 runs of 10-fold cross-validation. Our method is abbreviated as BWF-FAGA.Results: Our method achieves a sensitivity of 98.00 ± 0.95%, a specificity of 97.78 ± 0.95%, and an accuracy of 97.89 ± 0.94%. The area under the curve of our method is 0.9876.Conclusion: The results show that the proposed BWF-FAGA method is better than 10 state-of-the-art MS recognition methods, including eight artificial intelligence-based methods, and two deep learning-based methods.

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.

Predictability-dependent encoding of statistical regularities in the early visual cortex

2022 ◽  
Author(s):  
Andrea Kóbor ◽  
Karolina Janacsek ◽  
Petra Hermann ◽  
Zsofia Zavecz ◽  
Vera Varga ◽  
...  
Keyword(s):  
Visual Cortex ◽  
The Other ◽  
Block Type ◽  
Statistical Knowledge ◽  
Early Visual Cortex ◽  
Statistical Regularities ◽  
Visual Cortical ◽  
Increased Activity ◽  
The Brain ◽  
Subsequent Task

Previous research recognized that humans could extract statistical regularities of the environment to automatically predict upcoming events. However, it has remained unexplored how the brain encodes the distribution of statistical regularities if it continuously changes. To investigate this question, we devised an fMRI paradigm where participants (N = 32) completed a visual four-choice reaction time (RT) task consisting of statistical regularities. Two types of blocks involving the same perceptual elements alternated with one another throughout the task: While the distribution of statistical regularities was predictable in one block type, it was unpredictable in the other. Participants were unaware of the presence of statistical regularities and of their changing distribution across the subsequent task blocks. Based on the RT results, although statistical regularities were processed similarly in both the predictable and unpredictable blocks, participants acquired less statistical knowledge in the unpredictable as compared with the predictable blocks. Whole-brain random-effects analyses showed increased activity in the early visual cortex and decreased activity in the precuneus for the predictable as compared with the unpredictable blocks. Therefore, the actual predictability of statistical regularities is likely to be represented already at the early stages of visual cortical processing. However, decreased precuneus activity suggests that these representations are imperfectly updated to track the multiple shifts in predictability throughout the task. The results also highlight that the processing of statistical regularities in a changing environment could be habitual.

The Effect of Brain Based Learning on Improving Students Critical Thinking Ability and Self Regulated

2020 ◽  
Vol 2 (2) ◽  
pp. 162
Author(s):  
Sarifah Sari Maryati ◽  
Irma Purwanti ◽  
Melinda Putri Mubarika
Keyword(s):  
Critical Thinking ◽  
Thinking Skills ◽  
Critical Thinking Skills ◽  
Learning Approach ◽  
School Students ◽  
Critical Thinking Ability ◽  
Thinking Ability ◽  
Brain Based Learning ◽  
The Brain ◽  
Better Than

This research is motivated by the low ability of mathematical critical thinking and Self Regulated Cimahi 10 Public Middle School students, so that a learning approach is needed to overcome these problems. The alternative approach applied is the Brain Based Learning Model approach.The objectives of this researcher are: 1) to examine students' mathematical critical thinking skills; 2) reviewing the Self Regulated attitude of students who obtain Brain Based Learning learning with students who have expository learning; 3) examine there is a positive correlation between Critical Thinking with Self Regulated students who obtain Brain Based Learning and expository learning. The population in this study was grade VII students of SMP Negeri 10 Cimahi. The samples in this study were class VII-B (Brain Based Learning) and class VII-D (expository). The instruments used in this study were the Critical Thinking test and the student's Self Regulated questionnaire. The test used is a subjective type test (description). The way to analyze data is with IBM SPSS Statistics 18.0 for Windows. The results showed that: 1) the mathematical critical thinking ability of students who obtained learning based on the Brain Based Learning approach was better than students who gained expository learning; 2) Self Regulated  attitude, students who get Brain Based Learning are better than students who get expository approach learning; 3) there is no correlation between critical thinking with Self Regulated students who obtain Brain Based Learning and expository learning.

scholarly journals Human-specific microglial Siglec-11 transcript variant has the potential to affect polysialic acid-mediated brain functions at a distance

Glycobiology ◽  
2020 ◽  
Author(s):  
Masaya Hane ◽  
Dillon Y Chen ◽  
Ajit Varki
Keyword(s):  
Polysialic Acid ◽  
Innate Immune ◽  
Set Domain ◽  
Tissue Macrophage ◽  
Brain Functions ◽  
Functional Consequences ◽  
Molecular Patterns ◽  
The Brain ◽  
Better Than ◽  
Human Specific

Abstract CD33-related Siglecs are often found on innate immune cells and modulate their reactivity by recognition of sialic acid-based “self-associated molecular patterns” and signaling via intracellular tyrosine-based cytosolic motifs. Previous studies have shown that Siglec-11 specifically binds to the brain-enriched polysialic acid (polySia/PSA) and that its microglial expression in the brain is unique to humans. Furthermore, human microglial Siglec-11 exists as an alternate splice form missing the exon encoding the last (fifth) Ig-like C2-set domain of the extracellular portion of the protein, but little is known about the functional consequences of this variation. Here, we report that the recombinant soluble human microglial form of Siglec-11 (hSiglec-11(4D)-Fc) binds endogenous and immobilized polySia better than the tissue macrophage form (hSiglec-11(5D)-Fc) or the chimpanzee form (cSiglec-11(5D)-Fc). The Siglec-11 protein is also prone to aggregation, potentially influencing its ligand-binding ability. Additionally, Siglec-11 protein can be secreted in both intact and proteolytically cleaved forms. The microglial splice variant has reduced proteolytic release and enhanced incorporation into exosomes, a process that appears to be regulated by palmitoylation of cysteines in the cytosolic tail. Taken together, these data demonstrate that human brain specific microglial hSiglec-11(4D) has different molecular properties and can be released on exosomes and/or as proteolytic products, with the potential to affect polySia-mediated brain functions at a distance.

rethinking brain asymmetries in humans

2005 ◽  
Vol 28 (4) ◽  
pp. 598-599
Author(s):  
bianca dräger ◽  
caterina breitenstein ◽  
stefan knecht
Keyword(s):  
Bimodal Distribution ◽  
Language Lateralization ◽  
Evolutionarily Conserved ◽  
Organizational Principle ◽  
Directional Asymmetries ◽  
The Right ◽  
The Brain ◽  
Biological Advantage

similar to directional asymmetries in animals, language lateralization in humans follows a bimodal distribution. a majority of individuals are lateralized to the left and a minority of individuals are lateralized to the right side of the brain. however, a biological advantage for either lateralization is lacking. the scenario outlined by vallortigara & rogers (v&r) suggests that language lateralization in humans is not specific to language or human speciation but simply follows an evolutionarily conserved organizational principle of the brain.

scholarly journals Efficient inverse graphics in biological face processing

2020 ◽  
Vol 6 (10) ◽  
pp. eaax5979 ◽  
Author(s):  
Ilker Yildirim ◽  
Mario Belledonne ◽  
Winrich Freiwald ◽  
Josh Tenenbaum
Keyword(s):  
Face Processing ◽  
Three Dimensional ◽  
Generative Models ◽  
Neural Data ◽  
Primate Brain ◽  
Scene Structure ◽  
Analysis By Synthesis ◽  
The Brain ◽  
Processing Circuit ◽  
Better Than

Vision not only detects and recognizes objects, but performs rich inferences about the underlying scene structure that causes the patterns of light we see. Inverting generative models, or “analysis-by-synthesis”, presents a possible solution, but its mechanistic implementations have typically been too slow for online perception, and their mapping to neural circuits remains unclear. Here we present a neurally plausible efficient inverse graphics model and test it in the domain of face recognition. The model is based on a deep neural network that learns to invert a three-dimensional face graphics program in a single fast feedforward pass. It explains human behavior qualitatively and quantitatively, including the classic “hollow face” illusion, and it maps directly onto a specialized face-processing circuit in the primate brain. The model fits both behavioral and neural data better than state-of-the-art computer vision models, and suggests an interpretable reverse-engineering account of how the brain transforms images into percepts.

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