The Double Take of Expertise: Neural Expansion Is Associated With Outstanding Performance

2018 ◽  
Vol 27 (6) ◽  
pp. 462-469 ◽  
Author(s):  
Merim Bilalić
Keyword(s):  
Pattern Recognition ◽  
Human Brain ◽  
Brain Activation ◽  
Cognitive System ◽  
Brain Areas ◽  
Opposite Hemisphere ◽  
Computational Burden ◽  
Complex Skills ◽  
Neural Efficiency ◽  
The Brain

The performance of experts seems almost effortless. The neural-efficiency hypothesis takes this into account, suggesting that because of practice and automatization of procedures, experts require fewer brain resources. Here, I argue that the way the brain accommodates complex skills does indeed have to do with the nature of experts’ performance. However, instead of exhibiting less brain activation, experts’ performance actually engages more brain areas. Behind the seemingly effortless performance of experts lies a complex cognitive system that relies on knowledge about the domain of expertise. Unlike novices, who need to execute one process at a time, experts are able to recognize an object, retrieve its function, and connect it to another object simultaneously. The expert brain deals with this computational burden by engaging not only specific brain areas in one hemisphere but also the same (homologous) area in the opposite hemisphere. This phenomenon, which I call the double take of expertise, has been observed in a number of expertise domains. I describe it here in object- and pattern-recognition tasks in the domain of chess. I also discuss the importance of the study of expertise for our understanding of the human brain in general.

scholarly journals Processing of a spoken narrative in the human brain is shaped by family cultural background

2020 ◽  
Author(s):  
M. Hakonen ◽  
A. Ikäheimonen ◽  
A. Hultèn ◽  
J. Kauttonen ◽  
M. Koskinen ◽  
...  
Keyword(s):  
Social Identity ◽  
Human Brain ◽  
Native Speakers ◽  
Family Background ◽  
Cultural Background ◽  
The Other ◽  
Mutual Understanding ◽  
Brain Areas ◽  
Narrative Processing ◽  
The Brain

ABSTRACTUsing neuroimaging, we studied influence of family cultural background on processing of an audiobook in human brain. The audiobook depicted life of two young Finnish men, one with the Finnish and the other with the Russian family background. Shared family cultural background enhanced similarity of narrative processing in the brain at prelexical, word, sentence, and narrative levels. Similarity was also enhanced in brain areas supporting imagery. The cultural background was further reflected as semantic differences in word lists by which the subjects described what had been on their minds when they heard the audiobook during neuroimaging. Strength of social identity shaped word, sentence, and narrative level processing in the brain. These effects might enhance mutual understanding between persons who share family cultural background and social identity and, conversely, deteriorate between-group mutual understanding in modern multicultural societies wherein native speakers of a language may assume highly similar understanding.

scholarly journals Temporal pattern recognition in the human brain: a dual simultaneous processing

2021 ◽  
Author(s):  
Leonardo Bonetti ◽  
Elvira Brattico ◽  
Silvia EP Bruzzone ◽  
Giulia Donati ◽  
Gustavo Deco ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Human Brain ◽  
Temporal Pattern ◽  
Brain Regions ◽  
Machine Learning Algorithms ◽  
Brain Areas ◽  
Simultaneous Processing ◽  
Neural Information ◽  
Complex Procedure ◽  
Magnetic Resonance Imaging Mri

Pattern recognition is a major scientific topic. Strikingly, while machine learning algorithms are constantly refined, the human brain emerges as an ancestral biological example of such complex procedure. However, how it transforms sequences of single objects into meaningful temporal patterns remains elusive. Using magnetoencephalography (MEG) and magnetic resonance imaging (MRI), we discovered and mathematically modelled an inedited dual simultaneous processing responsible for pattern recognition in the brain. Indeed, while the objects of the temporal pattern were independently elaborated by a local, rapid brain processing, their combination into a meaningful superordinate pattern depended on a concurrent global, slower processing involving a widespread network of sequentially active brain areas. Expanding the established knowledge of neural information flow from low- to high-order brain areas, we revealed a novel brain mechanism based on simultaneous activity in different frequency bands within the same brain regions, highlighting its crucial role underlying complex cognitive functions.

Classification of Multiple Interleaved Human Brain Tasks in Functional Magnetic Resonance Imaging

2011 ◽  
pp. 123-148
Author(s):  
Manel Martínez-Ramón ◽  
Vladimir Koltchinskii ◽  
Gregory L. Heileman ◽  
Stefan Posse
Keyword(s):  
Pattern Recognition ◽  
Human Brain ◽  
Ad Hoc ◽  
Small Data ◽  
Data Sets ◽  
Reasonable Approach ◽  
Small Data Sets ◽  
Voxel Selection ◽  
Functional Areas ◽  
The Brain

Pattern recognition in functional magnetic resource imaging (fMRI) is a novel technique that may lead to a quantity of discovery tools in neuroscience. It is intended to automatically identify differences in distributed neural substrates resulting from cognitive tasks. Previous works in fMRI classification revealed that information is organized in coarse areas in the neural tissues rather than in small neural microstructures. This fact opens a field of study of the functional areas of the brain from the multivariate analysis of the rather coarse images provided by fMRI. Nevertheless, reliable pattern classification is challenging due to the high dimensionality of fMRI data, the small number of available data sets, interindividual differences, and dependence on the acquisition methodology. The application of kernel methods and, in particular, SVMs, to pattern recognition of fMRI is a reasonable approach to deal with these difficulties and has given reasonable results in accuracy and generalization ability. Some of the most relevant fMRI classification studies using SVMs are analyzed in this chapter. All of them were applied in individual subjects using ad hoc techniques to isolate small brain areas in order to reduce the dimensionality of the problem. Some of them included blind techniques for feature selection; others used the previous knowledge of the human brain to isolate the areas in which the information is presumed to lie. Nevertheless, these methods do not explicitly address the dimensionality, small data sets, or cross-subject classification issues. We present an approach to improve multiclass classification across groups of subjects, field strengths, and fMRI methods. We use an approach based on the segmentation of the brain in functional areas using a neuroanatomical atlas, and each map is classified separately using local classifiers. A single multiclass output is applied using an Adaboost aggregation of the classifier’s outputs. This Adaboost combined the region-specific classifiers to achieve improved classification accuracy with respect to conventional techniques without previous ad hoc area or voxel selection.

scholarly journals The Brain Is Globally Symmetric: An Analysis of Intra and Interhemispheric Symmetry of Subcortical Structures in the Aging Human Brain

2021 ◽  
Author(s):  
Jaime Gomez Ramirez ◽  
Javier J González-Rosa
Keyword(s):  
Human Brain ◽  
Resonance Imaging ◽  
Subcortical Structures ◽  
Brain Areas ◽  
Elderly Individuals ◽  
Statistical Dependency ◽  
Magnetic Resonance Imaging Mri ◽  
Effects Of Aging ◽  
The Brain ◽  
Adjacent Brain

Abstract Here we address the hemispheric interdependency of subcortical structures in the aging human brain. In particular, we investigate whether volume variation can be explained with the adjacency of structures in the same hemisphere or is due to the interhemispheric development of mirror subcortical structures in the brain. Seven subcortical structures in both hemispheres were automatically segmented in a large sample of over three 3,312 magnetic resonance imaging (MRI) studies of elderly individuals in their 70s and 80s. We perform Eigenvalue analysis to find that anatomic volumes in the limbic system and basal ganglia show similar statistical dependency when considered in the same hemisphere (intrahemispheric) or in different hemispheres (interhemispheric). Our results indicate that anatomic bilaterality is preserved in the aging human brain, supporting the hypothesis that coupling between non-adjacent brain areas could act as a mechanism to compensate for the deleterious effects of aging.

scholarly journals A taxonomy of the brain’s white matter: Twenty-one major tracts for the twenty-first century

2021 ◽  
Author(s):  
Daniel N. Bullock ◽  
Elena A. Hayday ◽  
Mark D. Grier ◽  
Wei Tang ◽  
Franco Pestilli ◽  
...  
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Brain Connectivity ◽  
First Century ◽  
Brain Areas ◽  
Behavior Development ◽  
The Brain ◽  
Brain Connections ◽  
Computational Properties

The functional and computational properties of brain areas are determined, in large part, by their connectivity profiles. Advances in neuroimaging and network neuroscience allow us to characterize the human brain noninvasively and in vivo, but a comprehensive understanding of the human brain demands an account of the anatomy of brain connections. Long-range anatomical connections are instantiated by white matter and organized into tracts. Here, we aim to characterize the connections, morphology, traversal, and functions of the major white matter tracts in the brain. It is clear that there are significant discrepancies across different accounts of white matter tract anatomy, hindering our attempts to accurately map the connectivity of the human brain. We thoroughly synthesize accounts from multiple methods, but especially nonhuman primate tract-tracing and human diffusion tractography. Ultimately, we suggest that our synthesis provides an essential reference for neuroscientists and clinicians interested in brain connectivity and anatomy, allowing for the study of the association of white matter’s macro and microstructural properties with behavior, development, and disordered processes.

scholarly journals Numerical Simulation of Higher-Order Nonlinearity of Human Brain Functional Connectivity Using Hypergraph p-Laplacian

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2345 ◽  
Author(s):  
Jichao Ma ◽  
Chunyu Du ◽  
Weifeng Liu ◽  
Yanjiang Wang
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Dominant Role ◽  
Pearson Correlation ◽  
Structural Connectivity ◽  
Laplacian Matrix ◽  
Graph Laplacian ◽  
Resting State Functional Connectivity ◽  
Brain Areas ◽  
The Brain

Unravelling how the human brain structure gives rise to function is a central question in neuroscience and remains partially answered. Recent studies show that the graph Laplacian of the human brain’s structural connectivity (SC) plays a dominant role in shaping the pattern of resting-state functional connectivity (FC). The modeling of FC using the graph Laplacian of the brain’s SC is limited, owing to the sparseness of the Laplacian matrix. It is unable to model the negative functional correlations. We extended the graph Laplacian to the hypergraph p-Laplacian in order to describe better the nonlinear and high-order relations between SC and FC. First we estimated those possible links showing negative correlations between the brain areas shared across subjects by statistical analysis. Then we presented a hypergraph p-Laplacian model by embedding the two matrices referring to the sign of the correlations between the brain areas relying on the brain structural connectome. We tested the model on two experimental connectome datasets and evaluated the predicted FC by estimating its Pearson correlation with the empirical FC matrices. The results showed that the proposed diffusion model based on hypergraph p-Laplacian can predict functional correlations more accurately than the models using graph Laplacian as well as hypergraph Laplacian.

Classification of Multiple Interleaved Human Brain Tasks in Functional Magnetic Resonance Imaging

2011 ◽  
pp. 878-900
Author(s):  
Manel Martínez-Ramón ◽  
Vladimir Koltchinskii ◽  
Gregory L. Heileman ◽  
Stefan Posse
Keyword(s):  
Pattern Recognition ◽  
Human Brain ◽  
Ad Hoc ◽  
Small Data ◽  
Data Sets ◽  
Reasonable Approach ◽  
Small Data Sets ◽  
Voxel Selection ◽  
Functional Areas ◽  
The Brain

Pattern recognition in functional magnetic resource imaging (fMRI) is a novel technique that may lead to a quantity of discovery tools in neuroscience. It is intended to automatically identify differences in distributed neural substrates resulting from cognitive tasks. Previous works in fMRI classification revealed that information is organized in coarse areas in the neural tissues rather than in small neural microstructures. This fact opens a field of study of the functional areas of the brain from the multivariate analysis of the rather coarse images provided by fMRI. Nevertheless, reliable pattern classification is challenging due to the high dimensionality of fMRI data, the small number of available data sets, interindividual differences, and dependence on the acquisition methodology. The application of kernel methods and, in particular, SVMs, to pattern recognition of fMRI is a reasonable approach to deal with these difficulties and has given reasonable results in accuracy and generalization ability. Some of the most relevant fMRI classification studies using SVMs are analyzed in this chapter. All of them were applied in individual subjects using ad hoc techniques to isolate small brain areas in order to reduce the dimensionality of the problem. Some of them included blind techniques for feature selection; others used the previous knowledge of the human brain to isolate the areas in which the information is presumed to lie. Nevertheless, these methods do not explicitly address the dimensionality, small data sets, or cross-subject classification issues. We present an approach to improve multiclass classification across groups of subjects, field strengths, and fMRI methods. We use an approach based on the segmentation of the brain in functional areas using a neuroanatomical atlas, and each map is classified separately using local classifiers. A single multiclass output is applied using an Adaboost aggregation of the classifier’s outputs. This Adaboost combined the region-specific classifiers to achieve improved classification accuracy with respect to conventional techniques without previous ad hoc area or voxel selection.

scholarly journals Towards Sentence-Level Brain Decoding with Distributed Representations

2019 ◽  
Vol 33 ◽  
pp. 7047-7054
Author(s):  
Jingyuan Sun ◽  
Shaonan Wang ◽  
Jiajun Zhang ◽  
Chengqing Zong
Keyword(s):  
Human Brain ◽  
Language Processing ◽  
Comprehensive Evaluation ◽  
Brain Activation ◽  
Systematic Evaluation ◽  
Activation Patterns ◽  
Distributed Representations ◽  
Brain Decoding ◽  
High Level ◽  
The Brain

Decoding human brain activities based on linguistic representations has been actively studied in recent years. However, most previous studies exclusively focus on word-level representations, and little is learned about decoding whole sentences from brain activation patterns. This work is our effort to mend the gap. In this paper, we build decoders to associate brain activities with sentence stimulus via distributed representations, the currently dominant sentence representation approach in natural language processing (NLP). We carry out a systematic evaluation, covering both widely-used baselines and state-of-the-art sentence representation models. We demonstrate how well different types of sentence representations decode the brain activation patterns and give empirical explanations of the performance difference. Moreover, to explore how sentences are neurally represented in the brain, we further compare the sentence representation’s correspondence to different brain areas associated with high-level cognitive functions. We find the supervised structured representation models most accurately probe the language atlas of human brain. To the best of our knowledge, this work is the first comprehensive evaluation of distributed sentence representations for brain decoding. We hope this work can contribute to decoding brain activities with NLP representation models, and understanding how linguistic items are neurally represented.

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