Comparative approach to cognitive neuroscience review ofFrom Monkey Brain to Human Brain edited by Stanislas Dehaene, Jean-Rene Duhamel, Marc D. Hauser, and Giacomo Rizzolatti. Cambridge, Massachusetts, The MIT Press, 2005, xvii+400 pp, $55.00.

M. Babette Fontenot

doi:10.1002/ajp.20407

The Mystery of Louis Verrey (1854-1916)

Gesnerus ◽

10.1163/22977953-0500102008 ◽

1993 ◽

Vol 50 (1-2) ◽

pp. 96-112

Author(s):

Semir Zeki

Keyword(s):

Visual Cortex ◽

Human Brain ◽

Primary Visual Cortex ◽

Cortical Area ◽

Functional Specialization ◽

Colour Centre ◽

Monkey Brain ◽

Experimental Discovery ◽

Separate Area

In 1888, Louis Verrey, a Swiss ophthalmologist, stated emphatically that there is a "centre for the chromatic sense" in the human brain and that it is located in the lingual and fusiform gyri. He did not, however, consider the “colour centre” to be a separate area but a large sub-division of the primary visual cortex. His evidence was quickly dismissed and forgotten. It was not to be taken seriously again until after the experimental discovery of functional specialization in the monkey brain. This paper considers why it is that Verrey did not consider the “colour centre” to be a separate cortical area, distinct from the primary visual cortex, why his evidence was so quickly and effectively dismissed, and why it is that Verrey did not pursue the logic of his findings.

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LSTM Neural Network for Textual Ngrams

10.20944/preprints201811.0579.v1 ◽

2018 ◽

Author(s):

Shaun C. D'Souza

Keyword(s):

Neural Network ◽

Deep Learning ◽

Human Brain ◽

Cognitive Neuroscience ◽

Probabilistic Models ◽

Prediction Models ◽

Machine Learning Algorithms ◽

Language Models ◽

Brain Functions ◽

The Web

Cognitive neuroscience is the study of how the human brain functions on tasks like decision making, language, perception and reasoning. Deep learning is a class of machine learning algorithms that use neural networks. They are designed to model the responses of neurons in the human brain. Learning can be supervised or unsupervised. Ngram token models are used extensively in language prediction. Ngrams are probabilistic models that are used in predicting the next word or token. They are a statistical model of word sequences or tokens and are called Language Models or Lms. Ngrams are essential in creating language prediction models. We are exploring a broader sandbox ecosystems enabling for AI. Specifically, around Deep learning applications on unstructured content form on the web.

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Conceptual Combination in The Cognitive Neurosciences

10.31234/osf.io/9jptv ◽

2019 ◽

Cited By ~ 1

Author(s):

Marc N Coutanche ◽

Sarah Solomon ◽

Sharon L. Thompson-Schill

Keyword(s):

Human Brain ◽

Cognitive Neuroscience ◽

Angular Gyrus ◽

Neural Systems ◽

Conceptual Combination ◽

Behavioral Studies ◽

Cognitive Neurosciences ◽

Conceptual Combinations ◽

Neural Underpinnings ◽

Future Work

Much has been learned about how individual concepts and semantic dimensions are represented in the human brain using methods from the field of cognitive neuroscience; however, the process of conceptual combination, in which a new concept is created from pre-existing concepts, has received far less attention. We discuss theories and findings from cognitive science and cognitive neuroscience that shed light on the processing stages and neural systems that allow humans to form new conceptual combinations. We review systematic and creative applications of cognitive neuroscience methods, including neuroimaging, neuropsychological patients, neurostimulation and behavioral studies that have yielded fascinating insights into the cognitive nature and neural underpinnings of conceptual combination. Studies have revealed important features of the cognitive processes that are central to successful conceptual combination. Furthermore, we are beginning to understand how regions of the semantic system, such as the anterior temporal lobe and angular gyrus, integrate features and concepts, and evaluate the plausibility of potential resulting combinations, bridging work in linguistics and semantic memory. Despite the relative newness of these questions for cognitive neuroscience, the investigations we review give a very strong foundation for ongoing and future work that seeks to fully understand how the human brain can flexibly integrate existing concepts to form new and never-before experienced combinations at will.

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Joint action with iCub: a successful adaptation of a paradigm of cognitive neuroscience in HRI

10.31234/osf.io/zer3w ◽

2019 ◽

Author(s):

Jairo Pérez-Osorio ◽

Davide De Tommaso ◽

Ebru Baykara ◽

Agnieszka Wykowska

Keyword(s):

Cognitive Psychology ◽

Human Brain ◽

Cognitive Neuroscience ◽

Human Robot Interaction ◽

Human Cognition ◽

Social Environments ◽

Social Signals ◽

Robot Interaction ◽

Robot Behavior ◽

The Impact

Robots will soon enter social environments shared with humans. We need robots that are able to efficiently convey social signals during interactions. At the same time, we need to understand the impact of robots’ behavior on the human brain. For this purpose, human behavioral and neural responses to the robot behavior should be quantified offering feedback on how to improve and adjust robot behavior. Under this premise, our approach is to use methods of experimental psychology and cognitive neuroscience to assess the human’s reception of a robot in human-robot interaction protocols. As an example of this approach, we report an adaptation of a classical paradigm of experimental cognitive psychology to a naturalistic human- robot interaction scenario. We show the feasibility of such an approach with a validation pilot study, which demonstrated that our design yielded a similar pattern of data to what has been previously observed in experiments within the area of cognitive psychology. Our approach allows for addressing specific mechanisms of human cognition that are elicited during human-robot interaction, and thereby, in a longer-term perspective, it will allow for designing robots that are well- attuned to the workings of the human brain.

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LSTM neural network for textual ngrams

10.7287/peerj.preprints.27377 ◽

2018 ◽

Author(s):

Shaun C. D'Souza

Keyword(s):

Neural Network ◽

Deep Learning ◽

Human Brain ◽

Cognitive Neuroscience ◽

Probabilistic Models ◽

Prediction Models ◽

Machine Learning Algorithms ◽

Language Models ◽

Brain Functions ◽

The Web

Cognitive neuroscience is the study of how the human brain functions on tasks like decision making, language, perception and reasoning. Deep learning is a class of machine learning algorithms that use neural networks. They are designed to model the responses of neurons in the human brain. Learning can be supervised or unsupervised. Ngram token models are used extensively in language prediction. Ngrams are probabilistic models that are used in predicting the next word or token. They are a statistical model of word sequences or tokens and are called Language Models or Lms. Ngrams are essential in creating language prediction models. We are exploring a broader sandbox ecosystems enabling for AI. Specifically, around Deep learning applications on unstructured content form on the web.

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Brain phylogeny, ontogeny and dysfunction: integrating evolutionary, developmental and clinical perspectives in cognitive neuroscience

Acta Neuropsychiatrica ◽

10.1111/j.1601-5215.2007.00205.x ◽

2007 ◽

Vol 19 (3) ◽

pp. 149-158 ◽

Cited By ~ 5

Author(s):

Anthony J. Hannan

Keyword(s):

Human Brain ◽

Cognitive Processes ◽

Cognitive Neuroscience ◽

Brain Dysfunction ◽

Advantages And Disadvantages ◽

Research Areas ◽

Functional Variants ◽

On Line ◽

The Mind ◽

Diverse Groups

Objective:One of the most popular approaches in cognitive neuroscience has been to study the normal adult human brain. However, there are likely to be limits to the knowledge that can be obtained from such studies. If we assume that no single approach can ever provide us with knowledge of causative processes whereby the mind emerges from the brain, then we need to consider how to combine more disparate approaches. I aim to illustrate here how the parallel study of brain phylogeny, ontogeny and dysfunction may bring us towards an integrative understanding of fundamental aspects of cognitive neuroscience.Methods:A review of published literature in these research areas was carried out and representative articles selected.Results:Comparative approaches, utilizing the extraordinary behavioural abilities as well as the structural and functional variants that evolution has thrown up across diverse groups of species, can inform the core neural systems that may be necessary and sufficient to support specific cognitive processes. Similarly, detailed studies of human brain development, focusing on structural and functional maturation correlated with temporal mapping of cognitive processes as they come ‘on-line’, may provide unique mechanistic insights. Finally, the study of brain dysfunction in neurological and psychiatric disorders such as Huntington’s disease, Alzheimer’s disease, schizophrenia and depression, may have the beneficial side-effect of greatly enhancing our understanding of healthy brain function.Conclusion:Each approach has its own epistemological advantages and disadvantages, but combined they may lead to more sophisticated, and empirically testable, models. In this review, I outline evidence for their utility, illustrate the approaches using specific examples and suggest how new advances in fields such as genomics, neurophysiology and neuroimaging may provide unprecedented opportunities in cognitive neuroscience.

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Investigation of Precipitins to Human Brain in Sera of Psychotic Patients

The British Journal of Psychiatry ◽

10.1192/bjp.111.479.1003 ◽

1965 ◽

Vol 111 (479) ◽

pp. 1003-1006 ◽

Cited By ~ 17

Author(s):

Robert T. Rubin

Keyword(s):

Human Brain ◽

Complement Fixation ◽

Serum Proteins ◽

Psychiatric Patients ◽

Double Diffusion ◽

Brain Extract ◽

Monkey Brain ◽

Functional Psychoses ◽

Schizophrenic Patients ◽

Altered Serum

Altered serum proteins in mental illness have been reported by workers in various parts of the world, and a review of these studies has led to the consideration of an autoimmune mechanism in the pathogenesis of some functional psychoses (Fessel, 1962a). Several reports have appeared in recent years which suggest the presence of antibodies in the serum of certain psychiatric patients to central nervous system tissue. Many of these have been reviewed by Vartanyan (1963). Fessel (1962b, 1963) demonstrated agglutination of latex particles coated with monkey brain extract and agglutination of tanned sheep red blood cells coated with human brain extract, but no precipitins in double diffusion in agar of the sera against monkey brain extract. As an alternative to antibrain antibodies he suggested a less specific physicochemical abnormality of the serum which caused the agglutination. Yokoyama, Trams, and Brady (1962), using a sheep red blood cell haemagglutination technique, showed the presence of anti-asialoganglioside antibodies in the sera of 3 of 14 schizophrenic patients and anti-ganglioside antibody in the serum of another. Kuznetzova and Semenov (1961), by complement fixation, demonstrated antibodies in the sera of 22 of 84 schizophrenics, mainly to human brain and not to other organs. The antibodies appeared more frequently in the later stages of the illness (Semenov, Morozov, and Kuznetzova, 1961). Skalickova and Jezkova (1961), also with a complement fixation technique, demonstrated blood and cerebrospinal fluid antibodies to grey and white matter during the “infectious” onset of schizophrenia, but not in the chronic, demented phase.

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Modeling Individual Differences in Brain Development

10.31234/osf.io/73byf ◽

2019 ◽

Author(s):

Andrik Becht ◽

Kathryn L. Mills

Keyword(s):

Mental Health ◽

Individual Differences ◽

Human Brain ◽

Health Outcomes ◽

Brain Development ◽

Cognitive Neuroscience ◽

Mental Health Disorders ◽

Group Level ◽

Human Brain Development ◽

Health Disorders

Within the field of developmental cognitive neuroscience there is an increasing interest in studying individual differences in human brain development in order to predict mental health outcomes. So far, however, most longitudinal neuroimaging studies focus on group-level estimates. In this review, we highlight longitudinal neuroimaging studies that have moved beyond group-level estimates to illustrate the heterogeneity in patterns of brain development. We provide practical methodological recommendations on how longitudinal neuroimaging datasets can be used to understand heterogeneity in human brain development. Finally, we address how taking an individual-differences approach in developmental neuroimaging studies could advance our understanding of why some individuals develop mental health disorders.

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