scholarly journals Fear and Trembling

2015 ◽  
Vol IX (2) ◽  
pp. 101-114
Author(s):  
John Crutchfield
Keyword(s):  
Brain Activity ◽  
Cognitive Activity ◽  
Learning Processes ◽  
Natural State ◽  
Fear And Trembling ◽  
State Of Affairs ◽  
The Brain ◽  
The Way

As teachers, we have every reason to take seriously the findings of neuroscience. Learning is after all a brain activity, and those who teach would do well to consider how the brain actually learns. “Neuroscientific research,” writes Michaela Sambanis, “offers powerful insights into the brain mechanisms that underly learning processes. These findings can give a better understanding of how learning happens, how the brain as organ of learning copes with stimuli, how it stores information, how it forms networks, and how competences are developed. In a nutshell, neuroscience can make substantial contributions when it comes to answering the multifaceted question of what helps and what hinders learning” (Sambanis 2016). One of the more powerful neuroscientific findings, though at the same time perhaps one of the least surprising, has to do with the role of emotions in learning: the brain learns more efficiently when cognitive activity is accompanied by “positive” or pleasant emotions (Spitzer 2003). In fact, there is reason to suppose that this is the natural state of affairs, i.e. that learning is in itself pleasurable, and that Nature arranged things for us this way because, with neither sharp teeth and claws nor very much in the way of fur, ...

scholarly journals Consciousness, decision making, and volition: freedom beyond chance and necessity

2021 ◽  
Author(s):  
Hans Liljenström
Keyword(s):  
Decision Making ◽  
Free Will ◽  
Brain Activity ◽  
Brain Structures ◽  
Complex Process ◽  
Neural Information ◽  
Neural Information Processing ◽  
Stochastic Population Model ◽  
The Brain

AbstractWhat is the role of consciousness in volition and decision-making? Are our actions fully determined by brain activity preceding our decisions to act, or can consciousness instead affect the brain activity leading to action? This has been much debated in philosophy, but also in science since the famous experiments by Libet in the 1980s, where the current most common interpretation is that conscious free will is an illusion. It seems that the brain knows, up to several seconds in advance what “you” decide to do. These studies have, however, been criticized, and alternative interpretations of the experiments can be given, some of which are discussed in this paper. In an attempt to elucidate the processes involved in decision-making (DM), as an essential part of volition, we have developed a computational model of relevant brain structures and their neurodynamics. While DM is a complex process, we have particularly focused on the amygdala and orbitofrontal cortex (OFC) for its emotional, and the lateral prefrontal cortex (LPFC) for its cognitive aspects. In this paper, we present a stochastic population model representing the neural information processing of DM. Simulation results seem to confirm the notion that if decisions have to be made fast, emotional processes and aspects dominate, while rational processes are more time consuming and may result in a delayed decision. Finally, some limitations of current science and computational modeling will be discussed, hinting at a future development of science, where consciousness and free will may add to chance and necessity as explanation for what happens in the world.

scholarly journals Inferring brain-computational mechanisms with models of activity measurements

2016 ◽  
Vol 371 (1705) ◽  
pp. 20160278 ◽  
Author(s):  
Nikolaus Kriegeskorte ◽  
Jörn Diedrichsen
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Summary Statistics ◽  
Activity Data ◽  
Individual Measurement ◽  
Activity Measurements ◽  
The Brain ◽  
The Way

High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. To avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic representational similarity analysis (pRSA) with MMs, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects, and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognize the data-generating model in each case. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

Is Artistic Composition in Abstract Art Detected Automatically?

2021 ◽  
pp. 102-106
Author(s):  
Claudia Menzel ◽  
Gyula Kovács ◽  
Gregor U. Hayn-Leichsenring ◽  
Christoph Redies
Keyword(s):  
Human Brain ◽  
Significant Role ◽  
Mismatch Negativity ◽  
Brain Activity ◽  
Perceptual Processing ◽  
Abstract Art ◽  
Electrical Brain Activity ◽  
Image Set ◽  
The Brain

Most artists who create abstract paintings place the pictorial elements not at random, but arrange them intentionally in a specific artistic composition. This arrangement results in a pattern of image properties that differs from image versions in which the same pictorial elements are randomly shuffled. In the article under discussion, the original abstract paintings of the author’s image set were rated as more ordered and harmonious but less interesting than their shuffled counterparts. The authors tested whether the human brain distinguishes between these original and shuffled images by recording electrical brain activity in a particular paradigm that evokes a so-called visual mismatch negativity. The results revealed that the brain detects the differences between the two types of images fast and automatically. These findings are in line with models that postulate a significant role of early (low-level) perceptual processing of formal image properties in aesthetic evaluations.

scholarly journals Spiritual Activism. Saving Mother Earth in Sápmi

Religions ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 342
Author(s):  
Siv Ellen Kraft
Keyword(s):  
Power Plant ◽  
Learning Processes ◽  
Political Climate ◽  
The South ◽  
Spiritual Activism ◽  
Mother Earth ◽  
Different Parts ◽  
The Way

Arctic Shaman Circle was founded in Oslo in November 2018. This article discusses what the Circle’s founding document refers to as “spiritual activism”, and how this was translated into action over the year that followed. I will follow one case in particular, which concerns plans for a power plant at the base of the mountain Aahkansnjurhtjie in the South Sámi area. Aahkansnjurhtjie is a sacred Sámi mountain, the shamans claim, and should be protected accordingly. My focus is on the learning processes that have emerged as the shamans have explored and argued the case, locally and nationally. I examine the negotiations that have happened along the way, in a political climate that has so far been hostile to religious arguments of any sorts, and in this example, involves a group that is contested among the Sámi. Finally, I look at the role of “indigeneity” in regard to claims, performances and responses to these particular concerns, as these have played out in different parts of the Sámi geography.

The role of consciousness and sub-consciousness in teaching of grammar in ELT

2018 ◽  
Vol 5 (1) ◽  
pp. 115-120
Author(s):  
Selin Ozdemir ◽  
Fatih Yavuz
Keyword(s):  
Literature Review ◽  
Language Learning ◽  
Explicit Knowledge ◽  
Learning Processes ◽  
Grammar Teaching ◽  
High Level ◽  
Teaching Grammar ◽  
The Way

Teaching grammar has been regarded as one of the most crucial issues in the field of language. It gains its importance since it helps learners attain high level of accuracy and proficiency in language learning processes. During these processes, the way of teaching grammar differs under some certain circumstances and is divided into some sub-categories such as conscious grammar teaching and subconscious grammar teaching. In this study, a literature review of issues on the role of consciousness and sub-consciousness in teaching of grammar has been widely discussed since there are numerous views, claims and approaches related to choosing one of them as an ideal way to teach grammar. Both of them have a significant impact on the knowledge of grammar .The study revealed that neither conscious grammar teaching which lays emphasis on the structures and rules of a language nor subconscious grammar teaching without attention to explicit knowledge of grammar should be neglected. Keywords: Grammar teaching, consciousness, sub-consciousness, deductive, inductive.

scholarly journals Complex of global functional network as the core of consciousness

2021 ◽  
Author(s):  
Keiichi Onoda
Keyword(s):  
Dynamic System ◽  
Brain Activity ◽  
Posterior Part ◽  
Neural Basis ◽  
Attention Networks ◽  
The Core ◽  
Hierarchical Partition ◽  
Brain Fmri ◽  
The Brain

Finding the neural basis of consciousness is a challenging issue, and it is still inconclusive where the core of consciousness is distributed in the brain. The global neuronal workspace theory (GNWT) emphasizes the role of the frontoparietal regions, whereas the integrated information theory (IIT) argues that the posterior part of the brain is the core of consciousness. IIT has proposed “main complex” as the core of consciousness in a dynamic system, which is a set of elements that the information loss in a hierarchical partition approach is the largest among that of all its supersets and subsets. However, no experimental study has reported the core of consciousness using the main complex for actual brain activity. This study estimated the main complex of brain dynamics using a functional MRI. The whole-brain fMRI data of eight conditions (seven tasks and a rest state) were divided into multiple elements based on network atlases, and the main complex of the dynamic system was estimated for each condition. It is assumed that, if there is a set of elements in the complex that are common to all conditions, the set is likely to contain the core of consciousness. Executive control, salience, and dorsal/ventral attention networks were commonly included in the main complex across all conditions, implying that these networks are responsible for the core of consciousness. This finding is consistent with the GNWT, as these networks are across the prefrontal and parietal regions.

scholarly journals Synchronization in the connectome: Metastable oscillatory modes emerge from interactions in the brain spacetime network

2022 ◽  
Author(s):  
Joana Cabral ◽  
Francesca Castaldo ◽  
Jakub Vohryzek ◽  
Vladimir Litvak ◽  
Christian Bick ◽  
...  
Keyword(s):  
Coupled Oscillators ◽  
Brain Activity ◽  
Power Spectra ◽  
Brain Areas ◽  
Neural Mass ◽  
Spatially Distributed ◽  
Weakly Stable ◽  
Human Brains ◽  
The Brain

A rich repertoire of oscillatory signals is detected from human brains with electro- and magnetoencephalography (EEG/MEG). However, the principles underwriting coherent oscillations and their link with neural activity remain unclear. Here, we hypothesise that the emergence of transient brain rhythms is a signature of weakly stable synchronization between spatially distributed brain areas, occurring at network-specific collective frequencies due to non-negligible conduction times. We test this hypothesis using a phenomenological network model to simulate interactions between neural mass potentials (resonating at 40Hz) in the structural connectome. Crucially, we identify a critical regime where metastable oscillatory modes emerge spontaneously in the delta (0.5-4Hz), theta (4-8Hz), alpha (8-13Hz) and beta (13-30Hz) frequency bands from weak synchronization of subsystems, closely approximating the MEG power spectra from 89 healthy individuals. Grounded in the physics of delay-coupled oscillators, these numerical analyses demonstrate the role of the spatiotemporal connectome in structuring brain activity in the frequency domain.

scholarly journals The modular and integrative functional architecture of the human brain

2015 ◽  
Vol 112 (49) ◽  
pp. E6798-E6807 ◽  
Author(s):  
Maxwell A. Bertolero ◽  
B. T. Thomas Yeo ◽  
Mark D’Esposito
Keyword(s):  
Human Brain ◽  
Cognitive Functions ◽  
Topic Model ◽  
Brain Activity ◽  
Modular Function ◽  
Imaging Data ◽  
Functional Architecture ◽  
Brain Imaging Data ◽  
The Brain

Network-based analyses of brain imaging data consistently reveal distinct modules and connector nodes with diverse global connectivity across the modules. How discrete the functions of modules are, how dependent the computational load of each module is to the other modules’ processing, and what the precise role of connector nodes is for between-module communication remains underspecified. Here, we use a network model of the brain derived from resting-state functional MRI (rs-fMRI) data and investigate the modular functional architecture of the human brain by analyzing activity at different types of nodes in the network across 9,208 experiments of 77 cognitive tasks in the BrainMap database. Using an author–topic model of cognitive functions, we find a strong spatial correspondence between the cognitive functions and the network’s modules, suggesting that each module performs a discrete cognitive function. Crucially, activity at local nodes within the modules does not increase in tasks that require more cognitive functions, demonstrating the autonomy of modules’ functions. However, connector nodes do exhibit increased activity when more cognitive functions are engaged in a task. Moreover, connector nodes are located where brain activity is associated with many different cognitive functions. Connector nodes potentially play a role in between-module communication that maintains the modular function of the brain. Together, these findings provide a network account of the brain’s modular yet integrated implementation of cognitive functions.

Non-Linear Mechanisms in the Brain

1998 ◽  
Vol 53 (7-8) ◽  
pp. 677-685 ◽  
Author(s):  
Gottfried Mayer-Kress
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
External Noise ◽  
Neuronal System ◽  
Neuron Models ◽  
General Role ◽  
Non Linear ◽  
Spatio Temporal ◽  
The Brain

Abstract Non-linear dynamical models of brain activity can describe the spontaneous emergence of large-scale coherent structures both in a temporal and spatial domain. We discuss a number of discrete time dynamical neuron models that illustrate some of the mechanisms involved. Of special interest is the phenomenon of spatio-temporal stochastic resonance in which co­herent structures emerge as a result of the interaction of the neuronal system with external noise at a given level punitive data. We then discuss the general role of stochastic noise in brain dynamics and how similar concepts can be studied in the context of networks of con­nected brains on the Internet.

scholarly journals Intuitive Cognition-Based Method for Generating Speech Using Hand Gestures

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5291
Author(s):  
Eldad Holdengreber ◽  
Roi Yozevitch ◽  
Vitali Khavkin
Keyword(s):  
Cognitive Process ◽  
Brain Activity ◽  
Hand Movement ◽  
Cognitive Activity ◽  
Depth Camera ◽  
New Approach ◽  
Speech Interface ◽  
Vocal Speech ◽  
The Brain ◽  
Basic Level

Muteness at its various levels is a common disability. Most of the technological solutions to the problem creates vocal speech through the transition from mute languages to vocal acoustic sounds. We present a new approach for creating speech: a technology that does not require prior knowledge of sign language. This technology is based on the most basic level of speech according to the phonetic division into vowels and consonants. The speech itself is expected to be expressed through sensing of the hand movements, as the movements are divided into three rotations: yaw, pitch, and roll. The proposed algorithm converts these rotations through programming to vowels and consonants. For the hand movement sensing, we used a depth camera and standard speakers in order to produce the sounds. The combination of the programmed depth camera and the speakers, together with the cognitive activity of the brain, is integrated into a unique speech interface. Using this interface, the user can develop speech through an intuitive cognitive process in accordance with the ongoing brain activity, similar to the natural use of the vocal cords. Based on the performance of the presented speech interface prototype, it is substantiated that the proposed device could be a solution for those suffering from speech disabilities.

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