EEG Frequency and Phase Coupling during Human Information Processing

2001 ◽  
Vol 40 (02) ◽  
pp. 106-111 ◽  
Author(s):  
P. Rappelsberger ◽  
N. Vath ◽  
S. Weiss ◽  
E. Möller ◽  
G. Grießbach ◽  
...  
Keyword(s):  
Information Processing ◽  
Information Transfer ◽  
Network Activity ◽  
Single Frequency ◽  
Human Information Processing ◽  
Phase Coupling ◽  
Memory Processing ◽  
Neuronal Ensembles ◽  
Non Linear ◽  
Oscillatory Network

AbstractNeuronal activity during information processing is represented by oscillations within local or widespread neuronal networks. These oscillations may be recorded by the EEG (electroencephalogram). The oscillatory interaction between neuronal ensembles may be at one single frequency or at different frequencies due to non-linear coupling. The investigation of momentary coherence and phase enables the examination of synchronized oscillatory network activity during fast-changing cognitive processes. On this basis information transfer from occipital areas towards frontal areas could be described during processing of visual presented words. Non-linear phase coupling between oscillations with different frequencies during memory processing was detected by means of cross-bicoherence.

scholarly journals Amplitude and phase coupling optimize information transfer between brain networks that function at criticality

2021 ◽  
Author(s):  
Arthur-Ervin Avramiea ◽  
Anas Masood ◽  
Huibert D Mansvelder ◽  
Klaus Linkenkaer-Hansen
Keyword(s):  
Information Processing ◽  
Long Range ◽  
Information Transfer ◽  
Distributed Processing ◽  
Brain Networks ◽  
Local Network ◽  
Phase Coupling ◽  
Scale Free ◽  
Functional Connections ◽  
Network Properties

Brain function depends on segregation and integration of information processing in brain networks often separated by long-range anatomical connections. Neuronal oscillations orchestrate such distributed processing through transient amplitude and phase coupling; however, little is known about local network properties facilitating these functional connections. Here, we test whether criticality—a dynamical state characterized by scale-free oscillations—optimizes the capacity of neuronal networks to couple through amplitude or phase, and transfer information. We coupled in silico networks with varying excitatory and inhibitory connectivity, and found that phase coupling emerges at criticality, and that amplitude coupling, as well as information transfer, are maximal when networks are critical. Our data support the idea that criticality is important for local and global information processing and may help explain why brain disorders characterized by local alterations in criticality also exhibit impaired long-range synchrony, even prior to degeneration of physical connections.

Information transformation: Some missing links

2007 ◽  
Vol 26 (3) ◽  
pp. 157-172
Author(s):  
Ivan P. Vaghely ◽  
Pierre-André Julien ◽  
André Cyr
Keyword(s):  
Decision Making ◽  
Grounded Theory ◽  
Information Processing ◽  
Knowledge Creation ◽  
Best Practice ◽  
Participant Observation ◽  
Human Information Processing ◽  
Process Information ◽  
Information Transformation ◽  
Efficient Information

Using grounded theory along with participant observation and interviews the authors explore how individuals in organizations process information. They build a model of human information processing which links the cognitivist-constructionist perspective to an algorithmic-heuristic continuum. They test this model using non-parametric procedures and find interesting results showing links to efficient information processing outcomes such as contributions to decision-making, knowledge-creation and innovation. They also identify some elements of best practice by efficient human information processing individuals whom they call the “information catalysts”.

scholarly journals Information Rate in Humans during Visuomotor Tracking

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 228
Author(s):  
Sze-Ying Lam ◽  
Alexandre Zénon
Keyword(s):  
Information Processing ◽  
Information Transfer ◽  
Cost Benefit ◽  
Cognitive Effort ◽  
Task Demands ◽  
Information Rate ◽  
Present Contribution ◽  
Visuomotor Tracking ◽  
Processing Rate ◽  
Choice Tasks

Previous investigations concluded that the human brain’s information processing rate remains fundamentally constant, irrespective of task demands. However, their conclusion rested in analyses of simple discrete-choice tasks. The present contribution recasts the question of human information rate within the context of visuomotor tasks, which provides a more ecologically relevant arena, albeit a more complex one. We argue that, while predictable aspects of inputs can be encoded virtually free of charge, real-time information transfer should be identified with the processing of surprises. We formalise this intuition by deriving from first principles a decomposition of the total information shared by inputs and outputs into a feedforward, predictive component and a feedback, error-correcting component. We find that the information measured by the feedback component, a proxy for the brain’s information processing rate, scales with the difficulty of the task at hand, in agreement with cost-benefit models of cognitive effort.

BASE RATE NEGLECT FOR THE WEALTH OF INTERACTING PEOPLE

2010 ◽  
Vol 13 (05) ◽  
pp. 607-619 ◽  
Author(s):  
DIEMO URBIG
Keyword(s):  
Information Processing ◽  
Social Learning ◽  
Learning Process ◽  
Base Rate ◽  
Human Information Processing ◽  
Base Rates ◽  
Base Rate Neglect ◽  
Positive Externalities ◽  
Rate Information

Previous research investigating base rate neglect as a bias in human information processing has focused on isolated individuals. This study complements this research by showing that in settings of interacting individuals, especially in settings of social learning, where individuals can learn from one another, base rate neglect can increase a population's welfare. This study further supports the research arguing that a population with members biased by neglecting base rates does not need to perform worse than a population with unbiased members. Adapting the model of social learning suggested by Bikhchandani, Hirshleifer and Welch (The Journal of Political Economy100 (1992) 992–1026) and including base rates that differ from generic cases such as 50–50, conditions are identified that make underweighting base rate information increasing the population's welfare. The base rate neglect can start a social learning process that otherwise had not been started and thus base rate neglect can generate positive externalities improving a population's welfare.

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