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Related Concept Videos

Brain Waves01:23

Brain Waves

Brain waves are electrical signals generated by the neurons in the brain, which are regularly monitored to measure mental activities. Brain waves and their frequency ranges can be measured using an electroencephalogram or EEG. There are four main types of brain waves, each with distinct characteristics:
High-Level and Low-Level Awareness01:19

High-Level and Low-Level Awareness

Controlled processes in human consciousness represent high-alert mental states where individuals deliberately focus their attention on achieving specific goals. Controlled processes can be seen in situations like mastering new technology, where a person might become so absorbed that they ignore surrounding distractions. Such processes involve selective attention, requiring one to concentrate on particular elements of experience while disregarding others. These are governed by executive...
Sleep-Wake Cycles01:24

Sleep-Wake Cycles

Sleep is an essential physiological process vital to maintaining overall well-being. The reticular activating system (RAS), a network of neurons in the brainstem, regulates wakefulness and sleep. While it may seem passive, sleep consists of distinct cycles, each with its unique characteristics and functions. Two key sleep phases are non-rapid eye movement (NREM) and  rapid eye movement (REM).
NREM Sleep
NREM sleep comprises four progressive stages that seamlessly merge:

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Related Experiment Video

Updated: May 9, 2026

Evaluation of Hemisphere Lateralization with Bilateral Local Field Potential Recording in Secondary Motor Cortex of Mice
07:03

Evaluation of Hemisphere Lateralization with Bilateral Local Field Potential Recording in Secondary Motor Cortex of Mice

Published on: July 31, 2019

Brain rhythms in mental time travel.

Sean M Polyn1, Per B Sederberg

  • 1Department of Psychology, Vanderbilt University, USA.

Neuroimage
|July 16, 2013
PubMed
Summary

Exploring human memory search, this study links mental time travel to neural oscillations. Retrieved-context theory bridges behavior and brain signals, confirmed by new analytic techniques for memory research.

Area of Science:

  • Cognitive Neuroscience
  • Neuroscience
  • Psychology

Background:

  • Mental time travel, the ability to revisit past experiences, involves reactivating past mental states.
  • Memory search engages widespread neural circuitry, reflected in rhythmic oscillatory brain signals.
  • Retrieved-context theory offers a framework linking memory search behavior to neural dynamics.

Purpose of the Study:

  • To review the cognitive theory of memory search and recent empirical work.
  • To explore how neural activity patterns confirm theoretical predictions.
  • To discuss analytic techniques for integrated neural-behavioral models of memory search.

Main Methods:

  • Review of cognitive theory and computational models.
  • Analysis of topographic patterns in oscillatory neural activity.

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Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice
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Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

Published on: August 2, 2017

Related Experiment Videos

Last Updated: May 9, 2026

Evaluation of Hemisphere Lateralization with Bilateral Local Field Potential Recording in Secondary Motor Cortex of Mice
07:03

Evaluation of Hemisphere Lateralization with Bilateral Local Field Potential Recording in Secondary Motor Cortex of Mice

Published on: July 31, 2019

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice
10:56

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

Published on: August 2, 2017

  • Application of multivariate analytic techniques.

    • Main Results:

    • Recent research confirmed critical predictions of retrieved-context theory.
    • Multivariate analyses of neural activity support the theory's predictions.
    • Established a link between behavioral phenomena and neural signals.

    Conclusions:

    • Retrieved-context theory provides a robust model for understanding memory search.
    • Advanced analytic techniques are crucial for integrating neural and behavioral data.
    • Future research can develop more sophisticated neural-behavioral models of memory search.