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Related Concept Videos

State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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Encoding01:19

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Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
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The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
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Control Volume and System Representations01:16

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Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
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Vector Representation of Complex Numbers01:16

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Complex numbers, represented in Cartesian coordinates, can also be visualized as vectors. These vectors can be expressed in polar form, emphasizing their magnitude and angle. When a complex number is input into a function, the output is another complex number, highlighting the function's zero point from which the vector representation can originate.
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Graphical and Analytic Representation of Sinusoids01:20

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Analyzing two sinusoidal voltages with equal amplitude and period but different phases on an oscilloscope, an instrument used to display and analyze waveforms, involves a three-step process.
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Updated: Feb 9, 2026

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis
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Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

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Human midcingulate cortex encodes distributed representations of task progress.

Clay B Holroyd1, José J F Ribas-Fernandes2, Danesh Shahnazian2

  • 1Department of Psychology, University of Victoria, Victoria, BC V8W 2Y2, Canada; holroyd@uvic.ca.

Proceedings of the National Academy of Sciences of the United States of America
|June 6, 2018
PubMed
Summary
This summary is machine-generated.

The midcingulate cortex (MCC) encodes dynamic action sequences, not just single events. This brain region sustains contextual information crucial for distinguishing between task states.

Keywords:
fMRImidcingulate cortexrecurrent neural networkrepresentational similarity analysissequence execution

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Area of Science:

  • Cognitive Neuroscience
  • Neuroimaging
  • Computational Neuroscience

Background:

  • The precise function of the midcingulate cortex (MCC) remains unclear.
  • MCC neurons exhibit diverse responses, and activation is widespread across various tasks.
  • Existing research often overlooks the complexity of MCC's role.

Purpose of the Study:

  • To elucidate the function of the midcingulate cortex (MCC).
  • To investigate how MCC encodes information during goal-directed actions.
  • To explore the dynamic representations within the MCC.

Main Methods:

  • Utilized a model-based cognitive neuroscience approach.
  • Employed neural network simulations and functional magnetic resonance imaging (fMRI).
  • Applied representational similarity analysis (RSA) to analyze neural data.

Main Results:

  • Demonstrated that the human MCC encodes distributed, evolving representations of action sequences.
  • Showed MCC representations are sensitive to the stage and identity of goal-directed actions.
  • Identified MCC's role in sustaining contextual information for task discrimination.

Conclusions:

  • Standard univariate analysis methods may miss significant information processed by the MCC.
  • The MCC plays a critical role in representing and distinguishing complex, sequential behaviors.
  • Future research should focus on dynamic, representational analyses of MCC function.