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Motor and Sensory Areas of the Cortex01:14

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The cerebral cortex, the brain's outermost layer, is pivotal in processing complex cognitive tasks, emotions, and various sensory inputs and executing voluntary motor activities. This intricate structure is divided into three primary functional areas: the motor areas, sensory areas, and association areas.
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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Neural Circuits01:25

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Visual System01:26

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Association Areas of the Cortex01:21

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Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
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What is a Sensory System?01:31

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Sensory systems detect stimuli—such as light and sound waves—and transduce them into neural signals that can be interpreted by the nervous system. In addition to external stimuli detected by the senses, some sensory systems detect internal stimuli—such as the proprioceptors in muscles and tendons that send feedback about limb position.
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Related Experiment Video

Updated: Apr 9, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Using Artificial Neural Networks to Relate External Sensory Features to Internal Decisional Evidence.

Marshall L Green1, Mingjia Hu2, Rachel N Denison3

  • 1School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA.

Open Mind : Discoveries in Cognitive Science
|April 8, 2026
PubMed
Summary

Perceptual decision-making involves transforming external sensory data into internal evidence. This study reveals a non-linear transformation for orientation, with artificial neural networks (ANNs) modeling this process.

Keywords:
convolutional neural networkorientation discriminationperceptual decision-makingsignal detection

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Psychophysics

Background:

  • Perceptual decision-making theories assume sensory information transforms into internal evidence.
  • The precise mechanism of this external-to-internal sensory transformation remains largely unknown.
  • Understanding this transformation is crucial for deciphering how stimuli are identified.

Purpose of the Study:

  • To investigate the transformation of a specific stimulus feature, orientation, into internal evidence.
  • To determine if this transformation is linear or non-linear.
  • To utilize artificial neural networks (ANNs) as a model to understand this transformation process.

Main Methods:

  • Two experiments were conducted where subjects judged the orientation of Gabor stimuli (clockwise vs. counterclockwise tilt).
  • Stimulus tilt was manipulated using fine-scale and coarse-scale increments.
  • Artificial neural networks (ANNs) were trained on the same orientation discrimination task.

Main Results:

  • Experiment 1 showed a linear increase in sensitivity with fine-scale orientation increments.
  • Experiment 2 revealed minimal sensitivity increase with coarse-scale orientation increments, indicating non-linearity.
  • ANN internal activations mirrored empirical findings, showing increased discriminability with tilt magnitude up to a saturation point.

Conclusions:

  • The transformation of orientation into internal evidence for perceptual decisions is highly non-linear.
  • Artificial neural networks (ANNs) can effectively model and elucidate this external-to-internal sensory transformation.
  • ANNs provide a valuable framework for studying the neural mechanisms underlying perceptual decision-making.