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Related Concept Videos

Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

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The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
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Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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The auditory system is essential for sound perception, utilizing various critical structures. When sound waves enter the outer ear, they travel through the ear canal and cause the eardrum to vibrate. These vibrations are then transmitted to the middle ear, where three tiny bones – the malleus, incus, and stapes – amplify the sound. This amplification is crucial, as it ensures that the sound vibrations are strong enough to be conveyed to the inner ear. These vibrations then reach the...
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The human ear is not equally sensitive to all frequencies in the audible range. It may perceive sound waves with the same pressure but different frequencies as having different loudness. Moreover, the perception of sound waves depends on the health of an individual's ears, which decays with age. The health of one's ears may also be affected by regular exposure to loud noises.
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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Categorical Perception: A Groundwork for Deep Learning.

Laurent Bonnasse-Gahot1, Jean-Pierre Nadal2

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Deep learning models exhibit categorical perception, similar to humans, where category learning enhances between-category separation and within-category compression. Deeper network layers show stronger categorical effects, influencing how noise impacts representations.

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Categorization is a fundamental cognitive ability, leading to categorical perception in humans and animals.
  • Categorical perception involves within-category compression and between-category separation.
  • Deep learning excels at classification tasks, mirroring cognitive abilities.

Purpose of the Study:

  • To investigate categorical effects in artificial neural networks (ANNs).
  • To analyze the geometry of neural representations in deep learning layers.
  • To understand how category learning influences neural representations and perception-like phenomena in ANNs.

Main Methods:

  • Combined theoretical analysis using mutual and Fisher information with numerical simulations on ANNs.
  • Used psychophysical methods (morphed continua) and introduced a categoricality index to assess neural representations.
  • Analyzed networks of increasing complexity, from shallow to deep architectures.

Main Results:

  • Category learning in ANNs automatically induces categorical perception, characterized by expanded space near category boundaries and contracted space far from them.
  • Deeper layers in neural networks exhibit stronger categorical effects.
  • The geometry of neural representations influences the impact of noise, with implications for regularization techniques like dropout.

Conclusions:

  • Deep learning models demonstrate categorical perception, providing insights into the cognitive process.
  • The depth of a neural network layer correlates with the strength of categorical effects.
  • Understanding the geometry of neural representations is crucial for optimizing deep learning models and interpreting their behavior in relation to biological systems.