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Perception01:28

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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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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Perceiving others accurately is fundamental to effective communication and relationship-building. Social perception, a key concept in social psychology, refers to the cognitive processes through which individuals gather and interpret information about others to understand their actions, intentions, and motivations. This process extends beyond spoken words and overt behaviors, incorporating subtle nonverbal cues and contextual factors.Nonverbal Cues and Their SignificanceNonverbal cues play a...
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A Survey on Probabilistic Models in Human Perception and Machines.

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Summary

This study explores Bayesian probabilistic models for signal processing (SP) in both biological and artificial systems. It highlights commonalities in noise reduction and source separation to foster interdisciplinary convergence.

Keywords:
audiovisual integrationautomatic speech recognitioncausal inferencehuman psychophysicsmultisensory perceptionoptimal cue integrationsignal processingspeech enhancement

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Area of Science:

  • Computational neuroscience
  • Machine learning
  • Signal processing

Background:

  • Extracting information from noisy signals is crucial for biological and artificial perception.
  • Bayesian probabilistic models are powerful tools in human perception and machine signal processing (SP).
  • Limited integration exists between these fields despite analogous problems like noise reduction and source separation.

Purpose of the Study:

  • To compare applications of probabilistic models in machine SP and human psychophysics.
  • To identify commonalities between models used for brain processes and intelligent machines.
  • To foster interdisciplinary convergence between computational neuroscience and artificial intelligence.

Main Methods:

  • Review and comparison of selective applications of probabilistic models.
  • Focus on audio and audio-visual processing.
  • Illustrative examples including speech enhancement, automatic speech recognition, audio-visual cue integration, source separation, and causal inference.

Main Results:

  • Probabilistic models offer a unified framework for tackling signal processing challenges.
  • Significant overlap exists in the application of Bayesian methods across disciplines.
  • Common principles can be identified in how biological and artificial systems process noisy signals.

Conclusions:

  • Commonalities in probabilistic models can bridge the gap between human perception and artificial intelligence.
  • This interdisciplinary approach can lead to more robust and intelligent systems.
  • Further integration of these fields holds significant potential for future research and development.