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Related Concept Videos

Perception01:28

Perception

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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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Computational evolutionary perception.

Donald D Hoffman1, Manish Singh

  • 1Department of Cognitive Science, University of California, Irvine, CA 92697, USA. ddhoff@uci.edu

Perception
|February 16, 2013
PubMed
Summary
This summary is machine-generated.

Evolutionary pressures favor perceptual systems that enhance fitness over those that accurately describe the world. Computational Evolutionary Perception (CEP) offers a new framework, reinterpreting Bayesian decision theory (BDT) through the lens of fitness, not just truth.

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Last Updated: May 14, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

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Published on: November 2, 2012

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
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Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

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Area of Science:

  • Cognitive Science
  • Evolutionary Biology
  • Computer Vision

Background:

  • Marr's theory posits human vision creates a "true description" of the world.
  • Bayesian decision theory (BDT) is a current probabilistic framework for vision research.
  • Marr's influential work guides contemporary vision studies.

Purpose of the Study:

  • Challenge the assumption that perception prioritizes objective truth.
  • Propose a new framework, Computational Evolutionary Perception (CEP), for studying vision evolution.
  • Reframe BDT within an evolutionary context focused on fitness.

Main Methods:

  • Utilized evolutionary game theory to model perceptual systems.
  • Introduced the Computational Evolutionary Perception (CEP) framework.
  • Demonstrated CEP's ability to subsume and reinterpret BDT.

Main Results:

  • Evolutionary dynamics favor perceptual systems optimized for fitness, not absolute truth.
  • Fitness is context-dependent, varying with organism, state, and action.
  • Natural selection optimizes perceptual systems for fitter behavior, not objective accuracy.

Conclusions:

  • Perceptual systems are shaped by natural selection to maximize fitness, not necessarily to reflect objective reality.
  • Computational Evolutionary Perception (CEP) provides a robust framework for understanding the evolutionary drivers of perception.
  • BDT can be reinterpreted as a measure of expected fitness within the CEP framework.