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Perception. Question of the Day. Why is recognizing an object so easy for humans, but so difficult for computers?. Points of Confusion. Perception:

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Question of the Day

  • Why is recognizing an object so easy for humans, but so difficult for computers?

Points of Confusion


attaching meaning to incoming sensory information

What is


Figure 2-1Distal Stimuli, Proximal Stimuli, and Percepts.

Perceptual Constancies

  • Shape Constancy

    • Tendency to see an object as keeping its form despite changes in orientation.

Perceptual Constancies

  • Size Constancy

    • Tendency to view an object as constant in size despite changes in the size of the retinal image.

Perceptual Constancies

  • Illusions

Gestalt ApproachFigureGround Figure 2-2

Gestalt ApproachSubjectiveContoursFigure 2-3

Gestalt principles of Perceptual Organization

  • Proximity

Gestalt principles of Perceptual Organization

  • Similarity


not this

what most people

would see

Gestalt principles of Perceptual Organization

  • Good continuation


Gestalt principles of Perceptual Organization

  • Closure

Gestalt principles of Perceptual Organization

  • Common fate

Figure 2-5

Gestalt principles of Perceptual Organization

Two Interacting Aspects of Perception

  • Bottom-up processing

    • Processing based on incoming stimuli from the environment

    • Also called data-based processing

  • Top-down processing

    • Processing based on the perceiver’s previous knowledge

    • Also called knowledge-based processing

Bottom-Up Processes

The number “4” from the check is compared to a list of stored templates.



Bottom-Up Processes

Problems with Template Matching

  • Large number of stored templates needed

  • How are new templates made?

  • An object can be “more or less” like the template

    • We can recognize many variations of a template

Bottom-Up Processes

Figure 2-8

Bottom-Up Processes

Featural Analysis

features (“parts”) of a stimulus are recognized by feature detectors and added together to help us perceive an object

  • Lines or edges

  • Geons

  • Phonemes

  • Parts of a face (eyes, nose…)

Featural Analysis


Figure 2-14 A depiction of Selfridge’s (1959) Pandemonium model.





Bottom-Up Processes

Featural Analysis

Feature Properties

  • Detectors can respond at different intensities

  • Connections between detectors can have different strengths

  • It is possible to change what a detector will respond to

Bottom-Up Processes

Prototype Matching

Bottom-Up Processes

Prototype Matching

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