Perception:
Pattern or object recognition
Chapter 3
Perception
 Sensation vs. perception
 What are the mechanisms responsible?
 What is the process?
 Q: How do we interpret lines and patterns as objects?
 Q: How do we program a computer to perceive objects and scenes?
 Start simple: How do we recognize these letters as A’s?
Template approach
 Stimulus is compared to stored pattern
 Examples?
 Bar code, bank check, scantron, etc.
 Problems:
 There are an infinite number of templates to remember
 Have to learn a template first
 Any change in stimuli will not be recognized
Bottom-up processing
Cell’s
responses
Stimulus
 Receptors in retina -> optic nerve -> occipital lobe (visual cortex)
 Specialized receptors in visual cortex
 Simple cells: feature detectors
 e.g. Orientation specific
 Complex cells
 Combination of 2 simple features
 Perception due to pattern of neural firing (neural code)
McClelland & Rummelhart (1981)
Interactive Activation Model
Pandemonium (Selfridge, 1959)
Visual perception by neurons
 Respond to things that occur most often in environment
 e.g orientation: horizontal and vertical lines vs. oblique
 Experience-dependent plasticity
 Animal reared in certain environment – brain changes to more strongly
respond to those cues (Blakemore & Cooper, 1970)
 Gauthier et al. (1999): “Greebles” study
 Measure FFA (fusiform area)
 IV: experience with Greebles
Recognition by components
 Biederman’s RBC (recognition by component) theory
 36 geons (3D)
 Basic building blocks
 Emphasis on
intersections
 Recognition with missing information possible
Geons:
Identify objects
Principle of
componential recovery
 Resistance to visual
“noise”
 ‘View invariant’
properties
 Discriminability
Biederman’s Geons
 Intersections are important to recognition
Beyond bottom-up processing
Pattern or object recognition
 Bottom-up processing
 Information from sensory receptors
 Processing driven by stimulus
 Data-driven
 Top-down processing
 Information from knowledge and expectations
 Processing driven by higher level knowledge
 Conceptually-driven
 Problems with pure bottom-up theories:
 How does brain pull all the feature information together?
 How do theories deal with complex objects?
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 Context and knowledge fills in the rest!
 The redundancy of stimuli provide more features than required
Oliva & Torralba (2007)
 Q: Does perception depend on more
than just stimulation of receptors?
 Method:
 Use same “blob” in multiple contexts
 Result:
 Perceived as different objects due to top-down processing
 Conclusion:
 Signal from object
 Signal from context
 Feedback signal: influence of knowledge
Theory of perception
 Bottom-up AND top-down
 Bi-directional or connectionist model
 Depth perception
 Relative size
 Size constancy
 Odor intensity
 Controlled sniff intensity
 Perception of language
 Speech segmentation
Treisman & Schmidt (1982)
1
 Q: Does knowledge change perception?
 Method
 Flash display of #s & objects 200 ms
 Ask Ss to report #s then objects
 IV: Give description of objects (“carrot, lake, tire”) or not
 Results
 Info significantly improved accuracy
 Conclusion
 Top-down knowledge changes perception
 Able to “bind” features (group information) together more rapidly
 Orange & triangle = carrot
3
Hollingworth (2005)
 Question
 How does knowledge of what objects
belong in a scene influence perception?
 Semantic regularities (knowledge of function
of objects)
 Method
 Study scene 20s
 IV: w/ or w/o target object
 Test: Place target object in scene
 By memory or expectation
 Result
 Accurate position in both conditions
 Prediction based on experience
Palmer (1975)
 Method
 Present scene
 Ss ID flashed pics
 (a) or (b) or (c)
 IV: type of picture
 DV: accuracy
 Results
 Appropriate pictures: 83%
 Inappropriate pictures: 50%
 Misleading pictures 40%
 Conclusion
 Bottom-up perception interacts with prior knowledge (top-down) to
influence response