Cog5 lecppt chapter03
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Transcript of Cog5 lecppt chapter03
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© 2010 by W. W. Norton & Co., Inc.
Recognizing Objects
Chapter 3Lecture Outline
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Chapter 3: Recognizing Objects
Lecture OutlineForm PerceptionObject RecognitionWord RecognitionFeature NetsDifferent Objects, Different Recognition
Systems?Top-down Influences on Object Recognition
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Recognizing Objects
Why is object recognition important?
Crucial for applying your knowledgeCrucial for learning
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Form Perception
How do we perceive and recognize objects?
Form perception: shape and size Object recognition: identification
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Form Perception
Jerome Bruner Gestalt Psychology
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Form Perception
One set of visual features
Two possible interpretations
But only one can be seen at a time
Necker Cube
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Form Perception
Knowledge can change our interpretation
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Form Perception
People resolve ambiguity in everyday situations
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Form Perception
Your ability to interpret these scenes is governed by a few basic principles
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Form Perception
Good Continuation
Proximity
Similarity
Closure
Simplicity
Single objects
How our mind creates objects
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Form Perception
Parallel Processing
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Form Perception
Simpler to interpret this as one X and not two v’s
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Form Perception
What is this? Hint: The black is the background.
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Form Perception
Proximity, good continuation, closure
Letter and Word Recognition
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Form Perception
Brain areas for basic visual features brain areas for large-scale form
Interactive
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Object Recognition
Now let’s turn from form perception, the process through which the basic shape and size of an object are seen
And discuss object recognition, the process through which the object is identified
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Object Recognition
Can recognize objects even when incomplete
Incomplete information
From the back
From the front
Context helps
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Object Recognition
Same stimulus
H A
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Top-Down Influences on Object Recognition
Bottom-up (or data-driven) processing Stimulus-driven effects
Top-down (or concept-driven) processing Knowledge- or expectation-driven effects
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Object Recognition
Recognition begins with features—the small elements that result from the organized perception of form
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Object Recognition
FeaturesBuilding blocksCommonalities for variable objectsPlay a role in visual search
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Object Recognition
Visual Search Demo
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Object Recognition
Find the vertical line (standing up)
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Object Recognition
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Object Recognition
Find the green-colored line
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Object Recognition
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Object Recognition
Find the vertical red-colored line (standing up)
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Object Recognition
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Object Recognition
Which one was harder?
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Object Recognition
Difficulty in judging how more than one feature is bound together in objects
Integrative agnosia, parietal cortex damage Disruption of parietal cortex via transcranial
magnetic stimulation (TMS)
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Word Recognition
Some methodology for studying word recognition:From tachistoscope to computers
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Word Recognition
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Word Recognition
Masked words Repeated words
40 ms
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Word Recognition
Word-superiority effect: response when asked whether “DARK” has an “E” or a “K” faster than within a letter string such as “JPERW”
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Word Recognition
Better at identifying letters in a word
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Word Recognition
Why word superiority?Probability
How likely is it that letter combinations appear in English?
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Word Recognition
Errors also driven by probabilityLikely to misread words predictably “TPUM” is likely to be misread as “TRUM” or
even “DRUM.”But the reverse errors are rare: “DRUM” is
unlikely to be misread as “TRUM” or “TPUM”
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Feature Nets
Complex
Simple
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Feature Nets
“Neural Network” Have receptive fields Fire above threshold Like complex assemblies of neurons
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Feature Nets
Recent firing = higher starting activation levelFrequency leads to higher recencyRepetition increases recency
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Feature Nets
To explain the word-superiority effect,
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Feature Nets
Stronger baseline activity Better recognition
recover from confusion
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Feature Nets
TH more frequent CA and AT more frequent
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Feature Nets
Stronger baseline activity Will correct recognition
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Feature Nets
Knowledge not locally represented But rather, distributed knowledge
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Feature Nets
Errors arise from the network’s ability to deal with ambiguous inputs and to recover from errors
Accuracy sacrificed for efficiency
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Feature Nets
A much more complex feature net with feedforward and feedback loops
More like a brain
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Feature NetsBuilding blocks for objects
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Feature Nets
Bottom-up recognitionGeon recognition leads to object recognitionViewpoint invariant
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Object Recognition
Geon Demo
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Object Recognition
Write the objects you see
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Feature Nets
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Feature Nets
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Feature Nets
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Feature Nets
Recognition by components
viewpoint independent viewpoint dependent
Whole objects need to be rotated
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Different Objects, Different Recognition Systems?
Some categories are specialFaces
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Different Objects, Different Recognition Systems?
Prosopagnosia is a type of agnosia also known as face blindness
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Different Objects, Different Recognition Systems?
Houses about the same upright and inverted
Faces much worse Inverted and much betterupright
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Different Objects, Different Recognition Systems?
Do these two faces look different?
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Different Objects, Different Recognition Systems?
Do these two faces look different?
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Different Objects, Different Recognition Systems?
Viewpoint dependence appears when Interpreting facesExpertise is high (e.g., dog judges)Specific individuals have to be recognizedConfigurations of component parts are
important
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Different Objects, Different Recognition Systems?
Face Expertise Car Expertise
Bird Expertise
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Different Objects, Different Recognition Systems?
Holistic processingComposite faces
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The Importance of Larger Contexts
Most of the accounts we have covered in this chapter depend on bottom-up processing
However, there is a great deal of knowledge that guides our recognition
Later chapters will discuss this further
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Chapter 2 Questions
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Which of the following is supportive of the claim that perception is in the “eye of the beholder” and not in the stimulus itself:
a) When presented with ambiguous letters, the visual system uses context to determine their identity.
b) A traffic light can be identified even if partially occluded by a tree branch.
c) Whether someone remembers having seen an ambiguous figure (e.g., face-vase) before depends on whether the interpretation of the figure is the same.
d) all of the above
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Which of the following is evidence for a feature theory of perception?
a) The visual system is specialized with cells that detect single features.
b) When researchers are able to stabilize the retinal image for an individual, preventing tiny eye movements (saccades) that refresh the rods and cones, the image stays the same.
c) In visual search paradigms, in which a single target must be found in an array of other items, target identification is faster when it shares features with the distractors.
d) Detecting an embedded figure (including its features) is independent of the way the form is parsed.
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When Betty (an English speaker) is shown strings of letters tachistoscopically, they are overregularized to follow the rules of common English spelling. This is because
a) of the word superiority effect. b) all humans are predisposed toward the
visual configurations evident in “regular” bigrams; this is why English uses them.
c) of a lifetime of strengthening the bigram detectors for common English letter pairs.
d) Betty is reluctant to give answers that she cannot easily pronounce.
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Which of the following methodologies does not measure brain activity or structure?
a) magnetic resonance imaging (MRI)
b) computerized axial tomography (CT)
c) positron emission tomography (PET)
d) transcranial magnetic stimulation (TMS)
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The use of geons is associated with
a) the recognition-by-components (RBC) model.
b) the word superiority effect.
c) visual masking.
d) feature nets.
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The “recognition-via-multiple-views” approach to object recognition is also known as _____ recognition.
a) viewpoint dependent
b) viewpoint independent
c) object
d) face
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Which of the following is the clinical term we use to describe a disturbance in the initiation or organization of voluntary action?
a) aphasia
b) neglect
c) agnosia
d) none of the above