Chapter 3

Perception:

Pattern or object recognition

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

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)

Bottom-up processing

Stimulus

Cell’s

responses

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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To xllxstxatx, I cxn rxplxce xvexy txirx lextex of x

sextexce xitx an x, anx yox stxll xan xanxge xo rxad xt –

ix wixh sxme xifxicxltx

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)

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

1 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