1. Cognitive Processes PSY 334
Chapter 2 – Perception
July 3, 2003

2. Law of Pragnanz
Of all the possible interpretations, we will select the one that yields the simplest or most stable form.
Simple, symmetrical forms are seen more easily.
In compound letters, the larger figure dominates the smaller ones.

3. Visual Illusions Depend on experience.
Influenced by culture.
Illustrate normal perceptual processes.
These are not errors but rather failures of perception in unusual situations.

4. Visual Pattern Recognition
Bottom-up approaches:
Template-matching
Feature analysis
Recognition by components

5. Template-Matching
A retinal image of an object is compared directly to stored patterns (templates).
The object is recognized as the template that gives the best match.
Used by computers to recognize patterns.
Evidence shows human recognition is more flexible than template-matching:
Size, place, orientation, shape, blurred or broken (ambiguous or degraded items easily recognized by people.

6. Feature Analysis Stimuli are combinations of elemental features.
Features are recognized and combined.
Features are like output of edge detectors.
Features are simpler, so problems of orientation, size, etc., can be solved.
Relationships among features are specified to define the pattern.

7. Evidence for Feature Analysis
Confusions – people make more errors when letters presented at brief intervals contain similar features:
G misclassified: as C (21), as O (6), as B (1), as 9 (1)
When a retinal image is held constant, the parts of the object disappear:
Whole features disappear.
The remaining parts form new patterns.

8. Object Recognition Biederman’s recognition-by-components:
Parts of the larger object are recognized as subobjects.
Subobjects are categorized into types of geons – geometric ions.
The larger object is recognized as a pattern formed by combining geons.
Only edges are needed to recognize geons.

9. Tests of Biederman’s Theory
Object recognition should be mediated by recognition of object components.
Two types of degraded figures presented for brief intervals:
Components (geons) missing
Line segments missing
At fast intervals ( ms) subjects could not recognize components when segments were missing.

10. Speech Recognition
The physical speech signal is not broken up into parts that correspond to recognizable units of speech.
Undiminished sound energy at word boundaries – gaps are illusory.
Cessation of speech energy in the middle of words.
Word boundaries cannot be heard in an unfamiliar language.

11. Phoneme Perception No one-to-one letter-to-sound correspondence.
Speech is continuous – phonemes are not discrete (separate) but run together.
Speakers vary in how they produce the same phoneme.
Coarticulation – phonemes overlap.
The sound produced depends on the sound immediately preceding it.

12. Feature Analysis of Speech
Features of phonemes appear to be:
Consonantal feature (consonant vs vowel).
Voicing – do vocal cords vibrate or not.
Place of articulation – where the vocal track is constricted (where is tongue placed).
The phoneme heard by listeners changes as you vary these features.
Sounds with similar features are confused.

13. Categorical Perception
For speech, perception does not change continuously but abruptly at a category boundary.
Categorical perception – failure to perceive gradations among stimuli within a category.
Pairs of [b]’s or [p]’s sound alike despite differing in voice-onset times.

14. Two Views of Categorical Perception
Weak view – stimuli are grouped into recognizable categories.
Strong view – we cannot discriminate among items within such a category.
Massaro – people can discriminate within category but have a bias to same items are the same despite differences.
Category boundaries can be shifted by fatiguing the feature detectors.

15. Top Down Processing
General knowledge (context, high-level thinking) combines with interpretation of low-level perceptual units (features).
Context limits the possibilities so fewer features must be processed:
Word superiority effect – D or K vs WORD or WORK – words do 10% better.
To xllxstxatx, I cxn rxplxce xvexy txirx lextex of x sextexce xitx an x, anx yox stxll xan xanxge xo rxad xt wixh sxme xifxicxltx.

16. Context and Speech Phoneme restoration effect:
It was found that the *eel was on the axle.
It was found that the *eel was on the shoe.
It was found that the *eel was on the orange.
It was found that the *eel was on the table.
The identification of the missing word depends on what happens after it.

17. Faces and Scenes
When parts are presented in isolation, more feature information is needed to recognize them.
Face parts are recognized with less detail when in the context of a face.
Subjects are better able to identify objects when they are part of coherent novel scenes rather than jumbled scenes.

18. Models of Object Perception
Two competing models explain how context and feature information are combined:
Massaro’s FLMP (fuzzy logic model of perception) -- Context and detail are two independent sources of information.
McClelland & Rumelhart’s PDP model – connectionist model in which both sources of information interact.

19. Testing the FLMP Model Four kinds of stimuli:
Only an e can make a real word.
Only a c can make a real word.
Both letters can make a word.
Neither letter can make a word.
Within each group, stimuli go from e to c.
Subjects saw each stimulus word briefly and had to identify the letter, e or c.

20. FLMP Results
Observed frequencies for naming a letter e increase as it has more e features, but also as the context demands an e.
Baye’s theorem gives a formula for combining the independent contributions of two sources of information.
Massaro’s results conform to predictions of Baye’s theorem, suggesting that the information sources must be independent of each other.

21. Testing the PDP Model
Activation spreads from features to excite letters and from letters to excite words (bottom up processing).
Activation also spreads from words to the component letters (top-down processing).
The more activation, the more likely the correct letter will be identified:
TRAP vs TRIP

22. Comparing the Two Models
Subjects heard a phoneme that varied from r to an l in two contexts:
A syllable beginning with t – tr or tl.
A syllable beginning with s – sl or sr.
Both the FLMP and PDP models were compared to actual subject data.
FLMP was close to what subjects did.
PDP was too strongly affected by context.

23. PDP Model Describes More
The PDP model suggests that information is not separately processed but each letter affects each other letter.
Recognition of “a” in MAVE is almost as good as recognizing it in MADE.
This occurs because MAVE is similar to many other words with an A in that position.
We do not have a context but four letters that each influence the others.

24. Marr
Depth cues (texture gradient, stereopsis) – where are edges in space?
How are visual cues combined to form an image with depth?
Primal sketch – extracts features.
2-1/2 D sketch – identifies where visual features are in relation to observer (depth).
3-D model – refers to the representation of the objects in a scene, combines context.

25. Putting it All Together
The output of these stages (see Fig 2.31) is a representation of an object and its location.
This output is used as input to higher-level cognitive processes.
Conscious awareness (a higher-level process) involves the recognition stage, but lots of processing occurs first.