PSY 369: Psycholinguistics

Language Comprehension:Visual perception

Beyond the segment Prosody and intonation

English: Speech is divided into phrases. Word stress is meaningful in English. Stressed syllables are aligned in a fairly regular rhythm, while unstressed syllables take very little time.

Every phrase has a focus. An extended flat or low-rising intonation at the end of a phrase can indicate that a speaker intends to continue to speak.

A falling intonation sounds more final.

Beyond the segment Prosodic factors (supra segmentals)

Stress Emphasis on syllables in sentences

Rate Speed of articulation

Intonation Use of pitch to signify different meanings

across sentences

Beyond the segment Stress effects

On meaning “black bird” versus “blackbird”

Top-down effects on perception Better anticipation of upcoming segments when syllable

is stressed

Beyond the segment Rate effects

How fast you speak has an impact on the speech sounds

Faster talking - shorter vowels, shorter VOT Normalization

Taking speed and speaker information into account

Rate normalization Speaker normalization

Visual language Why so much research using visual language

We do use it Easy to use in research

The parts Letters Words Eye movements (next lecture)

Visual perception of language

Same object category (‘e’) may have different shapes, sizes, and orientations EE E

E

E

EE

EE

E

E

E E

E

E

E

E

E EE

E

E

E

Perhaps the brain is able to represent these objects in a way that is “translationallyinvariant” and “size invariant”.

Invariance a problem in vision too?

Letter Recognition

How do we recognize a group of lines and curves as letters?

Two common explanations: Template matching Feature detection

Template matching Store in brain a copy of what every possible

input will look like. Match observed object to the proper image in

memory Costly: think of all the possible fonts,

handwriting styles etc. Normalization before matching

Perceptual Representation Memory Representations

Template matching

Prolblems with Template matching

Massive numbers of templates are required (remember all those E’s?)..

Predicts no transfer to novel views of the same object

Objects are often obstructed/occluded

E

Feature detection

Analysis-by-synthesis1. Letter broken down to its constituent parts

2. List of parts compared to patterns in memory

3. Best matching pattern chosen

A fixed set of elementary properties are analyzedIndependently and in parallel across visual field.

Possible examples

Line Orientations:

Different Sizes:

Curvature:

+45deg. -10deg.

Free line endings:

Colors:

Feature detection

PerceptualRepresentation

3 Horizontal lines1 Vertical line4 Right angles

MemoryRepresentation

3 Horizontal lines 1 Vertical line 4 Right anglesE

F2 Horizontal lines 1 Vertical line 3 Right angles

A simple theory of Feature detection

Evidence for Features:

The visual search task is straightforward, you are given some target to look for, and asked to simply decide, asquickly as possible, whether the target is present or absentin a set of objects.

For example, let’s try a few searches to give you a feel forthis.

Search 1 - Is there an O present in the following displays?

Is an O present?

T T T T

T OT T T

T T T T TTTT T T T T T TT T T O TTT T TT

TT T TT T T TT TT T TT T TTT T TT

Is an O present?

Q Q QQ Q Q

O Q QQ Q Q Q

Is an O present?

Q QQ Q Q QQQ QQQ Q QQQ Q O QQ QQ Q Q QQ Q QQ Q QQQ Q QQ QQ QQQ Q QQQQ Q

Is an O present?

Is an O present?

T T T T

T OT T T

T T T T TTTT T T T T T TT T T O TTT T TT

TT T TT T T TT TT T TT T TTT T TT

Q Q QQ Q Q

O Q QQ Q Q Q

Q QQ Q Q QQQ QQQ Q QQQ Q O QQ QQ Q Q QQ Q QQ Q QQQ Q QQ QQ QQQ Q QQQQ Q

Another theory of Feature detection

Interactive Activation Model (AIM)

McClelland and Rumelhart, (1981)

Nodes: • (visual) feature• (positional) letter• word detectors

• Inhibitory and excitatory connections between them.

Previous models posed a bottom-up flow of information (from features to letters to words).

IAM also poses a top-down flows of information

Inhibitory connections within levels If the first letter of a word is “a”, it isn’t “b” or “c” or …

Inhibitory and excitatory connections between levels (bottom-up and top-down)

If the first letter is “a” the word could be “apple” or “ant” or …., but not “book” or “church” or……

If there is growing evidence that the word is “apple” that evidence confirms that the first letter is “a”, and not “b”…..

Interactive Activation Model (AIM)

+

Until the participant hits some start key

The Word-Superiority Effect (Reicher, 1969)

COURSE

Presented briefly … say 25 ms

The Word-Superiority Effect (Reicher, 1969)

U &&&&&

A

Mask presented with alternatives above and belowthe target letter … participants must pick one as theletter they believe was presented in that position.

The Word-Superiority Effect (Reicher, 1969)

The Word-Superiority Effect (Reicher, 1969)

+

E E

& T

+

PLANE E

&&&&& T

+

KLANE E

&&&&& T

Letter only Say 60%

Letter in Nonword Say 65%

Letter in Word Say 80%

Why is identification better when a letter is presented in a word?

IAM & the word superiority effect

We are processing at the word and letter levels simultaneously Letters in words benefit from bottom-up and

top-down activation But letters alone receive only bottom-up

activation.

Other Relevant Findings?

.

Bias towards “well-formed” stimuli Bisidentify words with uncommon spelling patterns

BOUT as BOAT misidentify nonwords (e.g., SALID) as words that are like it

(SALAD). Difficulty identifying nonwords with irregular spelling patterns

(e.g., ITPR) more than those with regular spelling patterns (e.g., PIRT).

Sublexical units bigger than phonemes and graphemes? onsets and rimes

onset: initial consonant or consonant cluster in a word or syllable

rime: following vowel and consonants if words broken at onset-rime boundary, resulting letter

clusters more easily recognized as belonging together than if broken at other points

example: FL OST ANK TR

vs. FLA ST NK TRO

Sublexical units

Adding a bigram level

By adding a frequency-sensitive bigram level, we can accountfor the findings of well-formedness along with the others.

Summing up

Based on all of this, we are left with the claim that humanword recognition is based on a feature-detector system thatis biased to perceive common or recently occurring features.

Based on this model, we can make explicit predictions aboutsituations where the system will do well, and others where itwill make errors … thus the system can be further tested andrefined.