LEXICAL PROCESSING ANOMALIES IN TASK COMPARISONS

Kenneth I. ForsterUniversity of Arizona

“Any genuine lexical effect should be obtained in any task that requires lexical access.”--AnonIs this really true?

We, and others, have encountered surprising differences between lexical decision (LD) and semantic categorization (SC) tasks.

Both tasks clearly involve lexical access.

So, what are the differences?

How are they to be explained?

Difference #1Sensitivity to Semantic Effects

SC is more sensitive than LD to semantic SC is more sensitive than LD to semantic effectseffects

Frenck-Mestre & Bueno (1999)

• strong masked priming effects for exemplars

rifle-pistol whale-dolphin

(prime duration 28 ms)

• highly unlikely with LD

Sensitivity to Semantic Effects (cont.)

Hector (2002)Hector (2002)

Associative-semantic Priming for non-Associative-semantic Priming for non-exemplars exemplars (42 ms prime)(42 ms prime)

Lexical decision Semantic cat. (animal)

Related 530 570

Unrelated 529 583

-1 13**

Difference #2Cross-language Translation Priming

In LD, strong L1-L2 priming, no L2-L1 priming

BUT in SC, priming is symmetric

• Grainger & Frenck-Mestre, 1998

• Finkbeiner, Forster, Nicol & Nakamura (2002)

L1 L2

Difference #3Insensitivity to Orthographic EffectsNeighborhood Density (N) Effects

In lexical decision,

• high-N words are faster (debatable)

• high-N nonwords are slower (non-debatable)

What happens to nonwords in semantic categorization?

There is no effect for words (Forster & Shen, 1996).

However this is also being debated.

N effects for Nonwords

Lexical decision

500520540560580600620640660680700

LD time (ms)

0 1 2 3

Neighborhood Size

500

520

540

560

580

600

620

SC time (ms)

1 2 3

Neighborhood Size

Semantic Semantic categorizationcategorization

(Forster & Hector, M&C in press)

Neighbors seem to be Neighbors seem to be ignored.ignored.

GOAN

loan

moan

gown

goad

goat

goal

CADEL POTHE

cadetcamel

CANDIDATES

SC Times 631 644 607

(Forster & Hector, M&C in press)

Are neighbors really ignored?

Only the non-animal neighbors are Only the non-animal neighbors are ignored.ignored.

Category: Animal

How is this achieved?How is this achieved?

How can you tell which neighbor to evaluate without testing the semantic properties of each?

This should produce a cost for all neighbors.

What does SC have that LD doesn’t?

This may “focus” the semantic This may “focus” the semantic activation produced by the prime activation produced by the prime and the target.and the target.

Prime

sense1sense2sense3sense4sense5

Target

sense10sense11sense3sense12sense13

Is it the contextual effect of the Is it the contextual effect of the category?category?

What does SC have that LD doesn’t?

This may “focus” the semantic This may “focus” the semantic activation produced by the prime activation produced by the prime and the target.and the target.

Prime

sense1sense2sense3sense4sense5

Target

sense10sense11sense3sense12sense13

Is it the contextual effect of the Is it the contextual effect of the category?category?

CONTEXT

Semantic Focussing

sense1sense2sense3sense4sense5

word

Context Filter

i.e., this is non-i.e., this is non-interactiveinteractive

The Focussing Effect

This produces an increase in the proportion of This produces an increase in the proportion of primed senses. primed senses.

This could explain:This could explain: • enhanced L2-L1 translation priming

• enhanced semantic priming (for exemplars)

It could not explain:It could not explain:

• enhanced semantic priming for non-exemplars

• absence of N effects

Difference #4Frequency Effects in SC

Balota & Chumbley (1984)

No frequency effect for non-exemplars in SC

NOT SO

Monsell, Doyle & Haggard (1989)Forster & Shen (1996)

HOWEVER…..

Category Size Effects

The size of the category affects the frequency effect for non-exemplars.

LARGE CATEGORIES SMALL CATEGORIES(animal, living thing) (number, month)

Strong frequency effect No frequency effect

IMPLICATION: “No” decisions for small categories are reached without lexical access.

Category Search

If a category is very small, and well-learned

• “No” decisions can be reached by exhaustive search of the category

• therefore, no frequency effect

• no masked repetition priming

Category Search (cont.)

Categories: month, Categories: month, number, body parts, etc.number, body parts, etc.

NON-EXEMPLARS: HFREPORT

LF TURBAN

HF LF

Primed 563 583 573

Control 626 610 618

594 596

Results for Non-exemplars

Category Search (cont.)

HF LF

Primed 558 579 569

Control 579 602 591

569 591

Results for Non-exemplars

Could this be a pre-lexical Could this be a pre-lexical effect?effect?

Try again with a large category.Try again with a large category.Category: Animal

Feature Monitoring

O

S

P

Decision maker monitors specific features

Nonexemplar decisions are made at semantic level without waiting for network to settle.

Neighbors are irrelevant (unless they activate the right features)

But feature monitoring But feature monitoring also predicts no also predicts no frequency effects for non-frequency effects for non-exemplars in any exemplars in any category.category.

And, no priming.And, no priming.

Where to nextWhere to next?

Current hypothesis:Current hypothesis:with small categories, category search is fast enough for the prime to generate task-relevant output.

Category: number

###### turban TURBAN

tentative “No” output generated

That’s all. Thank you.That’s all. Thank you.