LEXICAL PROCESSING ANOMALIES IN TASK COMPARISONS Kenneth I. Forster University of Arizona

“Any genuine lexical effect should be obtained in any task that requires lexical access.” --Anon Is 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 #1 Sensitivity to Semantic Effects SC is more sensitive than LD to semantic effects Frenck-Mestre & Bueno (1999) strong masked priming effects for exemplars rifle-pistolwhale-dolphin (prime duration 28 ms) highly unlikely with LD

Sensitivity to Semantic Effects (cont.) Hector (2002) Associative-semantic Priming for non- exemplars (42 ms prime) Lexical decisionSemantic cat. (animal) Related Unrelated **

Difference #2 Cross-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) L1L2

Difference #3 Insensitivity to Orthographic Effects Neighborhood 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 Semantic categorization (Forster & Hector, M&C in press) Neighbors seem to be ignored.

GOAN loan moan gown goad goat goal CADELPOTHE cadet camel CANDIDATES SC Times (Forster & Hector, M&C in press) Are neighbors really ignored? Only the non-animal neighbors are ignored. Category: Animal

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 activation produced by the prime and the target. Prime sense1 sense2 sense3 sense4 sense5 Target sense10 sense11 sense3 sense12 sense13 Is it the contextual effect of the category?

What does SC have that LD doesn’t? This may “focus” the semantic activation produced by the prime and the target. Prime sense1 sense2 sense3 sense4 sense5 Target sense10 sense11 sense3 sense12 sense13 Is it the contextual effect of the category? CONTEXT

Semantic Focussing sense1 sense2 sense3 sense4 sense5 word Context Filter i.e., this is non-interactive

The Focussing Effect This produces an increase in the proportion of primed senses. This could explain: enhanced L2-L1 translation priming enhanced semantic priming (for exemplars) It could not explain: enhanced semantic priming for non-exemplars absence of N effects

Difference #4 Frequency 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, number, body parts, etc. NON-EXEMPLARS: HFREPORT LFTURBAN HFLF Primed Control Results for Non-exemplars

Category Search (cont.) HFLF Primed Control Results for Non-exemplars Could this be a pre-lexical effect? 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 also predicts no frequency effects for non-exemplars in any category. And, no priming.

Where to next Where to next? 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.