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LEXICAL PROCESSING ANOMALIES IN TASK COMPARISONS

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?.

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LEXICAL PROCESSING ANOMALIES IN TASK COMPARISONS

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  1. LEXICAL PROCESSING ANOMALIES IN TASK COMPARISONS Kenneth I. ForsterUniversity of Arizona

  2. “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?

  3. Difference #1Sensitivity to Semantic Effects SC is more sensitive than LD to semantic effects • Frenck-Mestre & Bueno (1999) • strong masked priming effects for exemplars • rifle-pistol whale-dolphin • (prime duration 28 ms) • highly unlikely with LD

  4. Sensitivity to Semantic Effects (cont.) Hector (2002) Associative-semantic Priming for non-exemplars (42 ms prime) Lexical decision Semantic cat. (animal) Related 530 570 Unrelated 529583 -1 13**

  5. L1 L2 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)

  6. Difference #3Insensitivity to Orthographic Effects Neighborhood Density (N) Effects • In lexical decision, • high-N words are faster (debatable) • high-N nonwords are slower (non-debatable) There is no effect for words (Forster & Shen, 1996). However this is also being debated. What happens to nonwords in semantic categorization?

  7. N effects for Nonwords Lexical decision Semantic categorization (Forster & Hector, M&C in press) Neighbors seem to be ignored.

  8. Are neighbors really ignored? Category: Animal GOAN CADEL POTHE loan moan gown goad goat goal cadet camel CANDIDATES SC Times 631 644 607 (Forster & Hector, M&C in press) Only the non-animal neighbors are ignored.

  9. 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.

  10. What does SC have that LD doesn’t? Is it the contextual effect of the category? This may “focus” the semantic activation produced by the prime and the target. Prime Target sense1sense2sense3sense4sense5 sense10sense11sense3sense12sense13

  11. What does SC have that LD doesn’t? Is it the contextual effect of the category? This may “focus” the semantic activation produced by the prime and the target. Prime Target sense1sense2sense3sense4sense5 sense10sense11sense3sense12sense13 CONTEXT

  12. Semantic Focussing Context Filter sense1sense2sense3sense4sense5 word i.e., this is non-interactive

  13. 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

  14. 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…..

  15. 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.

  16. 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

  17. Category Search (cont.) Categories: month, number, body parts, etc. NON-EXEMPLARS: HF REPORT LF TURBAN Results for Non-exemplars HF LF Primed 563 583 573 Control 626 610 618 594 596

  18. Category Search (cont.) Could this be a pre-lexical effect? Try again with a large category. Category: Animal Results for Non-exemplars HF LF Primed 558 579 569 Control 579 602 591 569591

  19. Feature Monitoring Decision maker monitors specific features S P O Neighbors are irrelevant (unless they activate the right features) Nonexemplar decisions are made at semantic level without waiting for network to settle.

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

  21. 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

  22. That’s all. Thank you.

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