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Size Matters

Size Matters. Sara C. Sereno Patrick J. O’Donnell Margaret E. Sereno. Bigger is better. Large vs. small visual object Activation of more neurons Attract attention more easily May hold attention for longer. Bigger is better. Ethology Mate selection (e.g., alpha males)

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Size Matters

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  1. Size Matters Sara C. Sereno Patrick J. O’Donnell Margaret E. Sereno

  2. Bigger is better • Large vs. small visual object • Activation of more neurons • Attract attention more easily • May hold attention for longer

  3. Bigger is better • Ethology • Mate selection (e.g., alpha males) • Supernormal stimulus (Tinbergen & Perdeck, 1950)

  4. > 50 20 Bigger is better • Size-value effect (Bruner & Goodman, 1947)

  5. + + ZEBRA LAMP LAMP + + ZEBRA Bigger is better • Size-congruity effects • Pavio (1975) • Rubinsten & Henik (2002)

  6. 1 9 8 2 Bigger is better • Line bisection with numbers (Fischer, 2001)

  7. Bigger is better • Linguistic markedness • Unmarked = usual, dominant, basic, default form • Marked = (not the above) • Examples: Gender marking: general/malefemale lion, actor lioness, actress Size: How tall is X? How big is y? How wide is z?

  8. Semantic Size • Hypothesis • Words denoting “big” entities are easier to process than those denoting “small” entities. • RTs for semantically “big” words will be faster than those for semantically “small” words.

  9. Lexical Decision Experiment • Subjects: N=28 • 14 female, 14 male • 26 years old • right-handed • Apparatus • Mac G4 using PsyScope 1.2.5 PPC software • 24-pt Courier font (black on white) • 3 characters = 1o vis. angle

  10. Lexical Decision Experiment • Materials • Big/Small defined in relation to human size NLengthSylFreqImageability Big 45 6.20 2.00 24.40 6.08 Small 45 6.20 2.00 23.74 6.07 Frequency BNC (occurrences per million) Imageability MRC Psycholinguistic Database (scale 1-7) Bristol Norms (Stadthagen-Gonzalez & Davis, 2006) Cortese & Fugett’s (2004) Imageability Norms • 90 length-matched pseudowords (e.g., blimble)

  11. Materials BIGSMALLBIGSMALLBIGSMALL bed cup truck thumb buffalo apricot bay fly whale peach gorilla parsley jet lip camel glove giraffe emerald cow pin comet snail mountain magazine park rose moose tulip motorway bacteria tree neck planet button elephant molecule bear ring jungle needle wardrobe sandwich lake nose galaxy insect dinosaur parasite tank tape rocket bullet downtown mosquito bull leaf walrus peanut bookcase teaspoon river glass monster diamond cathedral cigarette train phone stadium battery submarine butterfly horse video mansion vitamin skyscraper fingernail ocean apple tractor sausage supermarket handkerchief shore robin volcano aspirin hippopotamus hummingbird

  12. + string 1000 ms 200 ms 500 ms until response Lexical Decision Experiment • Procedure • Instructed that words represent a selection of several different categories of objects. NW W • Response mapping: left right

  13. Results • Data exclusion • Overall: RTs < 250 ms & RTs > 1500 ms • Per subj per cond: RTs < –2SD & RTs > +2SD • 4.72% data loss

  14. Results RT (ms)%Err Big words 513 (8.6) 1.6 (.3) Small words 527 (9.3) 2.3 (.5) t1(27)=5.22, p<.001 ts<1.15, ps>.25 t2(44)=3.29, p<.01

  15. 4 · 9 9 · 4 Discussion • Potential confound of response mapping: • Spatial markedness • Right is for WORD response, Big or Small. • Spatially, however, Left is marked and Right is unmarked. • Consistency of markedness (Right, Big) confers benefit only to Big words. • SNARC (Daheane et al., 1993) • Spatial Numerical Association of Response Codes • Faster right-sided responses to larger numbers; faster left-sided responses to smaller numbers. • E.g., Which is bigger? vs.

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  17. Discussion • Test potential confound: W NW • Reverse response mapping: left right • Subjects: N=14 (7F,7M), 23 years old • Materials: identical • Procedure: identical • Results: 5% data exclusion

  18. Discussion • Replication: RT (ms)%Err Big words 514 2.4 Small words 527 3.1 t1(13)=2.71, p<.05 ts<1.05, ps>.30 t2(44)=2.08, p<.05 • Combined data: RT (ms) Big words 513 Small words 527 F1(1,40)=28.12, p<.001 F2(1,88)=12.72, p<.001

  19. Discussion • Is size coded in lexical representations? • Yes, for size words and for some like dwarf, giant. • Is size a feature of concrete nouns? • Yes, according to size-congruity studies. • However, these studies use a size comparison task. • Yes, according to current Lexical Decision results. • But, response criteria can still play a role.

  20. Possible Explanation • Larger objects contain more Low SF information. • Low SF is transmitted faster thru magno pathway. • 1o vis cortex & LGN are activated during imagery. • If imagery accompanies word recognition, this information may become available earlier for words referring to larger objects. • Thus, while both Big and Small items can be equally highly imageable, the relative speed of accessing a stored visual representation is faster when the object is bigger.

  21. Conclusion • Need to establish effect in other paradigms: • EM-reading in neutral context. • EM-reading in different contexts (e.g., large ant). • EEG, MEG, fMRI, & WHATNOT. • The Bottom Line…………………..

  22. Bigger is FASTER

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