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Size Matters

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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 ... – PowerPoint PPT presentation

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Title: Size Matters


1
Size Matters
  • Sara C. Sereno
  • Patrick J. ODonnell
  • 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
Bigger is better
  • Size-value effect (Bruner Goodman, 1947)

5
Bigger is better
  • Size-congruity effects
  • Pavio (1975)
  • Rubinsten Henik (2002)

6
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/male female
  • 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 N28
  • 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
  • N Length Syl Freq Imageability
  • 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 Fugetts (2004) Imageability Norms
  • 90 length-matched pseudowords (e.g., blimble)

11
Materials
BIG SMALL BIG SMALL
BIG SMALL 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 riv
er glass monster diamond
cathedral cigarette train phone stadium
battery submarine butterfly horse video
mansion vitamin
skyscraper fingernail ocean apple
tractor sausage supermarket handkerchief shore rob
in volcano aspirin hippopotamus
hummingbird
12
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 lt 250 ms RTs gt 1500 ms
  • Per subj per cond RTs lt 2SD RTs gt 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, plt.001 tslt1.15,
    psgt.25
  • t2(44)3.29, plt.01

15
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.

16
888888888888888888888888888888
22222222222222222222222
17
Discussion
  • Test potential confound
  • W NW
  • Reverse response mapping
  • left right
  • Subjects N14 (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, plt.05
    tslt1.05, psgt.30
  • t2(44)2.08, plt.05
  • Combined data RT (ms)
  • Big words 513
  • Small words 527
  • F1(1,40)28.12, plt.001
  • F2(1,88)12.72, plt.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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