1
Reading skill and semantic transparency
L. B. Feldman (The University at Albany-SUNY, Haskins Laboratories),
P. Milin (University of Novi Sad, Tubingen University),
F. M. del Prado Martín (University of California - Santa Barbara)
K Cho (The University at Albany-SUNY)
& H. Baayen (Tubingen University)
((( Haskins
Labs)))
National Institute Of Child
Health and Development
Grant HD-01994 to Haskins
Laboratories
2
Semantically Transparent (S+): stem
morpheme’s meaning recurs in complex and
simple words
farmer = farm + er
buzzer = buzz + er
Semantically Opaque (S-): stem morpheme’s
meaning changes in the complex word
corner NOT corn + er
sadist NOT sad + ist
Mapping between form and meaning is graded

3
Exhaustive decomposition: stem + affix
stem occurs with a possible affix
farmer = farm + er
corner = corn + er
ratify = rat + ify
Partial decomposition: stem but no affix
stem occurs in complex form w/ left over letters
cornea NOT corn + ea
rattan NOT rat+ tan
Mapping between form and structure is graded

4
Form-with-meaning:
form and meaning attributes of morphemes
contribute concurrently to processing even at very short
SOAs (34 ms).
facilitation: batter-BAT> battery-BAT=battle-BAT
Form-then-meaning:
initial analysis is morpho-orthographic but
semantically blind. (semantic processing arises later)
facilitation: ratty-RAT = ratify-RAT > rattan-RAT
Two perspectives on early morphological processing
Forward masked priming at 48 ms SOA

5
unrelated
UR
unrelated
opaque
opaqueO
Transparent
transparentT
UR-T 13 ms*
UR-O 5 ms
O-T 8 ms#
Transparent
Transparent
unrelated
Form-with-meaning at 34-100 ms SOAs
Feldman et al., 2012 ms
6
unrelated
UR
unrelated
opaque
opaqueO
Transparent
transparentT
UR-T 13 ms*
UR-O 5 ms
O-T 8 ms#
Transparent
Transparent
unrelated
Form-with-meaning at 34-100 ms SOAs
Feldman et al., 2012 ms
7
Andrews and Lo 2013
SOA 48 ms
Spelling high
low
Vocab
high
low
15
15
Overall, ss show a transparency effect
facilitation: batter-BAT> battery-BAT
Skill influences
early
morphological
processing
8
Andrews and Lo 2013
SOA 48 ms
Spelling
high
low
Vocab
high low
N=15
N=15
Overall, ss show a transparency effect
facilitation: batter-BAT> battery-BAT
Skill influences
early
morphological
processing
15: stronger semantic skill so T-O
differ more than average
15: stronger form skill so UR-O
differ more than average
9
Reading skill and semantic transparency
Q1: Are transparency effects reliable at SOA 34 ms?
Q2: Do vocabulary (meaning) and/or spelling (form) skill
interact with transparency?
Q3: If not, why did we think they did?
10
Targets with semantically transparent and opaque
primes
N = 63

11

12
Forward masked primed lexical decision task
(Forster & Davis, 1984)
Semantically related trial
prime and target overlap
Forward mask:
########
500 ms
batter
Prime: 34 ms
BAT
TARGET: requires lexical
decision; visible until
response.
13
Forward masked primed lexical decision task
(Forster & Davis, 1984)
prime and target overlap
Semantically unrelated trial
########
Forward mask:
500 ms
Prime: 34 ms
TARGET: requires lexical
decision; visible until
response
battery
BAT
14
RT
ERR
ANOVA Mean RT SOA 34 ms
N= 73
ANOVA:
Trans vs. Opaq vs. UR p < .001
Trans > Opaq p = .046
Hi spell
Lo spell
Hi V/Lo Sp
Lo V/ Hi Sp
Trans Opaq
626 639
0.04 0.05
O-T UR-T
13* 28*
18
24
8
32
UR
654
0.05
UR-O
15*
6
24
O-T UR-T
21
33
8
28
UR-O
12
20
15
Trans Opaq
626 639
0.04 0.05
UR
654
0.05
Hi spell
Lo spell
O-T UR-T
18
24
8
32
UR-O
6
24
Hi V/Lo Sp
Lo V/ Hi Sp
O-T UR-T
21
33
8
28
UR-O
12
20
RT
ERR
ANOVA Mean RT: spelling skill
ANOVA
Prime type p < .001
no main effect spelling p < .12
no interaction w/prime type
16
RT
ERR
ANOVA Mean RT: Vocabulary
Hi spell
ANOVA
no main effect vocabulary (vocab) Lo spell
no interaction w/prime type
Hi V
Lo V
Trans Opaq
626 639
0.04 0.05
UR
654
0.05
O-T UR-T
18
24
8
32
UR-O
6
24
O-T UR-T
15
23
11
33
UR-O
8
22
17
ANOVA Mean RT:
High Vocabulary and
low spelling (N=15)
vs.
Low Vocabulary and
high spelling (N=15)
pattern replicates Andrews & Lo
RT
ERR
Hi spell
Lo spell
Hi V/Lo Sp
Lo V/ Hi Sp
Trans Opaq
626 639
0.04 0.05
O-T UR-T
28
13
18
24
8
32
UR
654
0.05
UR-O
15
6
24
O-T UR-T
21
33
8
28
UR-O
12
20
18
Generalized additive model (GAMM)
1. Predict response RT from predictor variables as in general
linear model
2. Works with mixed effect of factors, covariates (numeric
predictors), random effects simultaneously
3. Works with non-linear effects (smooths) and interactions
(tensor products)
-Smooths for ss over trials
-Smooths for items
4. AIC includes penalty as each smooth function is added
(wiggle vs. fit)
5. Can give better fit than other models
19
GAMM analysis:
Principal Component
Analysis
Frequency
HAL
K-F
Form-related
number of orthographic neighbors
number of phonological neighbors
word length
20
fast
slow
Log HAL freq; Log K-F
Differences between
targets (primes ns)
high scores on
PC1 pair longer
words and
words w/
small(er) # of
neighbors
21
RESULTS:
Transparency effect
SOA 34 ms (SE)
GAM N= 73
non-linear interaction of
vocabulary size by spelling
proficiency
… consistent w/ claims of
Andrews and Lo 2013
Prime type
22
Ss vary over trials
Generalized additive mixed model
(GAM) smooths for random byparticipant variation over trials
23
Ss vary over trials
Generalized additive mixed model
(GAM) smoothes for random byparticipant variation over trials.
Inclusion of participant variation over
trials in model renders interaction of
vocabulary size and spelling
proficiency unnecessary!
24
Properly modeled by-participant random variation
leaves little evidence for skill effects
AIC values of goodness-of-fit (small is better)

25
FINAL MODEL: transparency, nonlinear PCs and
ss/trial, target effects but no skill effects
AIC values of goodness-of-fit (small is better)

26
Ramscar, Hendrix, Shaoul, Milin, & Baayen (2014):
"psychometric vocabulary measures are virtually guaranteed
to fail (...) because they attempt to extrapolate vocabulary
sizes from sets of test words that are biased towards frequent
types (Raven, 1965; Heim, 1970; Wechsler, 1997)".
Reliable estimation of vocabulary sizes from small samples is
mathematically impossible (Baayen, 2001).
Individual differences vs. “skill”
effect?

27
34 ms SOA
unrelated
opaque
transparent
Transparent
Transparent
unrelated
Q1: Form-with-meaning at 34-100 ms SOAs

28
34 ms SOA
unrelated
opaque
transparent
Transparent
Transparent
unrelated
Q2: Reading skill plays a minor role when individual 
differences are are modeled
29
Q3: Apparent “skill effect” derives from:
differences between participants within a session
“Interaction” with skill
O-T
Hi V/Lo Sp
21
Lo V/ Hi Sp
8
UR-T
33
28
UR-O
12
20
random smooths for trials
30
Form-with-meaning acct of word
recognition
-Even early in word recognition (34ms)
-Not tied to reading skill
CONCLUSION:
-characteristic of all readers despite
their idiosyncrasies
31
Thank-you
Form-with-meaning
Even early in word recognition
(34ms)
Not tied to reading skill
32
unrelated
UR + Opaque
UR-opaque
Opaque
Transparent
Transparent
Transparent
Transparent

Transparent
Transparent
FORM-WITH-MEANING: EVEN AT SHORT SOAS
Transparent
Form-with-meaning: even at short SOAs
33
Vector representation
of variables in PCs
Frequency
HAL frequency
Brown frequency
Form-related
number of orthographic neighbors
number of phonological neighbors
word length variables
Log HAL freq; Log K-F