The slow developmental timecourse of real-time spoken word recognition
1
Supplemental Materials
The Slow Developmental Timecourse of Real-Time Spoken Word Recognition
by H. L. Rigler et al., 2015, Developmental Psychology
http://dx.doi.org/10.1037/dev0000044
The slow developmental timecourse of real-time spoken word recognition
2
S1. Stimuli and their properties
Table S1: Summary of auditory stimuli including phonological neighborhood density (number of
words that differ from base word by only one phoneme (Density A), gathered from Sommers,
2015), phonotactic probability (a combination of phoneme frequency and biphone frequency,
from Vitevitch & Luce, 2004), age of acquisition (where available, from Coltheart, 1981), and
Verbal Frequency (from the Brown corpus)
Word
Density
Phonotactic
Probability
AOA
Frequency
Duration (s)
Bag
Ball
Band
Baseball
Bat
Beach
Bead
Bean
Bed
Bees
Bell
Bike
Boat
Bone
Book
Bowl
Box
Broom
Bug
Building
Bus
Bush
Button
Can
Candle
Candy
Cane
Cap
Cat
Chain
Chair
Chin
Chips
Coal
Coat
Comb
Cone
Corn
26
21
13
0
33
14
21
23
21
24
22
18
27
25
17
28
3
7
25
-17
5
5
27
9
6
30
28
30
18
18
20
22
32
26
20
29
14
0.1296
0.2209
0.2481
0.2980
0.1777
0.1082
0.1411
0.1476
0.1184
0.1449
0.1896
0.1509
0.2103
0.2110
0.1613
0.1411
0.1248
0.2103
0.0913
0.2293
0.1522
0.1556
0.3318
0.1641
0.2814
0.2129
0.1785
0.1051
0.1340
0.1464
0.1200
0.0733
0.1176
0.1127
0.1665
0.1241
0.1673
0.1819
217
150
236
-----169
-----214
256
192
--300
-256
192
------311
---278
197
-275
--
10
1
--1
1
--18
1
6
-9
2
108
2
15
--29
5
3
2
501
---1
-2
10
--3
5
----
0.631
0.544
0.679
0.770
0.598
0.625
0.573
0.565
0.451
0.623
0.454
0.494
0.558
0.627
0.524
0.570
0.720
0.581
0.463
0.630
0.562
0.547
0.415
0.598
0.681
0.707
0.605
0.572
0.677
0.630
0.634
0.571
0.701
0.673
0.623
0.673
0.642
0.649
The slow developmental timecourse of real-time spoken word recognition
Dog
Doll
Dress
Foot
Frog
Garden
Gate
Girl
Goat
Hair
Ham
Hammer
Hand
Handle
Hat
Honey
Hook
Horn
Horse
Hose
House
Jam
Key
Kite
Lake
Light
Line
Lips
Lizard
Log
Money
Mouse
Mouth
Mustard
Nail
Nest
Net
Nickel
Peach
Peas
Pen
Phone
Pickle
Picture
Plane
Plate
Pole
Rain
Rake
8
14
6
9
3
1
24
15
20
22
24
4
9
6
31
8
15
11
9
18
6
15
23
19
28
26
28
23
3
12
10
12
6
1
23
13
23
9
18
17
25
23
9
1
6
10
29
28
26
0.1191
0.2103
0.2624
0.2038
0.1701
0.3130
0.1669
0.1717
0.1850
0.1615
0.1493
0.2492
0.2362
0.3134
0.1659
0.2077
0.1495
0.2138
0.2418
0.1819
0.1782
0.1099
0.1247
0.2049
0.1625
0.1668
0.1764
0.1531
0.1883
0.1013
0.2255
0.1960
0.2137
0.3377
0.1387
0.2213
0.1190
0.1664
0.1414
0.1841
0.2097
0.1746
0.2403
0.3154
0.2209
0.2636
0.2218
0.1762
0.1785
169
161
222
-258
186
-183
---278
---286
-308
-314
------275
---247
242
--272
-269
-292
----219
---211
336
8
-1
10
-14
3
44
-11
-2
42
9
3
-2
1
1
-57
1
19
-1
12
36
1
-2
88
2
10
---1
1
--2
1
-24
4
2
-5
--
3
0.608
0.616
0.686
0.726
0.768
0.599
0.578
0.536
0.571
0.540
0.696
0.704
0.811
0.707
0.660
0.479
0.545
0.642
0.770
0.746
0.653
0.680
0.567
0.715
0.683
0.767
0.721
0.767
0.721
0.759
0.693
0.767
0.654
0.786
0.631
0.774
0.866
0.755
0.626
0.814
0.535
0.719
0.582
0.642
0.681
0.787
0.664
0.700
0.579
The slow developmental timecourse of real-time spoken word recognition
Rat
Rope
Rose
Rug
Shower
Skate
Sun
Towel
Tower
Truck
Well
Whistle
Wizard
31
23
22
20
6
10
24
7
8
5
22
3
4
0.1766
0.1510
0.1927
0.0902
0.1981
0.2643
0.2206
0.1582
0.1620
0.1926
0.1587
0.2526
0.1745
-281
-233
342
-181
-353
-----
2
-2
-1
-13
-1
-1752
2
--
4
0.660
0.601
0.784
0.617
0.707
0.759
0.647
0.625
0.640
0.629
0.598
0.629
0.592
The slow developmental timecourse of real-time spoken word recognition
5
S2. Analysis of Timecourse of fixation: All Trials.
In the main text we used non-linear curve-fitting to examine the timecourse of fixations
to each object. These data were based on only the trials in which the participant was not fixating
anything at 300 msec. Here we report the same analysis conducted on all trials regardless of
where the subject was looking at the onset of the auditory stimulus. Table S2 shows the complete
results. We highlight the differences in the findings here.
With respect to the target, the analysis in the main text showed significant effects of age
for both crossover and slope. Here we see only significant effects on the slope; there is no effect
of age on the crossover. We should point out that the overall effect is similar in both cases – 9
year olds are slower to fixate the target than 16 year olds, whether this manifests itself as both
slope and cross over changes or just slopes.
Analyses of the cohort were the same in both this analysis and the one presented in the
main text: 9 year olds showed higher peak heights and shallower offset slopes than 16 year olds.
However, the effects on rhyme fixations were not the same between the two analyses. The
analyses reported in the main text (excluded trials in which the subject is looking at an object at
the onset of the auditory stimulus) showed effects of age for onset slope (p=0.013), midpoint
(p=0.005), peak height (p=.001), and offset slope (p=0.011). The effect of offset baseline was
marginal (p=.075). However, with all trials contributing to the analysis, we see only an effect of
age for rhyme peak heights (p=0.001) and offset baseline (p=0.016). Again, though, the overall
picture is quite similar between the two analyses: 9 year olds fixate rhymes more initially and are
slower to suppress them. Effects on unrelated fixations were the same between the analyses.
It should be pointed out that the effect on the offset baseline for rhymes must be tempered
by the fact that the unrelated were also higher at offset baseline. The consequence of this is that
even as 9 year olds look more at rhymes (late in the timecourse) it is not more than they look at
other objects (as we demonstrated in the main text in the analyses examining rhyme minus
unrelated looking).
Table S2. Parameters describing the situational timecourse of target and competitor fixations
in 9 and 16 year olds including all correct trials. T-tests assume unequal variance.
Number of participants contributing to each group is shown in parenthesis
Target
maximum (p)
crossover (c)
slope (s)
Cohort
onset slope (1)
midpoint ()
peak height (p)
offset slope (2)
offset baseline (b2)
M (SD)
9 y.o. (N=24)
16 y.o (N=18)
0.874 (0.121)
0.895 (0.073)
726 (89)
691 (41)
0.001 (0.0002)
0.002 (0.002)
T (40)
0.66
1.56
2.57
p
0.51
0.13
0.014
D
0.21
0.49
2.33
M (SD)
9 y.o. (N=24)
16 y.o (N=18)
216 (57)
205 (63)
573 (81)
599 (72)
0.244 (0.047)
0.195 (0.053)
298 (102)
205 (45)
0.022 (0.018)
0.015 (0.010)
T (40)
0.59
1.09
3.15
3.58
1.43
p
0.56
0.28
0.003
0.001
0.16
D
0.18
0.34
0.98
1.12
0.45
The slow developmental timecourse of real-time spoken word recognition
Rhyme
onset slope (1)
midpoint ()
peak height (p)
offset slope (2)
offset baseline (b2)
Unrelated
onset slope (1)
midpoint ()
peak height (p)
offset slope (2)
offset baseline (b2)
6
M (SD)
9 y.o. (N=23)
16 y.o. (N=18)
167 (58)
170 (81)
494 (116)
508 (124)
0.184 (0.048)
0.131 (0.049)
357 (140)
294 (92)
0.034 (0.021)
0.020 (0.012)
T (39)
0.14
0.36
3.52
1.64
2.51
p
0.89
0.72
0.001
0.108
0.016
D
0.04
0.11
1.11
0.52
0.78
M (SD)
9 y.o. (N=24)
16 y.o (N=18)
151 (55)
170 (70)
453 (89)
496 (143)
0.117 (0.027)
0.078 (0.032)
312 (82)
239 (94)
0.015 (0.007)
0.009 (0.006)
T (40)
1.018
1.194
4.192
2.676
2.905
p
0.32
0.24
<0.001
0.011
0.006
D
0.32
0.37
1.31
0.84
0.91
The slow developmental timecourse of real-time spoken word recognition
7
S3. Analysis of Curve-fit parameters using ANCOVA
The analyses reported in the main text used simple t-tests to compare curve-fit parameters
as a function of age. However, we were concerned that basic oculomotor differences between 9and 16-year-olds could be driving some of these effects. Thus, effects were replicated using an
ANCOVA in which the fixation rate to both objects (frO) and to nothing (frN) during the prescan period (number of fixations/second) were used as covariates (both centered). The only fixed
effect in these ANCOVAs was age. These analyses were conducted on the same curve-fits
reported in the main text (using only trials on which the participant was fixating an object at the
onset of the auditory stimulus).
The results are shown in Table S3 and we highlight any differences here. In the main text
our analysis of target fixations found a main effect for age on the crossover and slope for the
target curves, but not for maximum looks to target objects. Here, we see both of the significant
effects again, but the maximum looks are now also significant. This suggests that some
difference in maximum looks to the target may have been masked by fixation rate to objects in
the prescan period (fixation rate to nothing was not a significant covariate). Additionally, we see
continued effects of age on target slope and cross-over even in this conservative analysis. This
suggests a strong effect of age that cannot be attributed solely to oculomotor differences and is
also consistent with the analysis presented in Table S2, which utilized looks to objects on all
correct trials, rather than just trials where subjects were looking at non-objects at 300
milliseconds.
The cohort analysis from the main text showed a main effect for age on peak heights and
offset slopes. With this analysis, we also see a main effect of age on cohort peak height, but this
is significant over and above an effect of fixation rate to objects during the prescan period. Also
consistent with the analysis in the paper, we see here a main effect of age on cohort offset slopes.
Thus, the changes we see in cohort fixations cannot be attributed solely to oculomotor
differences.
Again, the analyses are not the same for rhyme fixations. In the simple t-tests reported in
the main text, we found an effect of age on peak height, offset slope, and offset baseline. With
this analysis, we only see an effect of age on offset slope. However the fact that neither covariate
was significant suggests that many of these effects that were significant (without covariates) did
not fall out of significance because of significant oculomotor effects; rather they may be nonsignificant simply due to the high collinearity between the fixation rates and age.
Finally, with this new analysis, we did not find an effect for offset slope or offset baseline
for looks to unrelated objects (as we did in the primary analysis)—the only significant effect of
age was on height.
To sum up, the analyses here largely support the conclusions made in the main text –
even accounting for oculomotor factors, 9 year old participants are slower to build fixations to
the target; they fixate cohorts more at peak and suppress these fixations slower, and they retain
fixations to the rhyme for longer. The most glaring qualitative difference was the now
significantly lower target maximum. This would appear to match the pattern shown by LI
listeners in (McMurray, Samelson, Lee, & Tomblin, 2010). However, it is important to point out
that this effect was seen in none of the other analyses, and was not matched by effects on the
offset baseline for either cohort or rhyme competitors (which were also observed in the LI
study). Thus, there is not strong support for the constellation of late effects that McMurray et al
associate with LI.
The slow developmental timecourse of real-time spoken word recognition
8
Table S3. Results of ANCOVA. Shown is the main effect of age. frN and frO columns show
whether or not that covariate was significant (p<.05)
Target
maximum
crossover
slope
F (1,38)
4.90
7.15
5.69
p
0.033
0.011
0.022
frN
no
yes
no
frO
yes
no
no
onset slope
midpoint
peak height
offset slope
offset baseline
F (1,38)
1.01
0.00
4.49
5.53
0.40
p
0.32
0.99
0.041
0.024
0.53
frN
no
no
no
no
no
frO
no
no
yes
no
no
onset slope
midpoint
peak height
offset slope
offset baseline
F (1,37)
2.561
2.231
2.211
6.532
0.594
p
0.118
0.144
0.146
0.015
0.446
frN
no
no
yes
no
no
frO
no
yes
yes
no
no
F (1,37)
0.81
0.04
13.03
2.33
2.70
p
0.37
0.84
0.001
0.136
0.109
frN
no
no
yes
no
no
frO
no
no
yes
no
no
Cohort
Rhyme
Unrelated
onset slope
midpoint
peak height
offset slope
offset baseline
The slow developmental timecourse of real-time spoken word recognition
9
S4. Analysis of Individual Fixations
The foregoing analysis rely on the overall timecourse of fixation behavior. This measure is a
complex product of many individual saccade and fixation events. This can be a strength in that it
can show overall trends that affect multiple properties of the eye-movement; however, this
measure may have issues in that is also fundamentally a product of averaging together many
different discrete events. Thus, we complemented this analysis with a series of analyses looking
at the properties of individual fixations. All of the analyses below examined only the correct
trials. Analysis examining the first fixation, computed duration and latency from the first
fixation that was initiated after 300 msec (100 msec prior to the onset of the auditory stimuli +
200 msec of oculomotor planning). Duration of unrelated fixations was computed twice: once
for TC and TCRU trials (to compare with cohort durations), and once for TR and TCRU (to
compare with rhymes).
Overall results confirm the findings from the more detailed analysis of the timecourse of
processing. Nine year olds were singificantly slower (by 107 msec) to make their first fixation to
the target than 16 year olds (p<.0001) and marginally slower to fixate the cohort and rhyme
competitors. The first fixations to cohorts and rhymes were also significantly longer (by about
40 msec) in 9 year olds than 16 year olds (both p<=.001). There were also significant
differences in the latency of the first unrelated fixations on these trials. However, across both
groups, cohort and rhyme fixations were significantly longer than unrealted fixations on the same
trial types (Cohort: T(41)=4.9, p<.0001; Rhyme: T(41)=2.3, p=.024). Finally, 9 year olds made
more fixations to all three classes of competitors than 16 year olds (Target, p=.0016;
Cohort/Rhyme: p<.0001), suggesting more switching during the trial and therefore more
uncertainty.
Table S4. Results of analyses on individual fixations
Measure
Latency
(msec)
Duration
(msec)
Fixations /
Trial
1st target fixation
1st cohort fixation
1st Rhyme fixation
1st Cohort Fixation
1st Unrelated fixation
(cohort trials)
1st Rhyme Fixation
1st Unrelated fixation
(rhyme trials)
Target
Cohort
Rhyme
Mean (SD)
9 y.o.
16 y.o.
847 (99)
740 (52)
678 (95)
626 (87)
735 (126) 668 (114)
263 (47)
221 (19)
T(40)
p
4.2
1.8
1.7
3.5
<.0001
.076
.088
.0010
241 (42)
201 (32)
3.4
.0016
250 (44)
216 (24)
2.9
.0062
241 (31)
207 (26)
3.8
.00042
2.90 (.70)
.60 (.18)
.49 (.15)
2.26 (.43)
.40 (.09)
.31 (.08)
3.4
4.6
4.5
.0016
<.0001
<.0001
The slow developmental timecourse of real-time spoken word recognition
10
S5. A comparison of raw language scores as a function of typical development or LI.
Our eye-movement results are highly suggestive that differences in lexical processing that derive
from develpomental changes in language ability may have a different profile than differences
that derive from LI; that is LI may not be simply described as a developmental delay. We should
be careful in drawing too firm a conclusion across these studies as there were subtle differences
in methodology and the subjects were not intentionally matched across studies. However, that
said the perhaps unique nature of the deficit associated with LI is further underscored when we
consider the raw (absolute) ability of the various groups of subjects across these two studies
(Table S5).
In the present study, both ages differed markedly in overall language ability (the raw
scores); clearly between 9 and 16 there is considerable development of both vocabulary and
complex language. Similarly, in McMurray et al. (2010) the TD and LI children also showed
large differences despite being the same age. Yet the profiles appeared quite different. In fact, the
subjects in our study had a larger raw difference in vocabulary and an equivalent difference in
sentence comprehension, than did the LI and TD subjects in in McMurray et al. (2010). Yet these
quantitative differences in raw language ability were associated with very different pattern of
real-time processing when the differences derived from age, than when they derived from LI.
This suggests that differences in absolute language ability can affect the timecourse of lexical
processing differently, depending on the developmental history that gave rise to those language
differences. That is, children who are on a typically developing trajectory but have poorer
language because they are younger show different processing dynamics than children who are
have poorer language due to an impairment.
PPVT
Present Study
Young 166.8 (17.7)
Old 205.2 (8.1)
Diff 38.4
McMurray et al. (2010)
111.8 (10.2) LI
128.9 (12.7) TD
17.2 Diff
CELF
Table S5: Raw PPVT (Vocabulary) and CELF (language) scores from the present study and
McMurray et al., 2010. Results are aligned to treat the young group from this study (on the left
column) as analogous to the LI group (the average of SLI and NLI children) in McMurray et al.,
2010; and to treat the old group from this study as analogous to the TD children (TD + SCI) in
McMurray et al. (2010)
Present Study
Young 166.8 (17.7)
Old 205.2 (8.1)
Diff 38.4
McMurray et al. (2010)
111.8 (10.2) LI
128.9 (12.7) TD
17.2 Diff
The slow developmental timecourse of real-time spoken word recognition
11
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