word age-of-acquisition and lexical decision making
TRANSCRIPT
Acta Psychologica 50 (1982) 21-34 North-Holland Publishing Company
21
WORD AGE-OF-ACQUISITION AND LEXICAL DECISION MAKING *
K.J. GILHOOLY and R.H. LOGIE University of Aberdeen, Scotland, UK
Accepted March 1981
This paper reports two experiments concerning the effects of word age-of-acquisition and other word attributes on speed of lexical decision. Analyses of group average data indicated that word length, frequency and familiarity were the major determinants of decision speed. Previous reports of age-of-acquisition effects on lexical decision are attributed to failures to control for word familiarity.
A number of recent studies have reported effects of word age-of-acqui- sition on adult performance in a range of verbal tasks. In particular, age effects have been reported for tasks involving retrieval from lexical memory, such as picture naming (Carroll and White 1973a), category instance naming (Loftus and Suppes 1972) and word completion (Gil- hooly and Gilhooly 1979). In these tasks, earlier acquired words have generally been retrieved more readily than later acquisitions. It should be mentioned at this point that age-of-acquisition has usually been mea- sured by having adults rate words in terms of when they think that they learned the words. Although this procedure may seem implausible, such ratings (e.g. Carroll and White 1973b; Gilhooly and Hay 1977; Gilhooly and Logie 1980) have been found to be reliable. Furthermore, the valid- ity of the ratings has been checked with favourable results, (a) by making comparisons between adults’ ratings and the average age at
* This research was supported by grant HR 5957 from the (United Kingdom) S.S.R.C. Thanks are due to A. Thomassen, A. de Groot and two anonymous referees for helpful comments on an earlier version of this paper.
Requests for reprints should be sent to K.J. Gilhooly, Dept. of Psychology, University of Aberdeen, King’s College, Old Aberdeen AB9 2UB, Scotland, UK.
0001-69 18/82/0000-0000/$02.75 0 1982 North-Holland
22 K.J. Gilhooly, R.H. Logie / Lexical decision making
which children are able to define the words rated on age-of-acquisition and, (b) by comparing the rank order of words in a standard vocabulary test with adult ratings of age-of-acquisition (Gilhooly and Gilhooly 1980). For the remainder if this paper it will be assumed, on the basis of the above considerations, that age-of-acquisition ratings are valid indices of average chronological age-of-acquisition.
The available data on age effects were tentatively interpreted by Gil- hooly and Gilhooly (1979) in terms of an early version of the logogen model (Morton 1969). As a general explanation for age effects in pic- ture naming and other retrieval tasks, it was proposed that internal recognition units or ‘logogens’ corresponding to early acquired words have lower thresholds than logogens corresponding to later acquired words, and so such words become available more readily to any of a range of cues. According to Carroll and White’s ( 1973a) analysis of pos- sible explanations for their picture naming data, the lowered thresholds of early words would not appear to be due to simple word frequency or to total life-span frequency. However the presumed low thresholds of early acquired words might be explained, the general threshold hypoth- esis is attractive in view of the range of retrieval tasks involving pictorial (Carroll and White 1973a, b), semantic (Loftus and Suppes 1972) and graphemic cues (Gilhooly and Gilhooly 1979), in which age-of-acquisi- tion effects have been found. If the threshold interpretation outlined above is correct, then age-of-acquisition effects would be predicted in all lexical access and retrieval tasks.
In the experiments reported here, possible age effects were investi- gated in the lexical decision task. In this task the subject has to decide as rapidly as possible whether a presented letter string is a word or not. On half the trials “yes” is correct. If the non-words are well formed (i.e. are pseudo words) then a “yes” decision has to be based on accessing the lexical entry corresponding to the letter string and a “no” on failure to access such an entry. If “accessing a lexical entry” is taken to mean the same as “activating a logogen to an above threshold level” then the simple logogen explanation for age effects would predict that decisions on early words would be faster than decisions on later acquired words (other factors such as word length and frequency of occurrence in print being controlled for). Some, previous studies (Whaley 1978; Butler and Hains 1979) have obtained results consistent with the hypothesis of age effects. However, the present experiments go beyond these earlier studies in that they involve a number of word attributes, correlated
K.J. Gilhooly, R.H. Logic /Lexical decision making 23
with age-of-acquisition and known to be predictive of lexical decision speed, such as concreteness (James 1975), frequency, length (Whaley 1978) and ambiguity (Rubenstein et al. 1970) that were not all taken into account in previous experiments.
The experiments reported here involved use of multiple regression and partial correlation techniques (Cohen and Cohen 1975), as have a number of recent studies of word attribute effects e.g. Carroll and White 1973a, b; Loftus and Suppes 1972; Butler and Hains 1979; and Gilhooly and Gilhooly 1979. One major reason for using multiple regression methods is the practical difficulty of factorially manipulating the numerous intercorrelated attributes of known or possible relevance to the tasks. Attempts to factorially manipulate more than a very few word attributes quickly lead to designs where the numbers of words per cell are very small and that raises doubts about the generality of any results (as well as reducing the power of the experiments). On the other hand, in a multiple regression study, a large sample of words is normally used. The correlations of the word attributes with each other and with the criterion performance measure are analysed to determine which attributes seem to have the major independent effects in the particular task being investigated. In view of the practical problems of factorially manipulating even a few attributes we opted for the multiple regression approach. The necessary calculations were performed using the Statis- tical Package for the Social Sciences (SPSS) computer program (Nie et
al. 1975). For each of the experiments reported here, a different sample of 100
words was drawn from a list of 1,944 words that had been measured on age-of-acquisition, familiarity, concreteness, ambiguity and frequency (Gilhooly and Logie 1980). Experiment 1 involved a random sample in which the attributes were quite highly intercorrelated. In experiment 2, a technique was developed which resulted in a sample of words with lower intercorrelations between age-of-acquisition and the other attri- butes. This was done with a view to clarifying the results of regression analysis (which are simpler to interpret the less the predictor variables are intercorrelated). This innovation and the other procedures are described more fully below. The prediction being tested in both studies was that age-of-acquisition would affect lexical decision speeds inde- pendently of other word attributes.
24 K.J. Gilhooly, R.H. Logic /Lexical decision making
Experiment 1
Method
Subjects
Ss were 36 student volunteers (17 males, 19 females) attending Aberdeen Univer- sity, who belonged to the Department Subject Panel. Each was paid 50 pence for taking part.
Procedure
Each S was told that either a word or a non-word would appear on each trial and that the task was to classify the stimulus as quickly as possible. Stimuli were pres- ented on a VDU screen to the S one at a time in a random order, under control of a PDP 1 l/34 computer. The order of presentation was different for each S. Timing began from the onset of the stimulus which remained on the screen until the S pushed one of two response buttons. Response was by means of the ‘1’ and ‘9’ keys on the computer keyboard, for word and non-word decisions respectively. After a response, there was a gap of 2 set before the next stimulus item, during which time the VDU screen was blank.
The S started the experiment by pushing any one of the keys and was given 20 practice trials of 10 words and 10 pseudo words in random order before the 200 experimental trials. After 100 trials, the S was allowed a rest. The second 100 trials resumed after the S pressed any of the available keys.
The PDP 1 l/34 computer recorded the latencies and type of response made on each trial.
Word and pseudo word samples
A random sample of 100 words was taken from Gilhooly and Logie’s (1980) list of 1,944 words. The words had been measured on imagery, concreteness, ambiguity, familiarity and age-of-acquisition, using Aberdeen University student samples simi- lar to the S sample in the present experiment. Thorndike-Lorge (1944) frequencies were obtained for each word.
The instructions and general procedure for imagery ratings closely followed Paivio et al. (1968). Words arousing images most readily were to be rated 7 and words arousing images with great difficulty or not at all were to be rated 1. Inter- group reliability was 0.93. Concreteness ratings were obtained on a seven-point scale using instructions similar to those of Spreen and Schulz (1966). Words refer- ring to objects, materials or persons were to receive a high concreteness rating while words referring to abstract concepts that could not be experienced by the senses were to receive a low concreteness rating. Intergroup reliability was 0.96. Age-of- acquisition ratings were obtained following the Carroll and White (1973a) instruc- tions, except that a 7-point, rather than a 9-point scale was used. *he scale ranged from 1 (O-2 years old) to 7 (age 13 and older). Intermediate points on the scale were identified with two-year age bands. Intergroup reliability was 0.98. Familiarity ratings were obtained following Noble’s (1953) general instructions but using a 7-point scale (1 = “never seen, heard or used”, 7 = “seen, heard or used every day”)
K.J. Gilhooly, R.H. Logie /Lexical decision making 25
instead of a S-point scale. Intergroup reliability was 0.96. An information theory measure of meaning ambiguity was derived from the distribution of meanings pro- duced to the words by Ss who gave the first meaning that occurred to them for each word. Intergroup reliability was 0.74. Fuller details on these measures are reported in Gilhooly and Logie (1980). The intercorrelations among the attributes of these words closely matched the pattern for the list as a whole. See table 1 for sum- mary statistics of the word sample.
The pseudo word were made up of 100 letter sequences of various lengths. The sequences were fourth order approximations to English from Hirata and Bryden (197 1). The pseudo words were matched for length with the real words and were pronounceable.
Results
Group average results The average correct decision times per word were obtained (mean = 7 19.11 msec, SD = 182.74 msec). The error rate was 5.5%. The decision times appear to be quite reliable in that when average scores on each word for two subgroups of 18 Ss were correlated a Pearson r of 0.87 was obtained. The decision time distribution was po- sitively skewed and was best corrected by a reciprocal transformation. Subsequent analyses therefore are based on decision speeds rather than simple decision times. Thorndike-Lorge frequencies were transformed to log frequencies to correct for positive skew.
From the correlation matrix (table 1) it can be seen that average decision speed was negatively related to word age-of-acquisition and word length but was posi- tively related to frequency, familiarity and imagery. A number of analyses were car- ried out on the correlation matrix in order to assess the contribution of the inde- pendent variables in predicting the criterion variable of lexical decision speed. First, a stepwise multiple regression was undertaken (table 2). In this type of analysis, variables are added to the regression equation one at a time, on the basis of their contribution to improving the predictive power of the equation. Stepwise regression picked out word frequency as the first variable to enter the equation because it had the strongest simple correlation with lexical decision speed. When the effects of word frequency were partialled from the other variables, word length made the next largest contribution and was added to the equation. When word frequency and word length had been partialled from the remaining variables, rated familiarity emerged as the next most important variable and the stepwise regression continued in this way until all seven predictors had been added to the equation. The signifi- cance of the contribution of each variable was assessed as it entered the equation. On this stepwise analysis, word frequency, length and familiarity emerged as significant factors while age-of-acquisition and imagery made no significant contribution.
Next, a simultaneous multiple regression was carried out (table 3). In this anal- ysis, all variables are entered into the regression simultaneously and the effects of all other variables are partialled out from each variable. This form of analysis places all variables on an equal footing and should be less susceptible to small differences in simple correlations that can have disproportionate effects in stepwise regression
Tab
le
1
Cor
rela
tions
am
ong
mea
sure
s of
wor
d at
trib
utes
an
d de
cisi
on
spee
d,
toge
ther
w
ith
sum
mar
y st
atis
tics.
N
=
100
wor
ds.
(Exp
erim
ent
1.)
Mea
sure
1
2 3
4 5
6 I
8
(1)
Dec
isio
n sp
eed
(2)
Imag
ery
(3)
Age
-of-
acqu
isiti
on
(4)
Fam
iliar
ity
(5)
Con
cret
enes
s
(6)
Am
bigu
ity
(7)
Len
gth
(8)
Log
. Fr
eque
ncy
1.00
0.
50
-0.7
0 0.
67
0.24
0.
29
-0.6
2 0.
71
1 .o
o -0
.73
0.49
0.
80
0.11
-0
.41
0.48
1.00
-0
.73
-0.5
0 -0
.28
0.53
-0
.71
1.00
0.
09
0.16
-0
.21
0.76
1 .o
o 0.
11
-0.3
8 0.
19
1 .o
o -0
.18
0.27
1 .o
o -0
.35
1 .o
o
Mea
n 0.
15
4.65
4.
34
4.68
4.
60
0.31
6.
86
1.28
SD
0.03
1.
08
1.28
1.
10
1.33
0.
45
2.5
1 0.
59
Min
0.
06
1.35
1.
25
1.28
2.
17
0.0
3.00
0.
0
Max
0.
19
6.60
6.
91
6.83
6.
71
1.37
15
.oo
2.00
K.J. Gilhooly, R.H. Logie / Lexical decision making 21
Table 2 Stepwise multiple regression analysis on correct response speeds in lexical decision task. (Exper- iment 1.)
Variable Beta F df on entry R* change R
Log. frequency 0.71 97.18 a 1,98 0.50 0.71 Length -0.42 44.78 a 1,97 0.16 0.81 Familiarity 0.35 16.97 a 1,96 0.05 0.84 Ambiguity 0.09 2.24 1,95 0.01 0.85 Concreteness -0.03 0.20 1,94 0.00 0.85 Imagery 0.16 1.58 1,93 0.00 0.85 Age -0.01 0.00 1,92 0.00 0.85
ap < 0.01
(e.g. affecting the critical ‘order of entry’ of variables into the equation). The results of the simultaneous analysis were essentially the same as those of the step- wise regression. Again, length, frequency and familiarity emerged as the major vari- ables in predicting lexical decision speed.
Finally, we examined various partial correlations involving the variables of par- ticular interest here. When word length alone was taken into account, age, familiar- ity and frequency all had significant partial correlations with the criterion (-0.56. 0.69 and 0.67 respectively). When the effects of word length and frequency (both ‘objective’ variables) were taken into account, familiarity still had a clearly signifi- cant partial correlation with the criterion (0.39) while the partial correlation between age and the criterion fell to a level of borderline significance (-0.21). When the effects of length, frequency and familiarity had all been taken into account, then the partial correlation of age with the criterion shrank to zero (-0.02).
Table 3 Simultaneous multiple regression analysis on correct response speeds in lexical decision task. (Experiment 1).
Variable Beta F df
Log frequency 0.25 1.66 a 1,92 Length -0.43 49.21 a 1,92 Familiarity 0.30 8.87 a 1,92 Ambiguity 0.09 2.70 1,92 Concreteness -0.15 1.70 1,92 Imagery 0.16 1.58 1,92 Age -0.01 0.00 1,92
a p < 0.01
28 K.J. Gilhooly, R.H. Logie / Lexical decision making
The conclusion from these various forms of analysis is that word age-of-acquisi- tion effects in this lexical decision experiment were redundant on those of length, frequency and familiarity. In other words, the apparent effect of age, in the simple correlation matrix, merely reflected the high correlations of age with the effective variables of length, frequency and familiarity. So, the results of this experiment do not support the general logogen (or threshold) hypothesis regarding age effects since that hypothesis predicted age effects independent of more easily explained effects such as those of length, frequency and familiarity.
Experiment 2
Word and pseudo word samples
The 100 word sample used in experiment 1 had been drawn at random from Gil- hooly and Logie’s list of 1,944 words measured on various attributes. The word attributes in the sample were quite highly intercorrelated (as indeed they are in the pool of 1,944). In particular, age-of-acquisition was highly correlated with familiarity, frequency and length. Because of these intercorrelations with other important predictors of lexical decision speed, any separate, possibly small, effects of age might not have emerged in the analysis. So, in order to give any age-of-acqui- sition effects a better chance to emerge we attempted to select words for a new sample in such a way as to reduce the correlation between age and familiarity (and also indirectly to reduce the frequency-age correlation). The desired type of sample was obtained by having a computer draw 10,000 random samples of 100 words from the pool of 1,944 and select that sample with the minimum age-familiarity correlation. The relevant correlation was -0.48 which compared favourably with the value of -0.73 obtained in experiment 1. The words selected gave a good spread of values on the dimensions measured. See table 4 for summary statistics of the word sample. The word measures were as described for experiment 1. The inter- correlations among attributes in this sample were generally slightly lower than in the sample used in experiment 1. It might be said that this technique ‘capitalises on chance’. Indeed, that is the intention. The method used chance to produce a sample in which attribute intercorrelations were reduced, especially the age-famil- iarity and age-frequency intercorrelations. Ideally, one would wish for a sample in which all intercorrelations were zero (while maintaining a good range of values per variable). If such a sample were obtained then the experiment would represent a perfectly orthogonal design. (In factorial experiments, all the independent variables have zero correlation with each other.) When the independent variables are uncorre- lated regression analysis becomes very simple since redundancy effects cannot arise. However, as explained in the general introduction to this paper, perfect orthogonal samples of words cannot be drawn when many correlated variables are being con- sidered. The technique used to draw this second sample, then, produces a compro- mise, “quasi orthogonal” sample in which the independent variables are less corre- lated than they would be in a purely random sample but more correlated than in a purely orthogonal design. Of course the sample is no longer completely random,
Tab
le
4 3
Cor
rela
tions
am
ong
mea
sure
d of
w
ord
attr
ibut
es
and
deci
sion
sp
eed,
to
geth
er
with
su
mm
ary
stat
istic
s.
N
= 10
0 w
ords
. (E
xper
imen
t 2.
) .%
Q
Mea
sure
3
1 2
3 4
5 6
I 8
0 Y Fi.
(1)
Dec
isio
n sp
eed
1.00
0.
39
-0.5
1
0.5
3 0.
23
0.37
-0
.49
0.61
?
(2)
Imag
ery
1.00
-0
.63
0.22
0.
85
0.13
-0
.53
(3)
Age
-of-
acqu
isiti
on
0.23
.a
1.
00
-0.4
8 -0
.50
-0.2
4 0.
59
(4)
Fam
iliar
ity
-0.6
2 6
1.00
0.
02
0.23
-0
.05
(5)
Con
cret
enes
s
0.72
2.
1.00
0.
08
-0.5
0 (6
) A
mbi
guity
0.
05
‘;=
1.00
-0
.22
(7)
Len
gth
0.23
2
1.00
(8
) L
og.
freq
uenc
y -0
.33
$
1.00
%
-$
Mea
n 0.
17
4.78
4.
21
4.83
4.
59
0.29
6.
93
1.24
8.
SD
0.03
1.
04
1.16
0.
86
1.34
0.
39
Min
2.
63
0.64
8’
0.09
1.
59
1.12
2.
14
2.17
0.
0 M
ax
3.00
0.
00
.S
0.22
6.
65
6.91
6.
42
6.69
1.
23
14.0
0 2.
00
%
G
30 K.J. Gilhooly, RN. Logie / Lexical decision making
Table 5
Stepwise multiple regression analysis on correct response speeds in lexical decision task. (Exper-
iment 2.)
Variable Beta F df on entry R* change R
Log. frequency 0.6 1 57.19 a I,98 0.37 0.61
Length -0.33 17.88 a 1,97 0.10 0.68
Familiarity 0.34 9.99 a 1,96 0.05 0.72
Ambiguity 0.16 5.03b 1,95 0.02 0.74
Imagery 0.08 0.89 1,94 0.01 0.74
Age 0.17 1.97 1,93 0.01 0.75
Concreteness 0.10 0.46 1,92 0.00 0.75
a p < 0.01 b p < 0.05
but neither would a sample selected to fit an orthogonal design be completely random.
The pseudo words were constructed from the Hirata and Bryden ( 197 1) tables as in experiment 1.
Subjects
Ss were 18 student volunteers (9 male, 9 female) attending Aberdeen University, Each was paid 50 pence for taking part.
Procedure
Procedure and instructions were identical to those of experiment 1.
Table 6
Simultaneous multiple regression analysis on correct response speeds in lexical decision task.
(Experiment 2.)
Variable Beta F df
Log frequency 0.29 5.78 b 1,92
Length -0.37 14.23 a 1,92
Familiarity 0.29 6.72 b 1,92 Ambiguity 0.17 5.49 b 1,92
Imagery 0.22 2.204 1,92
Age 0.16 1.73 1,92
Concreteness -0.09 0.46 1,92
a p < 0.01 b p < 0.05
K.J. Gilhooly, R.H. Logie / Lexical decision making 31
Results and discussion
Group average results The average correct decision times were obtained for each word (mean = 653.62 msec, SD = 144.26 msec). The decision times were quite reliable. When average scores on each word for two subgroups of 9 Ss were correlated a Pearson r of 0.70 was obtained. The decision time distribution was positively skewed and was satis- factorily corrected by a reciprocal transformation. Subsequent analyses thus are based on decision speeds rather than decision times. The Thorndike-Lorge word fre- quencies were transformed to log frequencies to correct for positive skew. Error rate was 4.4%.
From the correlation matrix (table 4) it can be seen that average decision speed was negatively related to word length and rated age-of-acquisition and was posi- tively related to frequency, familiarity, imagery and ambiguity. On a stepwise regression, frequency, length, familiarity and ambiguity emerged as important fac- tors. See table 5. The same variables also remained significant on a simultaneous regression (table 6). When frequency and length had been taken into account (by prior entry into the equation), age-of-acquisition had a zero partial correlation with decision speed (partial r = -0.01). So, as in experiment 1, these results do not sup- port the general threshold (logogen) hypothesis regarding age effects, which pre- dicted age effects independent of length, frequency and familiarity. The significant factors are as in experiment 1, with the possible addition of word ambiguity - a finding consistent with some previous studies (Rubenstein ef al. 1970).
General discussion
Overall, it appears from both these studies, which used different sam- ples of words and different subjects, that word age-of-acquisition is not an effective factor in lexical decision, compared to length, frequency and familiarity. Even in experiment 2, in which the age-familiarity and age-frequency correlations in the word sample were markedly reduced, no age effects survived either simultaneous or stepwise regression.
The present results will now be compared with those of previous studies. Butler and Hains (1979) obtained significant age effects in lexi- cal decision. Although they had controlled for frequency, familiarity measures were not included in their study. When familiarity is not taken into account in our experiment 1 data, age-of-acquisition does make a significant contribution, (the partial correlation of age with the criterion when only length and frequency were taken into account = -0.21, p < 0.05). So, it is plausible to regard the results of Butler and Hains as due to a failure to control for familiarity. The results from
32 K.J. Gilhooly, R.H. Logie / Lexical decision making
both experiments reported here indicated that the familiarity variable is important and that it makes a contribution separate from frequency. The familiarity measure involves a rating of the frequency with which particular words have been seen, heard or used. Thus, the familiarity measure is broader than the usual “frequency of occurrence in print” measure and could be expected to have effects independent of fre- quency, as it did in both the present studies.
Whaley (1978) found no effect of age on lexical decision times when frequency, letter structure variables and meaningfulness had been taken into account. He did obtain an effect for a compound factor (‘richness of meaning’) composed of imagery, concreteness, age-of-acquisition and meaningfulness scores. However, again, familiarity was not included as a variable in Whaley’s study and would generally be correlated with age, frequency and meaningfulness. Adding familiarity to Whaley’s measures would probably reduce further the already minor independent role of age in his results.
What implications do our results hold for the study and explanation of age-of-acquisition effects? One implication is that both familiarity and frequency should be taken into account when assessing possible age-of-acquisition effects. Reports that have not done so may be mis- leading. Unfortunately, most reports of age effects have not involved suitable controls for both familiarity and frequency. Two experiments which did take familiarity and frequency into account and still reported age effects are Lachman’s (1973) study of picture naming latencies and Gilhooly and Gilhooly’s (1979) word completion study. Both of these ‘positive’ results came from tasks in which word production was required, and in view of that and of the present results it may be that age-of-acquisition effects are localised in lexical output processes as against lexical access processes. If so, we would predict age effects on say, word naming but not on recognition thresholds.
How do our ‘positive’ results on the effects of length, frequency and familiarity fit current views on lexical decision making? They appear to be consistent with the ‘modal’ view (e.g. Seymour 1979, Butler and Hains 1979) that there is an initial stage of graphemic encoding (affected by word length) followed by logogen arousal (affected by word frequency and familiarity). This latter stage may be in turn be fol- lowed by a semantic information retrieval stage (affected by semantic variables such as ambiguity). Although such a stage may not be strictly necessary to task performance, there is evidence of semantic effects,
K.J. Gilhooly, R.H. Logie f Lexical decision making 33
e.g. faster decisions on more concrete words (James 1975). Apart from ambiguity (in experiment 2), we did not obtain any effects for other semantic variables such as concreteness or imagery. However, since it is conceivable for lexical decisions to be made without any semantic retrieval, simply on the basis of logogen activation, semantic effects would not always be expected.
In conclusion, our result are quite consistent with the ‘modal model’ of lexical decision making and, in conjunction with previous findings, suggest that effects due to age-of-acquisition are not found in the lexi- cal access system but may be localized in lexical output processes.
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