Contextual Diversity vs. Word Frequency 1

Running head: CONTEXTUAL DIVERSITY VS. WORD FREQUENCY

Contextual Diversity Not Word Frequency Determines Word Naming and Lexical Decision
Times

James S. Adelman, Gordon D. A. Brown, and José F. Quesada
University of Warwick

Word count: 3,457 words.
Corresponding Author:
James S. Adelman
Department of Psychology,
University of Warwick,
Gibbet Hill Road,COVENTRY,
CV4 7AL,UK.
Telephone: +44 (0) 24 7652 3158
Electronic mail: J.S.Adelman@warwick.ac.uk

Contextual Diversity vs. Word Frequency 2
Abstract
Word frequency is an important predictor of word naming and lexical decision times. It is,however, confounded with contextual diversity, the number of contexts in which a word
has been seen. Using a normative, corpus-based, measure of contextual diversity, word
frequency effects were eliminated by contextual diversity (but not vice versa) across threenaming and three lexical decision datasets, using any of three corpora to derive thefrequency and contextual diversity values. This result is incompatible with existingmodels of visual word recognition, which attribute frequency effects directly to frequency,
and is particularly problematic for accounts in which frequency effects reflect learning. It
is argued that the result reflects the importance of likely need in memory, and that thecontinuity with memory suggests using principles from memory research to informtheorizing about reading.

Contextual Diversity vs. Word Frequency 3
Contextual Diversity Not Word Frequency Determines Word
Naming and Lexical Decision Times

What determines how quickly a word can be read? Empirically, in both word
naming and lexical decision, frequency of occurrence is among the strongest known
factors: Frequent words are read more quickly than infrequent words (Forster &Chambers, 1973; Frederiksen & Kroll, 1976; Balota, Cortese, Sergent-Marshall, Spieler, &Yap, 2004). Thus it appears that repeated experience with or exposure to a particularword makes it more readable or identifiable. A key assumption of theoretical explanationsof the word frequency (WF) effect is that the effect is due to the number of experienceswith a word; each (and every) exposure has a long-term influence on accessibility.
In learning-based accounts of reading, such as connectionist models (e.g., Seidenberg& McClelland, 1989; Plaut, McClelland, Seidenberg, & Patterson, 1996; Zorzi, Houghton,& Butterworth, 1998), learning occurs upon each experience of a word, strengthening theconnections needed to process that word and allowing it to be processed more quickly. In
lexicon-based models, the accessibility of individual lexical entries (words) is governed
directly by frequency, either with thresholds of activation based on WF (e.g., Coltheart,
Rastle, Perry, Langdon, & Ziegler, 2001), or by a serially searched frequency-ranked list
(e.g., Murray & Forster, 2004).
Research on memory, however, has found that the extent to which the number of
repeated exposures to a particular item affects that item’s later retrieval depends upon
the separation of the exposures in time and context (Glenberg, 1976, 1979). Indeed, undersome conditions, if neither changes, there may be no benefit of repetition at all(Verkoeijen, Rikers, & Schmidt, 2004). If the memory for words that subserves word
recognition operates in the same fashion, then the effect of repetitions, that is, word
frequency, will be diminished or abolished when these repetitions occur in the same

Contextual Diversity vs. Word Frequency 4context. Instead, the number of contexts in which a word is experienced, its contextual
diversity (CD), should determine its accessibility and hence response times in word
naming and lexical decision.
A normative measure of a word’s CD may be obtained by counting the number ofpassages (documents) in a corpus that contain that word; such a measure has shown CD
to have effects on recognition memory that are distinguishable from WF effects (Steyvers& Malmberg, 2003). Here we compare the ability of CD and WF to predict six existing
sets of data regarding response times in word naming and lexical decision, basing ouranalyses on CD and WF measures from each of three corpora.
MethodDependent Variables
Item mean RTs from the word naming (reading aloud) and lexical decision (judging
whether the stimulus is a word or not) tasks from six datasets made available by Balota
and colleagues were used. Two of these datasets contain data for the word naming of
2,820 (2,776 analysed here) uninflected one-syllable words by young adults (Spieler &Balota, 1997) and older adults (Balota & Spieler, 1998); a further two contain data on
lexical decision for young and older groups for the same words (Balota, Cortese, & Pilotti,1999); and the last two contain data for young adults on both tasks for a broader selectionof 40,481 (39,383 analysed here) words (Balota et al., 2000).
Independent Variables
Word frequency (number of occurrences) and contextual diversity (number ofpassages/documents in which a word occurs) were calculated from three corpora. Kučera
and Francis (1967) provide these counts for the Brown corpus. This is a samples corpus
containing 500 samples (target length 2000 tokens) from distinct documents spread evenly

Contextual Diversity vs. Word Frequency 5over 15 genres. These have a mean length of 2030 tokens (SD 42). Counts were compiledby the present authors from the 12th grade level portion of the LSA/TASA (Landauer,
Foltz, & Laham, 1998) corpus which is formed from texts used in the compilation of theZeno, Ivens, Millard, and Duvvuri (1995) frequency norms, which are designed to sample
the likely experience of students through the American school system1 . The 28,882
samples from distinct documents in this section of the corpus have a mean length of 286
tokens (SD 25). The authors also compiled counts from the written portion of the BNC(British National Corpus Consortium, 2000). This corpus is designed have the largest
possible samples, ideally whole texts. There are 3144 samples of various forms and lengths
from pamphlets through book chapters to whole issues of newspapers2. The mean numberof tokens in each passage is 26,892 (SD 25,914). Where logarithm and power-law fits arecalculated, all counts are incremented by 1, to avoid problems from zero counts. Items
with zero counts are excluded from the rank analyses.
The following factors from CELEX3 (Baayen, Piepenbrock, & Gulikers, 1995) werecovaried out in the analyses: letter length; orthographic neighborhood size; for themonosyllabic databases only, rime consistency; where applicable, number of syllables; and,for word naming only, initial phoneme.
Results
In the next section of the paper we show that CD predicts word processing times
independently of WF, and, moreover that there is no evidence for a facilitatory effect of
WF independent of CD. Subsequent sections exclude a number of possible explanations of
the results that are inconsistent with our contention that CD per se determines
accessibility, and provide evidence for the validity of the CD measure.

Contextual Diversity vs. Word Frequency 6
Does corpus CD or corpus WF predict word naming and lexical decision times?
Table 1 presents the results of analyses using log-transformations of WF and CD;
log-WF is generally agreed to approximate a linear predictor of naming and lexicaldecision RTs. After variance attributed to covariates, introducing either WF or CD
accounted for significant additional variance (lines two and three of the table give thesesquared semipartial correlations (∆R2 ) after the covariates described above), with high
WF and high CD both being associated with faster RTs. Moreover, the improvement inprediction was always greater for CD than for WF. When the unique effects are examined,
in all 18 analyses there was a unique effect of CD (line five of the table gives squared
semipartial correlations after covariates and log-WF). Six analyses showed a unique effectof WF (line four of the table giving squared semipartial correlations after covariates andlog-CD), all such that high WF led to slow RTs, that is, WF acted as a suppressor
variable. These results suggest not only that CD, rather than WF, best predicts lexicaldecision and word naming times for both young and older participants, but also that WF
does not contribute to such RTs, except insofar as it is correlated with CD and thecovariates.
Since it is the addition of CD to the regression equation that eliminates the uniqueeffect of WF, CD must be a critical component of the confound. However, it need not be
the only component. When only log-WF and log-CD were entered into the equation, there
was always a facilitatory effect of CD, but in some cases there was also a facilitatory effectof WF. The raw correlations between the variables4 suggest that (letter) length is a likely
candidate for a contributor to the confound, as its correlation with log-WF is greater thanwith log-CD. Consistent with this, the analyses summarized in Table 2 with log-WF,
log-CD and length (only) as predictors showed no unique facilitatory effect of log-WF, but
a unique facilitatory effect of log-CD. Moreover, for the critical analyses where log-WF
had appeared to have an effect when length was omitted, there was evidence of a unique

Contextual Diversity vs. Word Frequency 7(inhibitory) effect of length. Figure 1(a) illustrates the semipartial correlations of log-WF
and log-CD with response times on a length-by-length basis for the Elexicon data.
Facilitatory effects of CD are consistently present, but this is not so for WF.
Do semantic variables account for the effect of CD?
Of course, CD may itself be confounded with some variable that has not been
controlled in this analysis. Whilst WF is more strongly subject to effects of structuralvariables, CD seems more likely to be influenced by semantic variables. Ambiguity, forinstance, might be important here, as words with multiple meanings should be used in
multiple contexts. Abstract words are also likely to be used in a larger number ofcontexts. Indeed, Galbraith and Underwood (1973) find that abstract words are rated tohave more different contextual uses than concrete words, and Schwanenflugel and Shoben(1983) find that context availability and diversity of contexts are correlated withconcreteness, and predict lexical decision response times. Imageability is conceptuallyrelated to concreteness, and often substituted for it in experimental designs. We
conducted analyses using the concreteness, imagery and ambiguity norms from Gilhooly
and Logie (1980) for the 1812 words they had in common with the Elexicon database.
The correlations5 between concreteness and CD appeared to be more negative than thosebetween concreteness and WF, although TASA, in general, appeared to be biased towards
more concrete words. Despite this, none of these variables eliminated the CD effect: As
can be seen in Table 3, after these variables’ effects, there remained a significant
facilitatory effect of CD, and none of WF. Also, here the BNC counts accounted for morevariance than the TASA counts, consistent with its being a larger corpus (by tokens); thismay indicate that the apparent advantage for TASA comes from its relationship toimageability and concreteness, not its greater number of passages. High CD is associatedwith faster responses regardless of imageability, concreteness, ambiguity and other factors,

Contextual Diversity vs. Word Frequency 8
and high WF is not.
Can the results be explained away by the high correlation between WF and CD?
The high correlation between log-WF and log-CD might cause concern to somereaders in the context of our regressions, although the inferential logic is unaffected by thecollinearity6 . One way to illustrate that no simple problem exists is to remove thecorrelation, by examining the effect of one variable whilst holding the other constant.
Figure 1(b) shows the effect of log-WF on response times for individual values of CD;
there is little or no evidence for a unique effect of WF. By contrast, Figure 1(c), showing
log-CD effects for individual values of WF, demonstrates a consistent (and necessarily
unique) facilitatory effect. Moreover, all the analyses described in Table 1 give evidence
for a unique effect of CD in the facilitatory direction. Such a pattern would be unlikely
even if Type I errors were occurring at random in every analysis (as the signs should beinconsistent between the analyses in this case).
Nonetheless, the high correlations between measures of WF and CD also raise thepossibility that this result comes about despite WF being the better predictor because
log-CD correlates better (more linearly) with the ‘correct’ transformation of WF thandoes log-WF; both Balota et al. (2004) and Murray and Forster (2004) have foundevidence of nonlinearity in the prediction of latencies in reading from log-WF.
One such possibility is that the rank of a word’s WF is a more linear predictor ofthese RTs. Murray and Forster (2004) provide some evidence that rank-WF is a better
predictor than log-WF from Kučera and Francis (1967) for lexical decision times; this iswhat they predict from their model of lexical access, as it serially searches for lexical
entries in lists that are frequency-ordered. Across the 18 dataset-corpus combinations
examined here, however, rank-WF7 accounted for more variance than log-WF for only
eight, including all six analyses with Kučera and Francis (1967); this count is the least

Contextual Diversity vs. Word Frequency 9reliable estimate of WF, is the least predictive of RTs, has the smallest range of CDvalues, and is most subject to negative bias in the estimation of ranks of low-frequency
words8 . In Table 4, the comparison between rank-WF and rank-CD may be seen for theeight cases where rank-WF accounted for more variance than log-WF. In all eight of theseanalyses there is a unique effect of rank-CD, such that high rank-CD leads to fast
responses. Six of the eight analyses yield a significant unique effect of rank-WF. The threeof these involving monosyllabic data use the K-F frequency count, but for these data
TASA accounts for more variance than K-F, even when ranked, and log TASA accounts
for even more variance. This suggests that K-F counts and a ranking transformation areboth inappropriate here. Moreover, the power transformation discussed below accounts formuch more variance in all cases. Furthermore, rank WF does not in these instances
eliminate a unique effect of rank CD, with the consequence that the resulting regression
formula does not correspond to any simple (or readily interpretable) version of arank-hypothesis serial search model. Moreover, in every case, CD is a stronger predictor
than WF, even when ranked measures are used.
Another possibility is that the power law of practice (e.g., Newell & Rosenbloom,1981) is followed by WF effects in reading (Kirsner & Speelman, 1996). This would meanthat the best transformation of WF is some (negative) power function. The analyses
presented in Table 5 tested the possibility that the advantage of CD over WF would
disappear when both measures underwent a power-law transformation (with the exponent
always as a negative free parameter). Broadly speaking, using this transformation led tolarge increases in the variance accounted for by WF or CD. As can be seen in Table 5, in
17 of the 18 analyses, CD accounted for more residual variance than WF. In all 18
analyses, CD led to a significant increase in R2 based on a relationship such that high CD
was predictive of fast responses, whilst significant increases in R2 from WF were based on
low WF being predictive of fast responses.

Contextual Diversity vs. Word Frequency 10
Is corpus CD just a better indicator of real world WF?
A final possibility that we consider is that these results come about because the CD
measure from a corpus is more correlated with real world WF (the frequency in thelanguage as a whole) than is the WF measure from the same corpus9 . This could occur asa result of WF being more influenced by idiosyncratic properties of individual passages
than is CD, as one obscure word might occur many times in one passage10 , inflating WF
greatly, but CD only slightly.
As an extreme example of this, if words did cluster, but not to differing degrees, thiswould necessarily be the case. Suppose that each word occurred in a particular documentwith a probability proportional to its frequency, and if it did occur, it occurred with equal
probability either once or 25 times. In this scenario, (proportional) real world WF and CD
are the same thing, and (proportional) corpus WF and CD are both unbiased as
estimators of real world WF, but corpus CD has much lower variance11 , because it is not
distorted by low frequency words that by chance occur 25 times in more than half thepassages that they occur in. Consistently different levels of clustering between words arenecessary for CD to be conceptually distinct from WF. The ratio of CD to WF can beused as a clustering index. This index correlates well between the different corpora (K-F
vs. TASA: .362; K-F vs. BNC: .485; TASA vs. BNC: .414, words in Elexicon data),
indicating that much of the clustering here is not idiosyncratic to any particular corpus,
that is, CD is reliable for reasons unrelated to corpus WF.
The preceding does not, however, address the more subtle possibilites; corpus WF
might be biased as an estimate of real world WF due to contextual factors, and corpus CD
more unbiased, leading to its better predictions. One way to approach the question ofwhether corpus CD better reflects real world WF is to use pairs of corpora to see whether(i) WF is consistently predicted better by CD than by WF, and (ii) CD consistently
predicts WF better than CD; either such eventuality would be damaging for the case that

Contextual Diversity vs. Word Frequency 11
there are true CD effects. Examination of the raw correlations does not yield consistentanswers (3 out of 6 pairs are consistent with (i), and 2 out of 6 with (ii)), and hence does
not support the suggestion that CD is consistently acting as a ‘better’ measure of WF,
but nor does this pattern allow us to reject the suggestion. However, similar analyses canbe conducted with randomly chosen halves (half the passages) of each corpus. We
conducted 100 such random splits for each corpus, and investigated the predictions of
log-WF, and the predictions by log-CD. WF is predicted slightly (but highly significantly)
better by WF than CD (K-F: .7816 vs. .7798, SEdiff .00019; TASA: .9340 vs. .9333, SEdiff.00003; BNC: .9721 vs. .9553, SEdiff .00012) , and CD predicts CD somewhat (and highly
significantly) better than it does WF (K-F: .7928 vs. .7812, SEdiff .00019; TASA: .9423
vs. .9338, SEdiff .00003; BNC: .9790 vs. 9549, SEdiff .00011). These results appear to
exclude the possibility that CD is a better indicator of WF than observations of WF itself.
Finally, we used a standard adjustment for clustered sampling of word frequency
estimates, Carroll’s U : This adjusts frequency estimates downwards for words occurring in
few contexts. Table 5 presents the relevant analyses, analogously to Table 1. Essentially,
the same pattern of results obtains: All 18 analyses show a unique facilitatory effect ofCD, and none shows a unique facilitatory effect of adjusted WF (U ), with many showing
unique inhibitory effects.
Discussion
In both word naming and lexical decision contextual diversity was more predictiveof reaction times than word frequency. Moreover, CD had a unique effect such that highCD led to fast responses, whilst WF had no unique effect or a suppressor effect with high
WF leading to slow responses. This implies there is a CD effect, but no facilitatory effectof WF per se. This (i) held even when ambiguity, imagery and concreteness were
controlled, (ii) was not artefactual of the strong correlation between the CD and WF

Contextual Diversity vs. Word Frequency 12variables, and (iii) did not appear to be a result the clustering properties of corpora as CD
did not better predict WF, and the result held even when WF was adjusted for clustering.
According to the rational analysis of memory (Anderson & Milson, 1989; Anderson& Schooler, 1991), number of contexts has an effect because an item occurring in many
contexts is more likely to be needed in any new context, and since different words cluster
within particular contexts to differing degrees, WF is a relatively poor indicator of likely
need. Recently needed items also have high likely need, and recency certainly affects
memory (e.g. Rubin & Wenzel, 1996). Since CD is a good indicator of the probable
recency of an item, it is feasible that recency, and not CD per se, that drives the CD effect.
However, when the recency of items is controlled by introducing recent repetitions, the(apparent) WF effect is diminished, but not abolished (Kirsner & Speelman, 1996; Balota& Spieler, 1999), which would not occur if recency were the key factor in the CD effect.
Previously, attempts to link contextual diversity to lexical decision latencies havealso used local windows of semantic context to derive (information-theoretic entropy)
values based on contextual predictability (McDonald & Shillcock, 2001). Although thisvariable did have an effect distinct from WF, it did not entirely eliminate the WF effect.
Possibly this occurs because temporal, as well as semantic, aspects of context contributeto the CD effect.
Learning-based models of reading cannot accommodate these results without
modifications to learning mechanisms to make them sensitive to context not frequency.
Models of reading that attribute frequency effects to frequency-sensitive units in
dictionary-like lexicons, but do not specify the source of this sensitivity, could be modified
to be sensitive to CD. However, such modifications would seem to violate the principlethat only orthographic forms are stored in the orthographic lexicon, and only phonologicalforms in the phonological lexicon (Coltheart, 2004). By contrast, on a view that readinguses the same kind of memorial resources as recall, the result is natural. The present

Contextual Diversity vs. Word Frequency 13
results motivate a theory of reading based on principles from memory research.

Contextual Diversity vs. Word Frequency 14
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Contextual Diversity vs. Word Frequency 17
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Contextual Diversity vs. Word Frequency 18
Author Note
This work was supported by a Warwick Postgraduate Research Fellowship to JSA
and by grants RES 000221558 and PTA 026270716 from the Economic and Social
Research Council (UK) and grant F/215/AY from the Leverhulme Trust. We thankMarjolein Merkx, Chris Kent, Elizabeth Maylor, Neil Stewart and Matthew Roberts forcomments on this work.

Contextual Diversity vs. Word Frequency 19
Footnotes
1 This corpus is also described at http://lsa.colorado.edu/spaces.html . We use

this grade level (“TASA12”) because frequency computed from it is a better predictor of
RTs than frequency counted across the whole corpus. This probably reflects the fact thatthe full corpus is too heavily weighted toward college-level texts to be representative ofundergraduate participants or education-matched controls.
2 See also http://www.natcorp.ox.ac.uk/what/balance.html .
3 CELEX was not used as a frequency count because the corresponding CD values

were not readily obtainable. Additionally, the base corpus consists of only 243 documents,and so would yield a relatively coarse measure of CD.
4 These have been made available at

http://www.warwick.ac.uk/~psrcaj/cd/correls.html for reasons of space.
5 These are also presented at the web page referenced in Footnote 4.
6 Estimated coefficients are in this context unbiased, but are subject to higher error.

Power is therefore reduced, but Type I error rates are not thereby inflated. The
non-independence of estimates and comparatively high sensitivity to small changes in thedata speak against interpretation of coefficient magnitudes, but allows null-hypothesis
significance testing. The negative effects on power are mitigated by large sample sizes.
7 Our calculations of rank differ somewhat from those of Murray and Forster (2004)

because we do not consider any entries to be ‘spurious’.
8 The relationship between ranks estimated from different corpora is nonlinear.
9 We thank David Balota for highlighting this possibility.
10 This would be especially problematic for longer passages. The BNC is the only

corpus with sizeable variability in passage size. To counter this, each occurrence wasweighted by the reciprocal of the length of the passage, so that each passage gave an equal
contribution the WF count. However, this decreased the correlation with RTs, and theContextual Diversity vs. Word Frequency 20
analyses still favored CD.
11 It is generally the case that CD estimates are more stable: This is why CD

correlates better with itself than WF correlates with itself over split halves of a corpus.

TASA

BNC

K-F

TASA

BNC

K-F

TASA

BNC

Covariates

45.95c45.95c45.95c26.72c26.72c26.72c

.99c

.99c

.99c

log-WF

5.16c

6.73c

5.85c

10.39c13.73c11.74c27.89c38.15c32.60clog-CD

5.35c

6.82c

6.72c

10.90c13.82c13.29c30.05c38.79c37.84clog-WF unique

.00

.00

.07†

.00

.02

.08†

.21b

.00

.45c

log-CD unique

.19b

.09a

.94c

.51c

.11a

1.63c

2.37c

.64c

5.69c

Effect

Data
Corpus

BCP99 Older LDT

Elexicon Naming

Elexicon LDT

TASA

BNC

K-F

TASA

BNC

K-F

TASA

BNC

Covariates

.76c

.76c

.76c

37.24c37.24c37.24c32.04c32.04c32.04clog-WF

22.67c32.42c27.80c8.66c12.66c12.17c14.87c19.66c20.14clog-CD34.63c32.66c32.52c9.07c

12.90c13.12c15.53c20.03c21.05clog-WF unique

.12a

.02

.48c

.00

.00†

.03c

.00

.01a

.00†

log-CD unique

1.76c

.26c

5.20c

.41c

.24c

.98c

.66c

.38c

.91c

EffectNumbers in italics refer to improvement in prediction from an inhibitory effect of word frequency. SB97 refers to Spieler and Balota(1997); BS98 to Balota and Spieler (1998); BCP99 to Balota et al. (1999); and Elexicon to Balota et al. (2000). K-F refers to Kučera
and Francis (1967). TASA refers to Landauer et al. (1998) 12th grade. BNC refers to British National Corpus Consortium (2000).
† p < .1; a p < .05; b p < .01; c p < .001.

Contextual Diversity vs. Word Frequency 21

K-F

Table 1

K-F

Corpus

BCP99 Young LDT

Overall (after covariates) and unique effects (∆R2 in %) of log-transformed Word Frequency

BS98 Older Naming(log-WF) and log-transformed Contextual Diversity (log-CD) from three corpora across six

SB97 Young Naming

datasets.

Data

K-F

TASA

BNC

K-F

TASA

BNC

K-F

TASA

BNC

log-WF (after CD)

.05†

.27b

.27b

.03

3̇1b

.06

.21b

.01

.45c

log-CD (after WF)

.04

.02

.12†

.31b

.00

.79c

2.37c

.70c

5.85c

log-WF (after CD, Length)

.02

.01

.04

.05

.01

.13a

.24b

.01

.55c

log-CD (after WF, Length)

.37c

.16a

.93c

.79c

.13a

1.97c

2.44c

.65c

6.02c

Length (after CD, WF)

12.56c11.35c12.65c

8.82c

7.36c

9.08c

.08a

.00

.07b

Corpus

BCP99 Young LDT

Effect

Data
Corpus

BCP99 Older LDT

Elexicon Naming

Elexicon LDT

TASA

BNC

K-F

TASA

BNC

K-F

TASA

BNC

log-WF (after CD)

.10a

.02

.43c

.02b

.02b

.04c

.05c

.05c

.26c

log-CD (after WF)

1.71c

.28c

5.16c

.41c

.21c

.73c

.53c

.21c

.55c

log-WF (after CD, Length)

.13a

.02

.54c

.04c

.07c

.15c

.01b

.03c

.00

log-CD (after WF, Length)

1.78c

.27c

5.37c

.63c

.49c

1.43c

.78c

.47c

1.14c

Length (after CD, WF)

.11a

.00

.21b

21.36c13.15c19.49c20.36c11.57c17.94cEffectNotation as in Table 1.

Contextual Diversity vs. Word Frequency 22

K-F

Table 2

BS98 Older Naming

Unique effects (∆R2 in %) in analyses of Word Frequency (log-WF), Contextual Diversity

SB97 Young Naming(log-CD) and Length from three corpora across six datasets.

Data

K-F

TASA

BNC

K-F

TASA

BNC

Concreteness

.60c

.77c

.21b

.74c

1.14c

.21b

Imagery

3.10c

1.77c

1.70c

3.18c

1.48c

1.52c

Ambiguity

.14a

.12a

.08†

.30c

.28c

.12a

log-WF

.00

.01

.02

.01

.04

.00

log-CD

.39c

.24b

.86c

.50c

.14a

1.14c

Total

59.11c60.24c60.68c59.63c61.90c62.65cEffectNotation as in Table 1.

Table 3Elexicon LDTContextual Diversity vs. Word Frequency 23

Unique effects of CD, WF and semantic variables, after covariates, Elexicon data for which

CorpusElexicon Naming

semantic variables are available.

DataCorpus

K-F

K-F

K-F

Covariates

46.56c27.20c1.16c

rank-WF

5.04c

10.94c29.24crank-CD

5.09c

11.34c30.77crank-WF unique

.09a

.09a

.12a

rank-CD unique

.14b

.49c

1.65c

BCP99 Older LDT

Elexicon Naming

Elexicon LDT

K-F

K-F

Effect

Data
Corpus

TASA

TASA

K-F

Covariates

.94c

.66c

38.71c36.17c34.32crank-WF

26.07c31.66c8.55c12.10c13.37crank-CD

27.86c32.85c8.85c12.38c13.71crank-WF unique

.04

.05

.10c

.02b

.19c

rank-CD unique

1.85c

1.38c

.40c

.30c

.53c

Notation as in Table 1.

Contextual Diversity vs. Word Frequency 24

EffectTable 4

BCP99 Young LDT

Overall (after covariates) and unique effects (∆R2 in %) of rank Word Frequency (rank-WF)

BS98 Older Naming

and rank Contextual Diversity (rank-CD) for dataset-corpus combinations where rank-WF

SB97 Young Naming

performed better than log-WF.

Data

BNC

K-F

TASA

BNC

K-F

TASA

BNC

pow-WF

5.89c

7.16c

6.33c

11.70c14.50c12.31c33.12c41.35c35.21cpow-CD

5.67c

7.29c

6.74c

11.93c14.76c13.29c35.46c42.49c38.88cpow-WF unique

.21b

.13a

.16b

.60c

.19b

.24c

.40c

2.26c

.92c

pow-CD unique

.19b

.25c

.57c

.82c

.45c

1.23c

1.75c

3.40c

3.59c

log-U

4.84c

6.63c

6.03c

9.71c

13.37c11.92c26.18c38.12c33.71clog-U unique

.08a

.00

.04

.26c

.04

.08†

1.32c

.00

.28c

log-CD unique

.59c

.19c

.73c

1.45c

.49c

1.45c

5.19c

.65c

4.41c

Effect

Data
Corpus

BCP99 Older LDT

Elexicon Naming

Elexicon LDT

TASA

BNC

K-F

TASA

BNC

K-F

TASA

BNC

pow-WF28.04c37.20c31.33c9.78c

14.27c12.27c16.20c20.74c20.21cpow-CD

29.22c38.16c33.16c9.99c

14.61c13.12c16.56c21.20c21.05cpow-WF unique

1.31c

.69c

.18a

.35c

.34c

.25c

.37c

.20c

.24c

pow-CD unique

2.48c

1.65c

2.01c

.58c

.50c

1.10c

.73c

.66c

1.08c

log-U

20.92c31.11c28.58c8.66c12.66c12.17c14.87c19.66c20.14clog-U unique

1.71c

.37c

.40c

.00

.00†

.03c

.00

.01a

.00†

log-CD unique

5.41c

1.93c

4.35c

.41c

.24c

.98c

.66c

.38c

.91c

EffectNotation as in Table 1. Power-law and U analyses are conducted separately. Power-law analyses are after covariates (as presented inTable 1). U analyses are after covariates, and unique log-U effects are after unique log-CD and covariates (as in Table 1).

Contextual Diversity vs. Word Frequency 25

K-F

Table 5TASA

Overall (after covariates) and unique effects (∆R2 in %) of power-law-transformed Word

K-F

Corpus

BCP99 Young LDT

Frequency (pow-WF) and power-law-transformed Contextual Diversity (pow-CD), and log-

BS98 Older Namingtransformed Adjusted Word Frequency (log-U ) and log-transformed Contextual Diversity

SB97 Young Naming(log-CD) from three corpora across six datasets.

Data

Contextual Diversity vs. Word Frequency 26
Figure Captions
Figure 1. Semipartial Correlations of TASA log-WF and log-CD with Response Times in
Word Naming (+) and Lexical Decision (×), Elexicon Data, with orthographic N , numberof syllables, and onset (for naming only) partialled. Points based on fewer than 50 words
are omitted. (a) For each length, effects of WF after CD and covariates (dotted lines), andeffects of CD after WF and covariates (solid lines). (b) For each value of CD, effects of
WF after covariates. (c) For each value of WF, effects of CD after covariates.

Semipartial Correlation with Response Time(a) Effects by Length(b) Effects of WF(c) Effects of CD

0.4

0.2

0.0

−0.2

−0.4
5

10

Length (letters)

15

0

20

40

Contextual Diversity

60

0

20

40

60

Word Frequency

80