German and English bodies: No evidence for cross-linguistic differences in preferred grain size

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German and English bodies: No evidence for cross-linguistic
differences in preferred grain size
Xenia Schmalz1,2, Serje Robidoux1, Anne Castles1, Max Coltheart1, & Eva Marinus1
1 Department of Cognitive Science, ARC Centre of Excellence in Cognition and its
Disoroders, Macquarie University, Australia.
2 DPSS, University of Padova, Italy.

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Abstract
Previous studies have found that words and nonwords with many body
neighbours (i.e., words with the same orthographic body, e.g., cat, brat, at) are read
faster than items with fewer body neighbours. This body-N effect has been explored
in the context of cross-linguistic differences in reading where it has been reported that
the size of the effect differs as a function of orthographic depth: readers of English, a
deep orthography, show stronger facilitation than readers of German, a shallow
orthography. Such findings support the psycholinguistic grain size theory, which
proposes that readers of English rely on large orthographic units to reduce ambiguity
of print-to-speech correspondences in their orthography. Here we re-examine the
evidence for this pattern and find that there is no reliable evidence for such a cross-
linguistic difference. Re-analysis of a key study (Ziegler et al., 2001), analysis of data
from the English Lexicon Project (Balota et al., 2007), and a large-scale analysis of
nine new experiments all support this conclusion. Using Bayesian analysis
techniques, we find little evidence of the body-N effect in most tasks and conditions.
Where we do find evidence for a body-N effect (lexical decision for nonwords), we
find evidence against an interaction with language.
Keywords: Psycholinguistic grain size theory, failure to replicate, body-rime
correspondences, sublexical processing, Bayes Factor.

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1. Theories of reading across languages
While the majority of research on reading has traditionally come from
English-speaking countries (Share, 2008), a small body of important research has
moved beyond this anglocentricity, and towards theories and models that can be
generalised to orthographies other than English. A question that has attracted a great
deal of attention is the way in which orthographic depth affects reading processes
(Katz & Frost, 1992; Schmalz, Marinus, Coltheart, & Castles, 2015; Ziegler &
Goswami, 2005). Orthographic depth, broadly speaking, can be defined as the degree
of ambiguity in the relationship between print and speech, which varies across
languages. In shallow orthographies, such as Finnish, the relationship between each
grapheme and phoneme is simple and predictable, whereas in deep orthographies,
such as English, knowledge of complex conversion rules and whole words is needed
to achieve high accuracy in reading aloud.
The major problem for children learning to read in a deep orthography is
deriving the pronunciation of unfamiliar words, because the sublexical information of
deep orthographies is, by definition, incomplete, inconsistent, and/or complex (Katz
& Frost, 1992). The psycholinguistic grain size theory (Ziegler & Goswami, 2005)
proposes one possible solution to this problem for the reader: the ambiguity
associated with sublexical information can be reduced by relying on larger sublexical
units and print-to-speech correspondences. In the case of English, linguistic analyses
have shown that reliance on bodies, which consist of the vowel and coda of a
monosyllabic word, reduces the unpredictability of vowel pronunciation (Peereman &
Content, 1998; Treiman, Mullennix, Bijeljac-Babic, & Richmond-Welty, 1995). For
example, the word “talk” cannot be read aloud correctly using grapheme-phoneme
correspondences (which would predict the pronunciation /tælk/), but can be read

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aloud correctly based on the body-rime correspondence “-alk” à /o:k/, as in “walk”
and “stalk”. As a result, the psycholinguistic grain size theory proposes that readers of
deep orthographies such as English develop routine reliance on larger units. In
contrast, readers of shallow orthographies can rely on smaller units, such as letters or
graphemes, and still achieve high reading accuracy.
This main claim of the psycholinguistic grain size theory is intuitively very
appealing. It has been a highly influential theory for explaining the results of cross-
linguistic studies, with over 1000 citations of the Ziegler and Goswami (2005) review
paper (Google Scholar; see Goodwin, August, & Calderon, 2015; Rau, Moll,
Snowling, & Landerl, 2015, for some recent examples). The theory depends critically
on the assumption that there is an interaction between orthographic depth and reliance
on large sublexical units, as we will discuss below. If such an interaction does not
exist, this would suggest that orthographic depth does not affect a reader’s preferred
size of sublexical units. Although several studies have provided evidence for this
claim (discussed in Section 1.2. below), it is important in psychological science to
make sure that experimental findings are replicable (Earp & Trafimow, 2015;
Ioannidis, 2005). Specifically, the current paper was motivated by several failures in
our lab to find evidence for differential reliance on bodies in cross-linguistic
comparisons of English and German readers (see analyses and results of Section 3).
Our main aim in this paper is to determine to what extent the existing evidence
for the psycholinguistic grain size theory is compatible with the view that there is no
cross-linguistic difference in the reliance on bodies, over the alternative hypothesis of
a real population difference. If the existing and new evidence do not support the main
prediction of the psycholinguistic grain size theory, one needs to reconsider whether
there are any alternative predictions that could be used to support the psycholinguistic

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grain size theory, or whether other theories of reading across languages have stronger
explanatory power given the available data (see Section 4.3).
1.1. What counts as evidence for the psycholinguistic grain size theory?
Before evaluating the existing evidence for the psycholinguistic grain size
theory, it is important to consider what kind of evidence can directly support it. Its
explicit prediction is that the deeper the orthography of a language, the more its
readers should rely on sublexical units that are larger than letters or graphemes. Here,
we define a sublexical orthographic unit as one that is not directly linked to lexical or
semantic information (i.e., whole words and morphemes do not count as sublexical
units). As a reflection of the importance of this prediction, all five studies reporting
evidence for the psycholinguistic grain size theory include a manipulation to measure
the reliance on bodies (discussed in detail in Section 1.2; Goswami, Gombert, & de
Barrera, 1998; Goswami, Porpodas, & Wheelwright, 1997; Goswami, Ziegler, Dalton,
& Schneider, 2003; Ziegler, Perry, Jacobs, & Braun, 2001; Ziegler, Perry, Ma-Wyatt,
Ladner, & Schulte-Körne, 2003).
In addition to the sublexical-unit-size manipulations, Ziegler and colleagues
interpret stronger length effects in German than English as support for the
psycholinguistic grain size theory (Ziegler et al., 2001; Ziegler et al., 2003). Length
effects reflect the finding that words or nonwords with more letters are processed
more slowly than words or nonwords with fewer letters (New, Ferrand, Pallier, &
Brysbaert, 2006; Weekes, 1997). Such effects are proposed to be a marker of
sublexical decoding using small units, as the number of letters should matter if the
system relies on a letter-by-letter processing strategy (Weekes, 1997). Given that the
psycholinguistic grain size theory predicts that readers of shallow orthographies rely
to a lesser extent on large units (such as bodies) and to a greater extent on small units

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(such as letters or graphemes), an increased length effect in a shallow orthography is
consistent with the psycholinguistic grain size theory. However, this prediction is
shared with another theory of reading across languages, namely the orthographic
depth hypothesis (Katz & Frost, 1992). According to this hypothesis, the nature of the
sublexical correspondences in deep orthographies, by definition, impedes the process
of sublexical decoding. This leads to relatively greater reliance on lexical processes in
deep compared to shallow orthographies. Consequently, readers of shallow
orthographies should exhibit relatively stronger reliance on sublexical processing,
which would also manifest as stronger length effects in shallow than deep
orthographies.
To test the prediction that there is stronger reliance on lexical than sublexical
processes for deeper compared to shallow orthographies, one can use the frequency
effect as a marker of lexical processing (Frost, 1994; Frost, Katz, & Bentin, 1987).
Words with a high frequency are typically reported to be read faster than words with a
low frequency. This is proposed to reflect a lower activation threshold for lexical
entries of high-frequency words (Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001).
Stronger reliance on lexical processing in deep compared to shallow orthographies,
again is a shared prediction of the orthographic depth hypothesis and the
psycholinguistic grain size theory. According to the former, this should be driven by
the slow-down of the sublexical route in deep orthographies. According to the latter,
this would reflect the general notion that readers of deep orthographies rely on larger
units (with whole words listed at the top of Ziegler and Goswami’s proposed
hierarchy; see their Figure 1).
In sum, stronger length effects for shallow than deep orthographies and
stronger frequency effects for deep than shallow orthographies are consistent with the

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psycholinguistic grain size theory. However, by themselves these two marker effects
cannot provide specific evidence for this theory, because the predictions are shared
with the orthographic depth hypothesis. The only evidence that specifically supports
the psycholinguistic grain size theory is the existence of cross-linguistic differences in
the reliance on sublexical units of different sizes. Therefore, in the current paper we
focus on this prediction.
In addition, studies providing evidence for the psycholinguistic grain size
theory should exclude the possibility that correlated variables account for any cross-
linguistic differences in reading (Cutler, 1981; Marinus, Nation, & de Jong, 2015).
Generally speaking, potential confounds in psycholinguistic research can be
associated either with language-level or participant-level factors. This issue is
especially pertinent to cross-linguistic research, because languages tend to differ from
each other on many aspects, and therefore it is often unclear to what language-level
difference a cross-linguistic difference should be attributed (Schmalz et al., 2015). We
discuss potential confounds that could provide alternative explanations for previous
observations of differential reliance on bodies across languages in Section 1.2.
Observed cross-linguistic differences should also be considered in relation to
potential participant-level confounds. In the case of reading, there are systematic
differences as a function of orthographic depth in the instruction methods that are
used to teach children to read (Landerl, 2000; Wimmer & Goswami, 1994): In deep
orthographies, such as English, whole-word instruction methods tend to be more
popular, because of the assumption that teaching print-to-speech correspondences
does not help with reading if these are unreliable. This is relevant to the
psycholinguistic grain size theory: a previous study has shown that adults who had
received whole-word reading instruction at school relied to a greater extent on bodies

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in a nonword reading aloud task than adults who had received phonics instruction
(Thompson, Connelly, Fletcher-Flinn, & Hodson, 2009).
1.2. The current evidence for the psycholinguistic gain size theory
Taking into account the issues discussed above, we provide an overview of the
existing studies on the psycholinguistic grain size theory, and consider whether these
show evidence of cross-linguistic differences that can be unequivocally attributed to
orthographic depth. The behavioural evidence supporting the view that readers of
deep orthographies rely to a greater extent on large sublexical clusters than readers of
shallow orthographies comes from two different psycholinguistic manipulations. In
the first set of studies, participants are asked to read aloud nonwords with either an
existing or a non-existing body (e.g., “dake”, a body neighbour of “cake”, or “daik”,
which has a unique body). In the second set of studies, reading aloud latencies are
compared for both words and nonwords with many versus few body neighbours. In
both manipulations, the idea is that bodies that occur frequently have stronger
psychological salience than bodies that occur rarely or do not occur at all. Therefore,
if readers routinely rely on bodies, they should show facilitation associated with body
existence or frequency. If readers instead routinely rely on letters or graphemes, they
should show less or no facilitation associated with body existence or frequency.
1.2.1. Body-existence studies
The psycholinguistic grain size theory predicts stronger facilitation associated
with body-existence (i.e., faster response latencies for “dake” than “daik”) in deep
compared to shallow orthographies, because readers of deep orthographies should rely
on bodies to a greater extent (Goswami et al., 1998). Three studies have been
conducted to explicitly address this hypothesis, which compare reading of nonwords
with existing versus non-existing orthographic clusters across languages.

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The first is a cross-linguistic study of English, French, and Spanish (Goswami
et al., 1998). Here, they found stronger body-existence effects in children as an
increasing function of orthographic depth, both in accuracy and reading aloud
latencies. However, for the accuracy analyses they did not take into account cross-
linguistic differences in overall reading accuracy. Children learning to read in deep
orthographies lag in their reading ability behind children learning to read in shallower
orthographies (e.g., Frith, Wimmer, & Landerl, 1998; Seymour, Aro, & Erskine,
2003). This can create the illusion of interactions that are due to smaller absolute
effects driven by lower average error rates or RTs in shallow orthographies (Faust,
Balota, Spieler, & Ferraro, 1999). For example, in the accuracy analyses in
Experiment 3 of Goswami et al. (1998), for monosyllabic words, there was no body-N
effect on accuracy for Spanish children (with average accuracy rates of 95% across all
age groups and conditions), while French and English children showed a significantly
bigger body-N effect (for French, averaged across age groups, the accuracy rates were
78.0% for existing-body items and 64.9% for non-existing body items, and for
English, 51.4% for existing-body items, and 31.9% for non-existing-body items). In
fact, such overadditivity (apparent interaction due to lower overall accuracy or
reaction times in one group compared to the other) could provide an alternative
explanation for all accuracy analyses reported by Goswami et al. (1998).
The potential for false overadditivity is acknowledged in Goswami et al’s
analysis of the latency data: In both Experiments 1 and 3, they performed follow-up
latency analyses including only a subset of children who were matched, across
languages, on their overall reading speed, and failed to find evidence for a cross-
linguistic difference in the size of the body-existence effect: in Experiment 1, they
report a two-tailed p-value of 0.08, and in Experiment 3, p = 0.12. Thus, it cannot be

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concluded from this study that there are cross-linguistic differences that are
attributable to orthographic depth rather than overall differences in reading accuracy
and speed.
In a similar study using a Greek/English comparison, Goswami et al. (1997)
found support for stronger reliance on the rhymes of bi- and trisyllabic nonwords in
English compared to Greek children. Here, follow-up analyses of matched subgroups
were reported for all critical comparisons, and the cross-linguistic difference persisted
even when the children were matched across languages in their overall accuracy and
speed. However, the Greek nonwords with non-existing bodies in this experiment had
near-identical orthographic patterns across all items. Thus, even if the children were
not familiar with these larger grain-sizes from their knowledge of the Greek
orthography, these units would have become familiar to them after a few trials of the
experiment. This was not the case for English, as there was no repetition of
orthographic clusters within the English item set. Thus, while the Greek participants
may have learned the non-existing rhymes due to the repetition, the English-speaking
participants did not have this opportunity. This confound might lead to the interaction
with stronger apparent reliance on rhymes in English than German, which is not
related to orthographic depth. Therefore, it cannot be concluded from this study that
the cross-linguistic differences are attributable to orthographic depth rather than the
item characteristics.
The third cross-linguistic study on the body-existence effect was conducted by
Goswami et al. (2003). The focus of this study was the interaction between language
and blocking (i.e., whether nonwords with existing or non-existing bodies are read
differently depending on whether they are presented in separate or mixed blocks). The
authors addressed this question by performing a 2 (language) x 3 (age group) x 2

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(blocked versus mixed presentation) x 2 (body existence) x 3 (number of syllables)
ANOVA on the accuracy rates. They report and analyse only accuracy data. For the
current purposes, the critical effect is the two-way interaction between language and
body-existence. This two-way interaction was not significant. However, there were
significant three-way interactions that included this contrast: (1) a body-existence by
language by blocking interaction, and (2) a body-existence by language by age
interaction. The authors did not perform a post-hoc analysis to test under which
circumstances the body-existence by language interaction emerged (because it was
not relevant to their aims), but an inspection of the condition means suggests that the
latter interaction may well have been driven mainly by German older children
approaching ceiling accuracy for both types of nonwords. The three-way interaction
between body-existence, language, and blocking reflects a stronger blocking effect for
English than German children, but only for nonwords with existing bodies.
These results are difficult to interpret, because the critical two-way interaction
was not significant, and the three-way interactions were not predicted a priori. A five-
way ANOVA tests at least 15 contrasts. Due to this multiple comparison problem,
there is an increased chance that a statistically significant interaction reflects a false
positive (Cramer et al., 2015), especially if it was not predicted a priori. The study of
Goswami et al. (2003) also suffered from a lack of power with only 9-13 participants
per cell (as language, age, and blocking condition were between-participant factors).
Underpowered studies that report significant results that were not predicted a priori
are more likely to be false positives than the conventional 5%-rate (Button et al.,
2013; Christley, 2010; Ioannidis, 2005; Royall, 1986). Thus, the results of this study,
like the other two studies discussed above, do not provide convincing evidence for

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cross-linguistic difference in the reliance on bodies that could be attributed to
orthographic depth.
1.2.2. Studies on the body-N effect
The second set of studies that report support for the psycholinguistic grain size
theory manipulated body-N. Body-N for a given letter string is defined as the number
of words that have the same body. The word jazz and the corresponding nonword
blazz have a body-N value of 1, because jazz is the only word with this body; the
word blue and the nonword crue have a body-N count of eight, with body neighbours
such true, cue, and clue. In single-word reading aloud, words and nonwords with
many body neighbours are generally processed faster than words with fewer body
neighbours (Ziegler et al., 2001; Ziegler et al., 2003). In lexical decision, a high body-
N count has been shown to facilitate the processing of words, while no effect has been
found for nonwords (Ziegler & Perry, 1998). From the point of view of the
psycholinguistic grain size theory, the body-N effect reflects sublexical processing of
larger-than-grapheme units. According to an alternative view, the facilitatory body-N
effect in reading aloud and in lexical decision for words could also reflect facilitation
through the lexical activation of body neighbours (Forster & Taft, 1994; Goswami,
1993). In this case, lexical decisions to nonwords may be impaired by a high body-N
count, because lexical activation will erroneously bias the reader towards a “yes”-
response.
In two cross-linguistic studies, Ziegler and colleagues compared reading aloud
latencies for words and nonwords which had either many or few body neighbours, in
English and German adults (Ziegler et al., 2001) and children (Ziegler et al., 2003).
As predicted by the psycholinguistic grain size theory, English participants showed a

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stronger body-N facilitation effect than German readers (and German participants
showed a stronger length effect than English readers).
However, the items used in both studies contained a confound that undermines
the conclusion that the results support the psycholinguistic grain size theory: namely,
the body-N manipulation was significantly stronger for English than German. For the
German items, the mean body-N counts were 8.89 (SD = 3.82) and 3.82 (SD = 1.79)
for the high and low body-N items, respectively; for English, the corresponding
values are 12.55 (SD = 4.41) and 3.33 (SD = 1.79). As a result, the strength of the
manipulation was larger in English than German. To test whether this difference was
statistically significant, we used a linear model analysis with body-N condition and
language (both contrast-coded as -0.5 and 0.5) as the independent variables and body-
N, as a continuous measure, as the dependent variable. This analysis showed a main
effect of language, with an overall higher body-N count in English than German, t = -
7.3, p < 0.0001, a main effect of body-N condition , t = 21.4, p < 0.0001, and
crucially, a body-N condition by language interaction, reflecting the stronger
manipulation for English than German, t = 4.5, p < 0.0001. This stronger
manipulation for English than German provides a possible alternative explanation for
the stronger body-N effect in English than German.
In the Ziegler et al. (2001, 2003) studies, there was also a significant
correlation between body-N and orthographic N (r = 0.44, p < 0.0001). The effect of
this was that, as was the case for body-N, the orthographic N manipulation was
significantly stronger for English than for German. We confirmed this in a linear
model analysis, as above, with language and body-N condition and their interaction as
independent variable and orthographic N as a dependent variable. Language and
body-N condition interacted, t = 2.2, p = 0.0266. Orthographic N is the number of

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words that can be created from a letter string by substituting a single letter (Coltheart,
Davelaar, Jonasson, & Besner, 1977). Orthographic N has been shown to affect
reading latencies (see Andrews, 1997, for a review), and this effect differs across
orthographies (Marinus et al., 2015). Body-N and orthographic N are conceptually
different but highly correlated, therefore an item set failing to de-correlate these two
concepts needs to consider the possibility that a body-N effect, instead, reflects an
effect of orthographic N.1 Orthographic N reflects the degree of interference or
facilitation of similar words in the orthographic lexicon, rather than the psychological
salience of a specific orthographic unit (Andrews, 1989, 1992; Coltheart et al., 1977).
As lexical processing has been proposed to be more important for English than
shallower orthographies (Katz & Frost, 1992), a larger ‘body-N’ effect for English
than German in the item set of Ziegler and colleagues (Ziegler et al., 2001; Ziegler et
al., 2003) could also be a larger orthographic N effect, reflecting stronger reliance on
lexical processing in English compared to German readers.
In Ziegler et al.’s (2003) study of developing readers, the confound with
orthographic N is not addressed. However, in their study with adult readers, Ziegler et
al. (2001) performed a follow-up analysis which included orthographic N as a
covariate, but they did not report having tested for the presence of an interaction
between language and body-N. Instead, they tested the body-N effect separately for
each language, finding a significant effect for English but not German. However, this
pattern of findings does not constitute evidence for an interaction (Gelman & Stern,
2006). The possibility remains, therefore, that there are no cross-linguistic differences
in the size of the body-N effect once orthographic N is controlled for.
1 Note that this is also a problem for the studies on the body existence effect.

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2. Evaluating the empirical evidence
Given the questions arising as a result of our analysis of the original studies,
our aim in this section is to evaluate all available evidence on the body-N effect
across languages varying in orthographic depth, using a combination of analytic
approaches to examine the question thoroughly. First, we reanalyse Ziegler et al.’s
(2001) data, as well as the data for their English words from the English Lexicon
Project (ELP) database (Balota et al., 2007). We use linear models, which allows us to
treat body-N as a continuous rather than a dichotomised variable (as demanded by
within-participants ANOVA). This allows us to take into account the cross-linguistic
difference in the strength of the manipulation, and also increases the power of the
statistical analyses.
In a re-analysis of the original data of Ziegler et al. (2001), we aim to assess
the evidence for an interaction between body-N and language while using body-N as a
continuous variable (thus removing the confound of a stronger manipulation for
English). If we continue to find evidence for this interaction, this would suggest that
there is a cross-linguistic difference that might be attributed to orthographic depth.
The trial-level data of the original study by Ziegler et al. (2001) has been lost (J.
Ziegler, personal communication, 2.9.2014), therefore we relied on the item-level data
(i.e., data which has been averaged across items) reported by Perry and Ziegler
(2002). In addition, we aim to assess whether the effect is generalisable to other
participants. To this end, we retrieved the trial-level data (i.e., RT data which have not
been averaged across items or participants) from the English Lexicon Project (Balota
et al., 2007).

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2.1. Re-analysis of the body-N effect in Ziegler et al. (2001)
2.1.1. Multiple Regression
Using the item-level data provided in Perry and Ziegler (2002), we conducted
a multiple regression analysis with body-N as a continuous variable. A model
including body-N, lexicality, language, and their interactions as predictors, and RT as
the dependent variable, showed a significant effect of lexicality, t = -5.96, p < 0.0001,
with faster responses for words than nonwords. All other ps were greater than 0.1.
The effect of body-N was not significant, t = -1.53, p = 0.13, nor was the body-N by
language interaction, t = 1.34, p = 0.18.
2.1.2 Bayes Factors
We performed the same analyses with an alternative inference method, namely
Bayes Factors (Rouder, Speckman, Sun, Morey, & Iverson, 2009). Bayes Factors
quantify the degree to which the observed data are compatible with a pre-specified
alternative hypothesis or the null hypothesis of no effect. Thus, a Bayes Factor can
also provide evidence for a null effect, which is theoretically impossible with
conventional frequentist testing (Dienes, 2014; Rouder et al., 2009). For all of the
Bayesian analyses reported throughout the paper, we used the R package BayesFactor
and its default settings to calculate Bayes Factor values (BFs; Morey & Rouder,
2014). The Bayes Factor provides a continuous measure, with decreasing values
below 1 providing increasingly stronger evidence for the null, and increasing values
above 1 providing increasingly stronger evidence for the alternative hypothesis. For
easier interpretability, we use a set of guidelines, as recommended by Rouder et al.
(2009): Bayes Factor values between 3 and 1/3 provide anecdotal evidence for or
against the alternative hypothesis, respectively; Bayes Factor values greater than 3 (or

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smaller than 1/3) provide some evidence for (or against) the alternative hypothesis,
and values greater than 10 (or smaller than 1/10) provide strong evidence.
The first model comparison tests for any influence of body-N, by comparing a
‘full’ model to a ‘base’ model, which excludes the main effect of body-N and any
interactions with this term. Using Ziegler et al.’s (2001) item-level data, we compared
a full model, identical to the LME model reported in the previous section, to the
‘base’ model. We obtained very strong evidence against the full model, BF = 0.02
(±4.14%).
In the second comparison, we tested for a main effect of body-N. Here, we
compared a main-effects model to a main-effects model that excluded the main effect
of body-N. This provided anecdotal evidence against the presence of the body-N
effect, BF = 0.44 (±3.73%). In a third comparison, we assessed the evidence for a
body-N by language interaction. We compared the full model to an identical model
which excluded the interaction of body-N and language, while retaining their main
effects. For a body-N by language interaction, BF = 0.73 (±8.92%), providing
anecdotal evidence against it. Thus, in Bayesian terms, Ziegler et al.’s (2001) study
cannot distinguish between the presence or absence of a body-N by language
interaction, or whether there is substantial evidence for a main effect of body-N,
although the first comparison suggests that a model excluding the main effect and
interactions of body-N fares better than the full model.
2.1.3. Ziegler et al.’s (2001) items in the English Lexicon Project
An alternative question is whether a body-N effect can be found using the
same items as Ziegler et al. (2001) but a different set of participants. Firstly, this will
allow us to assess to what extent the findings of Ziegler et al. (2001) are generalisable
across samples. Secondly, using the English Lexicon Project (Balota et al., 2007)

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allows us to perform the analyses on a trial-level item set, using linear mixed effect
(LME) models (Baayen, Davidson, & Bates, 2008). LMEs are commonly used in
psycholinguistic research, as they can simultaneously fit both participant- and item-
level variance in the random effects structure. They also provide more power by using
the information available from every trial rather than averaged data across participants
or items, as is typically done in ANOVA approaches.
We retrieved the trial-level data for Ziegler et al.’s English words from the
English Lexicon Project (Balota et al., 2007). This corpus contains reading aloud
latencies for 79 of the original 80 words. The trial-level dataset contained 2309
correct RTs, with an average of 29 observations per item. After removing data points
with RTs < 300 ms or > 1800 ms (which resulted in an approximately normal
distribution of the data, as shown by a qq-plot), we were left with a total of 2268 data
observations. These data were fit with an LME model including random intercepts for
both participants and items, as well as a fixed effect of body-N as a continuous
predictor. The dependent variable was inverse RTs. This analysis did not show a
significant body-N effect, t = -0.43, p = 0.67. For the Bayes Factor analysis, we
compared the LME model against one which was identical except that it excluded the
main effect of body-N. This comparison provided evidence against the presence of a
body-N effect BF = 0.15 (±3.49%).
This set of analyses does not directly answer the question of whether there is a
cross-linguistic difference in the size of the body-N effect. However, we found
evidence against a main effect of body-N in English, using Ziegler’s item set and data
from the English Lexicon Project. As the interaction reported by Ziegler et al. (2001)
was driven by a stronger body-N effect in English than German, the absence of a

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body-N effect in English in incompatible with the prediction of a smaller effect in
German.
3. Large-scale analysis
The analyses above, at the very least, suggest that the body-N effect is not
very stable. However, we did not find evidence against a body-N by language
interaction. Given the results so far, it could be argued that a body-N effect exists in
the population, but is very small. If the effect is small, the probability of reliably
detecting this main effect – let alone an interaction involving this effect – in a
typically-sized psycholinguistic experiment is also small (Button et al., 2013; Cohen,
1962; Vadillo, Konstantinidis, & Shanks, in press). This issue can be addressed by
conducting a high-powered study, or alternatively, by combining the data from
multiple studies, if their design is sufficiently similar to allow this (Schmidt, 1992,
1996). We took the latter approach, as we have accumulated numerous experiments
on the body-N effect both in English and in German (described in a later section and
in the Appendix). The experiments were conducted with various a priori aims, which
are described, along with their individual analyses and results, in the Supplementary
Materials, downloadable from https://osf.io/myfk3/.
The main advantage of combining data from numerous studies is increased
statistical power, compared to smaller-scale studies. In frequentist terms, this
maximises our chance of finding significant body-N effects and body-N by language
interactions. In Bayesian terms, larger studies tend to have stronger evidential value,
which is quantified by more extreme BF values (i.e., large numbers when an effect is
present or small numbers when the effect is absent, whereas for studies with low
evidential value, the BF values hover around 1). More extreme BF values increase the
confidence in the results.

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As we had access to all trial-level data, we were able to perform LME
analyses, which further increased our statistical power. Large-scale analyses allow us
to statistically control for a number of covariates (Kliegl, Grabner, Rolfs, & Engbert,
2004; Yap, Balota, Sibley, & Ratcliff, 2012). While strong inter-correlation between
the independent variables is a problem even in a large-scale analysis, the data
presented here are drawn from individual studies, where the items were matched
across body-N conditions on variables such as orthographic N, length, and frequency.
This reduces the problem of multicollinearity compared to a large-scale analysis of an
unselected or exhaustive set of items (Protopapas & Kapnoula, 2013).
In addition to the data collected in our lab, we attempted to obtain published or
unpublished trial-level data on the body-N effect from other labs. This allowed us to
add a study by M. Taft (unpublished; personal communication, 3.9.2014). Two
studies on the body-N effect in adults by Ziegler and colleagues could not be
included, because there was no available trial-level data. These studies were a lexical
decision experiment by Ziegler and Perry (1998; J. Ziegler, personal communication,
23.1.2013), and the study described above by Ziegler et al. (2001). We were also
unable to address the question of whether a body-N effect, and its interaction with
language, might be more convincing in children. There is, to date, only one study of
body-N effects in children (Ziegler et al., 2003), and we could not obtain either trial-
or item-level data for this study. The studies by Goswami and colleagues also do not
report data that could be used in a re-analysis of the body-existence effect across
languages (Goswami et al., 1998; Goswami et al., 1997; Goswami et al., 2003).

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3.1. Method
3.1.1. Studies included in the analyses
We analysed all skilled adult reader studies with available trial-level data,
which used either single word reading aloud or lexical decision, and which
manipulated the number of body-neighbours for words and/or nonwords. These
included eight experiments from our lab and one by M. Taft, and both lexical decision
and reading aloud tasks in English and German (see Appendix).
The experimental procedures of all studies were typical of psycholinguistic
research. In the studies from our lab, each item was presented to the participant using
the software DMDX, for 5 seconds or until a response occurred (in the case of reading
aloud, this was measured by when a voice key was triggered; in lexical decision, by a
button press). All reading aloud responses were scored for accuracy offline with the
program CheckVocal (Protopapas, 2007), which allows the researcher to adjust vocal
response onsets, thus reducing the bias associated with voice key triggering for
different first phonemes. The study by Taft was a lexical decision study, where each
item was presented in random order until a response occurred. In the entire dataset,
fourteen trials with RTs < 300 ms were discarded, as these are likely to reflect
premature accidental button presses or voice-key triggers. Note that this trimming
decision – and all other decisions about data analysis – were taken before any of the
analyses were conducted.
All English participants were native speakers of English, recruited through
Macquarie University in Sydney, Australia (or University of New South Wales, for
Taft’s study), and the German participants were native speakers of German, recruited
through Potsdam University in Germany. A spreadsheet with the full (i.e., untrimmed)

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trial-level data (i.e., the data of Taft and our data) as well as the R scripts used in the
large-scale study can accessed via https://osf.io/myfk3/.
The overall item characteristics across all studies that were included in the
analyses (averages, SDs and correlations with body-N) are described in Table 1. The
body-N counts are based on the same corpus analysis as those of Ziegler et al., (2001)
to increase the comparability across languages (Ziegler, Stone, & Jacobs, 1997, for
English, and Ziegler, 2012, personal communication, for German). The frequency and
orthographic N values are taken from WordGen (Duyck, Desmet, Verbeke, &
Brysbaert, 2004), which is an interface for cross-linguistic research based on the
CELEX database (Baayen, Piepenbrock, & Gulikers, 1995). Regularity was defined
as compliance to grapheme-phoneme correspondence rules, as implemented in the
German and English versions of the DRC (Coltheart et al., 2001; Ziegler, Perry, &
Coltheart, 2000).
TABLE 1 ABOUT HERE
Before reporting the results of the large-scale analysis, we outline the
characteristics and basic results of the individual studies, and thereby show the degree
to which the body-N effect is stable across experiments. Note that we are restricted in
the types of conclusions that we can draw from a series of smaller studies: although
the sample sizes of each study are similar to those of a typical psycholinguistic study,
it is possible that they do not have enough statistical power to consistently detect a
true small effect of Body-N and systematic differences across languages (Button et
al., 2013; Meehl, 1990; Schmidt, 1992).
Basic descriptions and outcomes of the studies that were included in the meta-
analysis are summarised in the Appendix and in the supplementary material. We re-
analysed the trial-level data of all experiments with LMEs. The t and p values

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(calculated by the R packages "lme4" and "lmTest", respectively; Bates et al., 2015;
Hothorn et al., 2015) provided in the “Results” column are based on LMEs, using
body-N as a centred continuous predictor for inverse RTs (-1000/RT), with items and
participants as random effects. The body-N slope was allowed to vary across
participants. Inverse RTs were used to bring the distribution closer to normal.
To summarise the basic results of the item sets, we inspected the BFs as a
function of task, lexicality, and language. These are presented in Table 2.
TABLE 2 ABOUT HERE
Table 2 shows that for most types of items, the BF provides either anecdotal
evidence, or stronger evidence for the absence of the main effect of body-N than for
its presence. This does not support the notion of a real effect of body-N. An exception
is the lexical decision task for German nonwords, which provides evidence for an
inhibitory effect of body-N, albeit based on only one study.
3.2. Analyses and results
To further explore the pattern of results, we combined all studies described in
the previous section to obtain greater power in the assessment of stability of the
effects. Note that all analysis scripts (for R), as well as the untrimmed data, are
available in the supplemental materials.
For three reasons, we did not analyse accuracy data. Firstly, overall accuracy
rates were very high, mean = 95.25%, and the ceiling effects reduce our chances of
finding meaningful effects. Secondly, the original studies on body-N effects in adults
focussed on RTs: Ziegler et al. (2001) did not analyse the accuracy data, and Ziegler
and Perry (1998) report only a weak body-N effect in accuracy in the words
condition, which was significant by participants but not by items. Thirdly, the
BayesFactor package, at this stage, has not implemented the possibility to test logistic

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models against each other (in trial-level accuracy data, the outcome variable is
binomial).
For RTs, we performed four groups of analyses: for nonwords in reading
aloud, nonwords in lexical decision, words in reading aloud and words in lexical
decision. We analysed these conditions separately, because different cognitive
mechanisms may underlie response latency variance in each of the four conditions
(Coltheart et al., 2001). This should be reflected in different patterns of the body-N
effect. For example, we expect a facilitatory body-N effect for reading aloud words
and nonwords, and for lexical decision for words, as stronger activation of a body unit
may enhance lexical activation and/or the sublexical assembly process (Ziegler &
Perry, 1998; Ziegler et al., 2001). For lexical decision for nonwords, however, we
might expect an inhibitory body-N effect: if a high body-N nonword elicits more
lexical activation compared to a low body-N nonword, it will be harder to reject in the
lexical decision task (Coltheart et al., 1977). Each analysis included both the English
and German items, which enabled us to assess any interactions between body-N and
language, as this is relevant to the psycholinguistic grain size theory (Ziegler &
Goswami, 2005).
3.2.1. Measuring body-N: Types versus tokens
The published studies of the body-N effect used type body-N (the number of
words with the same body) to quantify the effect (Ziegler & Perry, 1998; Ziegler et
al., 2001; Ziegler et al., 2003). In the literature on word consistency effects, some
evidence suggests that reliance on large units is instead driven by token frequency
(Jared, McRae, & Seidenberg, 1990), which can be quantified in the context of the
body-N effect as the summed frequency of all body neighbours. Practically, type and
token counts are difficult to dissociate unless the item sets are created with the aim of

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de-correlating these variables, because they are correlated. For our combined item set,
the correlation was r(915) = 0.43, p < 0.0001.
At the beginning of each set of analyses, we compared models including type
versus token body-N as predictors. Our aim here was to isolate the more reliable
predictor rather than adjudicating between the two measures. In every model
comparison, type body-N provided a better fit to the data than token body-N
according to the Akaike Information Criterion (AIC). For this reason, and also
because previous research has relied on type body-N counts to quantify the body-N
effect, we used type body-N counts for all subsequent analyses.
3.2.2. Body-N effect for nonwords in reading aloud
The analyses were conducted on inverse RTs as the dependent variable2. The
predictors were body-N, language (German, English - contrast coded as 1 and -1,
respectively, in the LME analyses, in order to obtain estimates of the main effects of
language as a deviation from the grand mean), orthographic N, and onset complexity
(the number of consonants in the onset)3. The continuous predictors were centred by
subtracting their mean from each value, so as to obtain LME parameter estimates for
average values rather than extreme values of zero. We also included random
2 It has been suggested that all models should also allow the by-participant slopes of
body-N to vary as random factors, because a failure to do so increases the Type-I
error rate (Barr, Levy, Scheepers, & Tily, 2013). As all of our critical conclusions are
based on null-effects, they are not compromised by the possibility of an increased
Type-I error rate. In fact, recent simulations have shown that maximising the model
structure results in a substantial loss of power (Matuschek, Kliegl, Vasishth, Baayen,
& Bates, 2015), thus excluding by-participant slopes increases our chances of finding
a potentially true effect of body-N and interaction with language.
3 Onset complexity is mainly included to act as a covariate. Other than the item set of
Ziegler et al., all studies that were included in the analyses manipulated body-N while
keeping orthographic N constant, meaning that high body-N words tended to contain
more complex onset clusters to reduce the orthographic N value. As this may act to
suppress a body-N effect, we included the effect of onset complexity as a statistical
control.

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intercepts for participant, item, and study as in all models. We did not include
previous trial RT, previous trial accuracy, or trial order as predictors, because these
were not available for all experiments.4
3.2.2.1. LME model analysis. As a first pass, we compared models containing
(a) only the main effects of body-N, orthographic N, and language (no interactions),
as well as onset complexity as a covariate, (b) adding all two-way interactions not
including onset complexity, and (c) adding the three-way interaction between body-N,
orthographic N, and language. We found that the model containing two-way
interactions performed significantly better than the model with no interactions,
χ
2 (3)
= 18.41, p < 0.001, while there was no additional benefit of adding the three-way
interaction,
χ
2 (1) < 1.
The LME results for the two-way interaction model are summarised in Table
3. In the model including the main effects and all two-way interactions between body-
N, orthographic N and language, as well as the main effect of onset complexity, we
found a facilitatory main effect of orthographic N, and two-way interactions which we
describe in more detail below: namely, an interaction between body-N and language,
and an interaction between language and orthographic N. The interaction between
body-N and orthographic N approached significance. The main effect of body-N did
not approach significance.
TABLE 3 ABOUT HERE
4 Throughout the paper, we report analyses that include all items. There might be two
reasons to include only items with low body-N counts: firstly, it could be argued that
a body-N effect is evident only for low body-N items, if the psychological saliency of
bodies operates in an all-or-none manner. Secondly, body-N counts are not linearly
distributed, because there are more words with smaller body-N values. However,
conducting the same analyses while excluding items with body-N > 5 did not change
any of the critical results. We therefore report the full analyses, as they have higher
statistical power.

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In a set of follow-up contrasts, we explored the patterns of interactions in the
results. Specifically, since we are interested in the body-N by language interaction, we
sought to examine the effects of body-N for each language. Table 3 shows that the
body-N by language interaction is driven by a numerically inhibitory body-N effect
for German, and numerically facilitatory effect in English. Using appropriate
contrasts, we found that the body-N effect is not significant in German, β = 0.003, t =
1.48, p = 0.14, while it was significantly facilitatory in English, β = -0.004, t = -2.58,
p = 0.01.
3.2.2.2. Bayes Factor analysis. To mirror the LME analyses, we started with a
comparison of a main-effects model (including language, orthographic N and body-N,
as well as onset complexity as a covariate and items, participants, and study as
random effects) to one which also included all two-way interactions. This provided
evidence against the main-effects only model, BF = 0.12 (±1.46%). We further
compared this two-way interactions model to a model including the three-way
interaction, and again found evidence for the two-way interaction model, BF = 5.20
(±1.46%). We therefore adopted the two-way interaction model as a baseline for
further model comparisons. To establish the importance of the main effect of body-N,
we compared the two-way interactions model to one excluding both the main effect of
body-N, and any interactions associated with it. Here, BF = 0.42 (±3.65%), thus
providing anecdotal evidence against any influence of body-N.
Even though the BF analysis does not favour a model which includes both the
effects and interactions of body-N, it is not clear that we can conclude that there is
either a main effect or interactions of body-N. It is possible, for example, that the
main effect of body-N improves the model fit, but including the interactions decreases
it and thereby counteracts a meaningful main effect. We therefore followed up with

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further model comparisons to establish the importance of the relevant effect and
interactions.
To assess the importance of the main effect of body-N, we compared the
“base” model (language and orthographic N and their interaction, plus main effect of
onset complexity) to one which also included the main effect of body-N. For the
model including the main effect of body-N, BF = 0.19 (±1.81%), suggesting that as a
main effect, body-N is unlikely to have any influence on reading aloud nonword
latencies.
As this does not rule out the possibility of a body-N by language interaction,
which was significant in the LME analysis, we compared the model which included
body-N (same as the body-N model for the previous analysis) against one which also
included the interaction between body-N and language. Here, we found support for
the model which included the interaction: BF = 4.94 (±2.01%).
Akin to the LME model, the Bayes Factor shows some evidence for a body-N
by language interaction and evidence against a main effect of body-N. The LME
analyses showed that the body-N was significantly facilitatory for English, but
numerically inhibitory for German. As we did not control for multiple comparisons, it
is unclear to what extent the significant body-N effect for English reflects a stable
pattern: multiple comparisons increase the type-I error rate and thus compromise the
frequentist properties of p-values (Cramer et al., 2015; Simmons, Nelson, &
Simonsohn, 2011). In contrast to LMEs, the Bayesian approach is immune to multiple
comparison problems (Dienes, 2011). Using the English data only, we compared a
model with a main effect of body-N and orthographic N, to one which included only
the main effect of orthographic N. This comparison showed evidence against the main
effect of body-N in English only, BF = 0.19 (±1%).

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3.2.2.3. Summary. The original aims were to establish whether there is a main
effect of body-N, and whether body-N interacts with language. Both in the LME and
BF analyses, we found no evidence for the presence of a main effect of body-N. The
interaction between language and body-N emerges consistently in both analyses,
likely due to the inhibitory direction of the slope of body-N in German and
facilitatory direction of the slope in English. The slope in German was not supported
by either analysis suggesting that the inhibitory trend that drives the interactions is
spurious. Although the facilitation effect in English is significant in the LME analysis,
the BF provides evidence against it.
3.2.3. Body-N effects for nonwords in lexical decision
We performed an equivalent set of analyses for the nonwords in the lexical
decision task. The dependent variable and independent variables were identical to the
previous set of analyses.
3.2.3.1. LME model analysis. We found no advantage of any model including
interactions over one containing main effects only based on measures of model fit,
both χ2 < 4 and p > 0.2. The main-effects only model for type body-N showed an
inhibitory effect of body-N, t = 2.86, p < 0.005, and an inhibitory effect of
orthographic N, t = 3.23, p < 0.005. All other p > 0.4. Note that the slope of the body-
N effect was numerically steeper for German, β = 0.005, than for English, β = 0.002,
suggesting numerically stronger inhibition in German than English.
3.2.3.2. Bayes Factor analysis. We compared the main effects only model to
one which included two-way interactions, and to one which included three-way
interactions. In both cases, the evidence was strongly in favour for the main-effects
only model, BF > 100, which was adopted for further comparisons.

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A BF comparison of the full main-effects model compared to one which
excluded the main effect of body-N provided support for the body-N effect in
nonword lexical decision latencies: BF = 4.75 (±2.66%). A comparison of the model
which included an interaction between language and body-N as well as the main
effects against a main-effects-only model provided evidence against the interaction,
BF = 0.19 (±2.50%).
3.2.3.3. Summary. A relatively simple model that included no interactions was
supported in this set of analyses. We found a stable inhibitory body-N effect for
nonwords in lexical decision, in addition to an inhibitory effect of orthographic N.
There was evidence against an interaction with language.
Given that we found a main effect of body-N, but no interaction with
language, it is worth noting that inverse reaction time transformations, as used in the
analyses above, have been criticised for masking interactions (Balota, Aschenbrenner,
& Yap, 2013). Thus, it could be argued that we did not find an interaction, because
we used inverse instead of raw RTs. Therefore, we re-did the analyses using raw RT
as a dependent variable, and found no improvement of fit for an interactive model
compared to a main-effects only model in LME, p > 0.5, and evidence against a
model containing the interaction between body-N and language using Bayesian
techniques, BF = 0.11 (±1.9).
3.2.4. Body-N effects for words in reading aloud
In the third set of trial-level analyses, we explored body-N in the reading
aloud task for words. The dependent and independent variables were identical to those
for nonwords, but frequency was included as an additional predictor. An interaction
of body-N and frequency is theoretically important: If bodies are processed as
sublexical units, we should find a smaller effect for high-frequency words, because

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the rapid lexical activation associated with the processing of high-frequency words
would mask the sublexical effect (Coltheart et al., 2001).
LME model analyses. Initially, we compared four models with increasing
complexity: a main effects model including body-N, language, orthographic N and
word frequency; a model adding the two-way interactions; a three-way interactions
model; and the full model with the four-way interaction. The most complex model
(including the four-way interaction between body-N, frequency, orthographic N and
language) was favoured over the three-way interaction model, χ2 (1) = 4.22, p < 0.05.
The results of the full LME model are summarised in Table 4.
TABLE 4 ABOUT HERE
From the results presented in Table 4, there was a facilitatory main effect of
frequency, and a facilitatory main effect of onset complexity. The two-way interaction
between language and frequency was significant due to a stronger frequency effect for
English than for German. There was a significant interaction between body-N and
orthographic N. Importantly, the critical interaction between body-N and language did
not approach significance, β = 0.0001, t = 0.11, p = 0.91. The four-way interaction
was significant, β = -0.001, t = 2.03 p = 0.04. As this could suggest that the critical
body-N by language interaction emerges only for a subset of words, we followed up
with four contrasts, to estimate the interaction for (1) high-frequency, high
orthographic N words, (2) high-frequency, low orthographic N words, (3) low-
frequency, high orthographic N words, and (4) low-frequency, low orthographic N
words. These analyses showed a significant body-N by language interaction for the
two orthographic N contrasts: for high-frequency words, β = 0.0069, t = 3.265, p =
0.0012, and for low-frequency words, β = 0.0069, t = 3.265, p = 0.0012. The main
effect of body-N was not significant, p > 0.4. For the high orthographic N contrasts,

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neither the body-N main effect nor the interaction of body-N and language
approached significance, all ts < 1.
For the two low orthographic N contrasts, we followed up by estimating the
body-N effect for English and German. In English, for the high-frequency low
orthographic N words, the effect of body-N was not significant, β = -0.0049, t = -
1.514, p = 0.1310. For German, there was a significant inhibitory effect of body-N, β
= 0.0088, t = 2.856, p = 0.0045. For the low-frequency low orthographic N contrast,
there was a significant inhibitory body-N effect for English, β = 0.0099, t = 2.709, p =
0.0071. The body-N effect was not significant for German, β = 0.0038, t =1.225, p =
0.2213.
3.2.4.2. Bayes Factor analyses. In contrast to the LME analyses, the BF
analysis very strongly favoured the main effects model over any of the more complex
models, all BFs > 9000 for the main-effect model. Therefore, the model used in the
following BF analyses included only the main effects of body-N, orthographic N,
frequency, and language, as well as onset complexity as a covariate and study, item,
and participant as random factors.
To establish whether body-N had an effect on reading aloud latencies, we
compared a main effects model which excluded the body-N effect to one which
included it. Here, we obtained anecdotal evidence against the presence of a main
effect of body-N, BF = 0.42 (±1.78%).
As for the nonword lexical decision analyses, we compared a main effect
model which also included body-N by language interaction, to the main-effects only
model. We obtained evidence against the model which includes the body-N by
language interaction, BF = 0.23 (±2.76%).

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3.2.4.3. Summary. For the reading aloud word data, the LME analyses, at face
value, suggest a highly complex interactive pattern (though in the absence of a
significant body-N effect or a two-way body-N by language interaction), while the
Bayes Factors support a simple model which contains only the main effects of
language, frequency, body-N and orthographic N. Follow-up contrasts of the LME
four-way interaction showed that there was a significant body-N effect for the high-
frequency, low orthographic N, German words, and for the low-frequency, low-
orthographic N English words. Contrary to the predictions of the psycholinguistic
grain size theory, both significant body-N effects were inhibitory rather than
facilitatory. Furthermore, the two significant body-N by language interactions
reflected opposite directionalities: for high-frequency words, the German body-N
slope was steeper than the English slope, and for low-frequency words, the English
slope was steeper than the one for German.
As neither of these results have been expected a priori, they are likely to
reflect spurious interactions. We are testing for multiple contrasts (Cramer et al.,
2015), and the p-value for the four-way interaction just exceeds the 0.05-threshold. If
there is a true effect, and given a large sample sizes such as ours, p-values are likely
to be substantially smaller than the conventional cut-off of 0.05 (Lakens & Evers,
2014; Simonsohn, Nelson, & Simmons, 2014). Furthermore, an inhibitory body-N
effect was not predicted a priori, nor was the particular interactive pattern that we
report. In addition to the results of the Bayes Factor analyses, these points suggest that
it is unlikely that the four-way interaction reflects a real population pattern. Neither
the LME nor the Bayes Factor analyses supported a body-N main effect nor a two-
way interaction between body-N and language.

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3.2.5. Body-N effects for words in lexical decision
The last set of analyses was performed on lexical decision latencies for words.
The dependent and independent variables were identical to the reading aloud for
words analyses (and identical to the nonword analyses, except for the inclusion of
frequency and its interactions).
3.2.5.1. LME model analysis. A model comparison showed a significant
advantage for a model including all three-way interactions over one including only
the two-way interactions, χ2 (4) = 14.20, p < 0.01, but no further improvement of a
model including the four-way interaction, χ2 (1) = 2.19, p > 0.1. The results of the
body-N model including all three-way interactions are summarised in Table 5.
TABLE 5 ABOUT HERE
Table 5 shows a main facilitatory effect of frequency, but no effect of
language or body-N. The two-way interaction between language and body-N does not
reach significance, nor do any of the three-way interactions involving it.
3.2.5.2. Bayes Factor analysis. To mirror the LME analyses, we again
constructed a set of models to assess the stability of the interactions. The evidence
against the two-way interaction compared to the main effects model was anecdotal,
BF = 0.79 (±2.68%), as was the evidence against the two-way compared to the three-
way interaction model 0.38 (±2.9%). The main effect model, however, was supported
over the three-way interaction model, BF = 3.38 (±1.61%) and over a four-way
interaction model, BF = 7.43 (±1.56%). Therefore, the evidence suggests that a main-
effects only model performs substantially better than the models including the three-
and four-way interactions, and numerically better than the two-way interactions
model.

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Excluding all interactions, we compared a model including body-N to one
excluding it. Here, we found evidence against the model which included body-N, BF
= 0.11 (±3.36%). Furthermore, we examined the theoretically important interaction
between body-N and language. Here, the evidence for the body-N by language
interaction was 0.32 (±1.28%), suggesting that body-N does not interact with
language.
3.2.5.3. Summary. As for the reading aloud word results, the analyses for
lexical decisions of words seem to be characterised by higher-order interactions
according to the LME analyses, although the BF analyses showed little support for
any interactions. None of the analyses, however, showed any evidence for the
presence of a body main effect, nor for an interaction with language.
As a caveat, it should be noted that a meta-analysis of various lexical decision
studies for words may mask differences between studies. In the case of orthographic
N, it has been shown that the presence or absence of an effect depends on the types of
nonwords that are used as foils (Andrews, 1989). This may influence the participants’
decision criteria, such that they rely on summed lexical activation of all neighbours,
leading to a facilitatory neighbourhood effect, if the task is easy, and on full lexical
access when the task is difficult, resulting in an inhibitory neighbourhood effect if
access to the specific orthographic form is slowed down by inhibition from its
neighbours. In the case of our studies, there does not seem to be variability in the size
of the body-N effect for lexical decisions on words: as shown in Table 2, three out of
four studies provide evidence against a body-N effect, and one provides only
anecdotal evidence.

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4. General Discussion
The psycholinguistic grain size theory predicts facilitatory effects of body-N
overall, with a stronger effect in English than German due to the former’s greater
orthographic depth (Ziegler & Goswami, 2005; Ziegler et al., 2001; Ziegler et al.,
2003). Two of the key studies supporting the psycholinguistic grain size theory are
based on the body-N effect, and report evidence that the effect is stronger in English
than German (Ziegler et al., 2001; Ziegler et al., 2003). A closer inspection of these
two studies identified potential issues both with the methodology and the statistical
analyses. We therefore aimed to assess the strength of the evidence for the claim that
the size of the body-N effect differs across orthographies.
First, we conducted a re-analysis of the original data reported by Ziegler et al.
(Perry & Ziegler, 2002; Ziegler et al., 2001), and one using trial-level reading aloud
data from the English Lexicon Project (Balota et al., 2007) for the same items. Then,
we carried out a large-scale analysis of nine studies collected by two different labs. In
all of the analyses reported here, we found little evidence for a main effect of body-N
effect or a cross-linguistic difference between English and German readers. When
there was evidence for such a main effect (in the lexical decision data for nonwords of
the large-scale analyses), there was evidence against a body-N by language
interaction. When there was evidence for an interaction (reading aloud nonwords in
the large-scale analysis), this occurred in the absence of a main effect of body-N. In
the analyses of word (reading aloud and lexical decision) data, there was no support
either for a body-N effect, nor for an interaction between body-N and language. These
results suggest that the body-N effect is not a reliable marker effect for larger unit
processing. Moreover, the data do not support the main claim of the psycholinguistic

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grain size theory: that English readers rely routinely on bodies, while German readers
tend to rely on smaller units.
4.1. Some notes on interpreting LME and Bayes Factor analyses
The use of Bayes Factors is relatively new in psychological research, but has
several advantages over traditional frequentist approaches. For our purposes,
supplementing the LME analyses with Bayes Factors allowed us to provide direct
support for the null hypothesis (no body-N effects and/or no body-N by language
interaction), relative to the corresponding alternative hypotheses.
In several of the large-scale analyses, the LME showed statistically significant
interactions, while the Bayes Factor provided evidence against the same interactions.
It is possible that some of the interactions, in the population, are so small that they are
closer to the BF’s H0 than H1, and that the BF therefore erroneously provides
evidence for the null. Conversely, it is also likely that some of the significant p-values
are false positives, especially given the large number of comparisons presented in the
current analyses (Cramer et al., 2015).
4.2. Additional theoretical implications of body-N effects
The body-N effect has theoretical implications beyond the psycholinguistic
grain size theory. Specifically, it is not clear whether the body-N effect reflects a
lexical analogy strategy, where similar lexical entries facilitate word recognition
(Forster & Taft, 1994; Goswami, 1993), or reliance on larger sublexical units
(Coltheart, Curtis, Atkins, & Haller, 1993; Patterson & Morton, 1985; Perry, Ziegler,
& Zorzi, 2007). In the former case, we might expect overall stronger body-N effects
for lexical decision than reading aloud data, because performance on the lexical
decision task is assumed to rely to a greater extent on direct lexical access, while
reading aloud is influenced to a greater extent by sublexical-phonological processes.

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For words in lexical decision, the body-N effect would be facilitatory, as the summed
lexical activation of body neighbours would facilitate a correct “yes” response, while
for nonwords the effect would be inhibitory, because the lexical activation from the
body neighbours would push for a “yes” response even though the item is a nonword.
Conversely, if the effect of body-N reflects sublexical reliance on bodies, we
would expect the strongest effect for reading aloud of nonwords. Reading aloud of
nonwords must be achieved via a sublexical decoding mechanism, because lexical
activation is not sufficient for a correct response – all other tasks, in theory, can be
performed by relying solely on lexical activation (or the lack thereof, for lexical
decision of nonwords).
In the large-scale analyses, the only condition which showed a stable effect of
body-N was lexical decision for nonwords. Here, a higher body-N led to longer
latencies, meaning that high body-N nonwords are more difficult to reject than low
body-N nonwords. The body-N effect seems to exist in addition to an inhibitory
orthographic N effect (which is relatively consistently reported in the existing
literature on orthographic N; for a review, see Andrews, 1997). This suggests that
bodies reflect some aspect of the lexical system: a high body-N nonword appears to
cause lexical activation of its body neighbours, and this lexical activation makes it
more difficult to determine that it is a nonword.
In the other conditions, there was no trace of a main effect of body-N. We are
not implying that the results suggest that bodies have no psychological reality, as this
would be inconsistent with a growing body of research using other paradigms
showing reliance on bodies. As mentioned in the introduction, nonword reading
studies that manipulate the existence versus non-existence of a body in real words (is
dake easier to read than daik?) consistently show body effects (Andrews, Woollams,

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& Bond, 2005; Goswami et al., 2003; Rosson, 1985; Treiman, Goswami, & Bruck,
1990), as do nonword reading studies, where the use of bodies would predict a
different pronunciation compared to grapheme-phoneme correspondences, such as
dalk, which can be read to rhyme with “talk” (if body-rime correspondences are
used), or "talc" (if grapheme-phoneme correspondences are used; Andrews &
Scarratt, 1998; Brown & Deavers, 1999; Glushko, 1979; Schmalz et al., 2014). A
further set of studies on the psychological reality of bodies show that words and
nonwords with inconsistent bodies (e.g., “-eat”, which can be pronounced as in
“beat”, “great” or “sweat”) are read aloud more slowly than words with only one
possible pronunciation (e.g., “-eet”; Andrews, 1982; Cortese & Simpson, 2000; Jared,
1997, 2002; Jared et al., 1990). In light of this other research, we believe the
appropriate interpretation of the absence of the body-N effect in three out of four
conditions here is that the body-N effect is not a sensitive measure of reliance on
bodies.
4.3. Evidence for cross-linguistic differences in the reliance on bodies
As described in the introduction, the key prediction that distinguishes the
psycholinguistic grain size theory from the orthographic depth hypothesis is stronger
reliance on orthographic units that are larger than graphemes and smaller than letters
in readers of deep compared to shallow orthographies. We did not find support for
this prediction. However, there are alternative explanations for the absence of a body-
N by language interaction. This warrants a thorough examination of all possibilities.
A strong explanation should ideally account both for the current set of results, and
also for previous studies that have been reported to support the psycholinguistic grain
size theory. Future studies should aim to provide further evidence to distinguish
between possible explanations.

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There are three possibilities: First, that the psycholinguistic grain size theory is
correct: there may be small, but theoretically meaningful, cross-linguistic differences
in the reliance on bodies driven by orthographic depth. If these differences are
sufficiently small, our current study would not be able to provide evidence for them,
because it would have insufficient power (in frequentist terms) and because the prior
used for the current analyses was set too high (in Bayesian terms). Second, there may
be individual differences in the reliance on bodies, but these may not be driven by
orthographic depth, but rather by cross-cultural differences in the type of reading
instruction. Third, given the issues of the published studies which we identified in the
introduction, and the results of our current analyses, it is possible that any cross-
linguistic differences observed are noise around a true mean of zero.
4.3.1. Possibility 1: The psycholinguistic grain size theory is right
Our results do not support the psycholinguistic grain size theory, but they do
not unequivocally disprove it. Especially given that we have not found the body-N
effect to be a reliable marker effect, an alternative explanation is that the body-N
manipulation is ill-suited for exploring cross-linguistic differences in the reliance on
bodies. Furthermore, we report some slope differences which go in the direction
expected by the psycholinguistic grain size theory: For reading aloud of nonwords, we
find evidence for an interaction both in the LME and in the Bayes Factor analyses –
although the main effect is not significant, the Bayes Factors gives evidence against a
facilitatory effect of body-N in English, and the interaction is driven by an unexpected
inhibitory trend in German. In an individual-study cross-linguistic comparison
(reported in the supplementary materials), we find a significant body-N by language
interaction in the item set listed as Experiments 4 and 7 in the Appendix – though,

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again, this occurs in the absence of a main effect, and the Bayes Factor provides
evidence against the interaction, BF = 0.31 (±3.6%).
Note that the argument of a potential small effect can be almost always made
against a study supporting an H0. This is because most psychological theories,
including the psycholinguistic grain size theory, make directional rather than
quantitative predictions. Thus, if in the population, the benefit of each additional body
neighbour is 1.1 ms for German and 1.2 ms for English, this would still support the
psycholinguistic grain size theory, but one would need thousands of participants to
provide evidence for such an alternative hypothesis.
Therefore, all existing data (including the published studies and our analyses)
do not unequivocally rule out the possibility of a small cross-linguistic difference. To
provide stronger evidence for or against the unique prediction made by the
psycholinguistic grain size theory, future research could involve a large-scale
confirmatory study. Such a study would be stronger if the reliance on bodies was
tested using the body-existence effect, rather than the body-N effect: the body-
existence effect seems to be reliable, as it has been reported by studies from various
labs and with well-controlled stimuli (Andrews et al., 2005; Brown & Deavers, 1999;
Rosson, 1985; Treiman et al., 1990). It would furthermore need to control for
language-level, item-level, as well as participant-level confounds. Confirmatory
analysis with a body-existence manipulation would address a drawback of the current
study, namely that the body-N effect is not a reliable marker effect. Furthermore, the
data in the current study were collected for various purposes, making the large-scale
analyses exploratory. A future confirmatory study could plan, a priori, to collect
sufficient data to confirm or disconfirm a smallest effect of interest, thus making sure

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that the study is adequately powered in frequentist terms, and that the prior in a Bayes
Factor analysis is theoretically informed.
In summary, it is possible that the psycholinguistic grain size theory is
acccurate, and that there are cross-linguistic differences in the reliance on bodies and
other large sublexical units. However, the existing evidence to date – based on the
current study, as well as previous experiments – does not provide a convincing
evidence for it.
4.3.2. Possibility 2: Individual differences as a function of reading instruction
If a cross-linguistic difference exists, it does not automatically follow that this
difference is attributable to orthographic depth. As outlined in the introduction, an
alternative explanation is that reliance on bodies is driven by whole-word reading
instruction rather than orthographic depth (Thompson et al., 2009). Reading
instruction methods have been discussed by previous studies as a potential confound
associated with the German/English comparison (Landerl, 2000; Wimmer &
Goswami, 1994).
This would explain the trends in the right direction for the nonword reading
aloud data, in the absence of convincing evidence for a main effect. If only some of
the English participants show a body-N effect, this would lead to an overall
facilitatory slope of body-N, but with an increase in variability that would make the
overall results less clear-cut. The English-speaking participants were recruited in
Australia, where reading instruction methods are varied. Therefore it is likely that
some of the participants had received whole-word reading instruction while others
received phonics instruction.
Unfortunately, neither the published studies nor our own data allow us to
address this hypothesis, as information about the participants’ schooling was

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unavailable. In fact, such data are difficult to obtain for adult participants, unless a
primary school curriculum is implemented on a national level, as adult participants
rarely remember details about their reading instruction. Any follow-up work on the
reliance on bodies should take this potential confound into account by collecting data
in a country where reading instruction methods are standardised, or by recruiting
children whose teachers have been interviewed to confirm that there are no cross-
linguistic differences.
4.3.3. Possibility 3: There are no cross-linguistic differences in the body-N effect
Overall, we are confident in concluding that there is, to date, no reliable
evidence for a cross-linguistic difference in the reliance on bodies as a function of
orthographic depth. The existing published studies do not provide strong evidence,
and our attempts to provide evidence for a body-N effect or an interaction with
language have further weakened it. As discussed above, however, any stronger
conclusions about the evidence for an absence of a cross-linguistic difference in the
reliance on bodies need to be postponed until there is a confirmatory study addressing
this issue.
4.3.4.The Orthographic Depth Hypothesis and the Psycholinguistic Grain Size
Theory
Aside from the five studies conducted by Goswami and Ziegler and colleagues
(which we discussed in the introduction), other research has used the psycholinguistic
grain size theory to explain results from cross-linguistic studies. Two recent studies
used lexical and sublexical marker effects to address the hypothesis of cross-linguistic
differences in the reliance on large and small units (Goodwin et al., 2015; Rau et al.,
2015). These studies did not include a marker effect of reliance on orthographic units
that are larger than letters but smaller than whole words or morphemes. As discussed

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in the introduction, an increased reliance on small units (i.e., letters and graphemes) in
shallow compared to deep orthographies, and increased reliance on lexical units (i.e.,
words, morphemes) in deep compared to shallow orthographies, are shared
predictions by the psycholinguistic grain size theory and the orthographic depth
hypothesis (Katz & Frost, 1992). For example, Goodwin et al. (2015) compared the
reliance on morphemes as “large units” in English and Spanish. Rau et al. (2015) used
the length and the word frequency effect in German and English as critical markers.
Neither morphemes nor whole words can be used to assess the reliance on sublexical
units, as both contain lexical-semantic information. Thus, the results are in line with
the psycholinguistic grain size theory, but do not provide evidence for it, as they are
equally compatible with the orthographic depth hypothesis.
The orthographic depth hypothesis explicitly describes a cognitive mechanism
that would lead to the relatively larger reliance on sublexical processing in shallow
then deep orthographies (Katz & Frost, 1992): here, a deep orthography is defined by
sublexical correspondences which are either insufficient to derive a word’s
pronunciation without whole-word knowledge or semantics (e.g., for the English
word “yacht”), or where the sublexical correspondences are complex (e.g., in the
French word “aient”, where five letters map onto the single phoneme /ε/). By
definition, this makes the sublexical decoding process less efficient, thus increasing
the relative usefulness of the lexical procedure.
This proposed mechanism provides the orthographic depth hypothesis with
considerable explanatory power, as it makes some specific predictions. For example,
the ratio of lexical-to-sublexical processing, according to the orthographic depth
hypothesis, should increase within languages as a function of the complexity,
unpredictability, and incompleteness of the sublexical correspondences of each

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particular word. Indeed, this prediction is supported by empirical evidence (Frost,
1994; Schmalz, Beyersmann, Cavalli & Marinus, submitted).
4.4. Theoretical challenges for the psycholingustic grain size theory
While we have focussed here on explicit predictions of the psycholinguistic
grain size theory, future theoretical and empirical work could use the broad
framework of the psycholinguistic grain size theory in its current form to generate
more explicit predictions about the exact factors and mechanisms that drive the
reliance on various sublexical units and correspondences. From the existing literature,
it is clear that there is a considerable degree of diversity in the type of linguistic units
that underlie print-to-speech conversion across orthographies (Asfaha, Kurvers, &
Kroon, 2009; Duncan et al., 2013; Morais, Alegria, & Content, 1987; Nag, 2007;
Schmalz et al., 2014; Taft & Radeau, 1995). As the psycholinguistic grain size theory
focuses mainly on orthographic depth – the degree to which small units are predictive
of the correct pronunciation – it makes no direct predictions about other factors which
may influence the reliance on particular sublexical units or correspondences. A
description of language-level differences beyond orthographic depth, and how these
could affect specific cognitive mechanisms would generate a wealth of testable
predictions. Moving beyond orthographic depth would help to create a framework of
reading and reading acquisition that is not limited to alphabetic orthographies
(Schmalz et al., 2015; Share, 2014).
In addition to such language-level factors, future theoretical work on the
psycholinguistic grain size theory could also clarify psychological factors and
constraints that drive reliance on different types of sublexical units. The main claim is
that statistical inconsistency causes reliance on larger units. However, most
orthographies contain some level of inconsistency, and it is always necessarily the

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case that taking into account larger units – which maximises the informational value
of the processed string – reduces ambiguity. Indeed, research has shown that readers
of orthographies which are generally considered shallow rely on sublexical units that
are larger than graphemes, namely bodies in German (Schmalz et al., 2014) and
syllables in Spanish (Carreiras, Alvarez, & Devega, 1993). This is broadly consistent
with the psycholinguistic grain size theory, in the sense that it is in line with the
notion that readers of all orthographies reduce inconsistency by relying on larger
units. However, it becomes unclear why one would expect cross-linguistic differences
in “the size of the dominant spelling units, the number of different grain-size levels,
and the reader’s flexibility to switch between different levels” (p. 383, Ziegler et al.,
2001), as it is advantageous for readers of any orthography to rely on larger units and
to flexibly switch to smaller units when they are confronted with unfamiliar spelling
patterns.
In summary, there are two theoretical challenges for future work on the
psycholinguistic grain size theory. First, predictions about factors beyond
orthographic depth would help to provide a deeper understanding of the specific
language-level variables that influence the reliance on various sublexical units, and
how the cognitive mechanisms interact with language-level factors during reading
acquisition. Second, a consideration of cognitive mechanisms that drive cross-
linguistic differences may help to clarify how the specific statistical distributions of a
given orthography may encourage readers to prefer units of a particular type.
4.5. Conclusion
In summary, the psycholinguistic grain size theory has proposed that readers
of deep orthographies, such as English, rely on large units such as bodies to greater
extent than readers of shallow orthographies, such as German. In the current paper,

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we show that there is no convincing evidence for this claim. We conclude that a
confirmatory analysis is needed to provide stronger evidence for or against the
psycholinguistic grain size theory and its prediction that orthographic depth affects
the reliance on large sublexical units, after controlling for confounds such as reading
instruction.
In the meantime, we propose that the routine reliance on small versus large
orthographic units does not depend on the orthographic depth of the orthography.
Instead, the existing evidence supports the Orthographic Depth Hypothesis, that
lexical processing becomes relatively more important if the sublexical information is
difficult to decipher (Katz & Frost, 1992). An important contribution of the
psycholinguistic grain size theory lies in sparking interest in the use of different
sublexical units across orthographies. Even in the absence of cross-linguistic
differences in the reliance on orthographic bodies, future empirical and theoretical
research could aim to establish the linguistic and psychological factors that affect the
choice of units across orthographies.

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Author note
Correspondence concerning this manuscript should be directed to XS, email:
xenia.schmalz@gmail.com.
We thank Marcus Taft for providing his body-N data, and for helpful
comments on an earlier version of this manuscript, Sachiko Kinoshita for insightful
discussions about methodological and statistical issues, and David Balota and Melvin
Yap for providing the trial-level ELP data. We are further grateful for feedback from
Becky Treiman, Karin Landerl, and Conrad Perry, on an earlier version which was a
part of XS’s doctoral thesis, and to Jo Ziegler, for responding to our queries about the
body-N data.