Individual differences in reading aloud:A mega-study, item effects, and some models
James S. Adelmana,∗, Maura G. Sabatos-DeVitob , Suzanne J. Marquisa ,Zachary Estesc
a Department of Psychology, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, U.K.
b Department of Psychology, University of North Carolina at Chapel Hill, Davie Hall, Chapel Hill, NC

27599, U.S.A.

c Department of Marketing, Bocconi University, Via Roentgen 1, 20136 Milan, Italy

Abstract
Normal individual differences are rarely considered in the modelling of visual
word recognition — with item response time effects and neuropsychologicaldisorders being given more emphasis — but such individual differences caninform and test accounts of the processes of reading. We thus had 100 participants read aloud words selected to assess theoretically important item response time effects on an individual basis. Using two major models of reading aloud — DRC and CDP+ — we estimated numerical parameters to bestmodel each individual’s response times to see if this would allow the models to capture the effects, individual differences in them and the correlations
among these individual differences. It did not. We therefore created an alternative model, the DRC-FC, which successfully captured more of the correlations
among individual differences, by modifying the locus of the frequency effect.
Overall, our analyses indicate that (i) even after accounting for individual differences in general speed, several other individual difference in reading remain
significant; and (ii) these individual differences provide critical tests of modelsof reading aloud. The database thus offers a set of important constraints for
future modelling of visual word recognition, and is a step towards integrating∗ Corresponding Author. Telephone: +44 (0) 24 761 50233. Fax: +44 (0) 24 765 24225

Email address: j.s.adelman@warwick.ac.uk (James S. Adelman)

Preprint submitted to Elsevier

October 31, 2013

such models with other knowledge about individual differences in reading.
Keywords: visual word recognition, word naming, individual differences,computational modelling, reading aloud

1. Introduction
The identification of individual words is one important component of reading, and one that has been subject to extensive empirical studies, and also
theoretical work in the form of computationally explicit implemented models. One of the most extensively modelled tasks is word naming (or reading
aloud), in which participants read aloud words or pseudowords presented inisolation. The major empirical phenomena that models have addressed are impaired reading — found in acquired and developmental dyslexia — and item
effects. Item effects are comparisons between words that differ on some specificdimension, such as length or frequency, usually in terms of response times(RTs).
According to the developers of models such as the dual-route cascadedmodel (DRC; Coltheart, Curtis, Atkins & Haller, 1993; Coltheart, Rastle, Perry,
Langdon & Ziegler, 2001) and the connectionist dual process model (CDP+;
Perry, Ziegler & Zorzi, 2007; Zorzi, Houghton & Butterworth, 1998), and to
those who compare models to data (e.g., Adelman & Brown, 2008a; Besner,
1999; Reynolds & Besner, 2002, 2004), the goal of such modelling is a complete1
and detailed account of human visual word recognition, which is indexed bya precise correspondence to the observed data. Whilst our understanding of
impairments to reading has informed theories of visual word recognition, theconstraints from these data are at a relatively high level. Consequently, thefiner details of word recognition processes have been more readily examinedusing item effects shown by the average of a population of (mostly unimpaired,
1 Although as the science progresses, models will be in some way incomplete — for instance,these models lack semantic processes — the goal of completeness means that phenomena that themodels were not designed to explain can be used to test these posited details.

2young adult) undergraduate readers (but see Ziegler, Castel, Pech-Georgel,
George, Alario & Perry, 2008, for an exception).
1.1. Average data and individual differences
1.1.1. Importance of individual differences
This concentration on average effects in skilled readers has been an important source of progress, but one could also raise the concern that this focus if itstood alone would be too narrow for models that seek to be a complete explanation of word recognition, because of individual differences in reading. One
such concern is practical: Knowledge about word recognition can be applied toeducational settings, including reading difficulties. If this knowledge included
a theoretical understanding of individual differences in reading, this might beapplied in the development of individual reading (and reading-related) education programs, tailored to the cognitive strengths and weaknesses of theindividual student. A related concern is that the array of knowledge from developmental studies of individual differences in reading renders the scope ofmodels that do not account for individual differences incomplete.
1.1.2. Misleading nature of average data
Moreover, it is well-known that average performance patterns can differ
from the average’s constituent patterns of performance (e.g., Brown & Heathcote, 2003; Estes, 1956).
1.1.3. Inferences from individual differences
In any case, patterns of individual differences in performance may simply
form additional constraints on models, giving indications as to the correct accounts of the item effects that might not emerge from the average item effects.
Such indications may come because of the implications of common loci of effects. For instance, some effects — length and position of irregularity — are
attributed by models such as DRC and CDP+ to the left-to-right sequential processing in spelling-sound conversion. If this is the case, these effects should be3susceptible to the same causes of individual differences, and those individuals
who are particularly susceptible to one effect will also be particularly susceptible to the other, inducing a correlation in the sizes of the effects. In contrast,
effects attributed to separate components, such as length and frequency might
be expected to show no such relationship, or a negative relationship if there issome trade-off in emphasis between the components.
Using item RT effects nevertheless leaves a complex picture, because theseeffects are influenced by several parameters, of which at least some should reflect individual differences, and the values these parameters take might be correlated. Depending on these correlations (and other distributional properties),
different patterns of correlations among effects might be found. For instance, adual-route model might have routes whose strengths trade-off against one another, producing negative correlations among effects arising in the two routes,
but it could instead have positively correlated route strengths due to generalability or speed, which would result in all effects correlating positively.
As a consequence, without data to constrain the distribution of parameters of a given model for different individuals, it is essentially impossible togive a general characterization of the patterns that model predicts. Future developments of models may allow many or all these parameters to be set byreference to data about other cognitive abilities (and others may be fixed forall participants). Ziegler et al. (2008) used tasks to measure individuals’ levelsof letter processing, (phonological) word processing and phoneme processing
and added noise to the corresponding systems of the DRC to reflect deficits inthese processes, with the magnitude of the noise varying parametrically withthe magnitude of the deficit; however, this approach is currently incomplete
as other parameters are also important for model performance and could varyacross individuals.
The data that are available to be used to constrain any simulations are theto-be-explained reading aloud data. At first glance, this might seem to make it
too easy for a model to fit the data. To be sure, if we ask a complicated modelto adapt to one or two effects that may be summarized with a handful of data4

points, it may be so easy that any model that complicated could fit the data. If
we instead ask a model to fit a complex of data, comprising of many effects andtheir magnitudes, the constraints imposed by the structure of the model may
become more important than its flexibility (and this is true in general).
For instance, it may be true that for one set of parameter settings, a model
produces some effect A, and for another parameter set, effect B, but there areno parameter sets for which both of A and B occur, whilst both effects are evidenced in the same data set. That the explanations instantiated by the modelfor these two effects are not compatible would be evidence against such a model.
In practice, the precise cause of the model’s problems will be difficult to isolate
when this is the case: Attempts to select parameter sets that reproduce the data
will show that the model does not predict one or other of the effects (and which
it does not show may vary with slight variations in procedure). Such a patternneed not show that the model cannot predict the effect, but it nevertheless is
symptomatic of an inconsistency between model and data. The size of each
effect can also be considered a constraint for selecting parameter values, which
exacerbates the just-described difficulty. As more and more effects are considered, identifying any particular type of model misspecification becomes evenmore difficult. The ability to detect problems with a model remains even if parameters are permitted to vary. To say this another way: A demonstration ofincompatibility for a particular model would be a falsification of that particularmodel2 .
Examining individual differences not only allows us to take this approach
further but also addresses a problematic assumption implied by ignoring suchdifferences: A valid objection to rejecting a model that does not show effects A
and B together is that there may be individual participants who show effect A
and some others who show effect B, but none who show both, exactly as themodel proposes. Only individual data can address such claims. Moreover, ex2 A particular model here refers to a particular version of a model, not all models that mightbear the name.

5amining individual data allows more patterns to be added to each effect and its
average size: First, the several patterns of compatible effect sizes of each participant, and second, the summary of this by way of the correlations of effects.
In summary, constraints from data on models come from data patterns interpreted as effects and the compatibility of these effects, which we may augment with patterns from individual differences because these give evidence offurther (and less misleading) patterns of compatibility that models must address.
1.1.4. Insufficiency of comparisons of good and poor readers
It is thus important to attempt to isolate variation on several dimensions
to distinguish different possible mechanisms involved in visual word recognition. Whilst reading success is the ultimate phenomenon of interest, item
effects in word naming are side-effects of the processes involved in the visual
word recognition component of reading, and these effects constrain the natureof these processes beyond what they achieve to how they achieve it. Various
studies have examined how these effects differ between better and poorer readers (e.g., Ashby, Rayner & Clifton, 2005; Kuperman & Van Dyke, 2011) but several factors can lead to better or poorer reading in humans (see, e.g., McClung,
O’Donnell & Cunningham, 2012) and in models. Moreover, there might be informative (co)variation in item effects that does not affect reading success perse.
1.2. Previous research
1.2.1. Variability in responses
Ziegler et al. (2008) have successfully used external cognitive predictors tomodify parameters controlling noise injected into the DRC model so as to predict type of words that would be susceptible to word naming errors. Moreover,there is a further line of individual differences evidence relating to responses(rather than their latencies) that constrains models: Andrews & Scarratt (1998)
found that when several individuals were asked to read the same nonwords,

6many stimuli had multiple pronunciations. Pritchard, Coltheart, Palethorpe& Castles (2012) recently compared DRC and CDP+ on a new data set showing similar results. Zevin & Seidenberg (2006) obtained comparable levels ofvariation in PDP simulations by using different (randomly selected) sequencesof words in the training trials for different individuals. Thus, they attributed
the whole effect to unsystematic individual differences. However, this seems
unlikely to be the correct explanation. The magnitude of difference shown issimilar to that which can be explained by systematic individual differences introduced by reading instruction (Thompson, Connelly, Fletcher-Flinn & Hodson, 2009). Moreover, there are likely additional differences due to cognitive
ability (McClung et al., 2012; Kuperman & Van Dyke, 2011); and there may bevariability in responding that occurs within individuals (in none of these studies did a participant read the same stimulus twice) rather than being due toindividual differences.
1.2.2. Individual differences in item effects and external predictors
Some other studies have directly addressed the RT differences in word
recognition tasks. One such line of research seeks to link measures of orthographic knowledge or experience to lexical decision performance; there isevidence (Chateau & Jared, 2000; Sears, Siakaluk, Chow & Buchanan, 2008)
that print exposure predicts the strength or quality of orthographic-lexical processes in lexical decision as assessed by neighbourhood size and frequency
item effects. That the neighbourhood size and frequency effects are both predicted by the same variable suggests of a common locus related to the strengthor quality of orthographic-lexical processes. In turn, this suggests these effects
should be positively correlated.
A related line of research addresses a similar issue, but with form priming
effects: Better spellers show inhibitory priming effects where poorer spellers
show facilitatory ones, consistent with an account in terms of precision of lexical representations (Andrews & Hersch, 2010). Vocabulary has also been linked
to word length effects (Butler & Hains, 1979), and frequency effects in both first7

and second languages (Diependale, Lemhöfer & Brysbaert, 2013); and moregenerally Yap, Balota, Sibley & Ratcliff (2012) found that higher vocabulary
was associated with more rapid responding, and lesser sensitivity to item effects.
1.2.3. Individual differences in item effects and relations among themMore similarly to our present approach, Yap et al.’s (2012) analysis of word
naming and lexical decision latencies from the English Lexicon Project (ELP:
Balota, Yap, Cortese, Hutchison, Kessler, Loftis, Neely, Nelson, Simpson &Treiman, 2007) also included an analysis of the correlations of item effects (orrather their principal components). One important question they addressed
was whether two distinct processes — linked to larger (word-sized) andsmaller (grapheme-sized) units — might be seen to trade-off across individuals. They found no such evidence, but their measure of the process withsmaller units was based on effects not necessarily due to the smaller unit
process in implemented models such as the DRC (Coltheart et al., 2001). Moreover, the ELP followed the mega-study approach of seeking to obtain good
estimates of average performance of essentially the whole of English, withindividual differences analyses as a secondary goal (see also Balota, Cortese,
Sergent-Marshall, Spieler & Yap, 2004; Balota, Yap, Hutchison & Cortese, 2012).
Some of the ways in which the ELP was designed to address the primary goal
and makes it useful for other types of analysis did however mean that theindividual differences analyses Yap et al. could perform did not permit a directcomparison with model simulations. First, participants were not assessed on
the same words as one another, which weakens the comparability of effectmagnitude estimates from different individuals. Second, most words in theexperiment were polysyllabic because most English words are polysyllabic;
whilst this makes the sample more representative, many predictors and mostmodels are only applicable to monosyllables. This limitation on the scopeof the models meant that Yap et al. could not directly address these models.
Third, assessment was based on principal components of item variables, not8directly upon theoretically interesting item effects because lexical variables
underlying item effects are intercorrelated in the language. Such a proceduremight, however, subsume effects with different causes in a single principal
component3 . Finally, adjustments for general speed could not be based on
individual participant reliabilities, because participants did not read the sameword more than once.

Our observation that existing databases were not

optimal for the kind of individual differences modelling that interested us
motivated the collection of new data regarding major item effects for latermodelling.
1.3. Major item effects
Certain item effects are so ubiquitous as to be taken as basic effects that amodel must incorporate, whilst others are important because they highlightcontrasts between theoretical positions. The list of variables that have been
claimed to affect word naming response times is extensive, so this discussion
focusses on those that have received a modelling response and whose source
in a given model would be important for understanding individual differences(and so were included in the experiment described below; for a longer list, see
Adelman, 2012). We will argue that individual differences among these readers
can also be informative about the mechanisms of reading. Such individual
differences will be seen in theoretically interesting item effects, and not only
overall performance.
1.3.1. Frequency
Response times are shorter for more frequent words than less frequentwords (e.g., Forster & Chambers, 1973). In models that posit parallel activationof individual units for each word (localist models), the difference may reflect
a lower required threshold of activation for frequent words (e.g., Morton,
3 Correlations among effect sizes across people are suggestive of cognitive locus in visual word
recognition. But the correlations used in the principal components analysis are correlations amonglexical variables across words, wich are suggestive of locus in language evolution, not locus in
visual word recognition.

91964), or a biasing input that inhibits activation of infrequent words (e.g.,Coltheart et al., 2001).

In models that learn parallel connections without

such individualised units (distributed representation models), more relevant
weight adjustments occur for frequent words (e.g., Seidenberg & McClelland,1989). Other models suggest that more frequent words have higher priority
in a sequential search of known lexical items (e.g., Forster, 2012). In termsof individual differences, effects that correlate positively with the size of thefrequency effect might be related to a process sensitive to details of individual
words; such a process is a lexical process or route.

Those that correlate

negatively might be related to another process that complements and trades
off with the lexical process.
1.3.2. Length and lexicality
Nonwords and to a lesser extent low frequency words are read more slowly
if they are long (have many letters) than if they are short (e.g., Weekes, 1997).
Dual-route theorists have interpreted this as due to a left-to-right nonlexical
process (composing a pronunciation on the basis of the components of its
spelling) that is slower to complete for longer stimuli, and is more influential
for stimuli with an absent or weak lexical route contribution to pronunciation (Coltheart et al., 2001). Alternative interpretations include articulation
preparation being sensitive to utterance length and reduced quality of the
input code for longer words due to information compression (Chang, Furber& Welbourne, 2012); these mechanisms affect different stages of the word
naming process, and so are not mutually exclusive. Nonwords are also readslower than words, because of their lack of a stored representation. The size of
this advantage for words will depend on the relative efficiency and strength of
the two routes: A stronger lexical route and weaker nonlexical route should
lead to a greater lexicality effect in favour of words. Whether individual
differences in the length effect can be modelled as differences in the efficiencyof spelling-sound translation (in a manner compatible with other effects) will
be informative as to its cause.
101.3.3. Neighbourhood size
The neighbourhood of a word is the set of words that are similar to it — its
neighbours — and the neighbourhood size (often denoted N, following Coltheart, Davelaar, Jonasson & Besner, 1977) is the count of these neighbours. A
variety of types of neighbourhood size have been considered, most commonly
the orthographic (or Coltheart’s) N, where a neighbour is defined as a wordthat can be created by replacing one letter with another in the same position(e.g., BOG is a neighbour of DOG). On balance, words with more neighbours
are read faster (Andrews, 1997; Mathey, 2001) albeit with several qualifications. The effect is somewhat surprising insofar as identifying a word uniquely
should be harder if it has many neighbours with which it could be confused.
This effect arises in models because similarly spelled words are usually similarly pronounced thus providing partial support for the correct pronunciation,although Andrews (1992) offered an alternative explanation in terms of topdown support for letter identities. Adelman & Brown (2007) conducted analyses of megastudies that suggested the effect was limited to phonologically similar orthographic neighbours (the phonographic neighbours, Peereman & Content, 1997), consistent with the phonologically-based interpretation. Indeed,
the number of phonological neighbors (i.e., ignoring orthography) appears tohave a unique influence (Mulatti, Reynolds & Besner, 2006; Yates, 2005), although this may require orthographic overlap (Grainger, Muneaux, Farioli &Ziegler, 2005). Given the strong correlations among these variables, we did notselect items to manipulate them orthogonally for the present study. The extent
to which neighbors are activated is one way in which individual differencesin reading skill have been found to manifest in other paradigms (Andrews &Hersch, 2010; Sears et al., 2008).
1.3.4. Consistency
The kind of association of similar phonological forms for similar orthographic forms described above could be considered in more general terms asspelling-sound consistency. Most commonly, such consistency has been con11

sidered empirically in terms of rime consistency, the extent to which words of
one syllable that have the same bodies (the end of the orthographic word fromthe orthographic vowel onwards) have the same rimes (the end of the phonological word from the vowel onwards). For instance, MINT and LINT rhyme
and so are consistent, or friends, but PINT is inconsistent with them, and is anenemy of both. Words that have many friends and few enemies are read quicker(e.g., Jared, McRae & Seidenberg, 1990). This may be explained through partial support from similar words, although it has alternatively been proposedthat body-sized units may be used in a nonlexical conversion process (e.g.,Coltheart, 1980, 2012) of a type that is usually implicated in effects of regularity. The relationship of individual differences in consistency effects to those inneighbourhood size and regularity effects ought to constrain its interpretation.
1.3.5. Regularity and Position of Irregularity
Such regularity effects give an alternative or additional reason that a word
like PINT might be read slowly: If there is a nonlexical conversion that translates spelling to sound using (grapheme-sized) sublexical rules such as I → /I/,then this mechanism will produce the incorrect pronunciation, which would
conflict with or fail to support the correct (known) pronunciation. Words that
follow such rules, known as regular words, are read more quickly than exception
words that do not. Moreover, exception words whose departure from the rule
is to the left in the word exhibit a greater slowing relative to their controls than
do those whose irregularity is further to the right (Rastle & Coltheart, 1999).
One major debate in the reading aloud literature has surrounded whether such
effects can be subsumed under processes sensitive to various forms of consistency (e.g., Zorzi, 2000) or require a rule-based approach.
The relationship between regularity and consistency effects is important toascertain their interpretations, and the position of irregularity effect’s relationship to length effects gives a test of their supposed common locus.

121.4. Models
Models that have been implemented to make quantified predictions
about word naming are the dual-route cascaded model, the connectionist
dual-process model and a variety of backpropogation models.
1.4.1. Dual-route cascaded model
The DRC (Coltheart et al., 1993, 2001) as a dual-route theory combines tworoutes: a lexical route that uses knowledge of specific words, retrieving a stored
pronunciation, and a nonlexical route that parses stimuli into units smaller
than the word to determine a pronunciation. Its lexical route is based on an
extension of the interactive-activation and competition model (McClelland &Rumelhart, 1981). Its nonlexical route uses a set of classical rules for converting graphemes (letters or short sequences of letters that represent individualphonemes) into phonemes; these are applied in a temporally left-to-right fashion. The nonlexical rule route is responsible for length effects, the regularity
effect, and the regularity effect’s sensitivity to position. To the extent that thestrength or speed of this route might vary, we would expect the magnitudeof these three effects to be controlled by this strength/speed and so correlate
positively with another.
Frequency effects have their locus in the lexical route, as do neighbourhood
effects (Reynolds & Besner, 2002), so naı̈vely we would infer these should positively correlate. Lexicality effects relate to the advantage conferred by the lexical route to words, and so should also be in this group, although this may not
be the only influence on lexicality effects.
The situation with consistency effects is less clear. Whilst some consistency effects (those of Jared, 1997) have been explained as nonlexical route
confounds, Coltheart (2012) has suggested that parameter modifications mightmake the model predict consistency effects observed without such confounds
via the lexical route in the same manner as neighbourhood effects. Exploring
parameter sets to match individual participants will discover whether this isfeasible.
13

In any case, these can only be tentative predictions because (i) verbal analyses may not accurately reflect the properties of the mechanisms in the model;(ii) simulations with arbitrary parameter values need not reflect the behaviorof the model in other areas of the parameter space; and (iii) exhaustive analysisof the parameter space is computationally infeasible.
1.4.2. Connectionist dual-process model
The CDP+ (Perry et al., 2007) uses a similar set of lexical components to
the DRC, but its nonlexical components are based on a two-layer connectionist
learning procedure (in constrast to DRC’s symbolic rules), as well as having
slightly different phonological representations and decision rule. The connectionist learning procedure implies that this system uses information about how
often different phonemes associate with particular graphemes, rather than associating individual rules to graphemes. At the level of assigning effects tocomponents, this more graded nonlexical route is the source of consistency
effects. As such, consistency effects clearly should be more associated withlength and exception effects than lexical frequency effects. Otherwise, our (tentative) expectations regarding the CDP+ would be quite similar to those for theDRC.
1.4.3. Backpropagation (“PDP”) models
A variety of models have used the connectionst backpropagation learning
rule to learn connection weights in a multilayer network with hidden units
to model various aspects of visual word recognition (e.g., Plaut, McClelland,
Seidenberg & Patterson, 1996).

These are commonly known as parallel-

distributed processing — or PDP — models (although this is arguably a
broader term), and are conceptualised within a ‘triangle’ framework describedby Seidenberg & McClelland (1989). This framework posits three explicit
representations of stimuli — orthographic, phonological, semantic — that areeach linked to the other by way of distributed processing (in hidden units), diagramatically forming a triangle. Stimuli are read aloud by influences coming

14

from the two routes linking orthographic and phonological representations:the direct route (via only hidden units) and the semantically-mediated route.
Coltheart (2012, Table 1.1) lists six variants of this type of model that havebeen used to model various effects. Coltheart points out that these models
have each been developed to model a relatively circumscribed set of effects
that does not always subsume those of its predecessors, and, moreover, thatdifferences between a model and its predecessors are not always justified byreference to a better explanation of the particular phenomena the later model
explains. Thus there is no current model that represents the sum knowledgeof the best model of its class (to which its predecessors are an approximation).
This contrasts with the approach of DRC and CDP+ modelers, whose latermodels are presented as replacements of previous models as the state of theart, justified by improvements in overall compatibility with the data.
This difference reflects a difference in modeling goals: For the most part,
PDP modelers have argued that modeling is and should be used to elucidate
general principles (Seidenberg & Plaut, 2006) and to show that these can leadto a small number of empirically observed phenomena of current key interest.
For this reason, compatibility is only evidenced between models similar at thelevel of general principles, with lower-level detail free to vary for convenience.
In contrast, such lower-level detail is considered to be of theoretical interest
by other modelers (including Coltheart et al., 2001; Perry et al., 2007), becausesuch details affect the predictions the models make, and so presumably are
critical to understanding the precise behavior of humans across a variety ofsituations and phenomena. Thus (in the absence of an interacting factor that
influences a parameter; e.g., Rastle & Coltheart, 1999, Exp. 2) compatibility issought within a single model with no changes. For PDP modellers, the explanation of a phenomenon is created by the modeler and necessarily precedes
and motivates the model, which validates the explanation if it produces thephenomenon. For other modellers (in this context, Coltheart and colleagues
and Perry and colleagues), analysis and examination of a model may give theexplanation for a phenomenon (whilst examination of phenomena may also15

suggest mechanisms a model requires); such an explanation is validated by
the validation of the model by producing several phenomena.
The current approach to individual differences relies upon having a single
model — except for the possibility of parameter changes — for different effects
to see what the detailed mechanisms of that model imply about individual differences. This is only a coherent goal (i) if the model is an account of the effects
in which individual differences are being examined, and (ii) if the model is infact a hypothesis about a complex of mechanisms. This has not been the casefor the PDP models listed by Coltheart (2012), and so it makes little sense toinvestigate them in this manner4 . Thus, we could not simultaneously model
individual differences in these effects with these PDP models because no single
PDP model explains all these effects: each phenomenon has its own explanation within the broader framework, rather than them sharing mechanisms in asingle computational model. Our modelling, therefore, does not include a PDP
model.
1.5. The present study
We provide the first study designed to examine individual differences initem effects in a way that can directly assess models. We sought to apply
to word naming the correlational logic of individual differences and models
described above (§1.1.3), mindful that verbal analysis of models is simplisticcompared to the actual function of the model processes. We collected new
data to examine individual differences in the major item effects — frequency(e.g., Forster & Chambers, 1973), irregularity (e.g., Rastle & Coltheart, 1999; Seidenberg, Waters, Barnes & Tanenhaus, 1984), consistency (e.g., Glushko, 1979;
Jared et al., 1990), length (e.g., Weekes, 1997), and neighbourhood size (e.g., Andrews, 1992), and the correlations between them. We also examined the abilityof two existing implemented models of reading aloud — the DRC (Coltheart
4 One recent backpropagation model has gone some way towards including key effects addressed by no previous such model (Chang et al., 2012), but it does not simulate response times (itproduces error scores that do not relate directly to processing time).

16

et al., 2001) and CDP+ (Perry et al., 2007) models — to account for these differences in simulations. To the extent that the models cannot account for the data,
our results would identify problems with these models and suggest how theymight be corrected.

2. EXPERIMENT
2.1. Method
2.1.1. ParticipantsParticipants (N = 100) were 32 staff and 54 students of the University of
Warwick, supplemented by 14 others who responded to local advertisements.
Ages ranged from 17 to 55 years (mean 28 years, median 23 years). All were
tested as part of a larger study and were paid (prorated over the whole study)
approximately £10 for their participation in this experiment (ca. U.S. $15). A
questionnaire was administered to gather information about the participants’language, academic and work history and to screen for any potential readingor learning disabilities. All participants were native English speakers and all
but two reported normal or corrected-to-normal vision (the others reporting
essentially monocular vision with normal corrected acuity).
2.1.2. Stimuli
Details follow of the 592 monosyllabic words and 93 nonwords that wereused to measure key marker effects in visual word recognition including: frequency, length (and lexicality), neighbourhood size, position of irregularity(and regularity), and consistency (and regularity). For the first three effects,
new stimulus sets were selected using CELEX (Baayen, Piepenbrock & Gulikers, 1995). For the remaining two effects, stimulus sets were taken from Rastle
and Coltheart (1999, Exp. 1) and Jared (2002, Exp. 1). Due to programming
error, the 26 stimuli (of which 18 were exception words) that were in both Rastle and Coltheart’s and Jared’s experiments were repeated, and data from thesecond trial with a given word in any session overwrote that for the first.

17

Table 1: Mean stimulus characteristics (standard deviations in parentheses) for words selected totest frequency effects in naming.

1 ppm

3 ppm

9 ppm

27 ppm81 ppmCELEX
Freq.
loge Freq.

20.24
3.00

(3.16)(0.16)

67.24 (15.70)
4.19 (0.21)

187.535.21(44.28)(0.22)

546.626.30(45.02)(0.08)

1586.197.35(318.39)(0.18)SUBTLEX
Freq.
loge Freq.

2.10
0.15

(2.62)(1.15)

7.20
1.27(8.38)(1.30)

13.06
2.24(10.24)(0.89)

68.31
3.37(123.44)(1.22)

106.304.30(106.99)(0.85)

Orth. N
PhGr. N
Consist.

10.85
8.10
0.93

(4.85)(3.40)(0.10)

11.10
8.57
0.93

(6.28)(5.95)(0.17)

11.95
10.00
0.96

(4.59)(4.31)(0.07)

11.00
8.57
0.94

(6.55)(5.81)(0.17)

11.57
7.67
0.92

(4.86)(4.42)(0.17)

Note — F-values are for a repeated-measures ANOVA (on the basis ofmatched quintuples) predicting the lexical statistic from the frequency band.
Freq. stands for frequency. CELEX frequencies are based on raw values (per17.9 million); SUBTLEX frequences are per million. ppm stands for parts(i.e. tokens) per million. Orth. N stands for orthographic neighbourhood
size. PhGr. N stands for phonographic neighbourhood size. Consist.: Rime
consistency calculated on a types basis (friends per friends plus enemies).
Frequency stimuli. 21 quintuplets of words were selected from five CELEX frequency bands, defined by the ranges 15–26, 51–107, 132–308, 479–657, and
1087–2649 (raw frequency; per 17.9 million words), each designed to be 3 times(or 1.1 loge -units) more frequent than the last. Similar differences between
bands were shown with the SUBTLEX frequency norms (Brysbaert & New,
2009). Within each quintuplet, words were matched on orthographic lengthand onset, and as closely as possible on orthographic N and rime consistency.
All words were regular according to DRC rules. Summary statistics are given
in Table 1, and the items are listed in Appendix A.
Neighbourhood stimuli. 42 pairs of words were selected with one member of
each pair low on neighbourhood size (both orthographic and phonographic)
and the other high on neighbourhood size. Items in each pair were matchedon onset and orthographic length and approximately matched on total CELEX
frequency. All were regular according to DRC rules. After data collection,
SUBTLEX frequencies were examined, which revealed that two stimuli (BITCH
18

F (4, 80)

Sig.

0.275
1.184
0.272

ns
ns
ns

Table 2: Mean stimulus characteristics (standard deviations in parentheses) for words selected totest neighbourhood effects in naming.

Low N

High NF (1, 39)Sig.

Orthographic N
Phonographic N
Phonological N
Rime Consistency

4.85
3.68
19.75
.96

(1.96)(1.72)(13.09)
(.14)

15.68
12.65
29.43
.93

(3.97)(3.25)(9.75)
(.14)

1.12

ns

CELEX
Frequency
loge Frequency

745.3
4.93

(1833.2)(1.84)

787.4
4.99(1980.8)(1.78)

0.01
0.02

ns
ns

SUBTLEX
Frequency
loge Frequency

35.7
1.66

(96.1)(1.95)

42.1
2.02(129.3)(1.77)

0.12
2.48

ns
ns

Note — F-values are for a repeated-measures ANOVA (on the basis ofmatched pairs) predicting the lexical statistic from the neighbourhood condition. CELEX frequencies are based on raw values (per 17.9 million); SUBTLEX
frequences are per million. Rime consistency was calculated on a types basis(friends per friends plus enemies). N stands for neighbourhood size.
and NOPE) were of substantially higher spoken frequency than the stimuli towhich they were paired (BEARD and NOUN); these pairs were excluded fromanalysis.
Summary statistics (for the 40 analysed pairs) are given in Table 2, and theitems are listed in Appendix B.
Length stimuli. 31 triplets of three, four, and five-letter monosyllabic words
were selected. In each triplet, words were matched on onset and length andapproximately matched on CELEX frequency and rime consistency; SUBTLEX
frequency was also not significantly associated with length. Neighbourhood
sizes could not be matched. All stimuli were regular according to DRC rules.
Summary statistics are given in Table 3 and items are listed in Appendix C.
93 nonwords whose regular pronunciation was monosyllabic were chosen to
match the word stimuli in length and phonemic onset (when pronounced regularly).

19

Table 3: Mean stimulus characteristics (standard deviations in parentheses) for words selected totest length effects in naming.

3 letters

4 letters

5 letters

F (2, 60)

Sig.

CELEX
Frequency
loge Frequency

629.66 (634.79)
5.27 (1.33)

396.61 (515.93)
5.29 (1.27)

414.32 (522.42)
5.33 (1.27)

0.658
0.498

ns
ns

SUBTLEX
Frequency
loge Frequency

37.19 (64.76)
1.60 (2.54)

24.99 (37.18)
1.66 (2.17)

40.38 (85.99)
1.74 (2.30)

0.798
1.365

ns
ns

0.96
16.32
13.52

0.95
12.03
9.36

0.98
4.13
3.61

0.792
103.230
67.146ns
p < .001
p < .001

Rime Consistency
Orthographic N
Phonographic N(0.05)(4.32)(4.07)(0.13)(4.25)(4.03)(0.06)(1.77)(1.65)

Note — F-values are for a repeated-measures ANOVA (on the basis of matched
triples) predicting the lexical statistic from the length (as a factor). CELEX
frequencies are based on raw values (per 17.9 million); SUBTLEX frequences
are per million. Rime consistency was calculated on a types basis (friends per
friends plus enemies). N stands for neighbourhood size.
Consistency stimuli. These were taken from Jared (2002, Exp. 1). The stimuli were 160 low-frequency monosyllabic words, divided into 80 inconsistent
words (40 exception words, 40 regular words) and 80 consistent words (all regular according to DRC rules). Consistency was defined on the basis of wordbodies. The 80 regular-consistent words were divided into four groups of
20 and matched to the 40 exception-inconsistent and 40 regular-inconsistent
words for length, initial letter and phoneme, frequency, mean summed frequency of friends and mean bigram frequency.
Position of irregularity stimuli. These were selected from Rastle and Coltheart(1999, Exp. 1). They included 88 monosyllabic, three- to six-letter exception
words from the CELEX database whose divergence from the DRC’s graphemephoneme conversion rules occurred at the first letter position (20), second letter
position (39), or third letter position (29). Each exception word was matchedto one of 88 regular words on number of letters and initial phoneme, and all
stimuli were low frequency.

202.1.3. Apparatus
The stimuli were presented on a Sony CPD-G200 17” monitor driven by an
NVIDIA GeForce 7025 graphics card. Responses were collected via a Plantronics Audio 370 gaming headset with microphone attached to an Ensoniq 5880
AudioPCI sound card. To code response (voice onset) times, the third author
conducted a visual and auditory inspection of wave forms on the basis of an
estimate output by custom voice key software written by the first author forthe analysis of Adelman, Marquis, Sabatos-DeVito & Estes’s (2013) data. Error
responses, those where no single onset could be identified, and those where
the initial phoneme was realised in a non-standard manner (e.g., /θ/ for /t/)were excluded; for words, all dictionary-listed variants were accepted as correct, and for nonwords, any pronunciation that appeared to be reasonably constructed by rule or analogy from the properties of English words was accepted
as correct.
2.1.4. Procedure
Participants attended three separate sessions, each on a different day. During each session, all 711 stimuli5 were presented in newly randomised order.
Each stimulus was presented on the computer screen for 1500 ms; the intertrial interval varied between 600 and 1000 ms. Participants wore a gaming
headset with microphone, which recorded vocal responses. Participants were
instructed to read the stimuli aloud as quickly and clearly as possible as they
appeared. They were warned that some stimuli would be unfamiliar, and in
those cases, they should take their best guess.
2.2. Results
Our empirical analyses of the data covered the analysis of the replicationof the major item effects; the replicability of these effects over sessions; a com5 This number is made up of 105 (5 × 21) frequency stimuli; 84 (2 × 42) neighbourhood stimuli;
93 (3 × 31) word length stimuli and 93 nonword length stimuli; 160 Jared stimuli; and 196 (2 × 88)
Rastle and Coltheart stimuli. The number 711 thus includes the 26 stimuli that were common to
the Rastle and Coltheart and Jared stimuli and were erroneously repeated.

21

parison with a past mega-study; estimation of effects for individuals and theeffects’ (test-retest) reliabilities; observation of the effect distributions; and observation of the correlations of the item effects.
2.2.1. Replication of standard effects
As a check on our manipulations, we compared the conditions for each ofthem using by-subjects and by-items ANOVAs. The condition means associated with these comparisons are in the first numerical column in Table 4, anda summary of the pattern of significance is in Table 5. Two-tailed p-values arereported in the text except when F is very small or p < .001 (disambiguated by
the F-value).
Frequency effect. A frequency effect, such that more common words are readmore quickly, has been observed repeatedly in word naming (e.g., Forster &Chambers, 1973), though there are some alternative suggestions for the underlying causative factor (e.g., Adelman, Brown & Quesada, 2006; Cortese &Khanna, 2008).
Such an effect of frequency was significant by subjects, F1 (4, 396) = 33.59,
and by items, F2 (4, 80) = 11.16.6
The bulk of the effect was carried by the linear trend (in log. frequency),
F1 (1, 396) = 123.74, F2 (1, 80) = 40.60, with the departure from linearity being
significant only by subjects, F1 (3, 396) = 3.54, p = .015, F2 (3, 80) = 1.34, p =.266.
Overall, the frequency effect replicated previous studies.
Neighbourhood size. Greater orthographic neighbourhood size has often been
associated with shorter naming latencies, especially in English (see Andrews,
1997; Mathey, 2001, for reviews), although other neighbourhood measures
6 The 1 and 3 ppm bands clearly did not differ from one another, F < 0.04 in both analyses, andnor did the 9 and 27 ppm bands, F < 0.7. The difference between the 27 and 81 ppm bands was
significant by subjects, F1 (1, 396) = 7.35, p = .007, but not by items F2 (1, 80) = 2.55, p = .114,
but the difference between 9 and 81 ppm was significant in both analyses, F1 (1, 396) = 12.19,
F2 (1, 80) = 4.08, p = .047.

22

new data

SB97

DRC

CDP+

DRC-FC

552
552
540
538
532

469
460
457
456
453

555
551
547
543
540

554
552
550
548
548

553
550
549
548
549

Neighbourhood size
few
many

544
542

464
463

545
546

548
548

547
547

Word length
3 letters
4 letters
5 letters

542
551
550

454
458
466

544
549
552

551
553
555

549
551
553

Nonword length
3 letters
4 letters
5 letters

587
609
631

n/a
n/a
n/a

593
604
622

593
602
616

597
601
612

Jared (2002) stimuli
Exception (E>F)
Controls
Exception (F>E)
Controls
Inconsistent (E>F)
Controls
Inconsistent (F>E)
Controls

561
544
548
534
562
545
527
532

479
462
473
464
468
463
470
470

558
546
556
542
550
549
532
533

550
547
547
543
549
549
534
532

557
545
552
543
548
548
532
532

Rastle & Coltheart (1999) stimuli
Exception Pos. 1
617
500
Controls
546
464
Exception Pos. 2
563
472
Controls
548
462
Exception Pos. 3
553
490
Controls
552
484

591
552
557
548
559
553

581
555
549
549
549
552

586
553
553
549
556
552

Frequency
1 ppm
3 ppm
9 ppm
27 ppm81 ppmTable 4: Condition means (ms) for data and simulations. SB97 = Spieler & Balota (1997). DRC= Dual-route cascaded model. CDP+ = Connectionist dual process model. DRC-FC = DRC withfrequency-weighted connections.

have been proposed (e.g., Adelman & Brown, 2007; Yarkoni, Balota & Yap,
2008).
The slight advantage for words with dense neighbourhoods was only significant with a one-tailed correction by subjects, F1 (1, 99) = 2.91, p = .091, andnot at all by items, F2 (1, 39) = 0.74, p = .394.
The neighbourhood size effect interacted with age, F1 (1, 98) = 4.31, p =.041, F2 (1, 39) = 5.69, p = .022, with less facilitation for older participants,
23

new data

SB97

DRC

CDP+

DRC-FC

✔
s
✔
✔
✔
✔

✔
0
✔
NW
NW
NW

✔
-SI
✔
✔
✔
✔

✔
-S
✔
✔
✔
✔

✔
S
✔
✔
✔
✔

✔
Si
✔
-S

✔
✔
0
0

✔
✔
S
-S

S
S
S
S

✔
✔
0
0

✔
✔
0
✔

✔
✔
0
✔

✔
✔
✔
✔

✔
0
-Si
✔

✔
✔
Si
✔

✔
✔
✔
✔
✔
✔

ID
ID
ID
ID
ID
ID

✔
✘
0
✔
0
✔

✔
0
0
✔
✘
✘

✔
✔
0
✔
✔
✔

Effects
New stimulus sets
Frequency
Neighbourhood size
Word Length
Nonword Length
Lexicality
Length × Lexicality
Jared (2002) stimuli
Exception (E>F)
Exception (F>E)
Inconsistent (E>F)
Inconsistent (F>E)
Rastle & Coltheart (1999) stimuli
Exception Pos. 1
Exception Pos. 2
Exception Pos. 3
Position × Regularity

Correlations
Exception ∼ Pos. Irr.
Frequency ∼ Neigh.
Lexicality ∼ Neigh.
Lexicality ∼ Word Len.
Lexicality ∼ Exc. (J.)
Frequency ∼ Nonword Len.

Dir.
+ve
+ve
-ve
+ve
+ve
+ve

Table 5: Patterns of significance for effects and correlations of interest. ✔ indicates an effect wassignificant on a two-tailed test (p < .05) in the expected direction or a correlation was significant in
the indicated direction (Dir.); where both by-subjects and by-items analyses were used, ✔ means
significant in both analyses. A negative (-) sign indicates an effect was in the unexpected direction.
S and I mean significant by-subjects and by-items, respectively. s and i mean .05 < p < .1 inby-subject and by-item analyses, respectively (or equivalently p < .05 one-tailed). +ve = positive.
-ve = negative. 0 indicates an effect or correlation was not significant. ✘ indicates a correlation
was significant in the opposite direction to that indicated. SB97 = Spieler and Balota (1997). NW= effect not estimated due to absence of nonwords in experiment. ID = effect not estimated due tonon-publication of individual trial data.

24

consistent with the finding of Spieler & Balota (2000).
Length and lexicality. Nonwords are typically read aloud more slowly thanwords, and longer stimuli are typically read more slowly than shorter stimuli;
for monosyllabic stimuli it has been argued the effect is only present for
nonwords (Weekes, 1997), but analyses of mega-studies with monosyllabic
words do find a small effect (e.g., Adelman & Brown, 2007, 2008a; Balota et al.,2004).
Words. The effect of length such that longer words were read more slowly
was significant by subjects, F1 (2, 198) = 13.04, and by items F2 (2, 60) = 6.64,
p = .002.
Partialing the confounded variable of orthographic neighbourhood size
from item analyses gave adjusted mean RTs of 542, 551 and 549 ms for three,four, and five letter words, respectively. After the (facilitatory) effect oforthographic neighbourhood size, F2 (1, 59) = 5.97, p = .018, was partialled
as a preceding step, the effect of length remained significant F2 (2, 59) = 3.61,
p = .033. Orthographic neighbourhood size was not, however, significant,F2 (1, 59) = 0.10, when added as the last step.
In the uncovaried analyses, the linear trend was strong, F1 (1, 198) = 19.35,
F2 (1, 60) = 9.06, p = .004, but augmented by the quadratic trend, F1 (1, 198) =6.72 p = .010, F2 (1, 60) = 4.21 p = .045. The covaried analysis had a weakerlinear trend F2 (1, 59) = 3.05, p = .086, with a quadratic trend in evidence,
F2 (1, 59) = 4.16, p = .045.
Overall, the length effect for words was in the expected direction and nonlinear.
Nonwords.

The effect of nonword length was significant by subjects,

F1 (2, 198) = 130.99, and by items F2 (2, 60) = 15.10.
Partialing the confounded variable of orthographic neighbourhood size
from item analyses gave adjusted mean RTs of 590, 610 and 629 ms for three,four, and five letter words, respectively.

After the (facilitatory) effect oforthographic neighbourhood size, F2 (1, 59) = 15.59, was partialled, the effect25

of length remained significant F2 (2, 59) = 7.68, p = .001. Orthographic neighbourhood size did not, however, explain unique variance, F2 (1, 59) = 1.50,
p = .226, when added as the last step.
The effect was wholly due to the linear trend: F1 (1, 198) = 261.88,
F2 (1, 60) = 30.19, covaried F2 (1, 59) = 16.59, with no role for the quadratic
trend: F1 (1, 198) = 0.10, F2 (1, 60) < 0.01, covaried F2 (1, 59) = 0.01.
There was a clear, linear, effect of length for the nonwords.
Lexicality. The words were read more quickly (548 ms on average) than the
nonwords (611 ms on average), F1 (1, 495) = 966.57, F2 (1, 150) = 236.23; andthe interaction with length was significant F1 (2, 495) = 29.31, F2 (2, 150) = 7.30.
The lexicality effect was replicated, as was its interaction with length.
Regularity and Consistency. Words whose mapping from spelling-to-sound isunusual are read more slowly than those whose mapping is typical or common (e.g. Glushko, 1979; Seidenberg et al., 1984). Such an effect is indexed interms of following rules (regular words) or not following rules (exception or
irregular words), namely regularity, or in terms of a graded construct involving the number of matching exemplars (friends) and mismatching exemplars(enemies), namely consistency; both regularity and consistency appear to have
independent effects, though the relative frequencies of friends and enemies isimportant for both, with the cost being reliant on the enemies being higher
frequency (Jared, 2002).
Regularity. For Jared’s (2002) stimuli, the exception words whose enemies
were higher frequency than their friends were read more slowly than theirmatched control words, F1 (1, 99) = 56.37, F2 (1, 19) = 10.92, p = .004, as werethe exception words whose enemies were lower frequency then their friends,
though the by-items analysis was only significant one-tailed, F1 (1, 99) = 43.78,
F2 (1, 19) = 3.43, p = .080 (unadjusted for direction).
The regularity effect was therefore replicated, though the effect was lessdependent on the frequencies of friends and enemies than in previous studies.
Consistency.

For Jared’s (2002) stimuli, the regular-inconsistent words

26

whose enemies were higher frequency than their friends were read more
slowly than their matched control words, F1 (1, 99) = 79.37, F2 (1, 19) = 11.97,
p = .003. The opposite was true for the regular-inconsistent words whose
friends were higher frequencies than their enemies: Responses to these words
were faster than to their matched controls, though only signficantly so in theby-subjects analysis, F1 (1, 99) = 5.70, p = .019, F2 (1, 19) = 0.91.
This pattern of a strong consistency effect only for those words whose enemies were higher frequency than their friends replicated the previously observed data.
Position of irregularity. The latency cost for exception words is greater for those
whose irregularities are toward the beginning of the word (Coltheart & Rastle,
1994; Rastle & Coltheart, 1999), the effect being strong for position 1 irregularities, moderate for position 2 irregularities and for position 3 absent in smaller
studies (Rastle & Coltheart, 1999; Roberts, Rastle, Coltheart & Besner, 2003), or
present but weak in mega-study analyses (Adelman & Brown, 2007).
Words with an irregular grapheme-phoneme correspondence in the first
letter produced longer RTs than their matched controls, F1 (1, 99) = 437.80,
F2 (1, 19) = 18.89. Words whose irregularity was in the second letter also
showed an exception cost relative to their controls, F1 (1, 99) = 75.35,
F2 (1, 38) = 9.55, p = .004. No such effect was observed for the third position
irregulars whose RTs were equivalent to their controls’, both F < 0.5.
The interaction between position and regularity was significant,F1 (2, 495) = 256.73, F2 (2, 85) = 15.41.
This replicated the pattern typically found in standard-sized studies of theposition of irregularity effect.
2.2.2. Effects by Session
Given the use of multiple sessions, we examined the effect of session. Overall, responses were faster in session 1 (544 ms) than session 2 (556 ms) thansession 3 (562 ms), F1 (2, 198) = 10.06, F2 (2, 1368) = 409.01.

27

Frequency. The effect of frequency replicated in every session: session 1, with
RTs of 535, 539, 529, 521 and 518 ms, F1 (4, 396) = 19.33, F2 (4, 80) = 8.87;
session 2, with RTs of 554, 552, 541, 539 and 532 ms, F1 (4, 396) = 11.56,
F2 (4, 80) = 6.69; session 3, with RTs of 563, 561, 546, 550 and 545 ms,
F1 (4, 396) = 10.39, F2 (4, 80) = 6.27. Moreover, the effect did not interact withsession F1 (8, 1188) = 1.31, p = .236, F2 (8, 160) = 0.97.
Neighbourhood size. The weak neighbourhood effect was not significant in any
individual session (session 1: 532 vs. 529 ms.; session 2: 549 ms vs. 547 ms;
session 3: 554 vs. 550 ms). It also did not interact with session, both F < 0.5.
Word Length. The effect of word length was present in each session, though
it was not significant by items in the second session: session 1, RTs of 527,
525 and 536 ms (for 3, 4 and 5 letter words, respectively), F1 (2, 198) = 7.53,
F2 (2, 60) = 6.55, p = .003; session 2, RTs of 545, 549 and 552 ms, F1 (2, 198) =4.00, p = .020, F2 (2, 60) = 1.93, p = .153; session 3, RTs of 551, 560 and 558 ms,
F1 (2, 198) = 4.98, p = .008, F2 (2, 60) = 3.72, p = .030. There was no interaction
with session, both F < 0.6.
Nonword Length. The effect of nonword length was present in all sessions: session 1, with RTs of 577, 602 and 623 ms, F1 (2, 198) = 84.18, F2 (2, 60) = 12.88;
session 2, with RTs of 583, 600 and 622 ms, F1 (2, 198) = 70.21, F2 (2, 60) = 14.86;
session 3, with RTs of 592, 608 and 618 ms, F1 (2, 198) = 38.91, F2 (2, 60) = 8.56.
This effect did interact by session, F1 (4, 396) = 6.28, F2 (4, 120) = 4.77, p < .001,
because the longer nonwords were immune to the overall slowing by session.
Lexicality. The effect of lexicality and its interaction with length were repeatedin every session: session 1 (534 and 606 ms), F1 (1, 495) = 859.95, F2 (1, 150) =236.96 for lexicality and, F1 (2, 495) = 23.79, F2 (2, 150) = 6.56, p = .002 forthe interaction; session 2 (551 and 608 ms), F1 (1, 495) = 569.01, F2 (1, 150) =192.30 for lexicality and, F1 (2, 495) = 21.64, F2 (2, 150) = 6.99, p = .001 for theinteraction; and session 3 (558 and 610 ms), F1 (1, 495) = 507.74, F2 (1, 150) =28

181.95 for lexicality and, F1 (2, 495) = 7.87, F2 (2, 150) = 3.36, p = .037 for theinteraction.
The lexicality effect clearly interacted with session, F1 (2, 198) = 32.29,
F2 (2, 120) = 7.86. The lexicality by length by session interaction was only
significant by subjects, F1 (4, 396) = 3.25, p = .012, F2 (4, 240) = 2.18, p = .072.
Again, both these effects were driven by the immunity of the longer nonwords
to the slowing showed by other conditions.
Regularity and Consistency. Regularity. The cost for the high-frequency enemies
exception words was consistent across sessions: session 1 (545 and 529 ms),F1 (1, 99) = 26.99, F2 (1, 19) = 10.12, p = .005; session 2 (560 and 545 ms),F1 (1, 99) = 19.10, F2 (1, 19) = 8.34, p = .009; session 3 (568 and 552 ms),F1 (1, 99) = 15.88, F2 (1, 19) = 10.10, p = .005. This effect did not interact
with session, both F < .2.
The cost for the lower-frequency enemies exception words was less consistent over sessions: session 1, (536 and 520 ms), F1 (1, 99) = 30.02, F2 (1, 19) =6.22, p = .022; session 2, (548 and 536 ms), F1 (1, 99) = 11.88, F2 (1, 19) = 3.11,
p = .094; session 3, (552 and 542 ms), F1 (1, 99) = 7.75, F2 (1, 19) = 0.86,
p = .365. This effect did not, however, interact with session, both F < .8.
Consistency. The regular-inconsistent words whose enemies were higher
frequency than their friends were read more slowly than their matched control words across all sessions: session 1 (548 and 533 ms), F1 (1, 99) = 26.32,
F2 (1, 19) = 4.95, p = .038; session 2 (560 and 528 ms), F1 (1, 99) = 17.40,
F2 (1, 19) = 8.67, p = .008; session 3 (570 and 540 ms), F1 (1, 99) = 25.58,
F2 (1, 19) = 14.66, p = .001. There was no interaction with session, both F < .2.
Those whose friends were higher frequency than their enemies did
not show this difference in a consistent manner: session 1 (514 and 521ms), F1 (1, 99) = 5.15, p = .025, F2 (1, 19) = 2.17, p = .157; session 2 (530
and 528 ms), both F < 0.09; session 3 (534 and 540 ms), F1 (1, 99) = 3.95,
p = .050, F2 (1, 19) = 0.92. The interaction with session was not significant,F1 (2, 198) = 1.93, p = .197, F2 (2, 38) = 1.74, p = .189.

29

Position of irregularity. The first position exception cost was present in all sessions: session 1 (608 and 535 ms), F1 (1, 99) = 238.47, F2 (1, 19) = 18.43; session
2 (610 and 544 ms), F1 (1, 99) = 259.47, F2 (1, 19) = 19.17; session 3 (611 and551 ms), F1 (1, 99) = 202.90, F2 (1, 19) = 18.68. An interaction with session was
significant by items (and nearly so by subjects), F1 (2, 198) = 3.01, p = .052,
F (2, 30) = 4.05, p = .025, driven by the immunity of the exception words tothe slowing shown by other conditions.
The second position exception effect was consistent over sessions: session
1 (547 and 535 ms), F1 (1, 99) = 25.16, F2 (1, 38) = 7.12, p = .011; session 2 (561
and 548 ms), F1 (1, 99) = 35.50, F2 (1, 38) = 8.33, p = .006; session 3 (566 and554 ms), F1 (1, 99) = 17.91, F2 (1, 38) = 7.41, p = .010, with no interaction, both
F < .4.
A third position exception effect was absent in all sessions: session 1 (540
and 542 ms), session 2 (552 and 549 ms), and session 3 (557 and 559 ms), all
F < 1.05, and this did not interact with session, both F < 1.3.
A position by irregularity interaction was present in each session: session
1, F1 (2, 495) = 138.67, F2 (2, 85) = 15.45; session 2, F1 (2, 495) = 118.06,
F2 (2, 85) = 14.83; and session 3, F1 (2, 495) = 98.60, F2 (2, 85) = 15.48.
The position by irregularity by session interaction was not significant,F1 (4, 396) = 1.301, p = .269, F2 (4, 170), p = .070.
Summary of session effects. Overall, participants became slower over sessions.
This might reflect motivation (these three half-hour sessions were embedded
within three of nine one-hour sessions of the larger experiment), or a strategicchange with experience. If the motivation explanation is correct, the lack ofslowing for the slowest conditions (4-letter nonwords, 5-letter nonwords, first
position exceptions) might be an artifact of more trials falling foul of the 1500
ms response recording cutoff. A strategic explanation would involve a shift of
the response criterion (cf. Taylor & Lupker, 2001).

30

2.2.3. Comparison with previous mega-study data
To further establish that our findings were not unusual, we also extracted
the mean RTs for these words from Spieler & Balota’s (1997) undergraduate
word naming data set. Of the 592 unique words in our data set, 534 were in
the Spieler and Balota experiment. We examined our major effects of interestin terms of the condition means, presented in Table 4, and by-items analyses
like those we used on our data.
More frequent words were read more quickly than less frequent words,
F2 (4, 80) = 5.71, as in our data set. No significant effect of neighbourhood size
was found, F2 (1, 39) = 0.01, as in our data set. Longer words were read more
slowly than shorter words, F2 (2, 60) = 10.93, as in our data set. From Jared
et al.’s (1990) stimuli, exception words were read more slowly than their regular controls if they had enemies more frequent than their friends, F2 (1, 16) =11.82, p = .003, as in our data. This was also the case when the friends weremore frequent, F2 (1, 19) = 5.76, p = .027; this effect was only significant inour data if a one-tailed correction was applied. Inconsistent words tended tobe read more slowly than their consistent controls when their enemies weremore freqeunt than their friends, but this was not significant, F2 (1, 16) = 1.41,
p = .252. In our data, and in Jared’s original study, the effect in this direction was however significant. No difference was found between consistent and inconsistent stimuli when friends were of higher frequency than enemies, F2 (1, 18) = 0.00, as in our data. From Rastle & Coltheart’s (1999) stimuli, exception words were read more slowly than controls when the irregularity was in first position, F2 (1, 7) = 14.87, p = .006, and in second position,
F2 (1, 20) = 4.68, p = .043, but no such effect was significant for third position,
F2 (1, 16) = 1.35, p = .263. These were all the same as in our data, as was thepresence of the interaction between position and irregularity (such that earlier
irregularities produce more slowing), F2 (2, 43) = 5.32, p = .009.
In summary, of these 11 tests on Spieler & Balota’s (1997) data, 7 showed thesame signficant effects as in our data and 2 showed the same non-significant

31

effects as our data (though one was marginal by-subjects in our data). One
further test was significant where this was significant by-subjects but only
marginal by-items in our data, and the last was significant in our data, where
the effect was only numerically in the same direction in Spieler and Balota’s
data. Overall, our data were not unusual.
2.2.4. Raw effect estimation and reliability
Our purpose in collecting these data was to examine individual differencesin these effects, so it was necessary to summarise each effect for each participant as a single number. A description of how we did so, and the reliability ofthe effects follows.
The reliability of our session-average estimates was calculated on the basis of the average test-retest reliability and the Spearman-Brown formula. Thisreliability is used to adjust estimated variance of the effect because the estimated effects will show variability that is greater than that of the underlying
effects; indeed, the presence of measurement error or noise implies variability
will appear even on measures for which participants are identical. Significancelevels are omitted in this section: Observed correlations for 100 participants are
significant at the 5% level if their absolute values are at least .197.
Frequency. The frequency effect was estimated by applying the linear contrast
to participants’ band means, reversing coding so that the expected effect was
positive (positive effects if rare words were read slower than common words).
Cross-session correlations were (1 and 2) .101, (1 and 3) .388, and (2 and 3) .046. The mean correlation of .148 yields an estimate for the reliability of theaverage of .342.
In light of the negative correlation of second and third sessions, we examined methods that might improve the reliability. Using individual item frequencies was counterproductive (.120, .314, and -.119). Solutions involving different contrasts did improve reliability but did not improve correlations withother effects. We did not explore solutions that involved the full range of stimuli as these might artificially induce common variance with other effects.
32

Neighbourhood size. The simple difference between the high neighbourhood
size and low neighbourhood size stimuli was used as the neighbourhood effect estimate. Cross-session correlations were (1 and 2) .064, (1 and 3) .102, and(2 and 3) .072. The mean correlation of .080 implied a reliability for the averageof .206; given the weak effect, this is not surprising.
Length. The length effects were estimated as the linear contrast. Cross-session
correlations for the word length effect were (1 and 2) .334, (1 and 3) .192, and(2 and 3) .216. The mean correlation of .247 implies a reliability for the averageof .497. Cross-session correlations for the nonword length effect were (1 and 2).424, (1 and 3) .270, and (2 and 3) .313. The mean correlation of .336 implies areliability for the average of .603.
Lexicality. The lexicality effect was calculated as the difference between word
and nonword responses for four-letter stimuli, to avoid structural correlations
with the length effects (the four-letter condition has no weight in the calculation of the linear contrast for length). The cross-session correlations were (1and 2) .613, (1 and 3) .632, and (2 and 3) .345; the mean of these is .530, implying
a reliability for the average of .772.
Regularity (Jared). The exception cost was calculated from the Jared (2002)
stimuli as the difference of the mean of both exception conditions from theirmatched controls, and from the Rastle and Coltheart (1999) stimuli as thedifference of the mean of the first and second position exceptions and theirmatched controls. The cross-session correlations for the Jared measure were (1and 2) -.014, (1 and 3) .138, and (2 and 3) .206, whose mean correlation of .110
implies a reliability for the average of .270. The cross-session correlations for
the Rastle and Coltheart measure were (1 and 2) .309, (1 and 3) .332, and (2 and
3) .256, whose mean correlation of .299 implies a reliability for the average of.561.
Consistency. The consistency effect was calculated as the difference of the inconsistent stimuli with high-frequency enemies and their matched controls,33because only the advantage for consistent words was present in the data. The
cross-section correlations were (1 and 2) .113, (1 and 3) -.079, and (2 and 3) .012,
whose mean of .016 implies a poor reliability for the average of .047.
Regularity (Rastle & Coltheart). The cross-session correlations for the measureof the regularity effect based on the Rastle and Coltheart stimuli were (1 and 2).309, (1 and 3) .332, and (2 and 3) .256, whose mean correlation of .299 implies
a reliability for the average of .561.
Position of irregularity. The position of irregularity effect was calculated as the
double difference (interaction) subtracting the position 2 regularity effect (position 2 exceptions minus matched controls) from the position 1 regularity effect(position 1 exceptions minus matched controls). The cross-session correlations
were (1 and 2) .264, (1 and 3) .338, and (2 and 3) .165, whose mean of .256
implies that the average has a reliability of .508.
2.2.5. Effect distributions
Of immediate concern for the study of individual differences is whether
participants do in fact differ on the variables of interest. Figure 1 illustrates(crosses and solid lines) the distribution of the effect estimates calculated as described above. These distributions are, however, somewhat misleading as they
include both true differences between participants and differences that arise
from trial-to-trial variation (noise); indeed these plots would show variability
even if participants were identical. We therefore estimated the (true-score) variance by multiplying the observed variance of an effect by our estimate of that
effect’s reliability (as indicated by classical test theory); these are illustrated in
Figure 1 (dashed lines) by normal distributions with this estimate of the variance.
We also performed analyses based on the ANOVA Subject × Effect interaction to establish that effects varied across subjects; the statistics are superimposed on the corresponding panels of Figure 1. Significant variation was
found for all but two of the effects; the interaction involving neighbourhood
34

size approached significance, whereas consistency showed no such evidenceof reliability.
Variability in the neighbourhood size effect in naming is, however, evidenced from other more sensitive data (four participants reading 2820 words
50 times each: Adelman et al., 2013, indeed, these participants show inhibitory
or null effects). Moreover, as the next section shows, both consistency andneighbourhood size participate in significant correlations, which should not be
the case if there were no participant-based variability.
2.2.6. Effect correlations
We calculated the correlations between the effects as calculated above; forease of interpretation, the frequency effect was reverse coded, so that a greater
benefit from frequency was indicated by a larger number; these are presented
in Table 6 (above the diagonal), as are correlations adjusted for attenuation
using the test-retest reliabilities (below the diagonal). The high incidence of
estimates of a perfect correlation for the consistency variable could indicatethat the reliability estimate is an underestimate, or that the population correlation coefficient is undefined because there is no true variability. The perfectcorrelation estimated between the two measures estimated from Rastle & Coltheart’s (1999) stimuli (exception and position of irregularity) is suggestive that
there is more variability in some of the conditions than others: For instance,
the position 1 exception effect may carry most of the variability in both effects.
The overall high incidence of significant correlations is suggestive of a general
speed influence that could be readily modelled by modifying the coefficient
linking model cycles to RTs, rather than being indicative of the qualities of differing processes, as we go on to discuss.
Adjusting for general speed. A core difficulty in assessing individual differencesin response time effects in terms of correlations is that a correlation will be induced if there is a general speed coefficient that is a multiplier on the central
processes; in many models this is the slope for converting cycles into response

35

Figure 1: Distributions of effects: Crosses (×) indicate observed effect estimates, calculated as
described in the text. Solid lines are Gaussian kernel density estimates of the distribution of effectestimates. Dashed lines are normal distributions estimating the distribution of the true effects, thatis, with standard deviation adjusted for reliability. The reported F-statistic is for the Subject ×
Effect interaction (tested with the Subject × Effect × Session term as the MSE), indicating whether
there is evidence for individual differences in the effects. Frequency is reverse-coded (relative
to the regression coefficient) so that positive values indicate rare words were read slower thancommon words. R&C = Rastle and Coltheart.

Frequency
F(99,198) = 1.59
p < .001

Neighbourhood Size
F(99,198) = 1.26
p = .091

Word LengthF(99,198) = 1.97
p < .001

0.10

0.05

0.00
0

10

20

Probability Density

Nonword LengthF(99,198) = 2.53
p < .001−20 0

20 40 60

−20

Lexicality
F(99,198) = 4.42
p < .001

0

20

40

60

Exception (Jared)
F(99,198) = 1.41
p = .022
0.10

0.05

0.00
−20

0

20

40

60

Consistency
F(99,198) = 1.05
p = .386−50 0

50 100 150 200

Exception (R&C)
F(99,198) = 2.23
p < .001−20 0

20 40 60

Position of Irregularity
F(99,198) = 2.03
p < .001

0.10

0.05

0.00
−20

0

20

40

60

0

20 40 60 80 100

Estimated Effect (ms)36

020 40 60 80

1.
2.
3.
4.
5.
6.
7.
8.
9.

(neg.) Frequency
Neighbourhood Size
Word Length
Nonword Length
Lexicality
Exception (Jared)
Consistency
Exception (R&C)
Pos. of Irregularity

1.
-Frq

2.
Nei

3.
WLen

4.
NWLen

5.
Lex

6.
ExcJ

7.
Cons

8.
ExcRC

9.
PoI

—
.840
.512
1.000
.736
.747
1.000
.575
.650

.223
—
-.143
.448
-.263
.441
.691
.138
.099

.211
-.046
—
.727
.729
.386
.512
-.005
.228

.471
.158
.398
—
.858
.712
1.000
.568
.421

.378
-.105
.452
.585
—
.663
1.000
.470
.526

.227
.104
.141
.287
.303
—
1.000
.689
.557

.226
.068
.078
.283
.280
.356
—
1.000
.809

.252
.047
-.002
.331
.309
.268
.332
—
1.000

.271
.083
.115
.233
.329
.206
.125
.613
—

Table 6: Correlations between raw estimated effects, above diagonal; the frequency effect has been
reverse-coded (sign-flipped) so that a greater benefit from higher frequency is indicated by a larger
number. Correlations significant at α = .05 (|r | ≥ .197) are indicated in bold. The same values
adjusted for attenuation using test-retest reliability are presented below the diagonal; these are notappropriate for significance testing against zero. R&C = Rastle and Coltheart.

times. Whilst in modelling applications, this parameter can simply be included
for every participant, for a more qualitative understanding of correlations, removing such an effect is desirable. To do this when we assume (as in the models we use here) that the noise is independent from the central processes ofinterest requires a method that adjusts for central process variance with noise
removed, rather than all the variance (as in z-scoring); our method for suchadjustment is detailed in Appendix D.
Once these adjustments were made, we estimated reliabilities for the adjusted effect magnitudes using the correlations between sessions, in the samemanner as for the unadjusted effect magnitudes (§2.2.4). The reliabilities were:
frequency .114; neighbourhood size .197; word length .171; nonword length.408; lexicality .570; exception (Jared) .399; exception (Rastle & Coltheart) .238;
consistency .0007 ; and position of irregularity .395.
Correlations among the effects adjusted for general speed using these variance components, and the disattenuated versions of the correlations are presented in Table 7. The apparently structural correlation between position of
7 In fact, all three between-session correlations were negative, but a negative reliability is meaningless.

37

1.
2.
3.
4.
5.
6.
7.
8.
9.

(neg.) Frequency
Neighbourhood Size
Word Length
Nonword Length
Lexicality
Exception (Jared)
Consistency
Exception (R&C)
Pos. of Irregularity

1.
-Frq

2.
Nei

3.
WLen

4.
NWLen

5.
Lex

6.
ExcJ

7.
Cons

8.
ExcRC

9.
PoI

—
1.000
.515
.978
.322
-.070
—
.176
.438

.313
—
-.169
.085
-.704
-.696
—
.531
.323

.072
-.031
—
.529
.839
-.276
—
-.524
.213

.211
.024
.140
—
.360
.380
—
-.089
-.265

.082
-.236
.262
.174
—
.474
—
-.461
.115

-.015
-.195
-.072
.153
.226
—
—
-.590
-.109

.123
.069
.102
-.004
.139
.179
—
—
—

.029
.115
-.106
-.028
-.170
-.182
.030
—
1.000

.093
.090
.055
-.106
.054
-.043
-.074
.505
—

Table 7: Correlations between general-speed-adjusted estimated effects, above diagonal; the frequency effect has been reverse-coded (sign-flipped) so that a greater benefit from higher frequency
is indicated by a larger number. Correlations significant at α = .05 (|r | ≥ .197) are indicated inbold. The same values adjusted for attenuation using test-retest reliability are presented below
the diagonal; these are not appropriate for significance testing against zero; consistency had no
test-retest reliability. R&C = Rastle and Coltheart.

irregularity and the related exception measure remained, along with five othersignificant correlations. Frequency was positively related with neighbourhood
size and nonword length. As well as the positive correlation with frequency,
neighbourhood size correlated negatively with lexicality. In addition to thisnegative correlation with neighbourhood size, lexicality correlated positively
with word length and exception effect by the Jared measure. These relationships are illustrated in Figure 2.
Five other correlations approached significance: negative correlations of the
Jared exception measure with neighbourhood size, consistency, and the Rastle& Coltheart measure of the exception effect; and lexicality’s positive correlation
with the nonword length effect and lexicality’s negative correlation with the
Rastle & Coltheart exception effect.
2.3. Discussion
2.3.1. Replication of standard effects
Our data largely replicated the major effects of interest, with the exceptionof neighbourhood size.
Frequency had its usual effect such that rare words required more process-38

Figure 2: Scatterplots illustrating the significant individual differences correlations between
general-speed adjusted effects.

Exc. (J) ~ Lex.

1

1.0
0.5

0

0.0

Exception

2

1.5

Exc. (R&C) ~ Pos. Irr.

0.0

0.5

1.0

1.5

0.0 0.5 1.0 1.5 2.0 2.5

Position of Irregularity

0.0

0.5

−0.2 0.0 0.2 0.4 0.6

WLen. ~ Lex.

−0.5

LengthNWLen. ~ Freq.

−0.1 0.0

0.1

0.2

0.3

0.4

0.0 0.5 1.0 1.5 2.0 2.5

1.0
0.5−1.0 −0.5 0.0

0.5

1.0

Neigh. ~ Lex.

−1.0 −0.5 0.0

Neighbourhood Size

Neigh. ~ Freq.

−0.1 0.0

0.1

0.2

0.3

0.4

Frequency

0.0 0.5 1.0 1.5 2.0 2.5

Lexicality
39ing time. The neighbourhood size effect was not significant. We suspect thisis because there is a mixture of readers showing facilitatory and inhibitory effects, and our sample has a greater proportion of readers showing inhibitory
neighbourhood size effects than the typical experiment showing the facilitatory
effect. This seems likely because our sample was older than that in a typicalexperiment (with introductory psychology students), and the facilitatory effectis associated with younger participants (Spieler & Balota, 2000). Longer stimuli were read more slowly than shorter stimuli, with the effect being stronger
for nonwords than real words. Consistent words were read more quickly thaninconsistent words, only if their friends were more frequent than their enemies, as found by Jared et al. (1990). Exception words were read more slowly
than regular words. The dependence of this on the position of the irregularity— greater effects for irregularities to the left (beginning) of the word — wasreplicated, but the evidence for a dependence on consistency was not.
2.3.2. Effects by session
For the most part, participants responded slower in later sessions, but theslowest response categories were immune to this effect. This could be artifactual, or relate to a change in response criterion (cf. Taylor & Lupker, 2001).
2.3.3. Comparison with mega-study data
There was great consistency between our data and the same stimuli in
Spieler & Balota’s (1997) data. The consistency effect did not reach significancein Spieler and Balota’s data, but the effect was in the same direction in bothdata sets, and such effects are significant in their data in regression analyses
over a larger set of items (Adelman & Brown, 2008a). The only other minor
inconsistency was one test of the exception effect in our data required a onetailed correction to reach significance, where such as correction was not neededin Spieler and Balota’s data. Overall, this confirmed the typicality of our data.

40

2.3.4. Reliability and variability
There was clear evidence for reliability of seven of the nine measures
as measures of individual traits, marginal evidence for such reliability ofneighbourhood size effects, and no such evidence for consistency effects. This
type of statistical evidence is ambiguous between very high actual sessionto-session variability or very small (or absent) individual differences. Large
relative session-to-session variability may partially result from relatively few
items contributing to the effect estimate.
That the neighborhood size effect was correlated with other effects in thisexperiment, and can be predicted from age and external written language variables in this task (Adelman, Sabatos-DeVito & Marquis, in prep.) and lexicaldecision (e.g., Sears et al., 2008) suggest that individual differences are not absent, but the weak effects are largely outweighed by trial-to-trial noise.
Given the size and significance of the consistency effect, it is surprising thatthose who show the strongest such effects in one session, are not those showing
the strongest effects in the next. We suggest that this is due to large priming
effects that differ with different randomizations of the stimulus ordering thatoutweigh the individual differences. When word bodies are repeated within anexperiment, the pronunciation at the first presentation can facilitate a consistent future presentation or inhibit an inconsistent one (Seidenberg et al., 1984).
As such, the consistency effect may itself — in whole or in part — result fromthe probability that the most recent preceding occurrences of the word bodyin question (which need not be within the experiment itself) having been ina rime friend that primes the relevant pronunciation. Where such variation
is intrinsic to the causation of the effect, lower estimated reliability should be
expected than if an effect is caused by a fixed property of the underlying mechanism, because susceptibility to the effect interacts with the helpfulness of thestimulus ordering.
Moreover, to the extent that orthographic neighbours are more (or only)
facilitatory when they are also phonological neighbours (Adelman & Brown,41

2007; Peereman & Content, 1997), a similar consistency-based mechanism
might be proposed for neighbourhood size effects. If the effect of frequency islinked to contexts rather than occurrences per se (Adelman et al., 2006), thena similar argument may be made for these contextual diversity effects: Morecontextually diverse words are more likely to have an appropriate context
instantiated by a recently presented word than are contextually restrictedwords.
Overall, whilst it is clear that there are reliable individual differences, italso appears that within-individual variation may be very important for understanding effects in which priming might have a causal role.
2.3.5. Correlations among effects
Turning to the central aim of the experiment, raw correlations were positive among nearly all pairs of effect measures, indicative of overall general
speed differences. The interpretation of these general speed differences may
be difficult if there are genuinely two processes — lexical and nonlexical —of imperfectly correlated varying speeds. Correlations on measures adjusted
for general speed (Table 7) revealed six significant relationships, and five thatapproached significance, which will be discussed in turn.
We consider the effects largely in terms of principles of recent versions ofdual-route theory (Coltheart, 2012, or more concretely, Coltheart et al., 2001;
Perry et al., 2007, for examples); we are not aware of previous examples of
their use to explain these types of correlations. These principles are that: lexical access speed depends on frequency; nonlexical processing has a left-toright component; for words, lexical and nonlexical processes can co-operateand compete to produce pronunciations, and co-operation produces faster responses than competition; words are read quicker than nonwords because thelexical route produces pronunciations rapidly.
Significant correlations.
Exception (Rastle & Coltheart) and Position of Irregularity.

42

As described above, the positive correlation between the exception effectfrom the Rastle & Coltheart stimuli and the position of irregularity effect (fromthe same stimuli) is explained by the position one exception effect for the same
items contributing most of the variance to both these measures.
Frequency and Neighbourhood Size. Both frequency and neighbourhood size
effects have been taken to be an indication of the quality of lexical representations (e.g., Sears et al., 2008). Moreover, neighbourhood effects can be producedby the lexical route of the DRC (Adelman & Brown, 2008a; Reynolds & Besner,2002). In a lexical quality interpretation, participants who show these effects
strongly have representations that are weak and therefore slow to be activated,and poorly differentiated from other representations (which in naming are coincidentally helpful, cf. Adelman & Brown, 2007; Peereman & Content, 1997).
Their positive relationship can readily be explained by such a concept, possibly
linked to letter-word connections in models, and hence lexical route strengthor speed (linking into general speed). Any nonlexical contribution to neighbourhood size effects (Perry et al., 2007) would not directly give a positive linkwith the lexical frequency effect, though an indirect influence (i.e., cross-talk)could involve both routes.
Lexicality and Neighbourhood Size. The lexicality effect is an indication of theextent to which words are read more quickly than nonwords, which in a dualroute framework largely reflects the extent to which the lexical route operates
more quickly than the nonlexical route (e.g., Coltheart et al., 1993). That is,strong lexicality effects reflect a fast lexical route, whereas strong neighbourhood effects reflect, according to the argument above, a slow (poorly specified)
lexical route; this would explain the negative relationship.
Lexicality and Word Length. Length effects are often thought of as due toa left-to-right process in the nonlexical route (Coltheart & Rastle, 1994). The
slower such a process operates, the greater difference will occur between
shorter and longer words, at least insofar as the nonlexical route is a limiting
factor. In the present account, a larger lexicality effect also should indicate a
relatively inefficient nonlexical route, so the positive relationship might follow,
43

so long as it is not so slow as to have no effect. Alternatively, word length
effects may have both a lexical and nonlexical locus.
Lexicality and Exception (Jared). By the same token, a large lexicality effectsuggests that the lexical route is operating quickly and the nonlexical route isoperating slowly, so little variation can be contributed by the lexical route, as
everything is fast. This also means effects in the nonlexical route would belarge because its processing is protracted, and large when adjusted for general
speed, because the nonlexical route is contributing most of the variance. The
exception effect is one of these effects produced from nonlexical contributions,so should be large in this scenario where the lexicality effect is large, thus producing the positive correlation.
Frequency and Nonword Length. Finally, the positive relationship between
frequency and nonword length is harder to explain, because they appear to reflect relatively pure lexical and nonlexical processes respectively, and so should
be separately linked to these separate speeds. If a positive link between these
two speeds were the source, though, it should have been extracted in the general speed correction. That it remains after this correction required explanation.
One possible explanation is that the strong frequency effect occurs when there
is more generalised activation (and more competition) in the lexical route (dueto the poor differentiation of representations); as such, the lexical route interferes with, and slows, output from the nonlexical route, and disproportionately
more so for (more slowly read) longer nonwords. An alternative explanation,that is less consistent with the traditional dual route conception, is that when
the lexical route is operating rapidly, it is sometimes able to contribute a lexical
analogy pronunciation before the slower, length-sensitive nonlexical route cangenerate a response, reducing the influence of the nonlexical route.
Marginal correlations.
Lexicality and Nonword Length. Likewise, a large lexicality effect indicates anefficient lexical route, so that once general speed is accounted for, most variation is due to the nonlexical route, where the nonword length effect is gener-

44

ated.
Neighbourhood Size and Exception (Jared). When the neighbourhood size effect is large, this implies that the lexical route is not only slow, but also important in determining RTs for words; as such, factors that reflect nonlexicalinfluence, including costs from exceptions, should be relatively less important(once general speed is adjusted for), generating the observed negative correlation.
Exception (Jared) and Exception (Rastle & Coltheart). Thesenominally similar effects correlate negatively, perhaps because the Rastle
and Coltheart measure is dominated by the cost for irregularities in the initial
position — mostly involving consonants — and the Jared measure is based onlater irregularities — mostly involving vowels. Possibly these reflect different
influences of nonlexical pronunciation. Effects of early irregularities might reflect inhibitory processes, possibly at the lexical level, whilst the effect of later
irregularities might reflect a lack of facilitation for the correct pronunciation,rather than inhibition per se.
Exception (Jared) and Consistency. These inconsistent and irregular stimuli
both mostly involve differences in the pronunciation of the vowel, so might be
subsumed under some similar form of nonlexical process.
Exception (Rastle & Coltheart) and Lexicality. According to the description
above, inhibition to lexical access is involved in the exception effect with the
Rastle & Coltheart stimuli. For such inhibition to be effective, the lexical process cannot outpace the nonlexical process too severely, which would be associated with a small lexicality effect; this would produce a negative correlation.
2.3.6. Verbal analyses vs. simulated models
However, it is unclear whether this pattern of correlations is necessarily to
be expected in a complete dual-route model. It is one thing to describe thesecauses verbally, but it is another to show that they can all occur in the same
single system. Indeed, arguments could have been made for some of these
correlations in the opposite direction, such as a negative correlation between
45frequency and nonword length. Part of this is due to nonlinearity in the effectof speed in a system with routes that can co-operate in parallel: a fast system
can have little effect on RTs because there is little range in its contribution, anda slow system can have little effect on RTs because it is outpaced by a fastersystem. Determining what can in fact occur in a dual-route system therefore
requires simulations of a dual-route model.

3. MODELLING: DUAL-ROUTE CASCADED MODEL
The most well-known implemented dual-route model is the dual-route cascaded model (DRC: Coltheart et al., 1993, 2001). Although published versionsof this model lack semantics and the ability to read polysyllabic words, it iscomplete in the sense that both the lexical and nonlexical route are implemented in some detail, and it is able to produce predictions for any monosyllabic stimulus, and can produce all the effects considered here (see regressions
performed by Adelman & Brown, 2008a). This model stands as a conjunctive
hypothesis about the processes by which people read aloud; that is, its authors consider it a theory in the sense of a set of falsifiable statements about
the details of cognition, rather than only one possible implementation of somebroader principles. It combines a lexical route that is based on an extensionof the interactive-activation and competition model (McClelland & Rumelhart,
1981) with a set of spelling-sound rules that are applied in a temporally left-toright fashion.
In our simulations, we sought to examine whether the model’s descriptionof the structures and processes of reading aloud is sufficiently accurate to allow
simulation of these data. Does adjustment of numerical parameters allow themodel to capture the variation and correlations in our data? Or does the model
make assumptions about the mechanisms and parameters underlying the item
effects that are shown to be incorrect by our data?

46

3.1. Method
3.1.1. Models
Version of the Model. Coltheart et al. (2001) described the DRC with explanations for the motivation for its structure; we (Adelman & Brown, 2008a) redescribed it with some details that we used in our re-implementation to produce the behaviour of the program that Coltheart et al. made available but
which were not mentioned in their paper. For present purposes, one minorchange was made to the model in the way that the grapheme-phoneme conversion (GPC) route is initiated, and the phonological lexicon frequencies werechanged to be the same as used by Perry et al. (2007) (which were provided
to them by Coltheart). In terms of the GPC route, in the earlier version of themodel, the cycle on which the GPC first converted the first letter was directly
specified by a GPC delay parameter (α: on the tth model cycle, the left-most⌊1 + (t − α)/β⌋ letters are considered). The modification was to set a threshold on letter activation to initiate GPC processing: That is the GPC delay was
not directly a parameter, instead the start of GPC processing (α) was set to be
the first cycle on which a letter in the left-most channel exceeded a thresholdparameter. This change has been adopted in unpublished modifications to the
DRC by Coltheart and colleagues (DRC 1.2.1, used to model findings by, e.g.,
Mousikou, Coltheart, Finkbeiner & Saunders, 2010), in response to criticisms
such as those of Blais & Besner (2007) relating to the response of the model todegraded stimuli. The rate at which letters are identified in the model does notvary substantially across words (only across stimulus qualities) so the effect of
the old parameter is mimicked by the new parameter.
Parameter Settings. Two-hundred-and-fifty thousand potential parameter sets
were each produced by independently randomly selecting each parameterfrom a set of values based upon those found to be optimal for average data
by Adelman & Brown (2008a) and, for parameters also in the CDP+, the value
used by Perry et al. (2007). The values used are presented in Appendix E.

473.1.2. Stimuli and Procedure
All but two stimuli from the experiment were used for simulation, the exceptions being DIRE, which is bisyllabic in most accents including the model’s,and MOULD, as only MOLD is in the model’s vocabulary. All 250,000 parameter
sets were tested by sequentially presenting the stimuli to the model to obtain
responses and RTs in model cycles; a trial was ceased, counted as an error andexcluded from analysis if more than 300 cycles were required. Responses that
did not match the model’s stored pronunciation for words were also counted as
errors. If and when 60 stimuli had timed out or been erroneously pronounced
by the model with a given parameter set, that simulation was ceased, and theparameter set rejected; 2,674 sets were retained.
3.1.3. Selection to Correspond to Participants
For each participant, a multiple regression was performed for each parameter set that had been simulated (and retained), with the response being theparticipant’s mean RT for each word8 , and the predictors being the model’s RT
in cycles and the initial phoneme of the model’s response. For each participant,the parameter set whose regression produced the lowest mean-squared-error(highest R2 ) was retained, and the regression’s predictions were treated as thepredictions of the DRC (i.e., the DRC was permitted to treat the effect of firstphoneme as explicable but outside the model’s scope). The mean R2 obtained
was 36.10%.
3.2. Results
3.2.1. Standard Effects
We first examined whether the DRC had produced the effects observed in
the data; the predicted means for each condition are in Table 4. The corresponding ANOVAs follow, and are also summarized in Table 5.
8 One might imagine performing a similar calculation on condition means or effects, but thisdiscards information, and results in fewer points to use for fitting than parameters to fit them.

48

Frequency. Predicted RT decreased with increasing frequency, F1 (4, 396) =84.98, F2 (4, 80) = 113.4.
Neighbourhood size. The model predicted an inhibitory effect of neighbourhoodsize, F1 (1, 99) = 6.76, p = .011, F2 (1, 41) = 6.19, p = .017. However, it would
be unrealistic to expect a predicted effect of 1 ms to reach significance in thedata.
Length and lexicality. The model predicted longer RTs for longer words,
F1 (2, 198) = 52.35, F2 (2, 60) = 132.6, and the same pattern for nonwords,
F1 (2, 198) = 106.2, F2 (2, 60) = 152.2. The difference between words andnonwords, such that nonwords were slower, was significant F1 (1, 495) = 1056,
F2 (1, 150) = 4995, as was the interaction such that the length effect for
nonwords was greater, F1 (2, 495) = 13.61, F2 (2, 150) = 53.88. The estimatedmeans after partialling out neighbourhood size were: 544, 549, and 552 ms forwords and 594, 605, and 621 ms for nonwords.
Regularity and Consistency.
Regularity.

Jared’s (2002) exception stimuli with high frequency ene-mies were predicted to have slower responses than their matched controls,
F1 (1, 99) = 98.20, F2 (1, 17) = 13.00, p = .002, as were those with highfrequency friends, F1 (1, 99) = 67.75, F2 (1, 19) = 19.20.
Consistency. The regular-inconsistent stimuli with high frequency enemies
were predicted to have slower responses than their matched controls, though
this was significant only by-subjects, F1 (1, 99) = 46.15, F2 (1, 19) = 0.58, p =1.1.456. The predicted 1 ms effect was, however, tiny compared to that in the data.
The regular-inconsistent stimuli with high-frequency friends showed the opposite pattern, which was again only significant by-subjects, F1 (1, 99) = 17.80,
F2 (1, 19) = 0.04, as in the data.
Position of irregularity. Exception words whose irregularity was in the first
position were predicted to be read more slowly than their regular controls,
F1 (1, 99) = 311.0, F2 (1, 19) = 12.65, p = .002, as were the second position49

exceptions, F1 (1, 99) = 51.89, F2 (1, 38) = 22.44, and the third position exceptions, F1 (1, 99) = 23.85, F2 (1, 28) = 7.01, p = .013. The regularity by positioninteraction was significant, F1 (2, 495) = 132.2, F2 (2, 85) = 12.23.
3.2.2. Recovery of individual differences in effects
Correlations between the participants’ effects and the corresponding effects in the simulations were: frequency .447; neighbourhood size -.039; word
length .476; nonword length .784; lexicality .885; exception (Jared) .498; exception (R&C) .686; consistency .239; position of irregularity .671. Correlations
using the corrections for general speed (which for the model is equivalent to zscoring as there is no trial-to-trial variance in the model predictions) were: frequency .338; neighbourhood size .034; word length .319; nonword length .527;
lexicality .647; exception (Jared) .394; exception (R&C) .556; consistency .084;
1.11

position of irregularity .572. Again, the criterion for significance is |r | ≥ .197
(or |r | ≥ .164 one-tailed).
3.2.3. Effect correlations
We examined the correlations that the predicted RTs would produce between the effects, presented in Table 8. Most of the significant correlations
were positive, again consistent with the influence of general speed. The exception was neighbourhood size’s correlation with frequency, which is negative,
contrary to data. Overall, of 24 correlations that reached significance in thedata (Table 6), 19 were predicted to be significant in the correct direction, 2
not significant but in the correct direction, 2 not significant and in the incorrect
direction, and 1 significant in the incorrect direction.
The correction for general speed produced the correlations in Table 9, in
which the notable negative correlation between neighbourhood size and frequency persists. Of the 6 correlations significant in the data after the adjustment (Table 7), 3 were predicted significant in the correct direction (including
the artifactual correlation involving the two measures from the Rastle & Coltheart stimuli), 2 not significant but in the correct direction, and 1 significant in

50

1.
2.
3.
4.
5.
6.
7.
8.
9.

(neg.) Frequency
Neighbourhood Size
Word Length
Nonword Length
Lexicality
Exception (Jared)
Consistency
Exception (R&C)
Pos. of Irregularity

1.
-Frq

2.
Nei

3.
WLen

4.
NWLen

5.
Lex

6.
ExcJ

7.
Cons

8.
ExcRC

9.
PoI

—

-.287
—

.374
-.170
—

.568
-.072
.644
—

.592
-.186
.481
.666
—

.191
-.077
.167
-.098
.261
—

.757
-.133
.015
.385
.452
.110
—

.341
-.112
.478
.250
.435
.631
.211
—

.234
-.144
.241
.270
.206
-.052
.198
.605
—

Table 8: Correlations between raw DRC predicted effects; the frequency effect has been reversecoded (sign-flipped) so that a greater benefit from higher frequency is indicated by a larger number.
Correlations significant at α = .05 (|r | > .197) are indicated in bold. R&C = Rastle and Coltheart.

1.
2.
3.
4.
5.
6.
7.
8.
9.

(neg.) Frequency
Neighbourhood Size
Word Length
Nonword Length
Lexicality
Exception (Jared)
Consistency
Exception (R&C)
Pos. of Irregularity

1.
-Frq

2.
Nei

3.
WLen

4.
NWLen

5.
Lex

6.
ExcJ

7.
Cons

8.
ExcRC

9.
PoI

—

-.244
—

.232
-.086
—

.339
.056
.577
—

.459
-.092
.250
.437
—

.148
-.058
.087
-.262
.110
—

.774
-.134
-.083
.198
.412
.080
—

.216
-.038
.323
.013
.107
.565
.172
—

.064
-.067
.057
.007
-.161
-.211
.553
.140
—

Table 9: Correlations between general-speed-adjusted DRC predicted effects; the frequency effecthas been reverse-coded (sign-flipped) so that a greater benefit from higher frequency is indicatedby a larger number. Correlations significant at α = .05 (|r | > .197) are indicated in bold. R&C =Rastle and Coltheart.

the incorrect direction; again, this was the correlation between frequency andneighbourhood size effects. These relationships are illustrated in Figure 3.
3.3. Discussion
Parameter modification allowed the DRC to adjust to much of the individual variation in effects shown in the data: As well as simulating the average
effects of frequency, word and nonword length, lexicality, exception, and position of irregularity, in the data, the simulated individual differences correlated

51

Exc. (J) ~ Lex.

0.5

1.0

0.0 0.2 0.4 0.6 0.8 1.0

Exc. (R&C) ~ Pos. Irr.

0.0

Exception

1.5

Figure 3: Scatterplots illustrating the pertinent individual differences correlations between generalspeed adjusted effects simulated with the DRC.

0.0

0.5

1.0

0.5

1.0

1.5

2.0

Position of Irregularity

0.0 0.1 0.2 0.3 0.4 0.5

WLen. ~ Lex.

−0.2 0.0 0.2 0.4 0.6 0.8

LengthNWLen. ~ Freq.

0.0

0.1

0.2

0.3

0.5

1.5

2.0

0.1
0.0
−0.1

0.0

0.1

Neigh. ~ Lex.

−0.1

Neighbourhood Size

Neigh. ~ Freq.

1.0

0.0

0.1

0.2

0.3

Frequency

0.5

1.0

1.5

Lexicality52

2.0

with observed individual differences in these effects, both before and after the
adjustment for general speed.
However, closer examination revealed some problems; in particular, themodel did not succeed in capturing phenomena relating to the neighbourhood
size effect. First, although the model can produce a small facilitatory effect ofneighbourhood size under some parameter settings (Adelman & Brown, 2008a;
Coltheart et al., 2001; Reynolds & Besner, 2002, and the sets chosen for 20 of
the participants here), the mean effect for the fitted parameters was inhibitory,suggesting that parameter sets that produce facilitatory neighbourhood size effects fit other effects less well than those producing inhibitory neighbourhood
size effects. Second, no correlation emerged between the observed and predicted neighbourhood size effects. Third, the model predicted that participants
showing strong frequency effects would show more inhibitory neighbourhood
size effects, when in the data, participants showing strong frequency effects
showed more facilitatory neighbourhood size effects.
This occurs because of a trade-off between frequency and neighbourhood
effects that was noted by Forster (1976). Consider two neighbours of differing
frequency, such as BRIGHT and BLIGHT. The neighbourhood effect requiresthat a word’s neighbours are activated when that word is presented. When thelower frequency item (BLIGHT) is presented, not only is its higher frequency
neighbour (BRIGHT) activated, it has greater activation due to its frequency.
If the combination of these two effects is too great, the lower frequency item
will be mistaken as its higher frequency neighbour. As such, parameter setsthat produce both effects strongly are not acceptable due to their unreasonably
high error rate, and thus were rejected as candidates to represent participantsin the modeling. Thus, the parameter sets that were retained in the modelingto represent individual participants show either a strong frequency effect ora facilitatory neighbourhood size effect (and a weaker frequency effect), thus
producing a negative correlation.
One possible explanation of the pattern in the human data is given by theidea of lexical precision and the lexical tuning hypothesis (e.g. Andrews, 2012;53

Andrews & Hersch, 2010; Castles, Davis, Cavalot & Forster, 2007): Experience
with a word increases the quality of the representation of that word so that it
is easier to distinguish from its neighbours. That is, a higher frequency word
is not only more easily recognized, it is more easily rejected when it is not
the stimulus. As such, the higher frequency neighbor (BRIGHT) of a low frequency stimulus (BLIGHT) usually ceases to be a candidate for identification
sufficiently early that misidentification does not occur.
This is consistent with Andrews and Hersch’s (2010) criticism of interactiveactivation based models: These models simulate an individual with perfectorthographic knowledge — perfect spelling — rather than allowing for imprecise representation, which appears to be a key source of variability between
even individuals who can competently read. Modulation of the parametersof the model could not mimic this, probably because this kind of variation inlexical quality is selective to low-frequency words, whereas the parameters arenot. Whilst such individual differences in lexical quality of known words arecorrelated with vocabulary, the importance of spelling over vocabulary as apredictor of the priming effects in these studies implicates lexical quality as theactual cause9 .
Furthermore, the model underestimated the overall magnitude of regularity and consistency effects. This may be because nonlexical influences operate
directly only on pronunciation, and the effect on lexical activation is weak because it is indirect. That is, exceptional spelling-sound correspondences primarily affect pronunciation, rather than lexical access itself. Alternatively, theproblem could be to do with the assembly of nonlexical phonology itself, which
the CDP+ model was designed to improve relative to the DRC.
9 Moreover, allowing vocabulary to vary with an additional parameter (minimum frequency ofword known) did not improve the model’s ability to capture the frequency–neighbourhood-sizecorrelation.

54

4. MODELLING: CONNECTIONIST DUAL PROCESS MODEL
The first complete version of the connectionist dual process model, the
CDP+ (Perry et al., 2007), can be seen as a modification of the DRC to incorporate graded spelling-sound correspondences that are learned by the deltarule, and a more structured phonological representation; its authors argue it
offers a more complete explanation of the item effects attested in the literature.
4.1. Method
Modelling with CDP+ was similar to that performed for the DRC, except
the model was, of course, that described by Perry et al. (2007)10 , and the parameters based upon those used in that paper, detailed in Appendix E; wherea parameter was common to the DRC and CDP+, the same parameter range
was used as for the DRC. 3,505 parameter sets were retained as passing the
fewer-than-60-errors criterion. The mean R2 obtained was 36.97%.
4.2. Results
4.2.1. Standard effects
We checked the CDP+’s predictions for the standard effects with these parameters; the predicted means for each condition are in Table 4. The corresponding ANOVAs follow, and are also summarized in Table 5.
Frequency. A significant effect of shorter response times for more frequentwords was found, F1 (4, 396) = 47.77, F2 (4, 80) = 18.36.
Neighbourhood size. The words with lower neighbourhood size were predicted
to have more rapid responses than the matched words with high neighbourhood size. This miniscule (less than 0.1 ms) difference was only significant bysubjects, F1 (1, 99) = 4.12, p = .045, F2 (1, 39) = 0.00. This is suggestive of
the model being very sensitive to a slight mismatch of the neighbourhood size
10 There has been further development of this model, primarily in terms of extension to multisyllabic vocabulary, but this is the version that was available at the time we began the time-consuming
simulations, and the one most comparable to the published version of the DRC.

55

stimuli (that is not significant across pairs), and an effect of this magnitude
obviously cannot be treated as a substantive prediction of the model.
Length and lexicality. Predicted RTs increased with length for words,
F1 (2, 198) = 21.26, F2 (2, 60) = 30.71, and nonwords, F1 (2, 198) = 95.15,
F2 (2, 60)= 21.75.

F2 (1, 150)=

Nonwords were read slower, F1 (1, 495)

= 1014,

1091, and the length effect was stronger for nonwords,

F1 (2, 495) = 13.61, F2 (2, 150) = 13.41.

The estimated means adjustingfor neighbourhood size were 552, 553, and 554 ms for words, and 596, 603, and614 ms for nonwords.
Regularity and consistency.
Regularity. Jared’s (2002) exception stimuli with high-frequency enemies
had greater predicted RTs than their controls, which was significant bysubjects, F1 (1, 99) = 84.12, but not by-items, F2 (1, 17) = 0.01, and a similar
pattern held for the exceptions with low-frequency enemies, F1 (1, 99) = 67.83,
F2 (1, 19) = 0.10.
Consistency. The regular-inconsistent stimuli with high-frequency enemies
were given longer predicted RTs than their controls by a miniscule amount (different in the third decimal place), again only significant by subjects, F1 (1, 99) =6.063, F2 (1, 19) = 0.04. This predicted effect is not comparable in size to that in
the data. For those regular-inconsistent stimuli with low-frequency enemies,
the slower predictions than their controls again reached significance only by
subjects, F1 (1, 99) = 7.436, p = .008, F2 (1, 19) = 0.73; this is not the direction in
the data.
Position of irregularity. Exception words whose irregularity was in first
position were predicted to have RTs longer than their matched controls,
F1 (1, 99) = 193.0, F2 (1, 19) = 5.34, p = .032, but those in second position
were not, F1 (1, 99) = 1.47, p = .228, F2 (1, 38) = 0.12. The prediction thatthird position irregulars would be read more quickly than their controls was
significant only by subjects, F1 (1, 99) = 30.34, F2 (1, 28) = 3.66, p = .066; this56

is not the direction in the data. The position by regularity interaction was
significant, F1 (2, 495) = 141.1, F2 (2, 85) = 9.48.
4.2.2. Recovery of individual differences in effects
Correlations between the participants’ effects and the effects in the corresponding CDP+ simulations were: frequency .382; neighbourhood size .020;
word length .464; nonword length .612; lexicality .931; exception (Jared) .151;
exception (Rastle and Coltheart) .514; consistency .224; position of irregularity.491. With the adjustments for general speed applied, the correlations were:
frequency .221; neighbourhood size .113; word length .317; nonword length.558; lexicality .766; exception (Jared) .001; exception (Rastle & Coltheart) .557;
consistency .239; position of irregularity .438.
4.2.3. Effect correlations
We examined the correlations that would emerge from the predicted RTs,
presented in Table 10. Again, many positive correlations were obtained, consistent with the influence of general speed. Nevertheless, three of the significant correlations were negative, two involving consistency. Of these, one —the correlation between consistency and exception (Jared stimuli) effect — wassurprisingly contrary to a significant positive correlation in the data. Overall,of the 24 correlations significant in the data (Table 6), 7 were predicted significant in the correct direction and 6 not significant in the correct direction. Of the
11 predicted in the wrong direction, 3 were significant.
The correlations using the predictions adjusted for general speed in Table 11
showed negative correlations involving consistency and effects assumed to benonlexical, and a positive correlation with frequency; the corresponding effects
in the data did not reach significance. Of the six general-speed-adjusted correlations significant in the adjusted data (Table 7), two were predicted significant
in the correct direction, two significant in the incorrect direction, and two notsignificant in the correct direction. These relationships in the simulations areillustrated in Figure 4. The correlation between lexicality and word length was

57

1.
2.
3.
4.
5.
6.
7.
8.
9.

(neg.) Frequency
Neighbourhood Size
Word Length
Nonword Length
Lexicality
Exception (Jared)
Consistency
Exception (R&C)
Pos. of Irregularity

1.
-Frq

2.
Nei

3.
WLen

4.
NWLen

5.
Lex

6.
ExcJ

7.
Cons

8.
ExcRC

9.
PoI

—

.186
—

.490
.128
—

-.040
-.122
.095
—

.565
.091
.557
.345
—

-.093
.286
.022
.023
-.123
—

.422
.004
-.014
-.071
.260
-.298
—

.103
.343
.002
-.225
-.155
.365
-.214
—

.089
.225
.099
-.189
-.011
.159
-.111
.929
—

Table 10: Correlations between raw CDP+ predicted effects; the frequency effect has been reversecoded (sign-flipped) so that a greater benefit from higher frequency is indicated by a larger number.
Correlations significant at α = .05 (|r | > .197) are indicated in bold. R&C = Rastle and Coltheart.

1.
2.
3.
4.
5.
6.
7.
8.
9.

(neg.) Frequency
Neighbourhood Size
Word Length
Nonword Length
Lexicality
Exception (Jared)
Consistency
Exception (R&C)
Pos. of Irregularity

1.
-Frq

2.
Nei

3.
WLen

4.
NWLen

5.
Lex

6.
ExcJ

7.
Cons

8.
ExcRC

9.
PoI

—

.109
—

.326
.114
—

-.217
-.194
-.032
—

.421
-.001
.381
.134
—

-.209
.261
-.001
-.033
-.289
—

.450
-.048
-.096
-.156
.212
-.315
—

-.187
.315
.030
-.302
-.311
.312
-.204
—

-.018
.209
.081
-.289
-.199
.073
-.110
.933
—

Table 11: Correlations between general-speed-adjusted CDP+ predicted effects; the frequency effect has been reverse-coded (sign-flipped) so that a greater benefit from higher frequency is indicated by a larger number. Correlations significant at α = .05 (|r | > .197) are indicated in bold. R&C= Rastle and Coltheart.

predicted correctly, as was the artifactual correlation between exception (Rastle& Coltheart) and position of irregularity effects. The incorrectly simulated correlations were that between frequency and nonword length and that between
exception (Jared) and lexicality, which both were simulated as negative when
they were positive in the human data. Neighbourhood size’s correlations withfrequency and lexicality effects were in the correct direction (positive and negative, respectively) but not significantly so, and practically zero for lexicality.

58

Exc. (J) ~ Lex.

0.1

0.2

0.3

Exc. (R&C) ~ Pos. Irr.

0.0

−0.2 0.0 0.2 0.4 0.6 0.8 1.0

Exception

Figure 4: Scatterplots illustrating the pertinent individual differences correlations between generalspeed adjusted effects simulated with the CDP+.

−0.2 0.0

0.2

0.4

0.6

0.8

0.0

0.5

1.0

1.5

2.0

Position of Irregularity
WLen. ~ Lex.
0.0 0.1 0.2 0.3 0.4

0.0 0.2 0.4 0.6 0.8 1.0

LengthNWLen. ~ Freq.

0.0

0.5

1.0

1.5

2.0

−0.05

0.05

0.15

Neigh. ~ Lex.

−0.15

−0.05

0.05

0.15

Neigh. ~ Freq.

−0.15

Neighbourhood Size

−0.05 0.00 0.05 0.10 0.15 0.20

−0.05 0.00 0.05 0.10 0.15 0.20

Frequency

0.0

0.5

1.0

1.5

Lexicality
59

2.0

4.3. Discussion
Parameter modification allowed the CDP+ to capture much of the individual variation in effects. Moreover, the CDP+’s R2 s for the individual participants were better than those for the DRC. However, it did so in a way that wasinconsistent with some aspects of the correlational structure of the data, andsome effects were not predicted reliably with the chosen parameters.
In terms of effects, the most notable feature was the very weak predictionof consistency effects with the parameters chosen to fit these data. Given thatthese are a major motivation for the CDP+, and these effects were consistentin Perry et al.’s (2007) simulations, it is surprising that the present simulations
did not obtain them.
Given that there do exist parameter values that would produce the effect,
the problem must be that parameter values that produce the effect were notselected because they fit the data less well in terms of R2 . In particular, giventhat there was also no second-position exception effect in the predictions (alsopresent in Perry et al.’s (2007) simulations), it appears parameters were selectedthat slowed the nonlexical route so that it only processed the first letter during
word processing.
The selection of parameters that effectively remove the second-position exception effect and consistency effect could in principle have occurred either
because of a present effect problem — that is, the parameters that would produce the missing effects compromise an effect (or the magnitude of an effect)that accounts for more variance. However, it is difficult to see how candidate
variables for producing this type of problem — such as frequency and lexicality — could affect the second-position exception effect in this way for the CDP+
but not the DRC, given their similar structure. Even if this is the case, this is
not a flaw in the parameters, it is a problem with the model: The model cannot predict the magnitudes of effects shown simultaneously in the data with asingle parameter set (per participant).
An absent effect problem — that is, the parameters with a second-position
exception effect and a consistency effect introduce a predicted effect that does60

not occur in the data — might instead explain the problems in these simulations, as this could readily be attributed to a single difference between the
CDP+ and the DRC. For instance, the graded (proportional) spelling-soundconsistencies to which CDP+ is sensitive may include some to which people
are not sensitive; this would also explain why people more often read nonwords like the DRC than like the CDP+ (Pritchard et al., 2012).
Indeed, many of the stimuli have vowels in second position, and mostvowel graphemes have several possible pronunciations, so precisely how
people process the vowels (e.g., are correspondences used for the whole
body/rime of the word, rather than for the vowel alone) may strongly
influence the fits.
Two correlations were predicted with the wrong direction in these simulations. One was a predicted negative correlation between frequency and nonword length; this appears to reflect a straightforward trade-off between thelexical process producing the frequency effect and the nonlexical process producing the nonword length effect. Whilst the DRC has the same structure, thistrade-off is less marked in the DRC because in that model the nonlexical contribution is the same for all regular words of matched length.
The other problematic correlation was a predicted negative correlation between exception (Jared) and lexicality effects; this probably relates to the roleof the nonlexical route: When the nonlexical route is more efficient, this introduces an exception effect (albeit a slight one for these stimuli), and nonwords
are read more quickly, reducing the lexicality effect11 .
Given that the introduction of graded spelling-sound correspondences tothe nonlexical route did not allow the CDP+ to improve on the DRC’s patterns
of predictions for our individual differences data, we instead explored a variety of alternative models, including a modification to the DRC’s lexical routedesigned to mimic the alternative suggestion that differences in lexical repre11 Because the parameter sets chosen for the DRC do show second-position exception effects, thesize of these effects is more modulated by the size of the contribution of the nonlexical route, whichis greater when the lexical route is slow (and lexicality effects are large).

61

sentation quality (rather than acccessibility) are linked to frequency (see §3.3).
5. MODELING: DUAL-ROUTE CASCADED MODEL WITH FREQUENCYWEIGHTED CONNECTIONS
We explored a variety of modifications to the DRC that might make it moreconsistent with the data; we chose DRC as the base model because its base performance had fewer inconsistencies with the data, and because models that did
not require new training for each parameter set could be explored more rapidly.
These modifications included: using CDP+’s phonological representation; using a vowel-centered orthographic representation; modifying the functional
form of frequency effects (cf. Adelman & Brown, 2008b); allowing the parameter controlling the frequency effect to be different for the phonological lexicon
than the orthographic lexicon; introducing a direct set of connections from thenonlexical route to the phonological lexicon; allowing participants to vary invocabulary size by removing low-frequency items; and moving the locus of
the frequency effect from bias on lexical units to be in the weights connecting
orthography and phonology.
Of the various modifications we examined, the last-named — making
frequency have its effect through the connection strength between the orthographic and phonological lexical units (see Besner, Moroz & O’Malley,
2011, for arguments in favour of this mechanism for frequency effects), rather
than the bias on those units — made the most progress towards resolving
the inconsistencies with data that the DRC showed (without introducing newincorrect predictions), when combined with the changes to orthographic andphonological representations (to be vowel-centered and onset-vowel-coda,
respectively; the former was the more important change). We will call thismodel DRC-FC. We now describe in detail how DRC-FC differs from DRC,
and our simulations with the DRC-FC model.

625.1. Method
5.1.1. Model: Differences between the DRC and DRC-FC
Implementation of frequency. The direct influence on the input (the biased input) to the word units in the orthographic and phonological lexicons was removed; this is equivalent to setting the frequency scaling parameter to zero.
Instead, the excitation weight on each connection from an orthographic unit toa phonological unit was adjusted by multiplying by a value representing theorthographic frequency from CELEX (the same value as used to produce thebiasing input to the orthographic lexical units in DRC). Similarly, the excitation weight on each connection from a phonological unit to an orthographic
unit was adjusted by multiplying by a value representing the phonological frequency from CELEX (the same value as used to produce the biasing input tothe phonological lexical units in DRC). The multiplers were calculated by dividing the log. orthographic/phonological frequency of the word by the log.
orthographic/phonological frequency of the most frequent word (as is used forthe bias in the base DRC), and multiplying by a frequency weighting parameter(which was common to both directions of connection).
Orthographic coding scheme. Orthographic representations were left-paddedwith spaces so that the first vowel (counting Y as a vowel if it was not theinitial letter) was in fourth position, and the right-padding with spaces
reduced accordingly.

The grapheme-phoneme translation system ignored

these preceding spaces (but did not add additional following spaces), so thatwhen nonlexical processing was initiated, the first letter, not a space, wasimmediately processed. This, combined with the following modification, wasexamined because of the possiblity it might allow rime-based neighborhoodor consistency processing along the lexical route.
Phonological coding scheme. Phonological representations were modified to use
separate slots for onset, vowel, and coda, as in the CDP+ (rather than the leftalignment of the DRC) but the representation of the blank (missing) phoneme63

as a distinct unit (rather than a stable near-zero activity) was retained fromthe DRC. Thus, the phoneme representation for items with fewer than threephonemes in the onset had the blank phoneme unit active in some of the onset
slots. This permitted the rule for timing of initiation of pronunciation of the
DRC to be used with the minor alteration that the terminating blank needed
to be after the vowel. It was therefore necessary for the nonlexical route toactivate these blank units to produce a pronunciation. It did so at the sametime it activated the vowel, and the strength of the input to these blank units
was the same as that to the vowel unit.
5.1.2. Parameter settings, stimuli, procedure and per-participant selection
All other aspects of the methods were the same as those for the DRC, withthe exception that a new range of values was needed for the frequency weighting parameter of the new frequency mechanism; the range of all parametervalues is given in Appendix E. After the removal of parameters sets producing too many errors 3,548 parameter sets remained. The mean R2 of the fitted
sets was 35.72%.
5.2. Results
5.2.1. Standard effects
We examined DRC-FC’s predictions for the standard effects with these parameters; the predicted means for each condition are in Table 4. The corresponding ANOVAs follow, and are also summarized in Table 5.
Frequency. Shorter response times were predicted for more frequent words
than for less frequent words, F1 (4, 396) = 32.39, F2 (4, 80) = 6.12.
Neighbourhood size. The words with higher neighbourhood size were predicted
to have more rapid responses than the matched words with low neighbourhood size. This small (0.6 ms) difference was only significant by subjects,
F1 (1, 99) = 4.31, p = .041, F2 (1, 39) = 1.45, p = .235.

64

Length and lexicality. The longer a word, the slower its predicted latency, F1 (2, 198) = 34.04, F2 (2, 60) = 36.01, and likewise for nonwords,
F1 (2, 198) = 50.32, F2 (2, 60) = 31.82.

Nonwords were read slower,

F1 (1, 495) = 1106, F2 (1, 150) = 3558, and the length effect was stronger
for nonwords, F1 (2, 495) = 6.51, p = .001, F2 (2, 150) = 16.83. The estimatedmeans adjusting for neighbourhood size were 550, 551 and 553 ms for words,
and 602, 604 and 612 ms for nonwords.
Regularity and consistency.
Regularity. Jared’s (2002) exception stimuli had greater predicted RTs thantheir controls, whether in the group of items with higher-frequency enemies,
F1 (1, 99) = 75.79, F2 (1, 17) = 9.48, p = .007, or low-frequency enemies,
F1 (1, 99) = 52.30, F2 (1, 19) = 13.16, p = .002.
Consistency. Differences between the regular-inconsistent stimuli and theirregular controls were small and not significant, for both the items with highfrequency enemies F1 (1, 99) = 2.54, p = .114, F2 (1, 19) = 0.01, and those with
low-frequency enemies, F1 (1, 99) = 2.10, p = .151, F2 (1, 19) = 0.21.
Position of irregularity. First position exceptions were predicted to have RTs
longer than their matched controls, F1 (1, 99) = 222.26, F2 (1, 19) = 8.51, p =.009, as were second position exceptions, F1 (1, 99) = 15.80, F2 (1, 38) = 6.64,
p = .014. The cost for third position irregularities was small and significant
by-subjects, F1 (1, 99) = 9.24, p = .003, and significant with one-tailed correction by-items, F2 (1, 28) = 3.00, p = .094. The position by regularity interaction
was significant, F1 (2, 495) = 113.79, F2 (2, 85) = 10.30.
5.2.2. Recovery of individual differences in effects
Correlations between the participants’ effects and the effects in the corresponding DRC-FC simulations were: frequency .269; neighbourhood size .121;
word length .475; nonword length .597; lexicality .878; exception (Jared) .405;
exception (Rastle and Coltheart) .591; consistency .324; position of irregularity.600. With the adjustments for general speed applied, the correlations were:
65

1.
2.
3.
4.
5.
6.
7.
8.
9.

(neg.) Frequency
Neighbourhood Size
Word Length
Nonword Length
Lexicality
Exception (Jared)
Consistency
Exception (R&C)
Pos. of Irregularity

1.
-Frq

2.
Nei

3.
WLen

4.
NWLen

5.
Lex

6.
ExcJ

7.
Cons

8.
ExcRC

9.
PoI

—

.235
—

.297
.615
—

.463
.174
.421
—

.412
.071
.411
.408
—

-.133
-.198
.294
.065
.425
—

.569
-.279
-.145
.294
.361
.162
—

.002
.716
.442
.101
.275
.718
.088
—

.170
.375
.248
.062
.036
-.003
-.001
.638
—

Table 12: Correlations between raw DRC-FC predicted effects; the frequency effect has been
reverse-coded (sign-flipped) so that a greater benefit from higher frequency is indicated by a larger
number. Correlations significant at α = .05 (|r | > .197) are indicated in bold. R&C = Rastle andColtheart.

frequency .167; neighbourhood size .156; word length .274; nonword length.485; lexicality .670; exception (Jared) .294; exception (Rastle & Coltheart) .467;
consistency .234; position of irregularity .537.
5.2.3. Effect correlations
We examined the correlations of the effects given by the predicted RTs, presented in Table 12. The influence of general speed was again apparent in theabundance of positive correlations. Two correlations were nevertheless significantly negative in the predicted data, where those in the observed data were
not significant (postive). Overall, of the 24 correlations significant in the data
(Table 6), 14 were predicted significant in the correct direction and 8 not significant in the correct direction. Of the 2 predicted in the wrong direction, neither
was significant.
The correlations using the predictions adjusted for general speed in Table 13
showed four negative correlations, none of which was significant in the data.
Of the six general-speed-adjusted correlations significant in the adjusted data
(Table 7), five were predicted correctly (and significantly), and one not significant in the incorrect direction (see Figure 5). The negative correlation between
neighborhood size and lexicality was the effect not produced by DRC-FC.

66

Figure 5: Scatterplots illustrating the pertinent individual differences correlations between generalspeed adjusted effects simulated with the DRC-FC.

Exc. (J) ~ Lex.
1.0
0.5

0.5

0.0

0.0

Exception

1.0

Exc. (R&C) ~ Pos. Irr.

0.0

0.2

0.4

0.6

0.8

0.0

0.5

1.0

1.5

2.0

Position of Irregularity
WLen. ~ Lex.

0.1

0.2

0.5

0.0

0.0

Length

0.3

1.0

0.4

NWLen. ~ Freq.

0.00

0.05

0.10

0.15

0.0

1.0

1.5

2.0

0.6
0.4
0.2
0.0

0.2

0.4

0.6

Neigh. ~ Lex.

0.0

Neighbourhood Size

Neigh. ~ Freq.

0.5

0.00

0.05

0.10

0.15

Frequency

0.0

0.5

1.0

1.5

Lexicality
67

2.0

1.
2.
3.
4.
5.
6.
7.
8.
9.

(neg.) Frequency
Neighbourhood Size
Word Length
Nonword Length
Lexicality
Exception (Jared)
Consistency
Exception (R&C)
Pos. of Irregularity

1.
-Frq

2.
Nei

3.
WLen

4.
NWLen

5.
Lex

6.
ExcJ

7.
Cons

8.
ExcRC

9.
PoI

—

.249
—

.316
.587
—

.386
.137
.414
—

.269
.041
.264
.235
—

-.297
-.200
.117
-.054
.245
—

.464
-.263
-.168
.207
.285
.099
—

-.161
.153
.300
-.010
-.054
.624
-.004
—

.024
.282
.072
-.029
-.295
-.171
-.104
.598
—

Table 13: Correlations between general-speed-adjusted DRC-FC predicted effects; the frequency
effect has been reverse-coded (sign-flipped) so that a greater benefit from higher frequency is indicated by a larger number. Correlations significant at α = .05 (|r | > .197) are indicated in bold.
R&C = Rastle and Coltheart.

5.3. Discussion
Like its relatives, DRC-FC accommodated individual differences in effects
by changes in parameters. Unlike the other models, in its general-speedadjusted data, it correctly produced a significant positive correlation between
frequency and neighbourhood size effects. In common with the other models,
it did not produce a consistency effect comparable to the data, nor a negative
correlation between neighbourhood size and lexicality effects.
Introducing vowel-alignment in orthographic and phonological representations did not improve the model’s reproduction of the consistency effect. If(as previously discussed) the consistency effect largely results from residual
priming of a word’s friends and enemies — rather than purely the stimulus
activating its own friends and enemies — then this would explain why all themodels considered here — where activities are reset for each trial — do not
fully capture the effect.
The key improvement with the DRC-FC is correctly catpuring the positive correlation between frequency and neighbourhood size. DRC-FC’s ability
to capture this pattern — perhaps counterintuitively — appears to rely upon
the two effects occuring at different stages within the same route. Where the
DRC has both effectively occurring at the lexical units, high frequency tends
68to increase lexical inhibition, making neighbourhood size effects inhibitory for
those showing strong frequency (and overall). In the DRC-FC, a stronger frequency effect can increase the influence of neighbours by passing more activation from orthographic to phonological layers. Alternatively, if these effects
are now controlled by different parameters, these parameters are free to correlate in whichever direction is necessary to capture the relationship between theeffects.
From a learning perspective, it seems reasonable that the strength of association between orthographic and phonological forms should depend on thenumber of learning episodes. Whilst this may appear to run counter to otherfindings that suggest that orthographic units are sensitive to frequency, there
could be additional loci of the frequency effect beyond that in DRC-FC, andsome tasks that appear to be wholly orthographic may actually drive participants to make decisions that are based on phonological activity (Rastle & Brysbaert, 2006). It still, however, seems implausible that there is no purely visualorthographic frequency effect, but this may occur on the links between lettersand words. One way in which such an account could avoid the BLIGHT- BRIGHT
problem is if the frequency effect operates more effectively on inhibitory thanfacilitatory inputs, so that the frequency benefit is only seen for exact matches.

6. GENERAL DISCUSSION
We now recapitulate the main empirical and modelling findings, consider
notable aspects of the empirical findings, consider the implications for models,and conclude with future directions.
6.1. Summary of findings
6.1.1. Effects in the data
The data broadly showed the typical patterns that we sought: Frequentwords were read more quickly than rare ones (Forster & Chambers, 1973);
the slowing by length Frederiksen & Kroll (1976)was greater for nonwords

69

than words (e.g., Weekes, 1997); words violating spelling-sound rules wereread slower than those that did not (e.g., Seidenberg et al., 1984), especially
if those violations were early in the word (Rastle & Coltheart, 1999); (some)words whose pronunciations were inconsistent with those that shared an orthographic body (vowel onwards) were read slower than those with no suchinconsistency (e.g., Jared, 2002). The evidence was not strong for a facilitatoryneighbourhood size effect (see Andrews, 1997), because that effect was moderated by age, with our older participants showing less facilitation, and possibly
inhibition, consistent with past research (Spieler & Balota, 2000).
6.1.2. Correlations in the data
We sought correlations among key item effects in word naming, and foundmany. Most of these could be attributed to general speed, but others could
not: positive relationships with frequency effects for neighbourhood size andnonword length effects; positive relationships with lexicality for word lengthand exception effects; and negative relationships with neighbourhood size forlexicality and exception effects.
6.2. Modeling the data
Whilst we were able to offer an interpretation in terms of dual route theory,
we were concerned that most correlations could have been predicted either
way. This is a general problem with attributing individual differences to routes
without a detailed implementation of their properties and how their contributions are combined. This is because a route’s effect may be weak because it isfast or because it is too slow. If it is too fast, its fastest and slowest responses
differ little in speed. If it is too slow, it may be outpaced by the other route andhave little influence on the result. We therefore ran implemented dual route
models — the DRC and CDP+, and a modified DRC, the DRC-FC — to see ifthey could capture the data by changing parameters on a participant by participant basis.
In these simulations, we selected for each participant the parameter values

70

giving the best correlation with his or her individual item mean response times(once first phoneme was partialed out). This technique gives a more complete
view of model performance than techniques that fit only condition means oreffects. These non-item-level model selection procedures would indeed havefound parameters that made better predictions about the effects we built into
the experiment. However, the parameters could make very bad predictions
about other unexamined (and possibly unknown) effects that contribute to the
response times of the items in the experiment and still be selected. Regardless of the effects that we intend to use to interpret the modeling data, all effects
place constraints (of the compatibility type discussed in §1.1.3) on how the cognitive mechanisms must operate.
Whilst, unsurprisingly, the fitting procedure gave the models some capability to predict a large effect of some variable for those who showed a greater
effect of that variable, more detailed examination suggested that the models
did not capture this variation in a way that was compatible with the data.
6.2.1. DRC
The parameters best fitting the subject-and-item-level data for the DRC led
the model to produce no consistency effect, a negative correlation between thefrequency and neighborhood size effects, and no correlations between the lexicality effect and either the neighbourhood size or exception effect, inconsistent
with the data. The model with these parameters did correctly capture the threeother correlations of interest, as well as the other overall patterns, albeit notalways with their full magnitude.
6.2.2. CDP+
The parameters best fitting the subject-and-item-level data for the CDP+
led the model to produce no consistency effect, an exception effect that was restricted to first-position irregularities, a negative correlation between lexicality
and exception effects, a negative correlation between frequency and nonword
length effects, and no correlations of neighborhood size with either frequency71

or regularity, inconsistent with the data. The model with these parameters
did correctly capture the other two correlations of interest, as well as the otheroverall effects, albeit not always with their full magnitude.
6.2.3. DRC-FC
The parameters best fitting the subject-and-item-level data for the DRC-FC
led the model to produce no consistency effect, and no correlations between
the lexicality effect and the neighbourhood size effect, inconsistent with thedata. The model with these parameters did correctly capture the five othercorrelations of interest, as well as the other overall effects, albeit not always
with their full magnitude.
6.3. Interpretation of the data
6.3.1. General speed does not accommodate all individual differences
There was a strong overall pattern of positive correlations in the raw effects. Adjustments for general speed indicated that many of these positive
correlations were attributable to a general speed multiplier affecting all the(signal) processes. However, five significant non-artefactual correlations persisted, which were indicative that systematic individual differences exist thatare not due to general speed. Verbal dual route interpretations could be placedon these, but we sought to replace these verbal interepretations with the modeling.
6.3.2. Lexical quality and reading experience
The concept of lexical quality seemed important for interpreting the data,
despite its having no clear analog in the models of interest. Lexical quality(e.g., Andrews & Hersch, 2010; Perfetti, 1992) refers to the idea that the specificorthographic, phonological or semantic-syntactic information associated witha particular word can vary in how well-specified it is; being poorly specified
means that particular information — such as the ordering of adjacent I and E— about a word is missing. The degree of underspecification of a word’s information can vary so that receptive language outperforms production (e.g.,72

recognizing a correctly spelled word despite spelling it incorrectly yourself).
Although lexical quality is a concept that applies at the level of the individual
word within the individual person, it also can be applied as an average over
the words a person knows: individuals who have learned a language more
— due to greater experience and/or greater efficiency of learning — will haveon average better lexical quality across lexical items. This offers a natural explanation of otherwise difficult phenomena, such as the observed correlationof neighbourhood size and frequency effects, because it naturally links bothneighbourhood and frequency effects to (lack of effective learning) experience.
In particular, the constraint-based approach implied by lexical quality meansthat as words become better learned, they might be more activated by perfectmatches but they should also be more readily distinguished from close mismatches.
Consistent with this, older participants (having more reading experience)
also showed less facilitatory neighbourhood size effects, consistent with finertuned input representations — that is, higher lexical quality — reducing thegeneralization that would support such facilitation.
This interpretation also offers a possible view on analyses that suggest frequency effects can be effectively predicted from vocabulary size. Developmentally, lexical quality is determined by exposure to lexical items and by ability
to form and refine representations from such exposure. These are the same factors that would be responsible for vocabulary size; indeed vocabulary can beseen as a person-level measure of lexical quality computed on an all-or-nonebasis over possible lexical items (i.e., all words in the dictionary). It is therefore possible to interpret results that suggest an influence of vocabulary size
as instead being due to lexical quality (on average for an individual over the
relevant lexical entries).
6.3.3. Changes within individuals within the experiment
Session-to-session changes. For the most part, the average participant responded
slower in the later sessions of naming, although such patterns may not bear out
73

at the level of the individual participant (see Adelman et al., 2013, for data formany sessions). This was not associated with an increase in the sizes of theeffects of interest, which might be expected from a stretching of the main process of naming. That is, the slowing occurred in the “intercept” part of theprocess unaffected by the identity of the stimulus. Indeed, the only significant
interactions with session were such that effects became smaller over sessions,because the slowest conditions (exception words, long nonwords) were relatively immune to the slowing effect. This may reflect participants strategicallycompensating for their slowing only for those responses they perceived at riskof falling after stimulus offset.
Trial-to-trial changes. To be consistent with the models in question and typical word naming experimental analyses, we have excluded possible effects oflong-term priming from our analyses. The implied assumption is that such effects will average out over the several different orderings of the stimulus list
and result in all words being read on average the same amount faster than thebaseline notionally unprimed condition. This assumption is problematic forthe analyses here, however, because each participant only saw three orderings,
which does not allow for a great deal of averaging out, and the natural estimateof reliability is the test-retest one. Worse, though, there are good reasons to suppose the assumption is wrong. First, words may differ in their susceptibility topriming (e.g., Kinoshita, 2006); if a truly neutral baseline is not achieved, moreprimeable words will have shorter RTs on average, but not reliably. Second,words may differ in the number of possible preceding words that would prime
them12 ; words with more related primes will on average be more primed, thus
having shorter RTs. The latter type of process may be particularly relevant tothe consistency effect, as there is long-term rime priming (Seidenberg et al.,1984), and, indeed, this was an effect that showed low consistency in our data.
Clearly, models that do not adapt to the trial sequence could not give a correct
12 Indeed, this may be the source of primeability — words that have many related primes may

typically be in the primed state, and thus be difficult to prime.

74account of the effect if this were true.
6.4. Modeling interpretations
6.4.1. DRC
For the DRC, the key problems in the simulations related to effects that havepreviously been identified as problematic for this model and its relatives. The
patterns with the neighbourhood size effect — its overall inhibitory effect inthe model (albeit to a degree that would be undetectable in the data) and its
negative correlation with the frequency effect — were suggestive that a modification to the model is needed that weakens its knowledge of the spellingcorrespondences of lower-frequency words; it is unclear how this could occur
without broader changes in its letter level to remove the heavily criticised assumption of perfect knowledge of letter position (e.g., Davis, 2010; Grainger &van Heuven, 2003). A representation that is somewhat redundant could havethe desired effect. The somewhat weak but correct consistency effect with flexibility in parameters is suggestive of an incomplete ability to account for the effect. Indeed, although significant, the effect was so small once converted fromcycles to milliseconds that we would expect essentially no power to detect theeffect in the data, where a strong effect was in fact observed. As models become more and more refined, increased attention to the precision of predictedeffects will be warranted.
Given that (for instance) CRATE and LATE are wholly dissimilar in the DRC
model both orthographically and phonologically, the absence of a strong consistency effect is perhaps not surprising. A complete sensitivity to rime structure requires some alteration to the orthographic coding, as was already indicated, and to the phonological coding, perhaps to reflect the linguistic structureof the syllable, as in CDP+13 .
13 However, in modeling not presented in detail here, such changes were not sufficient to produce
a consistency effect of appropriate magnitude when the parameters are constrained by other effects
in the data.

75

6.4.2. CDP+
For the CDP+, the problems appeared more systemic. The model parameters that best fitted the data had an excessively severe position of irregularity
effect, such that late irregularities and inconsistencies did not exert an influence
from the nonlexical route. This is suggestive that properties of the predictions
from the nonlexical route were discrepant from the data in a way that meantthat fits to the data were better when the nonlexical route’s influence was minimised. Whilst it is known that the CDP+ model can produce consistency effects on the average of conditions selected to differ in consistency (Perry et al.,2007), examining only differences in condition means can obscure other problems in the predictions (see, e.g., Besner, Twilley, McCann & Seergobin, 1990),
and the item R2 criterion was chosen to prevent models taking advantage ofthis obfuscation opportunity. Although the ability to fit consistency conditionmeans has previously been taken as an advantage for the more graded nonlexical route of the CDP+, there are pseudoword stimuli for which human naming
responses are more like those of the DRC than those of the CDP+ (Pritchard
et al., 2012). One possible consideration here is that the CDP+ is trained on
monosyllabic words, whereas no such constraint applies in humans’ learningof spelling-sound relationships. If this is the problem, then the newer CDP++(Perry, Ziegler & Zorzi, 2010) would resolve the problem.
However, the problems the CDP+ has could also be more to do with thestructure of the network that learns the spelling-sound correspondences. The
network’s structure is sensitive to a form of consistency that is correlated with,
but not the same as, the type of consistency manipulated in experiments showing the effect in humans. CDP+ is sensitive only to grapheme-level consistency,
not rime-level consistency. CDP+’s nonlexical route has no way to represent
specifically that OOD is often pronounced /Vd/ (as in hood) or OOM is often
pronounced /u:m/; it instead would have to represent that (1) OO makes /V/and /u:/ both somewhat likely; and (2) that a post-vocalic M makes /u:/ a more
likely vowel and a postvocalic D makes /V/ a more likely vowel, and these ef-

76fects would be independent. (Over the whole vocabulary, this might not be theoverall learned pattern — the point here is to illustrate the types of representations possible in the two-layer network.) That is, the effect of the orthographic
consonant on the phonological vowel can not be specific to the particular orthographic vowel context: /u:/ would (ignoring the influence of the other learning
patterns) be more activated by the nonlexical route in response to RIM than RID,
and /V/ is more activated by the nonlexical route in response to RID than RIM.
This differs substantially from other connectionist accounts (e.g., Plaut et al.,1996) because it uses a network with no hidden units to map from graphemes
to phonemes, and so can not learn larger units than a grapheme. In common
with these models, however, the position-specific nature of the spelling-soundrelationships learned by CDP+ is a likely cause of problems with nonwords
that have graphemes in atypical positions, where people do clearly attempt togeneralize across position.
6.4.3. DRC-FC
The changes made to the DRC to make the DRC-FC circumvented someproblems with the fit to the effect correlations. By placing the frequency effecton the links between orthography and phonology, the deleterious effects of the
combination of neighborhood activity and a benefit in bias for frequent words
is achieved. By limiting the influence of frequency on orthographic nodes tofeedback from the phonological nodes, higher-frequency neighbors were unable to overwhelm items, even when neighborhood size effects were large.
However, it seems unlikely that there are no purely orthographic frequency
effects, as this model would suggest. Moreover, in this case, there is no obvious single root cause that produces neighbourhood and frequency effects —the positive correlation between them is likely produced by covarying parameters, rather than a constraint in the model. Moreover, the model also still did
not produce frequency and length effects of a magnitude consistent with the
data.

77

6.4.4. PDPOther connectionist, parallel-distributed-processing (PDP: e.g., Harm &Seidenberg, 1999; Plaut et al., 1996; Seidenberg & McClelland, 1989) models
were, however, excluded from our consideration for three main reasons. The
first is that it is at odds with the intent (and epistemology) of the modellers
who produced them: Whilst the modellers responsible for the DRC and CDP+
treat the details of each effect as a constraint that should be included into acomplete true model, modellers using PDP emphasise that the explanation or
principle of a certain phenomenon is true, and explicitly deny that the model
is itself a representation of the truth at any other level of abstraction. As such,an attempt to falsify such a model is meaningless, as it would falsify no claimof the author. The second reason is a corollary of this approach. Given that nomodel is intended to be correct, we would have no rule to select the correctmodel for our data: no model is correct, no model supersedes an earlier one,and each phenomenon deserves and requires its own model. The final reason
follows from both of the preceding ones: no PDP model qualitatively captures
all of the effects, because no model is intended be true and each phenomenon
may and indeed should be simulated separately from any other according to
the arguments of these PDP modellers (Seidenberg & Plaut, 2006).
Nevertheless, in light of these data, and some previous indications, we canoffer some idea how some of the patterns seen here might be accommodated
in models related to those seen in the PDP literature. In many respects, theconsiderations mirror those of trade-offs between routes in dual-route models insofar as there are factors in PDP models that produce trade-offs between
item-specific processing and processing based on generalization across items.
For instance, the number of hidden units between orthographic and phonological layers affects the extent of generalization: Lower numbers of hidden
units tend to require greater compression of the input information, producing
greater generalization, because having hidden units that read only one or a
few words cannot work when such units are scarce. Similarly, modulation of78

an input gain parameter can control item-based versus generalization-based
processing, with higher levels of input gain leading to more item-based processing. By either mechanism, more item-based processing would be expected
to be associated with less benefits from generalization, slowing low-frequency
words and pseudowords, but giving relative benefits to inconsistent or irregular words. That is, one could naı̈vely predict the frequency and lexicality effects
would be positively correlated with each other and negatively correlated withexception effects.
An alternative consideration is the amount of learning that has taken place.
First, as reading becomes more efficient, general speed should increase, reducing all effects. Further, as we are considering relatively expert readers,
we would expect that the asymptote of learning is a consideration for the
more frequent words, so that at greater levels of learning frequency effects
should diminish, and more rapidly so than just the general speed effect. At
the same time, as learning at the orthographic level proceeds, spurious activity
for neighbors should be eliminated, reducing the potential for neighborhood
facilitation.
Some research on modeling nonword pronunciation with PDP models has
been offered as evidence that PDP models account for individual differences(Zevin & Seidenberg, 2006). However, the individual differences in which pronunciation was chosen for nonwords was caused in these models by minor
random variations in the training schedule. Whilst it is easy to see how suchvariation could produce a bias in how novel stimuli are processed, it is in noway clear that such variation should offer an appropriate account of systematic
shifts in response times to stimuli with particular characterstics, let alone howsuch effects should correlate.
Indeed, as with the other models, this kind of verbal analysis can only offer
a set of possible considerations, which may be incomplete, and only some ofwhich may be relevant for the particular model structure and parameters selected to fit each participant’s data. To assess such predictions would require
an appropriate PDP model in terms of both producing response times and cov79

ering an adequate range of effects. To our knowledge, this would be a newmodel (the model of Chang et al., 2012, might, for example, be extended to
produce response times rather than only error scores that may be presumed
to be monotonic in response time). We do not attempt any such PDP model
development here, and indeed proponents of the PDP approach, and indeed
well-known proponents of the PDP approach have argued that the development of such a model would be undesirable (Seidenberg & Plaut, 2006); proponents of competing approaches view this as an argument for unfalsifiability(Rastle & Coltheart, 2006).
6.5. Conclusion and future directions
We replicated the key theoretical phenomena of reading aloud, and demonstrated that reliable individual differences are observed even after accounting
for differences in general speed. We then identified critical correlations among
these effects (within individual participants), and used these correlations as
criteria for testing computational models of reading aloud. After showing that
DRC and CDP+ capture some but not all of these correlations, we developed
and tested a model based on DRC, the DRC-FC, which did indeed yield moreaccurate predictions of these correlations.
These data offer important new constraints on theories of visual word
recognition and highlight areas of potential improvement within DRC and
CDP+ accounts of word naming. Extending models to accommodate individual differences is an important future step to allow more complete theories
of reading to be more accurate and more applicable to real-world concerns
about reading and reading acquisition. Moreover, investigations of individual
differences in more naturalistic reading tasks similarly need to be extended
beyond the comparison of good and poor readers (e.g., Ashby et al., 2005),
informative as these may be. However, more than just accommodating thesedifferences, models must also offer an understanding of these individual
differences.

Such an understanding would specify the processes — andthe parameters of these processes — that differ to underlie differences in80

the effects. Such an understanding would be augmented by linking theseparameters to other, more general, cognitive abilities. Such model-specificsteps would be premature without a model that can accommodate the effects.
We view the present data as one empirical step towards a more global modelof reading.

Acknowledgements
This work was funded by ESRC Grant RES-062-23-0545. We thank Gordon
Brown, Marc Brysbaert, Victor Kuperman and two anonymous reviewers fortheir constructive comments.

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Appendix A. Frequency stimuli
Stimuli used for estimating the frequency effect are presented in Table A.1.

Appendix B. Neighbourhood stimuli
Stimuli used for estimating the neighbourhood size effect are presented inTable B.1.

Appendix C. Length stimuli
Stimuli used to estimate length effects and the lexicality effect are presented
in Table C.1.

90

1 ppm

3 ppm

9 ppm

27 ppm81 ppm

boo
bash
brawl
dud
dent
foal
gull
hoot
chomp
cud
clove
lurk
mush
nigh
pelt
rink
seep
shank
steed
tab
teak

bug
bout
brink
din
dope
foil
gosh
hiss
chunk
kin
clown
lick
moth
knob
putt
rake
sage
shoal
stunt
tar
toil

ban
bike
bride
den
dock
fork
goat
hint
cheer
cab
clash
lust
mill
nail
pond
ripe
suck
shine
stack
tub
tilt

bay
bomb
brick
dad
damp
file
goal
hide
chain
kid
cloud
link
mess
knee
pack
rope
song
sheet
storm
tin
tent

box
base
brown
die
desk
film
game
hell
chair
cup
clean
lack
milk
note
pain
rock
send
share
stone
tax
team

Table A.1: Stimuli used for estimating frequency effectAppendix D. Adjusting for general speed
A core difficulty in assessing individual differences in response time effects
in terms of correlations is that a correlation will be induced if there is a general
speed coefficient that is a multiplier on the central processes; in many models
this is the slope for converting cycles into response times. Whilst in modelling
applications, this parameter can simply be included for every participant, fora more qualitative understanding of correlations, removing such an effect isdesirable. Consider a numerical example. Suppose that the to-be-modelledcognitive item-based task’s response times are affected by three (standardised,orthogonal) variables of the items — A, B, and C — in a manner that is consistent with a linear regression, and the residual noise process has an independent, irrelevant, weight (in contemporary models of word naming, this istrue: only item mean RTs come from central parts of the model; RT variability
91low Nhigh N

bird
buzz
beard
disc
dumb
dirt
dawn
foul
fact
gulf
garb
growl
helm
hurl
howl
high
yak
cusp
keel
cube
cult
keen
kiss
coal
leave
mesh
mere
romp
rich
surf
self
stump
void
veil
wisp
zinc
loin
noun
crypt
nil
loaf
loud

bell
buck
*bitch
deed
dole
doll
dare
fame
face
gang
gush
graze
hilt
hash
hack
home
yam
coop
cone
cork
cart
cake
cave
cope
light
mole
mile
rave
rate
sane
seed
stale
vest
vice
wane
zeal
loon
*nope
creed
nap
lest
lake

Table B.1: Stimuli used for estimating neighbourhood size effects. Pairs with an asterisked item
were not included in the analyses.

92

WordsNonwords

3

4

5

3

4

5

bib
bin
beg
bid
dig
dry
fun
gut
gun
hag
hug
hut
cod
cap
cut
lob
lid
leg
mar
mob
mix
nip
net
pun
peg
pen
rib
rub
sin
tip
web

bile
bunk
barn
bond
dull
drop
fill
gale
gate
hoop
hawk
hang
cape
code
cost
lobe
lump
loss
meek
mode
myth
nape
neat
pert
pike
pump
rout
rude
sing
tune
weep

budge
bulge
boost
bunch
depth
drive
fault
goose
guess
hunch
haunt
hence
curve
coach
court
lurch
lodge
lunch
munch
marsh
mount
nudge
nurse
purge
punch
pitch
roost
ridge
solve
toast
wedge

bul
bym
bov
bes
dyt
dro
fud
gol
gaz
hin
hab
hol
kem
kuc
kav
lec
loy
lig
moy
mib
mup
nev
nar
pag
pem
poy
rus
ruv
suz
tol
wec

beab
bibe
bobe
booc
deef
dran
filk
gise
goaf
hean
heek
hont
coth
cose
kive
lenk
lilk
loog
marf
meap
moop
noof
nowl
peef
perb
paim
rull
roog
soob
telp
wiln

beich
bouse
beash
boarm
dunch
drine
fodge
gunch
gudge
hulch
hetch
haise
cange
kaunt
kutch
lerge
lirge
louch
medge
mouch
metch
nalve
ninch
petch
pedge
purpe
raint
rorse
sudge
tarch
wouse

Table C.1: Stimuli used for estimating length and lexicality effects

93within items is treated as a purely statistical issue). Participant 1 has regression
coefficients 5, 15, and 5 respectively for these variables, and residual standard
deviation (s.d.) 25; Participant 2 has coefficients 10, 30, and 10, and s.d. 50;
Participant 3 has coefficients 10, 30, and 10, and s.d. 100; Participant 4 has coefficients 10, 10, 30, and s.d. 200; and Participant 5 has coefficients 5, 15, and15, and s.d. 50. Adjusting for general speed means identifying Participants 1,
2 and 3 as examples of the same 1:3:1 (A:B:C) ratio pattern, Participant 4 as an
example of a different 1:1:3 pattern, and Participant 5 as different from all ofthem, having a 1:3:3 pattern.
A common solution — and that used by Yap et al. (2012) — is to use standardised regression coefficients for regressions separately on each participant(or at least per-participant z-scored RTs in separate analyses). If there were noresidual noise, that is, all variability in observed item means were due to item
properties, this would have the desired effect. However, if there is contamination with noise that is not wholly proportional to overall speed (i.e. participants are not equally reliable) as in the example, then the strength of this noise
will affect the adjusted strength of the item variables. In the example, whilst
Participant 1 and 2 are correctly identified with the same set of strengths, Participant 3 receives weaker corrected strengths for all the item variables, due tothe greater noise influence. It is true that more variance in the individual trials
is explained by those variables for the first two participants, but this is unlikely
to be theoretically relevant: Response time variability is not usually attributed
to the central cognitive process of interest, instead in whole or in part being attributed to other processes, such as variability in response execution; certainly,
contemporary naming models give no account of within-participant, withinitem response time distributions. Perhaps more concerning is the comparisonof Participants 2 and 4. Standardising Participant 4’s highly variable response
times will give a much greater compression of the effects than occurs for Participant 2; as a consequence, Participant 4’s standardised effect for C will be lessthan Participant 2’s standardised effect for C, despite this variable accounting
for the majority of the systematic effect for Participant 4, and another variable94

(B) doing so for Participant 2.
Another (incomplete) solution might be to use something related to the actual ratio of the observed regression coefficients, such as weighting the coefficients so that they (or their squares) add to one. If all effects are known, thenthis achieves the goal (up to the quality of the data). However, if the empirical regression lacks one or more variables then it does not produce the correct
pattern: In our example, suppose C is omitted, then (assuming the remaining
coefficients are perfectly estimated, for simplicity) whilst Participant 4 is correctly identified as different (1:1), the other four participants are all identified
as having a 1:3 (A:B) ratio pattern. That is, the relatively smaller influence of A
and B for Participant 5 is not detected.
These two possibilities combined isolate the problem: What is needed is areasonably good estimate of the residual standard deviation for a given participant that remains after accounting for all the variance caused by item variables.
The z-scoring approach uses the whole observed variance as the estimate of thesystematic variance, underestimating the residual variance as zero. The naı̈ve
ratio approach uses observed variance attributable to known variables as theestimate of the systematic variance, overestimating the systematic variance by
including variance due to unknown variables as well as some noise variance.
Clearly, the true variance due to item variables — both known and unknown
together — is the same as the true variance due to items. If a participant has,
however, completed each item only once, directly estimating (by analysis ofvariance of the data for an individual participant) the variance due to items exhausts all the variance (because item and trial are aliased), including variancedue to noise; no correction is available because there is no independent estimate of the noise. On the other hand, if, as here, participants read the words
more than once, the estimated item variance does not soak up all the noise(only part of it); it is the item by session interaction that can not be separated
from the noise in this design. That is, the variance of the item means is less contaminated (as an estimate of variance due to items) with noise variance than thevariance of all the trials, but a further step can be taken: An unbiased variance95Factordf

Sums of squares

Mean square (MS)Session
Item
Residual

2
683
1309

99994
24026131478906

49997
3518
1130

Variance component for Item = (MS(Item) - MS(Residual))/(#Sessions)= (3518-1130)/3 = 795.98
Divisor√= 795.98 × 683/684= 28.19 ms

Table D.1: Calculation of the estimate of the variance component associated with Item.

component estimate for the items can be calculated. This variance component
can be used instead of the total variance as the square of the denominator in a zscore-like calculation; in the following, we do so using the (unbiased) ANOVA
estimate of the variance component for simplicity. If the assumption that thenoise process is scaled as part of the general speed is true, this method produces results equivalent to those produced by plain z-scoring (all adjusted RTs
are multiplied by the same constant); if it is not, then it does not allow differences in the noise process to affect the interpretation of the speed of the otherprocesses.
To make this concrete, Table D.1 shows the ANOVA table from which thevariance component was calculated for one participant.
The standard deviation estimated from the variance component for use instandardisation for each participant had mean 54.3 ms and standard deviation 14.7 ms; it correlated with the participants’ overall standard deviations.661, with their mean RTs .522, and with the individual effects: frequency .473;
neighbourhood size .148; word length .264; nonword length .665; exception(Jared) .380; Exception (Rastle & Coltheart) .581; consistency .388; position ofirregularity .384; and lexicality .616.

96

Appendix E. Parameter ranges used in DRC and CDP+ simulations
Table E.1 presents the lowest and highest value used for each parameterof the models. For parameters than can take only integer values, 21 equally
spaced values were used with equal probability; for other parameters, 51
equally spaced values were used with equal probability.

97Parameter

DRC, CDP+ or DRC-FC?

Activation
Frequency scaling
Frequency weighting
Stopping criterion
Resting criterion
Feature-letter excitation
Feature-letter inhibition
Letter-orthography excit.
Letter-orthography inhib.
Letter-letter inhib.
Letter decay
Orthography-phonology excit.
Orthography-letter excit.
Orthography-letter inhib.
Orthography-orthography inhib.
Orthography decay
Phonology-phoneme excit.
Phonology-phoneme inhib.
Phonology-orthography excit.
Phonology-phonology inhib.
Phonology decay
Phoneme-phonology excit.
Phoneme-phonology inihb.
Phoneme-phoneme inhib.
Phoneme decay
Nonlexical route strength
Nonlexical route threshold
Temperature
Learning rate
Nonlexical route start
Nonlexical route step
Phonics cycles
Vocabulary cycles

Min.

Max.

0.18
0.02
12
0.25
0.00001
0.001
−0.25
0.04
−0.8
0
0
0.1
0.2
0
−0.2
0
0.05
−0.4
0.02
−0.2
0
0
−0.2
−0.4
−0.02
0.01
0.005
1.0
0.005
0
0
0
0

0.38
0.6
360
1
0.00101
0.02
−0.05
0.24
−0.4
0.1
0.2
2.0
0.8
.1
0
0.1
0.25
0
2
0
0.1
0.4
−0.05
−0.05
−0.01
0.2
0.25
11.0
0.2
20
20
200
200

All
DRC and CDP+
DRC-FC
All
CDP+
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
CDP+
CDP+
CDP+
DRC and DRC-FC
All
CDP+
CDP+

Table E.1: Parameters ranges used for DRC and CDP+ simulations.

98