This is the accepted, before copy-editing, version of:Adelman, J. S., & Brown, G. D. A. (2007).
Phonographic neighbors, not orthographic neighbors,
determine word naming latencies. Psychonomic
Bulletin & Review, 14, 455–459.

1

Running head: PHONOGRAPHIC NEIGHBORS

Phonographic neighbors not orthographic neighbors determine word naming latencies

James S. Adelman and Gordon D. A. Brown
University of Warwick

Word count: 3,617, including refs.

Corresponding Author:
James S. Adelman
Department of Psychology,
University of Warwick,
Gibbet Hill Road,COVENTRY,
CV4 7AL,UK.
Telephone: +44 (0) 24 7615 0233
Electronic mail: J.S.Adelman@warwick.ac.uk

Abstract

The orthographic neighborhood size (N) of a word, the number of words that can be formed fromthat word by replacing one letter with another in its place, has been found to have facilitatory
effects in word naming. The orthographic neighborhood hypothesis attributes this facilitation tointeractive effects. A phonographic neighborhood hypothesis, in contrast, attributes the effectto lexical print-sound conversion. According to the phonographic neighborhood hypothesis,
phonographic neighbors (words differing in one letter and one phoneme, e.g., stove and stone)
should facilitate naming and other orthographic neighbors (e.g., stove and shove) should not.
The predictions of these two hypotheses are tested. Unique facilitatory phonographic N effects
were found in four sets of word naming mega-study data, along with an absence of facilitatory
orthographic N effects. These results implicate print-sound conversion, based on consistentphonology, in neighborhood effects, rather than word-letter feedback.

ord naming latencies
Phonographic neighbors not orthographic neighbors determine wword

The orthographic neighborhood of a word is defined as the set of words that may be formed fromit by replacing only one letter with another in the same position. The effect of the size of
this neighborhood (N, Coltheart, Davelaar, Jonasson, & Besner, 1977) on the speed of lexical
processing has been examined in many experiments (for reviews, see Andrews, 1997; Mathey, 2001).
Andrews (1997) and Mathey (2001) both concluded that large values of orthographic N are
associated with fast word naming. Specific evidence as to the source of this effect would
constrain word recognition models. Some accounts attribute N effects to purely orthographic
interactive-activation processes (Andrews, 1989, 1997). In contrast, as discussed below, several
models of word recognition attribute N effects to a print-sound conversion process -orthographic neighbors tend to have similar pronunciations, and so the effect is not one oforthographic neighbors per se (see Peereman & Content, 1995, for discussion of alternative
hypotheses and data from French pseudoword and word naming). Evidence for the former
interpretation would be difficult to accommodate within current models, as such interactions
have little effect on the word level in some (e.g Coltheart, Rastle, Perry, Langdon, & Ziegler,2001) or are absent from the model in others (e.g Plaut, McClelland, Seidenberg, & Patterson,
1996). Here we test between the competing explanations of N effects.

When Andrews (1989) first found an effect of orthographic N on word naming for low-frequency
words, she interpreted this in terms of the `gang effects' occurring in McClelland andRumelhart's (1981) interactive-activation (IA) model of visual word recognition, originally used
to account for word-superiority effects in the Reicher-Wheeler paradigm (Reicher, 1969; Wheeler,1970). She argued that processing units for words differing in one letter from the target would
receive moderate (spurious) activation in the early stages of word recognition, which would feedback to (mostly) correct letter units, increasing their activation, and in turn speeding therecognition of the correct word. Thus, words with many orthographic neighbors would be named
faster than those with few. Although Jacobs and Grainger (1992) failed to find such an effect in

an IA model, such effects are highly dependent on parameter values, and can be found in related
models with appropriate parameters (Reynolds & Besner, 2002). Andrews (1989, 1997) has argued
that a lexical identification account is most parsimonious because it may also account for theword-superiority effect, as well as a facilitatory effect of orthographic N in lexical decision(Andrews, 1989, 1992, 1997).

However, an alternative account is not only possible but is assumed by many current models. The
DRC model of Coltheart et al. (2001) accounts for neighborhood effects by the action of its IAbased lexical route. Coltheart et al. (2001) cite the connections between orthographic word
units and phonological word units, and the cascade into phonemes, in this route as causing
facilitatory N effects for word and pseudoword naming, although they need to alter parametervalues to find such an effect for words. With this modified parameter set, Reynolds and Besner(2002) have confirmed using lesion methods that the effect for words is in fact due to theseconnections alone; that is, lexical print-sound correspondences bring about effects oforthographic N. This explanation therefore is not one purely in terms of orthographic
identification; rather, it relies upon the orthographic neighbors of a word activating the samephonemes as that word.

Similarly, other computational models of visual word recognition, excepting those which haveassume all-or-nothing lexical access, can only account for N effects as due to print-soundcorrespondences. This is because they do not have orthographic word representations; rather,
letters are processed immediately into intermediate or phonemic representations. Such models
include parallel-distributed processing (PDP) models (e.g., Seidenberg & McClelland, 1989; Plautet al., 1996; Zorzi, Houghton, & Butterworth, 1998) and retrieval models (e.g., Kwantes &Mewhort, 1999). Moreover, PDP models do produce orthographic neighborhood size effects (Sears,
Hino, & Lupker, 1999).

Thus, there are two competing hypotheses regarding the source of orthographic N effects. The

orthographic neighborhood hypothesis states that facilitation from orthographic N results frompurely orthographic facilitation of word identification. The phonographic-orthographic orphonographic neighborhood hypothesis (cf. Peereman & Content, 1997) states that facilitationfrom orthographic neighborhood size occurs because phonology consistent with the desired
phonology is activated by most of the orthographic neighbors, and thus is not purely
orthographic.

Despite the widespread theoretical assumption that orthographic N effects are due to print-soundconversion at the lexical level, there is almost no direct evidence to support the claim,
especially in English. In regression analyses of their naming results on ninety-two Frenchwords, Peereman and Content (1995) divided the orthographic neighbors into those which were also
phonological neighbors (i.e., could be generated from the word by replacing one phoneme) andthose which were not, and concluded that only those which were also phonologically similar
contributed to the N effect. However, the sample was small, and the regression did not control
for known predictors of naming latencies aside from those relating to neighborhoods, including
orthographic length, which correlates with neighborhood size. Peereman and Content argue
specifically that the effects are due to lexical (whole-word) rather than sublexical print-soundconversion, since introducing pseudowords into the context, which should entail a strategic
preference for (or focussed attention upon) sublexical correspondences, decreases the N effect.
This effect does not however exclude a role for sublexical grapheme-phoneme correspondence
rules, as it may reflect a shift in time criterion rather than route emphasis (e.g., Chateau &Lupker, 2003).

The orthographic neighbors that are also phonological neighbors were termed phonographic
neighbors by Peereman and Content (1997). For instance (in English) shove and stone are bothorthographic neighbors of stove, but only stone is also its phonographic neighbor (since it is aphonological neighbor). Moreover, like stone, stoat is a phonological neighbor of stove, but it
is not its phonographic neighbor (since it is not an orthographic neighbor). Although Yates

(2005) has found evidence for phonological neighborhood effects in naming, and Yates, Lawrence
Locker, and Simpson (2004) have suggested that some orthographic neighborhood effects might bedue to phonological neighborhood, no evidence has been adduced for the latter point beyond theexistence of the confound between the variables. Phonographic neighbors (rather than simply
phonological neighbors) are of concern in the current paper, since it is these that should be
the cause of neighborhood effects according to the phonographic neighborhood hypothesis.

Peereman and Content (1997) analyzed these phonographic neighbors by comparing naming latencies
for French pseudowords which varied in phonographic neighborhood size, controlling orthographic
neighborhood size (having shown that phonological neighborhood size has no effect). Despite thiscontrol on orthographic N, there was a facilitatory effect of phonographic N. However, they did
not seek an orthographic neighborhood effect with phonographic neighborhood size controlled.
From further experiments, they concluded that the effect was isolated to those neighbors whose
difference was in the onset of the word1. In effect, this meant they were manipulating number offriends (i.e., words that share both orthographic and phonological vowel and coda, althoughcomplex onsets mean that friends need not be phonographic neighbors). They also showed afacilitatory effect of friends for French words, replicating the result in English of Kay andBishop (1987) and Brown (1987). Their conclusion that rimes are critical is surprising, giventhat their regression on their previous experiment with French words (Peereman & Content, 1995)
showed no unique effect for these. Moreover, their experiments do not rule out an effect ofother kinds of neighbors for words. Further, it is not clear that effects for pseudowords, which
do not have a stored phonological representation, will necessarily generalize to words.

Currently, from mega-study data regarding English words, there is no evidence that higher-levelsegmental spelling-sound correspondences, that is, consistencies, are the cause of orthographic
neighborhood effects in naming. Balota, Cortese, Sergent-Marshall, Spieler, and Yap (2004)
entered both feed-forward and feed-backward consistency variables based on orthographic segments(onset, rime) into their analyses, but continued to find orthographic neighborhood effects.

According to the phonographic neighborhood hypothesis, this might not indicate that apparent
orthographic neighborhood effects are due to something other than lexical spelling-soundcorrespondences, but rather that these kinds of measurement of lexical spelling-soundcorrespondence are too fine (are defined by similarities over too small a portion of the word).
A further competing alternative is that the correspondences represented in these consistency
variables are too coarse and it is grapheme-phoneme correspondences that drive orthographic N
effects.

The availability of mega-study data is crucial in situations such as this where predictors are
difficult to manipulate factorially. Attempts to manipulate similar variables whilst controllingothers result in small sets of stimuli (cf. Cutler, 1981) with insufficient power to detect
effects. This is the case when comparing orthographic neighborhood size and phonographic
neighborhood size; matched pairs of stimuli that do not differ on other spelling-sound measures
are few in English. With large sets of data containing many words, variables not of interest canbe covaried out of the analyses and power increased by the use of many items that would
otherwise have to be ignored.

The orthographic and phonographic hypotheses regarding neighborhood facilitation in English word
naming were therefore tested in the present work using word naming data from four mega-studies.
Since there could be effects of orthographic and phonographic neighbors from different sources,
both phonographic and orthographic neighborhood size effects were sought. Finding a phonographic
but not orthographic neighborhood effect would confirm the phonographic neighborhood hypothesis,
indicating that the word-letter feedback invoked by Andrews (1989) does not account forneighborhood effects.

Analyses

To assess the extent to which orthographic and phonographic neighbors affect word naming, weconducted regression analyses that assess the unique influence of each variable when it isentered last (this is equivalent to simultaneous regression).

Predictors

Predictors in the regression analyses were (where necessary and not otherwise specified, fromCELEX: Baayen, Piepenbrock, & Gulikers, 1995):


log of frequency plus one,

orthographic neighborhood size,phonographic neighborhood size, that is, the count of orthographic neighbors whose
phonology could be formed by changing at most one phoneme,first phoneme, as a (dummy-coded) factor with 38 levels (no attempt was made to code
phonetic features),

length (in letters),

exception cost by position of irregularity, following the DRC model (Coltheart et al.,2001), as a (dummy-coded) factor with 6 levels (regular, and five positions ofirregularity),

number of friends (words that look as though they rhyme, and do),

number of enemies (words that look as though they rhyme, and do not), andtypes rime consistency ratio (i.e., friends divided by [friends plus enemies]).

Any effect of Body N¹, which is defined as the sum of number of friends and number of enemies,
would be subsumed by the joint effects of number of friends and number of enemies in this typeof analysis.

Summary statistics for the predictors (and response time measures described later) are in Table
1. Correlations are presented in Table 2. Orthographic N correlates highly with phonographic N,
but only 630 (23.2%) of the words had identical orthographic and phonographic Ns. The highcorrelation causes only higher error in estimates, not bias, and does not modify the probability

of a Type I error. The negative effects on power that can result are mitigated by the large
sample sizes in the analyses we report here.

Response Times

Item mean response times were taken from the data of Spieler and Balota (1997, SB98) and Balota
and Spieler (1998, BS99) for 2720 monosyllabic words (those with all the variables above) in the
training set of the Plaut et al. (1996) model. Mean response times for the same words were also
extracted when available from the data of Seidenberg and Waters (1989, SW89) (2693 words) andElexicon (Balota, Cortese, et al., 2002; Balota, Yap, et al., in press; 2667 words). Summary
statistics are presented in Table 1.

Results

Raw correlations are presented in Table 2. Table 3 show the results of the regression analyses.
There is a significant unique facilitatory effect of phonographic N, but not of orthographic N,
across all four sets of data. The only evidence for an effect of orthographic N was for an
inhibitory effect in the SW89 data.

Given the evidence that suggests an interaction between

frequency and N, we conducted analyses that tested for orthographic N effects by testing theimprovement in R² from entering orthographic N and its interaction with log frequency
simultaneously as a block, and phonographic N effects with an analogous phonographic N block.
For all four data sets, the phonographic N block had a significant effect after orthographic N
and its interaction with log frequency (F(2, *) > 6, p < .05), and the orthographic N block did
not have such an effect (F(2, *) < 2.1, p > .12).

Discussion

In regression analyses on four sets of data, a facilitatory effect of phonographic N wasobserved in English, concurring with Peereman and Content's (1995) regression on theirexperiment with French words, and their later (Peereman & Content, 1997) experiments withnonwords. Moreover, this occurred in the absence of any additional facilitation from otherorthographic neighbors. This reliance on consistency between similar orthography and similar
phonology implicates print-sound conversion processes. Moreover, this effect appeared to be over
and above those of regularity and rime consistency, as these were partialed out in the analyses.

These results contradict the orthographic neighborhood hypothesis that neighborhood effects
result from interactions at purely orthographic levels. By contrast, the results are as would be
expected from a number of models of visual word recognition. The primary constraint arising isthat generalization of print-sound correspondence can be influenced by individual words. This
arises in IA models (e.g., McClelland & Rumelhart, 1981), and the IA-based portion of the DRC(Coltheart et al., 2001); as a consequence of the error arising from individual words inbackpropagation PDP models (e.g., Seidenberg & McClelland, 1989; Plaut et al., 1996; Zorzi etal., 1998); and from the retrieval of individual words in memory-based models (e.g., Kwantes &Mewhort, 1999).

Given that it appears that orthographic neighbors of a word aid its naming only insofar as theyclosely support the correct pronunciation of this word (by being phonological, and hencephonographic, neighbors) it is perhaps surprising that there is little or no inhibitory effectof orthographic N once phonographic N is controlled. Such inhibition might be expected because
the additional orthographic neighbors support incorrect pronunciations of the target word; if
phonographic neighbors are activated on the basis of the orthographic input, so should all otherorthographic neighbors. The lack of an inhibitory effect may occur because most of theorthographic neighbors which are not phonological neighbors only differ in two phonemes whose
pronunciation they hinder, but give the correct pronunciation for the remainder which they help;
this may be almost the correct balance to be equivalent to no neighbor at all. Within this
explanation, confusion arising from high neighborhood density, which ordinarily would slow

identification per se (cf. Snodgrass & Mintzer, 1993), speeds naming. Alternatively, there may
be no competitive inhibition between phonemes or words in normal reading, as in the model ofBrown (1987).

Since there is no evidence for an orthographic neighborhood size effect per se, no feedback fromword to letter recognition is necessary to account for neighborhood size effects. Moreover,models which lack this kind of feedback, but rather assume that responses in letter
identification can be augmented by word identification in a feedforward manner (e.g., Paap,
Newsome, McDonald, & Schvaneveldt, 1982) can predict word-superiority in the Reicher-Wheeler
task. If such word identification occurs independently, we would have neighborhood size
facilitation of pronunciation without such facilitation of identification. Indeed, one would
expect neighborhood effects to occur in processing before full identification is possible, thatis, where confusion is possible or likely.

It has been shown that in word naming, neighborhood size effects should be considered in termsof phonographic, rather than orthographic neighbors. As such, print-sound consistency, and hence
print-sound conversion, is implicated as the source of these effects, rather than orthographic
feedback. Such conversion could be based on the result of orthographically similar words
competing for identification and influencing pronunciation before correct identification iscomplete.

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Author Note
This work was supported by a Warwick Postgraduate Research Fellowship awarded to the firstauthor, and by grants F/215/AY from the Leverhulme Trust, and RES 000221558 from the ESRC. We
thank Marjolein Merkx for helpful comments on an earlier version of this paper.

Notes¹ Peereman and Content (1997) term the phonographic neighbors that differ in the onset bodyneighbors, and consider only those that have simple onsets. Other authors (e.g., Ziegler, Perry,
Jacobs, & Braun, 2001) use body N to refer to the number of words that have the same
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Table 1

Summary statistics for predictor and response variables in the regression analyses

Variable

MSD-----------------------------------Length

4.360

0.864

Log [Frequency + 1]

5.333

2.048

Orthographic N

8.774

5.803

Phonographic N

6.313

4.660

Number of Friends

10.5497.256

Number of Enemies

1.375

3.015

Consistency Ratio

.883

0.216

-----------------------------------SB98 Young RT (ms) 469.779

21.511

BS99 Older RT (ms) 661.622

37.867

SW89 RT (ms)569.00244.147

Elexicon RT (ms)622.92352.589

Table 2

Correlations among predictors and between predictors and naming latencies.

1.

2.

3.

4.

5.

6.

7.

1.

Length--2.

Log[Frequency+1]

-.108

--

3.

Orthographic N

-.658

.116

--

4.

Phonographic N

-.580

.056

.895

--

5.

Friends

-.281

-.002

.474

.539

--

6.

Enemies

-.048

.152

.114

-.111

-.137

--

7.

Consistency Ratio

-.033

-.153

.009

.220

.351

-.820

--

SB98 RT

.378

-.301

-.369

-.353

-.116

-.001

-.042

BS99 RT

.325

-.384

-.303

-.288

-.125

-.003

-.042

SW89 RT

.336

-.215

-.301

-.321

-.073

-.005

-.038

Elexicon RT

.371

-.343

-.333

-.331

-.091

-.001

-.027

Note - Predictor correlations are based on all 2720 words analyzed from the SB98 and BS99
databases.

Table 3
Regression analysis on naming RTs.
Data Set

Predictor

SB98 Young

BS99 Older-------------------------------------------------------------------------------------------------------------------------

Effect (ms)

Effect (ms)

Effect (ms)

Effect (ms)

t

Var.

SW89

t

Var.

Elexicon

t

Var.

t

Var.

----------------------------------------------------------------------------------------------------------------------------------------------------First Phoneme
Length} 28.44} 14.33} 32.07} 20.83

3.873

7.911 ***

1.07

6.086

6.142 ***

0.85

5.594

5.369 ***

0.53

7.971

6.193 ***

0.76

Log[Frequency + 1] -2.879-19.920 ***6.77-6.880-23.518 ***12.48-4.000-12.978 ***3.10-8.464-22.053 ***9.60

Orthographic N-0.132-0.927

0.02

0.074

0.259

0.01

0.593

1.962 *

0.07

-0.326-0.871

0.01

Phonographic N-0.708-4.187 ***0.30-0.816-2.385 *0.13-1.439-3.991 ***0.29-0.915-2.048 *0.08

Number of Friends

0.205

3.901 ***

0.26

-0.053-0.497

0.01

0.017

0.154

0.00

0.055

0.396

0.00

Number of Enemies -0.332-1.865 +0.06-0.535-1.4860.05-0.654-1.724 +0.05-0.148-0.316

0.00

Consistency Ratio -8.433-3.231 **0.18-14.335-2.714 **0.17-13.903-2.499 *0.11-11.287-1.6390.05

Exception Pos. 1

25.0008.965 *** }32.5395.765 *** }37.9136.304 *** }60.8268.364 *** }Exception Pos. 2

4.408

3.861 *** }7.086

3.067 ** }9.429

3.863 *** }12.485

4.126 *** }Exception Pos. 3

4.709

3.049 ** } 2.18

4.211

1.347

} 0.91

3.784

1.140

} 0.989.637

2.391 *

} 1.78

Exception Pos. 4

0.994

0.324

}

-1.142-0.184

}

2.525

0.380

}

-15.443-1.856 +}Exception Pos. 5

1.819

0.275

}

8.667

0.646

}

27.334

1.946 +}

30.686

1.778 +}----------------------------------------------------------------------------------------------------------------------------------------------------Total Variance

54.46

39.81

51.42

48.34

Note: + p < .1 ; * p < .05 ; ** p < .01 ; *** p < .001. Var refers to percentage unique variance accounted for by variable (except in final row).