## Abstract

Differential electrophysiological effects for regular and irregular linguistic forms have been used to support the theory that grammatical rules are encoded using a dedicated cognitive mechanism. The alternative hypothesis is that language systematicities are encoded probabilistically in a way that does not categorically distinguish rule-like and irregular forms. In the present study, this matter was investigated more closely by focusing specifically on whether the regular–irregular distinction in English past tenses is categorical or graded. We compared the ERP priming effects of regulars (baked–bake), vowel-change irregulars (sang–sing), and “suffixed” irregulars that display a partial regularity (suffixed irregular verbs, e.g., slept–sleep), as well as forms that are related strictly along formal or semantic dimensions. Participants performed a visual lexical decision task with either visual (Experiment 1) or auditory prime (Experiment 2). Stronger N400 priming effects were observed for regular than vowel-change irregular verbs, whereas suffixed irregulars tended to group with regular verbs. Subsequent analyses decomposed early versus late-going N400 priming, and suggested that differences among forms can be attributed to the orthographic similarity of prime and target. Effects of morphological relatedness were observed in the later-going time period, however, we failed to observe true regular–irregular dissociations in either experiment. The results indicate that morphological effects emerge from the interaction of orthographic, phonological, and semantic overlap between words.

## INTRODUCTION

A key aspect of human language is the use of highly regular patterns to mark grammatical relationships. Grammatical morphology provides many examples of this: Languages can productively combine elements of meaning (morphemes) through a variety of mechanisms including concatenation (prefixing, suffixing, and infixing). Perhaps the most closely studied example is past tense in English, which marks most verbs using a variant of the “–ed” ending (e.g., baked, robbed, tested). Its productive nature has led to the suggestion that a generative rule is used to concatenate the –ed suffix to verb roots (Pinker, 1998; Marcus, Brinkmann, Clahsen, Wiese, & Pinker, 1995). Likewise, morphologically complex forms are assumed to be recognized using a decomposition process that breaks them into their constituent morphemes (e.g., baked is recognized as bake and –ed; Clahsen, 1999; Pinker, 1991; Prince & Pinker, 1988).

One complication to this account is the presence of irregular forms that do not adhere to the regular default pattern. For instance, about 5% of all English verbs and 14% out of 1000 most frequent verbs are irregular (Marcus et al., 1995); that is, they are marked in other ways, including changing a vowel or consonant (take–took, sit–sat, make–made), changing the vowel and adding a final consonant (sleep–slept, think–thought), no change at all (hit–hit), or completely changing the verb altogether (go–went, be–was). Although some formal accounts have suggested ways that additional rules might be used to account for these forms (Albright & Hayes, 2003; Halle & Mohanan, 1985), one popular theory suggests instead that irregular forms are, in fact, processed using a qualitatively separate mechanism from regulars (Ullman, 2004; Clahsen, 1999; Sonnenstuhl, Eisenbeiss, & Clahsen, 1999; Pinker, 1991, 1998; Marcus et al., 1995). This “dual-mechanism” theory holds that irregular past tense forms are instead stored in an associative memory system alongside other lexical items (including regular stems such as bake).

Representation of morphologically complex words has traditionally been investigated using reaction time (RT) measures in behavioral tasks. Of particular interest are studies of morphological priming, which have shown faster recognition of a word such as bake following presentation of a morphologically related prime such as baked (Feldman, 2000; Frost, Deutsch, Gilboa, Tennenbaum, & Marslen-Wilson, 2000; Rastle, Davis, Marslen-Wilson, & Tyler, 2000; Drews & Zwitserlood, 1995; Marslen-Wilson, Hare, & Older, 1993; Grainger, Cole, & Segui, 1991; Bentin & Feldman, 1990; Stanners, Neiser, Hernon, & Hall, 1979). Such data might suggest that listeners maintain a shared underlying representation of the prime and the target, and that recognizing a complex form involves decomposing it into its root and affixes. The results for irregulars are less consistent. Some studies have found weaker or no priming effects for irregular forms (Sonnenstuhl et al., 1999; Marslen-Wilson et al., 1993; Kempley & Morton, 1982; Stanners et al., 1979), whereas others found facilitation for irregulars that was larger or equivalent to that of regular forms (Longworth, Marslen-Wilson, Randall, & Tyler, 2005; Tyler et al., 2002; Fowler, Napps, & Feldman, 1985). Although the dual-route theory can accommodate priming for both types of forms, it predicts differences in processing of regular and irregular verbs. Previous ERP studies have indeed found priming differences between regulars and irregulars, and these dissociations were often interpreted in support of the dual-mechanism theory. However, recent findings indicate that regular–irregular differences might be due to the formal rather than grammatical differences in the prime–target relationship (Justus, Larsen, de Mornay Davies, & Swick, 2008).

Not all psycholinguistic theories rely on the concept of rules and exceptions to explain morphology. In particular, the connectionist perspective (McClelland & Patterson, 2002; Plaut & Gonnerman, 2000; Joanisse & Seidenberg, 1999; Rumelhart & McClelland, 1986) proposes that morphology is encoded as statistical regularities that capture systematic relationships among forms. On this view, a single type of computational mechanism is used to process all morphologically complex words. Differences in how regular and irregular forms are processed stem from the extent to which processing can rely on phonological, orthographic, and semantic overlap among related forms. With respect to past tense, regular forms are phonologically and orthographically more similar to their present tense forms, leading to a greater reliance on these sources of information for producing and recognizing them. In contrast, irregulars are less similar to their stem forms. As a result, the system relies more heavily on semantic information for recognizing these forms (Joanisse & Seidenberg, 1999). Recent analyses of lexical statistics further indicate that irregulars have more semantic neighbors that are irregular and tend to cluster more densely in semantic space than regular verbs do (Baayen & Moscoso del Prado Martin, 2005), again indicating that semantic mechanisms play a stronger role for irregular inflections.

Consistent with this view, a number of studies have suggested that morphological priming effects can, in fact, be influenced by semantic, phonological, or orthographic factors (Pastizzo & Feldman, 2002a; Marslen-Wilson et al., 1993; Napps, 1989; Forster, Davis, Schoknecht, & Carter, 1987; Kempley & Morton, 1982; Stanners et al., 1979). Building on these results, it has been found that the strength of morphological priming effects can be modulated by orthophonological similarity (Kielar, Joanisse, & Hare, 2008; Basnight-Brown, Chen, Hua, Kostic, & Feldman, 2007; Pastizzo & Feldman, 2002a, 2002b) and semantic relatedness (Gonnerman, Seidenberg, & Andersen, 2007; Feldman, Soltano, Pastizzo, & Francis, 2004; Feldman & Soltano, 1999). Just as importantly, these effects are not all-or-none. Instead, priming can occur for both regular and irregular forms, depending on task parameters. For example, the processing time of the prime affects target recognition by modulating the influence of formal and semantic factors. Effects of formal overlap tend to decrease as the processing time of prime increases, whereas semantic similarity effects increase with processing time (Kielar et al., 2008; Feldman et al., 2004; Dominguez, Segui, & Cuetos, 2002; Feldman, 2000; Rastle et al., 2000; Feldman & Soltano, 1999). Similarly, presentation modality appears to influence the effect of regularity on priming (Kielar et al., 2008; Feldman et al., 2004; Feldman & Prostko, 2002; Pastizzo & Feldman, 2002b). For instance, whereas priming effects under visual presentation, especially at short SOAs, tend to be influenced by the orthographic similarity of primes and targets, auditory presentation is more sensitive to the semantic and phonological aspects of words. Stated generally, morphological effects in lexical decision tasks vary with SOA and presentation modality and the degree to which these task parameters allow for the interaction between form and meaning dimensions of similarity.

### ERP Indices of Morphological Processing

On the whole, then, it remains unclear whether dissociations in priming of regular and irregular forms can be better accounted by their morphological regularity or by the differential reliance on form and meaning information used in their computation. One potential source of information in this regard might come from ERPs, which have increasingly been used to more closely investigate morphological processing and representation. This may provide a more complete picture by showing how word recognition processes unfold in real time. That is, although behavioral measures such as RT represent the end point of multiple language processing stages, ERPs allow us to dissect the time course of word recognition in a way that might isolate processes uniquely related to morphology, rather than orthography, phonology, and semantics.

A primary focus in this respect has been the N400 ERP component, and in particular, priming-related reductions in N400 amplitude. For instance, N400 repetition priming is found when a word is repeated within a list, compared to its first presentation (Hamberger & Friedman, 1992; Bentin & Peled, 1990; Holcomb & Neville, 1990; Smith, Stapleton, & Halgren, 1986; Rugg, 1985). Similar effects are also observed when a word is preceded by a semantically or morphologically related word, compared to when it is preceded by an unrelated word (Münte, Say, Clahsen, Schiltz, & Kutas, 1999; Bentin, McCarthy, & Wood, 1985). The N400 is thought to be sensitive to the lexical–semantic aspects of language processing (Kutas & Federmeier, 2000; Kutas & Hillyard, 1980), and in particular, the ease of accessing the word in memory (Van Petten, Kutas, Kluender, Mitchiner, & McIsaac, 1991; Holcomb & Neville, 1990; Rugg, 1985). Although some results indicate that it is sensitive to unconscious, automatic meaning access (Deacon, Hewitt, Yang, & Nagata, 2000; Luck, Vogel, & Shapiro, 1996), it has also been linked to a postlexical integration process as well (Brown, Hagoort, & Chwilla, 2000; Brown & Hagoort, 1993; Holcomb, 1988, 1993). On this account, N400 priming occurs because the word is more easily integrated into an ongoing context, thanks to having been recently activated. If morphologically complex words are decomposed into stems and affixes during recognition, this should facilitate subsequent processing of the stem. This would suggest that priming a stem (bake) with a morphologically related word (baked) should lead to an attenuated N400, as both the stem and inflected forms are proposed to share a single lexical entry (Clahsen, 1999). By the same token, irregular words are not assumed to be decomposed, and thus, should produce either smaller N400 priming, or none at all.

There is some evidence to support this. Münte et al. (1999) used a delayed priming paradigm in which participants performed lexical decision on targets separated from primes by five to nine intervening items. The study found attenuated N400s for regular past tenses following a previously presented present tense form, but no similar effect for irregulars. Similar effects have also been found using other types of priming paradigms in German and Spanish (Rodriguez-Fornells, Münte, & Clahsen, 2002; Weyerts, Münte, Smid, & Heinze, 1996), again marked by stronger N400 priming effects for regulars than irregulars.

Other studies have used ERP measures of morphological violations to examine this same issue. In this paradigm, ERP responses to correctly formed complex words are compared to responses to words that have an incorrect suffix (e.g., German plurals: *muskel-s instead of the correct muskel-n; German past tense: *gelauf-t instead of the correct gelauf-en; *bringed instead of the correct brought; *book instead of correct baked; Penke et al., 1997; Weyerts, Penke, Dohrn, Clahsen, & Münte, 1997). Results of these studies showed that the amplitude and scalp distribution of ERP responses were different for incorrectly inflected regular versus irregular forms. In particular, applying a regular suffix to irregular items was associated with a left anterior negativity (LAN), whereas incorrectly inflected regulars elicited an N400 but no LAN. Similar differences have also been reported in English (Newman, Ullman, Pancheva, Waligura, & Neville, 2007; Morris & Holcomb, 2005), Italian (Gross, Say, Kleingers, Clahsen, & Münte, 1998), Catalan (Rodriguez-Fornells, Clahsen, Lleo, Zaake, & Münte, 2001), and Turkish (Münte, Anvari, Matzke, & Johannes, 1995).

Differences in ERPs to regulars and irregulars have typically been interpreted as supporting a dual-mechanism view. Note that such data may also be compatible with the alternative explanation, however, that these dissociations reflect differences in the representational characteristics of regulars and irregulars such as form and meaning. In the present study, we suggest a stronger test, which involves examining whether the regular–irregular distinction is fully bipartite, or is, in fact, a graded one as predicted by the connectionist theory (Kielar et al., 2008; Joanisse & Seidenberg, 2005). To test this, we compare priming for regulars along with two sets of irregular verbs. The first are “suffixed irregular” verbs such as kept, wept, and slept. Linguists have referred to these forms as semiweak, as they more closely resemble regulars in terms of their similarity to regular verbs, and their partial consistency. They are formally irregular because they do involve a somewhat unpredictable change to the stem; however, they also involve adding a regular-like suffix. The results of previous studies indicate that the brain responses for these verbs are more similar to regular than vowel-change irregular verbs (Justus et al., 2008; Joanisse & Seidenberg, 2005). These forms are contrasted with “vowel-change” irregulars such as take–took, which do not take a regular-like ending, and are highly inconsistent.

We previously investigated the distinction between regulars and two types of irregular verbs on lexical decision RTs, using cross-modal and visual priming (Kielar et al., 2008). The results revealed a similar pattern of results for regulars and suffixed irregulars, reflecting the degree of form overlap between primes and targets, compared to vowel-change irregulars that tended to yield weaker priming effects. Moreover, the exact pattern of effects varied depending on task dimensions that modulated sensitivity to orthographic and phonological similarity (i.e., prime modality, prime–target SOA). The results suggested that the differences among forms can be attributed to the orthographic similarity of prime and target. Similarly, Justus et al. (2008) found similar N400 priming effects for regulars and weak irregulars using auditory lexical decision and auditory primes. That said, the strongest priming was found for strong irregulars, compared to regulars and weak irregulars, which is the opposite of what we might have predicted given prior findings in visual lexical decision. The direction of this effect might be attributed to the sensitivity of the auditory immediate priming task to the semantic relationship between words.

In summary, the present study examines whether regularity effects in visual recognition of past tenses are categorical or graded. Evidence for continuity in how these different verb types are processed would suggest that morphological priming occurs not because of decomposition, per se, but is instead due to differences in the basic types of representations that are used in processing of these forms. Importantly, the study did not merely look for the differences between regulars and irregulars, but rather sought to understand the underlying reasons for these dissociations.

## EXPERIMENT 1: ERP VISUAL PRIMING

In this experiment, participants' ERPs were recorded as they performed a visual–visual priming task with lexical decision. Because visual priming has been used extensively to study morphological representation in RT studies, the study provided important continuity with this previous body of work. Further, employing visual modality allowed us to test the contribution of orthographic factors to morphological priming. Priming effects for morphologically related items were contrasted with the effects of shared meaning (couch–sofa) or form (panel–pan), to assess the extent to which such factors can explain observed ERP priming effects for morphological relatedness. A third control condition consisted of word pairs that overlapped with respect to both phonology and semantics (e.g., screech–scream, sneeze–snort); these items are termed the −M + P + S condition, following Gonnerman et al. (2007) and Rastle et al. (2000), and allowed us to examine whether ERP priming effects observed for past tense forms might occur due to the interaction of formal and semantic overlap, beyond what is observed for phonology or semantics alone.

### Methods

#### Materials and Procedure

The stimulus items in each experimental condition were the same as in Experiment 1. However, we did modify the filler items such that these lists consisted only of word prime–nonword target pairs, to reduce testing time (note, however, that each prime condition continued to include its own unrelated word–target items). In order to avoid strategic effects due to stimulus predictability, the filler condition included trials on which nonword targets were preceded by auditory primes that were past tense verbs (e.g., printed–DRASS), present tense verbs (e.g., blend–DASE; stamping–TRINK), nouns (e.g., brain–DEST), and adjectives or adverbs (e.g., humble–MEST, nicely–RIVE). The number of nonword filer trials was increased to 400, which preserved the proportion of related to unrelated trials across both experiments.

On each trial, a fixation cross was displayed at the center of a computer screen while the auditory prime was presented over earphones at a comfortable listening level. Following a 500-msec delay, the visual target was presented in uppercase letters. Targets appeared for 5000 msec or until a response was made. Participants performed lexical decisions to targets by pressing a key on a response pad. Trial order was randomized as in Experiment 1, such that order of primed versus unprimed word presentations was counterbalanced across subjects. All EEG recording procedures, materials, and analyses were the same as in Experiment 1.

### Results

#### Behavioral Results

Mean response latencies and error rates are presented in Table 4. Incorrect responses and RTs ±3SD from the mean were removed and treated as errors. The targets mast and earl (ortho/phonological condition) and slink (−M + P + S condition) were excluded from analyses based on error rates greater than 25% across participants.

Table 4.

Mean Latency (msec) (SD) and Accuracy (%) (SD) for CM ERP Experiment

Condition
RT
Accuracy
M
SD
M
SD
Regulars
Primed 535 76 100
Unprimed 591 82 97
Difference +55** +3

Suffixed Irregulars
Primed 549 81 99
Unprimed 583 79 97
Difference +34** +2

Vowel-change Irregulars
Primed 550 72 99
Unprimed 579 81 99
Difference +28**

Semantic
Primed 570 69 100
Unprimed 577 78 97
Difference +7 +3

Phonological
Primed 582 68 98
Unprimed 587 70 96
Difference +5 +2

M + P + S
Primed 613 83 96
Unprimed 647 99 94
Difference 34** +2
Condition
RT
Accuracy
M
SD
M
SD
Regulars
Primed 535 76 100
Unprimed 591 82 97
Difference +55** +3

Suffixed Irregulars
Primed 549 81 99
Unprimed 583 79 97
Difference +34** +2

Vowel-change Irregulars
Primed 550 72 99
Unprimed 579 81 99
Difference +28**

Semantic
Primed 570 69 100
Unprimed 577 78 97
Difference +7 +3

Phonological
Primed 582 68 98
Unprimed 587 70 96
Difference +5 +2

M + P + S
Primed 613 83 96
Unprimed 647 99 94
Difference 34** +2

**p < .01.

Statistical tests of behavioral and ERP data are presented in Table 5. For RTs, a repeated measures ANOVA with prime and word type as within-subject factors revealed significant main effects of prime, word type, and a Prime × Word type interaction, indicating the magnitude of priming effects was not equivalent for all conditions. Planned comparisons revealed significant facilitation for all morphologically related conditions [regulars: F1(1, 14) = 40.03, p < .01, F2(1, 34) = 55.03, p < .01; suffixed irregulars: F1(1, 14) = 13.04, p < .01, F2(1, 23) = 20.59, p < .01; vowel-change irregulars: F1(1, 14) = 10.99, p < .01, F2(1, 35) = 20.35, p < .01]. The comparison of priming effects for verb conditions revealed that suffixed irregulars did not differ from regulars or vowel-change irregulars [suffixed irregulars vs. regulars: F1(1, 14) = 3.19, p > .05, F2(1, 57) = 3.43, p > .05; suffixed vs. v/c irregulars: both Fs < 1]. However, the effect for regulars was significantly greater than that for vowel-change irregular verbs [F1(1, 14) = 12.53, p < .01, F2(1, 69) = 6.66, p < .05]. For the morphologically unrelated words, the −M + P + S condition showed significant facilitation [F1(1, 14) = 16.57, p < .01, F2(1, 25) = 15.65, p < .01], but the priming effects for the ortho/phonologically and semantically related words were not significant (all Fs < 1). These results replicate findings previously reported in a larger sample (Kielar et al., 2008).

Table 5.

Statistical Tests Performed on Behavioral and ERP Data in Experiment 2

Variable
Source

df
F
p
RT Word type (W) F1 5, 70 26.97 <.01
F2 5, 181 14.11 <.01
Priming (P) F1 1, 14 33.19 <.01
F2 1, 181 83.03 <.01
W × P F1 5, 70 5.56 <.01
F2 5, 181 6.14 <.01
Accuracy Word type (W) F1 5, 70 8.06 <.01
F2 5, 181 5.90 <.01
Priming (P) F1 1, 14 15.43 <.01
F2 1, 181 20.19 <.01
W × P F1 5, 70 0.73 ns
F2 5, 181 0.83 ns
N2 Electrode (E) 6, 84 12.32 <.01
Word type (W) 5, 70 6.12 <.01
Priming (P) 1, 14 1.44 ns
P × W 5, 70 0.29 ns
P × E 6, 84 4.31 <.01
E × W 30, 420 2.59 <.01
E × P × W 30, 420 0.74 ns
Early N400 Electrode (E) 6, 84 1.77 ns
Word type (W) 5, 70 4.09 <.05
Priming (P) 1, 14 15.36 <.01
P × W 5, 70 3.21 <.05
P × E 6, 84 7.52 <.01
E × W 30, 420 2.14 <.05
E × W × P 30, 420 1.26 ns
Late N400 Electrode (E) 6, 84 11.52 <.01
Word type (W) 5, 70 14.47 <.01
Priming (P) 1, 14 12.03 <.01
P × W 5, 70 2.08 ns
P × E 6, 84 5.92 <.01
E × W 30, 420 2.54 <.05
E × W × P 30, 420 1.26 ns
Variable
Source

df
F
p
RT Word type (W) F1 5, 70 26.97 <.01
F2 5, 181 14.11 <.01
Priming (P) F1 1, 14 33.19 <.01
F2 1, 181 83.03 <.01
W × P F1 5, 70 5.56 <.01
F2 5, 181 6.14 <.01
Accuracy Word type (W) F1 5, 70 8.06 <.01
F2 5, 181 5.90 <.01
Priming (P) F1 1, 14 15.43 <.01
F2 1, 181 20.19 <.01
W × P F1 5, 70 0.73 ns
F2 5, 181 0.83 ns
N2 Electrode (E) 6, 84 12.32 <.01
Word type (W) 5, 70 6.12 <.01
Priming (P) 1, 14 1.44 ns
P × W 5, 70 0.29 ns
P × E 6, 84 4.31 <.01
E × W 30, 420 2.59 <.01
E × P × W 30, 420 0.74 ns
Early N400 Electrode (E) 6, 84 1.77 ns
Word type (W) 5, 70 4.09 <.05
Priming (P) 1, 14 15.36 <.01
P × W 5, 70 3.21 <.05
P × E 6, 84 7.52 <.01
E × W 30, 420 2.14 <.05
E × W × P 30, 420 1.26 ns
Late N400 Electrode (E) 6, 84 11.52 <.01
Word type (W) 5, 70 14.47 <.01
Priming (P) 1, 14 12.03 <.01
P × W 5, 70 2.08 ns
P × E 6, 84 5.92 <.01
E × W 30, 420 2.54 <.05
E × W × P 30, 420 1.26 ns
##### Accuracy

The analysis of accuracy data revealed a significant main effect of prime and word type, but no interaction (Table 5).

#### ERP Results

Grand-average ERPs for each condition are illustrated in Figure 5. Figures 6 and 7 illustrate the topographical distributions and amplitudes of N400 priming effects, respectively.

Figure 5.

N400 results for each word type in cross-modal ERP experiment. Grand-average ERPs (n = 15) elicited by primed and unprimed words at the midline region (FC, CC, PC).

Figure 5.

N400 results for each word type in cross-modal ERP experiment. Grand-average ERPs (n = 15) elicited by primed and unprimed words at the midline region (FC, CC, PC).

Figure 6.

Topographical distribution of N400 priming effects across the scalp based on the differences waveforms (unprimed − primed), at early (324–400 msec) and late (400–476 msec) time intervals.

Figure 6.

Topographical distribution of N400 priming effects across the scalp based on the differences waveforms (unprimed − primed), at early (324–400 msec) and late (400–476 msec) time intervals.

Figure 7.

N400 priming results in cross-modal ERP experiment. (A) Difference waves (unprimed − primed) for each word type at the midline region (CC). The difference waves illustrate the electrophysiological effect of priming. (B) Mean differences in amplitude (unprimed − primed) for all word types at the midline region (CC) in 324–400 msec and 400–476 msec time intervals.

Figure 7.

N400 priming results in cross-modal ERP experiment. (A) Difference waves (unprimed − primed) for each word type at the midline region (CC). The difference waves illustrate the electrophysiological effect of priming. (B) Mean differences in amplitude (unprimed − primed) for all word types at the midline region (CC) in 324–400 msec and 400–476 msec time intervals.

##### N2 interval

A repeated measures ANOVA with prime, word type, and electrode region as within-subject factors examined effects at the 180–236 msec interval. This revealed a main effect of electrode region and word type. There were significant interactions of Region × Prime, and Region × Word type (Table 5). The interaction of prime and region might be suggestive of a small N2 priming effect restricted for certain scalp regions; however, a post hoc analysis failed to reveal significant primed − unprimed differences for any conditions [regulars, irregulars, suffixed irregulars, semantic, −M + P + S, all Fs < 1; phonological: F(1, 14) = 1.72, p > .05].

##### Early N400 interval

A repeated measures ANOVA revealed significant main effects of prime and word type. The Prime × Word type interaction was significant, as was Electrode region × Prime and Region × Word type. Planned comparisons on the mean amplitude of primed versus unprimed targets revealed significant N400 priming effects for all verb conditions at the early interval [regulars: F(1, 14) = 17.86, p < .01; suffixed irregulars: F(1, 14) = 5.79, p < .05; vowel-change irregulars: F(1, 14) = 4.68, p < .05]. Significant priming effects were also found for the semantic, −M + P + S, and ortho/phonological conditions [F(1, 14) = 9.35, p < .01; F(1, 14) = 8.56, p < .05; F(1, 14) = 6.99, p < .05]. Additionally, the comparison of the magnitude of priming effects for verb conditions, as indexed by mean amplitude of the difference waves, revealed significantly greater priming for regulars compared to vowel-change irregulars [F(1, 14) = 17.21, p < .01]. Suffixed irregulars showed an intermediate effect, such that they were not significantly different from regulars [F(1, 14) = 2.92, p > .05] or vowel-change irregulars [F(1, 14) = 2.50, p > .05].

##### Late N400 interval

There were significant main effects of prime, word type, and electrode region. Significant interactions were observed for Electrode region × Word type, and Region × Prime. Planned comparisons performed on the mean amplitudes of primed versus unprimed targets revealed significant priming effects for the regular, suffixed irregular, and −M + P + S conditions [regulars: F(1, 14) = 13.23, p < .01; suffixed irregulars: F(1, 14) = 6.16, p < .05, −M + P + S: F(1, 14) = 6.36, p < .05]. However, no priming effect was observed for the vowel-change irregulars [F(1, 14) = 1.31, p > .05], the semantic condition [F(1, 14) = 3.36, p > .05], or the ortho/phonological condition (F < 1). Here again, the comparison of verb conditions using mean amplitude of the difference waves (unrelated − related) revealed differential effects for verbs, such that regulars showed greater priming effects than vowel-change irregulars [F(1, 14) = 8.18, p < .05]. As in the early time window, suffixed irregulars showed an intermediate effect, such that the priming effects for suffixed irregulars did not differ from regulars [F(1, 14) = 1.80, p > .05], or vowel-change irregulars [F(1, 14) = 2.18, p > .05].

Finally, an additional ANOVA with interval, site, and word type as within-subject variables computed on the mean amplitude of the difference waves (unrelated − related), compared priming effects at the early versus late N400 time intervals. This analysis revealed a significant main effect of word type [F(5, 70) = 3.72, p < .01], but no interaction between interval and word type [F(5, 70) = 1.21, p > .05], or three-way Interval × Electrode region × Word type interaction [F(50, 700) = 1.50, p > .05]. The comparison of priming effects for each condition across the two time intervals revealed a significantly greater effect for regulars at the early time window [F(1, 14) = 4.62, p < .05]; similar effects were marginal for the vowel-change irregular [F(1, 14) = 3.80, p = .071], suffixed irregular [F(1, 14) = 3.69, p = .075], and phonological conditions [F(1, 14) = 3.64, p = .077]. The effects across two time windows did not differ significantly for semantically related targets [F(1, 14) = 2.65, p > .05] or the −M + P + S condition (F < 1).

### Discussion

ERP priming effects were compared for different degrees of morphological regularity, and for strictly meaning- and form-based priming. As expected, cross-modal presentation reduced the apparent effect of orthographic overlap observed in Experiment 1, and revealed stronger differences in the size of N400 priming for different morphologically related targets. At the early portion of the N400, significant priming effects were found for all conditions including the semantic, ortho/phonological, and −M + P + S conditions. However, at the later portion of the N400 component, this effect disappeared for vowel-change irregular verbs and the semantic and ortho/phonological conditions. Thus, the temporal characteristics of the ERP waveforms allowed us to tease apart effects of morphological, semantic, and phonological relatedness. As in Experiment 1, we did not find priming effects in the N2 component, which was not unexpected given that prior findings of this type have been restricted to short-SOA visual priming paradigms.

Of key theoretical interest was the observation that morphological effects were graded, such that we found greater priming for regular verbs, a smaller effect for vowel-change irregulars, and an intermediate effect for suffixed irregulars. This was most prominent at the later time interval, where priming was only found for regulars and suffixed irregulars and not vowel-change irregulars. This finding seems inconsistent with the theory that rule-based forms and irregulars are recognized via separate mechanisms, as predicted by a dual-mechanism account. Instead, this pattern seems to better fit the view that “regularity” is a strictly relative, rather than dichotomous, construct.

Just as importantly, the use of cross-modal priming suggests the observed morphological priming effects cannot be attributed to formal or semantic similarity alone. Specifically, the ortho/phonological condition showed appreciably smaller effects in this experiment compared to what was observed in visual–visual priming. In contrast, significant N400 priming effects were found for the −M + P + S condition, suggesting that the joint contribution of form and meaning representations can lead to facilitation for these items, especially when phonological similarity was enhanced under cross-modal presentation.

## GENERAL DISCUSSION

Prior ERP studies have observed differential effects of morphological regularity in ERPs (Newman et al., 2007; Morris & Holcomb, 2005; Rodriguez-Fornells et al., 2001, 2002; Münte et al., 1999; Gross et al., 1998; Penke et al., 1997; Weyerts et al., 1997), which might support the theory that regular and irregular forms are processed via dissociable neurocognitive mechanisms. As we argued in the Introduction, however, there is an alternative theory that suggests morphology is an emergent characteristic of the interaction between phonological and semantic relatedness (Plaut & Gonnerman, 2000; Seidenberg & Gonnerman, 2000). On this view, dissociations occur in morphology due to the degree of formal overlap between related forms (Kielar et al., 2008), along with a form's phonological typicality (Burzio, 2002) and semantic characteristics (Baayen & Moscoso del Prado Martin, 2005). We examined this hypothesis with respect to past tense regularity, using ERP priming. In particular, we assessed the hypothesis that it is possible to observe graded effects of regularity in ERP priming by manipulating the ortho/phonological relationship between prime and target.

The key finding was that N400 priming effects are not unique to regular forms, but can extend to irregulars as well. Moreover, these effects do appear to be specific to morphology, in that priming was not observed for forms related only with respect to semantics, orthography, or phonology. These effects cannot be attributed to morphological decomposition processes, however, as they were not specific to regular forms; rather, we interpret them as occurring due to the interaction of formal and semantic relatedness over the time course of word recognition (Plaut & Gonnerman, 2000).

The results also indicate that the degree to which formal and semantic dimensions of similarity influence word recognition changes over the time course of processing; hence, subtly different effects were observed when the N400 waveform was divided into “early” and “late” time windows. Furthermore, the contribution of both types of information varied as a function of the sensitivity of visual and cross-modal presentation modalities to the orthophonological and semantic properties of stimuli, leading to differences in N400 priming effects for visual–visual versus auditory–visual priming. In Experiment 1, orthophonological overlap seemed to have the greatest effect in the early time window, whereas the integration of semantic and formal factors influenced morphological effects in the later time window. Thus, these results seem to fit well with previous findings of the time-varying influence of form and meaning information in recognizing morphologically complex words (Feldman et al., 2004; Feldman & Prostko, 2002; Feldman & Soltano, 1999).

Of particular interest was the finding that the degree of priming N400 for past tenses was modulated in a graded way, such that we found stronger priming for irregular forms that were more similar to regulars (the suffixed irregular condition) compared to the vowel-change irregulars. This pattern of effects is compatible with the results of the auditory immediate priming study reported by Justus et al. (2008). Their results showed stronger and more prolonged priming effects for strong (vowel-change) irregular verbs compared to weak irregulars and regulars. These results are consistent with the greater reliance of irregular verbs on semantic representations, an effect that was enhanced by the immediate priming design. They also reveal the effect of the inconsistent orthophonological relationship between present and past tense forms on priming effects. Consistent with our results, the orthophonological effects were observed in the early but not in the later time window, confirming the time-varying contribution of formal overlap to morphological processing.

In contrast with our data, Münte et al. (1999) previously found significant N400 priming for regulars only, and not for either irregulars or formally related words. This difference might be due to the fact that the present study used an immediate priming paradigm, which appeared to elicit generally stronger N400 effects compared to the long-lag paradigm used by Münte et al. It is possible that N400 effects for irregulars were simply too small to be detected in that study, due to the interitem lag of 5 to 13 items that was used. This might also explain why their study failed to find priming effects for formally related items. It does seem to be the case that when primes and targets are separated by intervening items, orthographic effects tend to be reduced or eliminated (Drews & Zwitserlood, 1995). In contrast, orthographically similar items produce reliable behavioral effects in a contiguous procedure where the target immediately follows the prime (Napps & Fowler, 1987).

The present results are also relevant to the results of ERP morphological priming studies conducted in other languages such as German (Weyerts et al., 1996) and Spanish (Rodriguez-Fornells et al., 2002), which found generally stronger effects for regular than irregular inflections. The results of the present study suggest that this dissociation might be due to orthographic and/or phonological similarity rather than morphological status; the key prediction is that a weaker advantage for regulars should be observed using either a more rapid presentation paradigm, or by comparing priming for irregulars that pattern more closely with regular items. A similar prediction is made for dissociations in ERPs for regulars and irregulars in morphological violation studies (Newman et al., 2007; Rodriguez-Fornells et al., 2001; Gross et al., 1998; Penke et al., 1997; Weyerts et al., 1997). Again, such effects may reflect the degree of phonological similarity between correct and incorrect forms, rather than a categorical distinction between regular and irregular inflections.

The contributions of formal and semantic dimensions of similarity to morphology have been also documented by the recent behavioral investigations of morphological priming with derivational-suffixed words (Marslen-Wilson, Bozic, & Randall, 2008; McCormick, Rastle, & Davis, 2008; Diependaele, Sandra, & Grainger, 2005; Longtin & Meunier, 2005; Rastle et al., 2000, 2004; Longtin et al., 2003; Dominguez et al., 2002). Moreover, a similar findings have been reported in the present N400 priming studies of suffixed derivations that attempted to tease apart effects of semantic, orthographic, and morphological priming to the visual word processing using long SOAs (Dominguez, de Vega, & Barber, 2004; Barber, Dominguez, & de Vega, 2002). The findings of the recent masked priming studies of derivational morphology (Lavric et al., 2007; Morris et al., 2007) are consistent with the early influence of form overlap, and with semantic overlap constraining morphological processing at the later stages of word recognition. Notably, the Morris et al. study also found graded effects of morphological relatedness consistent with the present results.

### Conclusion

We argue that the present findings are consistent with the connectionist view that word recognition is achieved via the engagement of formal and semantic processes, that these processes unfold in different ways over time, and that morphology itself emerges as a consequence of the systematic mapping between these multiple codes (Kielar et al., 2008; Plaut & Gonnerman, 2000; Seidenberg & Gonnerman, 2000; Joanisse & Seidenberg, 1999; Rueckl, Mikolinski, Raveh, Miner, & Mars, 1997). On this view, every word is learned and processed in the context of many other words such that internal representations support the processing of all words, whether they are morphologically related or unrelated. Morphological representations are an emergent characteristic of this system, and occur because of statistical regularities arising in sound–meaning associations (e.g., walk–walked are related in the same way as type–typed). This can extend even to −M + P + S word pairs, which do not have clear morphological relationships but do have some degree of phonological and semantic consistency.

An important consequence of this view is that dissociations between different verb types are not a necessary consequence of rules per se, but can instead reflect differences in the representational codes used in recognizing words. This suggests that the distinction between regular and irregular verbs is a graded one, and that morphology is processed as a function of statistical regularities in the sound and meaning of words.

## APPENDIX A

Prime and target pairs used in ERP priming experiments. Items matched for natural log-frequency values from CELEX, orthographic neighborhood (N-watch), orthographic length, and phonological and orthographic overlap.

 Prime Target Phonological Overlap (% Phonemes) Orthographic Overlap (% Letters) Item Frequency Length N Item Frequency Length N Regular Verbs rented 2.3 6 4 rent 3.8 4 12 66.7 66.7 cleared 3.3 7 3 clear 5.5 5 2 80.0 71.4 agreed 4.6 6 1 agree 4.4 5 0 80.0 83.3 opened 4.9 6 2 open 5.7 4 3 80.0 66.7 played 4.7 6 6 play 5.6 4 5 75.0 66.7 talked 4.6 6 4 talk 5.6 4 8 75.0 66.7 hoped 4.1 5 9 hope 5.2 4 13 75.0 80.0 passed 4.9 6 6 pass 4.6 4 12 75.0 66.7 walked 5.0 6 4 walk 4.8 4 4 75.0 66.7 jumped 3.2 6 6 jump 3.3 4 6 80.0 66.7 pushed 4.1 6 5 push 3.8 4 8 75.0 66.7 filled 4.3 6 9 fill 3.7 4 15 75.0 66.7 caused 4.3 6 2 cause 5.0 5 1 75.0 83.3 saved 3.6 5 7 save 4.2 4 13 75.0 80.0 examined 3.4 8 2 examine 3.4 7 0 85.7 87.5 lifted 3.7 6 8 lift 3.8 4 9 66.7 66.7 baked 2.4 5 8 bake 1.7 4 16 75.0 80.0 reached 4.9 7 4 reach 4.5 5 7 75.0 71.4 named 4.0 5 4 name 5.6 4 9 75.0 80.0 pressed 3.6 7 2 press 4.9 5 4 80.0 71.4 chewed 1.8 6 0 chew 1.7 4 4 66.7 66.7 crawled 2.2 7 2 crawl 2.1 5 3 80.0 71.4 blamed 2.4 6 5 blame 3.6 5 5 80.0 83.3 chopped 2.4 7 5 chop 2.0 4 5 75.0 57.1 breathed 2.5 8 4 breathe 2.8 7 1 80.0 87.5 combed 1.6 6 1 comb 1.7 4 5 75.0 66.7 frowned 2.3 7 3 frown 1.8 5 5 80.0 71.4 prayed 2.2 6 4 pray 2.7 4 6 75.0 66.7 scrubbed 1.5 8 0 scrub 2.1 5 2 83.3 62.5 pulled 4.5 6 9 pull 4.2 4 13 75.0 66.7 sprayed 1.3 7 2 spray 2.4 5 2 80.0 71.4 touched 3.8 7 3 touch 4.6 5 5 75.0 71.4 splashed 1.6 8 1 splash 1.7 6 0 83.3 75.0 chased 2.1 6 4 chase 2.7 5 4 75.0 83.3 skated −0.5 6 4 skate 1.3 5 3 66.7 83.3 Mean 3.2 6.3 4.0 3.6 4.6 6.0 76.3 72.6 SD 1.3 0.8 2.4 1.4 0.8 4.5 4.6 7.8 Suffixed Irregular Verbs leapt 2.4 5 3 leap 2.6 4 7 50.0 80.0 dealt 3.1 5 1 deal 5.2 4 16 50.0 80.0 fled 2.8 4 8 flee 1.7 4 8 50.0 75.0 slept 3.5 5 2 sleep 4.8 5 5 60.0 60.0 kept 5.3 4 2 keep 5.9 4 9 50.0 50.0 sold 4.0 4 8 sell 4.0 4 11 50.0 50.0 meant 5.0 5 2 mean 6.0 4 9 50.0 80.0 heard 5.6 5 4 hear 5.2 4 15 66.7 80.0 built 4.8 5 3 build 4.3 5 2 75.0 80.0 bent 3.7 4 14 bend 3.1 4 10 75.0 75.0 crept 2.4 5 3 creep 2.1 5 3 60.0 60.0 swept 3.4 5 3 sweep 2.7 5 4 60.0 60.0 burnt 2.9 5 2 burn 3.3 4 7 75.0 80.0 sent 5.0 4 13 send 4.4 4 8 75.0 75.0 felt 6.0 4 7 feel 5.9 4 9 50.0 50.0 spent 4.9 5 3 spend 4.5 5 2 80.0 80.0 wept 2.2 4 5 weep 1.9 4 8 50.0 50.0 taught 4.2 6 2 teach 3.9 5 5 33.3 16.7 brought 5.5 7 2 bring 5.2 5 5 50.0 42.9 stood 5.4 5 3 stand 5.0 5 2 75.0 60.0 told 6.2 4 8 tell 6.1 4 12 50.0 50.0 caught 4.7 6 2 catch 4.2 5 6 33.3 50.0 lost 5.4 4 14 lose 4.4 4 12 50.0 75.0 dwelt 0.6 5 1 dwell 1.4 5 2 80.0 80.0 Mean 4.1 4.8 4.8 4.1 4.4 7.4 58.3 64.1 SD 1.4 0.8 4.0 1.4 0.5 4.0 13.9 16.8 Vowel-change Irregular Verbs tore 2.7 4 18 tear 2.9 4 16 66.7 25.0 wore 4.1 4 19 wear 4.2 4 14 66.7 25.0 gave 5.7 4 14 give 6.2 4 6 66.7 75.0 sang 2.9 4 15 sing 3.2 4 13 66.7 75.0 drew 4.1 4 4 draw 4.1 4 5 66.7 75.0 shook 4.2 5 3 shake 3.2 5 10 66.7 40.0 spoke 4.7 5 5 speak 4.9 5 4 75.0 40.0 stung 1.2 5 4 sting 1.7 5 6 75.0 80.0 won 4.3 3 14 win 4.1 3 15 66.7 66.7 grew 4.3 4 5 grow 4.5 4 6 66.7 75.0 woke 3.2 4 7 wake 3.5 4 15 66.7 75.0 shone 2.7 5 6 shine 2.0 5 10 66.7 80.0 hung 4.0 4 8 hang 3.5 4 11 66.7 75.0 fell 4.7 4 13 fall 4.7 4 12 66.7 75.0 blew 3.1 4 5 blow 3.7 4 8 66.7 75.0 drove 4.2 5 4 drive 4.5 5 1 75.0 80.0 rang 3.6 4 14 ring 4.2 4 11 66.7 75.0 drank 3.5 5 5 drink 4.8 5 3 80.0 80.0 ran 4.7 3 16 run 5.4 3 14 66.7 66.7 stole 2.4 5 7 steal 2.5 5 4 75.0 40.0 found 6.2 5 8 find 6.3 4 11 60.0 20.0 chose 3.5 5 6 choose 4.2 6 1 66.7 50.0 sprang 2.5 6 2 spring 4.2 6 5 80.0 83.3 rode 2.7 4 13 ride 3.5 4 14 66.7 75.0 sank 2.6 4 14 sink 3.3 4 13 75.0 75.0 swam 2.0 4 11 swim 3.2 4 5 75.0 75.0 broke 4.2 5 2 break 4.7 5 6 75.0 40.0 fought 3.6 6 3 fight 4.6 5 8 66.7 16.7 swore 1.9 5 7 swear 2.7 5 4 75.0 40.0 threw 3.9 5 3 throw 3.9 5 2 66.7 80.0 stuck 3.8 5 3 stick 4.0 5 5 75.0 80.0 began 5.9 5 3 begin 4.8 5 2 75.0 80.0 flew 3.1 4 8 fly 3.9 3 6 66.7 50.0 wrote 4.9 5 1 write 4.8 5 4 66.7 80.0 swung 3.1 5 3 swing 3.4 5 6 75.0 80.0 held 5.4 4 9 hold 5.1 4 10 75.0 75.0 Mean 3.7 4.5 7.8 4.0 4.4 7.9 70.0 63.8 SD 1.1 0.7 5.1 1.0 0.7 4.5 4.9 20.5 Phonological fairy 2.4 5 4 fair 4.5 4 4 75.0 80.0 belly 2.8 5 7 bell 3.7 4 13 75.0 80.0 corner 4.6 6 1 corn 3.2 4 10 80.0 66.7 master 3.9 6 7 mast 0.9 4 16 66.7 66.7 barn 2.3 4 12 bar 4.2 3 16 75.0 75.0 freak 1.6 5 3 free 5.3 4 3 75.0 60.0 keep 5.9 4 9 key 4.3 3 5 66.7 50.0 pitch 3.0 5 7 pit 2.6 3 18 66.7 60.0 bitter 3.6 6 8 bits 3.5 4 15 75.0 50.0 lawn 3.0 4 7 law 5.1 3 16 66.7 75.0 panel 3.0 5 1 pan 3.4 3 19 75.0 60.0 agent 3.8 5 0 age 5.5 3 9 40.0 60.0 plant 4.3 5 5 plan 4.6 4 3 80.0 80.0 seem 5.4 4 9 sea 5.1 3 10 66.7 50.0 blanket 2.8 7 0 blank 2.9 5 6 71.4 71.4 dear 4.8 4 15 dean 2.5 4 9 66.7 75.0 tank 3.0 4 12 rank 3.1 4 13 75.0 75.0 door 5.8 4 5 floor 5.1 5 2 50.0 40.0 match 4.0 5 7 mat 2.0 3 22 66.7 60.0 feet 5.4 4 8 fee 2.6 3 15 66.7 75.0 farm 4.2 4 6 far 6.2 3 17 75.0 75.0 early 5.8 5 1 earl 2.7 4 2 75.0 80.0 beef 2.8 4 6 bee 1.9 3 13 66.7 75.0 county 3.8 6 2 count 4.4 5 3 80.0 83.3 wink 1.2 4 11 sink 3.3 4 13 75.0 75.0 tent 3.6 4 15 ten 5.4 3 16 75.0 75.0 dollar 2.7 6 1 doll 2.9 4 11 60.0 66.7 army 4.7 4 2 arm 4.7 3 5 75.0 75.0 fancy 3.4 5 1 fan 2.4 3 16 60.0 60.0 party 5.9 5 6 part 6.2 4 17 80.0 80.0 start 5.4 5 4 star 4.0 4 7 80.0 80.0 home 6.2 4 10 some 7.6 4 7 66.7 75.0 pillow 2.6 6 2 pill 2.6 4 14 75.0 66.7 dragon 2.0 6 0 drag 2.9 4 7 66.7 66.7 Mean 3.8 4.9 5.7 3.9 3.7 10.9 70.3 68.9 SD 1.3 0.9 4.3 1.4 0.6 5.6 8.6 10.7 Semantic happy 4.9 5 2 sad 3.8 3 17 25.0 20.0 cat 3.7 3 22 dog 4.3 3 14 0.0 0.0 grass 4.4 5 7 green 5.1 5 3 50.0 40.0 dinner 4.5 6 2 lunch 4.4 5 6 20.0 16.7 boots 3.4 5 11 shoes 4.2 5 5 0.0 40.0 skirt 3.0 5 1 dress 4.4 5 5 0.0 0.0 toe 2.3 3 17 heel 2.5 4 7 0.0 25.0 cry 3.9 3 6 laugh 4.0 5 0 0.0 0.0 salt 3.8 4 4 pepper 1.9 6 1 0.0 0.0 gold 4.5 4 9 silver 4.0 6 0 20.0 16.7 pear 0.9 4 15 apple 2.9 5 2 0.0 0.0 cow 3.1 3 23 horse 4.4 5 5 0.0 20.0 jam 2.6 3 12 toast 2.7 5 3 0.0 0.0 queen 3.9 5 1 king 4.5 4 9 25.0 0.0 cherry 1.8 6 2 grape 0.7 5 8 0.0 0.0 chair 4.7 5 2 table 5.3 5 4 0.0 0.0 peace 4.5 5 3 war 5.8 3 15 0.0 0.0 hot 4.9 3 17 cold 5.2 4 10 0.0 25.0 summer 4.8 6 2 winter 4.4 6 2 40.0 33.3 rule 4.2 4 5 law 5.1 3 16 33.3 0.0 couch 2.2 5 6 sofa 3.0 4 3 0.0 20.0 below 4.8 5 0 above 5.4 5 1 25.0 0.0 mad 3.9 3 22 anger 4.0 5 1 0.0 0.0 south 5.3 5 2 north 5.0 5 2 25.0 60.0 west 5.3 4 14 east 4.9 4 10 50.0 50.0 bread 4.3 5 6 butter 3.3 6 10 25.0 16.7 forest 4.2 6 0 trees 4.8 5 4 16.7 33.3 sugar 4.0 5 0 sweet 3.8 5 5 0.0 20.0 doctor 4.9 6 0 nurse 3.5 5 2 0.0 0.0 jacket 3.5 6 2 coat 4.0 4 9 20.0 0.0 smooth 3.6 6 0 rough 3.8 5 5 0.0 0.0 white 6.0 5 3 black 5.8 5 4 0.0 0.0 rose 4.6 4 14 red 5.2 3 12 33.3 25.0 sour 2.4 4 11 lemon 2.6 5 1 0.0 0.0 Mean 3.9 4.6 7.1 4.1 4.6 5.9 12.0 13.6 SD 1.1 1.0 7.1 1.1 0.9 4.8 16.1 17.0 “−M + P + S” scald −0.8 5 4 scorch −0.1 6 0 40.0 33.3 scrape 1.6 6 1 scratch 2.4 7 0 60.0 57.1 screech 0.8 7 0 scream 2.6 6 1 80.0 57.1 shelve −0.6 6 0 shelf 2.6 5 2 75.0 66.7 shrivel −0.3 7 0 shrink 1.7 6 4 60.0 57.1 slither 0.1 7 0 slink −0.8 5 6 50.0 42.9 ghost 3.0 5 0 ghoul −0.4 5 0 50.0 60.0 groan 1.1 5 2 grumble 0.9 7 1 33.3 28.6 crinkle −1.1 7 2 wrinkle 0.0 7 1 83.3 85.7 loathe 0.7 6 0 loath −0.1 5 0 100.0 83.3 hotel 4.8 5 2 motel 1.9 5 4 80.0 80.0 nostril 0.6 7 0 nose 4.3 4 10 42.9 42.9 flutter 1.3 7 2 flurry 1.5 6 2 60.0 42.9 sneeze 0.3 6 0 snort 0.8 5 4 50.0 33.3 snarl 0.5 5 2 sneer 1.0 5 2 60.0 40.0 shovel 1.4 6 2 shove 1.4 5 4 75.0 83.3 shimmer 0.5 7 3 glimmer 0.7 7 2 80.0 71.4 converge 0.6 8 1 merge 1.2 5 3 50.0 50.0 plunge 2.0 6 0 plummet −0.2 7 0 50.0 42.9 flood 2.8 5 2 float 2.8 5 2 75.0 60.0 bustle 1.3 6 2 hustle 0.2 6 3 75.0 83.3 devil 3.3 5 0 evil 4.0 4 0 75.0 80.0 ham 2.6 3 17 spam 4.7 4 7 50.0 50.0 freeze 2.2 6 2 frost 2.4 5 1 50.0 33.3 mohair 0.0 6 0 hair 5.3 4 5 75.0 66.7 placard −0.6 7 0 card 3.8 4 12 66.7 57.1 elevator 2.1 8 0 escalator 0.3 9 0 62.5 11.1 Mean 1.1 6.1 1.6 1.7 5.5 2.8 63.3 55.6 SD 1.4 1.1 3.3 1.7 1.2 3.1 15.9 19.7
 Prime Target Phonological Overlap (% Phonemes) Orthographic Overlap (% Letters) Item Frequency Length N Item Frequency Length N Regular Verbs rented 2.3 6 4 rent 3.8 4 12 66.7 66.7 cleared 3.3 7 3 clear 5.5 5 2 80.0 71.4 agreed 4.6 6 1 agree 4.4 5 0 80.0 83.3 opened 4.9 6 2 open 5.7 4 3 80.0 66.7 played 4.7 6 6 play 5.6 4 5 75.0 66.7 talked 4.6 6 4 talk 5.6 4 8 75.0 66.7 hoped 4.1 5 9 hope 5.2 4 13 75.0 80.0 passed 4.9 6 6 pass 4.6 4 12 75.0 66.7 walked 5.0 6 4 walk 4.8 4 4 75.0 66.7 jumped 3.2 6 6 jump 3.3 4 6 80.0 66.7 pushed 4.1 6 5 push 3.8 4 8 75.0 66.7 filled 4.3 6 9 fill 3.7 4 15 75.0 66.7 caused 4.3 6 2 cause 5.0 5 1 75.0 83.3 saved 3.6 5 7 save 4.2 4 13 75.0 80.0 examined 3.4 8 2 examine 3.4 7 0 85.7 87.5 lifted 3.7 6 8 lift 3.8 4 9 66.7 66.7 baked 2.4 5 8 bake 1.7 4 16 75.0 80.0 reached 4.9 7 4 reach 4.5 5 7 75.0 71.4 named 4.0 5 4 name 5.6 4 9 75.0 80.0 pressed 3.6 7 2 press 4.9 5 4 80.0 71.4 chewed 1.8 6 0 chew 1.7 4 4 66.7 66.7 crawled 2.2 7 2 crawl 2.1 5 3 80.0 71.4 blamed 2.4 6 5 blame 3.6 5 5 80.0 83.3 chopped 2.4 7 5 chop 2.0 4 5 75.0 57.1 breathed 2.5 8 4 breathe 2.8 7 1 80.0 87.5 combed 1.6 6 1 comb 1.7 4 5 75.0 66.7 frowned 2.3 7 3 frown 1.8 5 5 80.0 71.4 prayed 2.2 6 4 pray 2.7 4 6 75.0 66.7 scrubbed 1.5 8 0 scrub 2.1 5 2 83.3 62.5 pulled 4.5 6 9 pull 4.2 4 13 75.0 66.7 sprayed 1.3 7 2 spray 2.4 5 2 80.0 71.4 touched 3.8 7 3 touch 4.6 5 5 75.0 71.4 splashed 1.6 8 1 splash 1.7 6 0 83.3 75.0 chased 2.1 6 4 chase 2.7 5 4 75.0 83.3 skated −0.5 6 4 skate 1.3 5 3 66.7 83.3 Mean 3.2 6.3 4.0 3.6 4.6 6.0 76.3 72.6 SD 1.3 0.8 2.4 1.4 0.8 4.5 4.6 7.8 Suffixed Irregular Verbs leapt 2.4 5 3 leap 2.6 4 7 50.0 80.0 dealt 3.1 5 1 deal 5.2 4 16 50.0 80.0 fled 2.8 4 8 flee 1.7 4 8 50.0 75.0 slept 3.5 5 2 sleep 4.8 5 5 60.0 60.0 kept 5.3 4 2 keep 5.9 4 9 50.0 50.0 sold 4.0 4 8 sell 4.0 4 11 50.0 50.0 meant 5.0 5 2 mean 6.0 4 9 50.0 80.0 heard 5.6 5 4 hear 5.2 4 15 66.7 80.0 built 4.8 5 3 build 4.3 5 2 75.0 80.0 bent 3.7 4 14 bend 3.1 4 10 75.0 75.0 crept 2.4 5 3 creep 2.1 5 3 60.0 60.0 swept 3.4 5 3 sweep 2.7 5 4 60.0 60.0 burnt 2.9 5 2 burn 3.3 4 7 75.0 80.0 sent 5.0 4 13 send 4.4 4 8 75.0 75.0 felt 6.0 4 7 feel 5.9 4 9 50.0 50.0 spent 4.9 5 3 spend 4.5 5 2 80.0 80.0 wept 2.2 4 5 weep 1.9 4 8 50.0 50.0 taught 4.2 6 2 teach 3.9 5 5 33.3 16.7 brought 5.5 7 2 bring 5.2 5 5 50.0 42.9 stood 5.4 5 3 stand 5.0 5 2 75.0 60.0 told 6.2 4 8 tell 6.1 4 12 50.0 50.0 caught 4.7 6 2 catch 4.2 5 6 33.3 50.0 lost 5.4 4 14 lose 4.4 4 12 50.0 75.0 dwelt 0.6 5 1 dwell 1.4 5 2 80.0 80.0 Mean 4.1 4.8 4.8 4.1 4.4 7.4 58.3 64.1 SD 1.4 0.8 4.0 1.4 0.5 4.0 13.9 16.8 Vowel-change Irregular Verbs tore 2.7 4 18 tear 2.9 4 16 66.7 25.0 wore 4.1 4 19 wear 4.2 4 14 66.7 25.0 gave 5.7 4 14 give 6.2 4 6 66.7 75.0 sang 2.9 4 15 sing 3.2 4 13 66.7 75.0 drew 4.1 4 4 draw 4.1 4 5 66.7 75.0 shook 4.2 5 3 shake 3.2 5 10 66.7 40.0 spoke 4.7 5 5 speak 4.9 5 4 75.0 40.0 stung 1.2 5 4 sting 1.7 5 6 75.0 80.0 won 4.3 3 14 win 4.1 3 15 66.7 66.7 grew 4.3 4 5 grow 4.5 4 6 66.7 75.0 woke 3.2 4 7 wake 3.5 4 15 66.7 75.0 shone 2.7 5 6 shine 2.0 5 10 66.7 80.0 hung 4.0 4 8 hang 3.5 4 11 66.7 75.0 fell 4.7 4 13 fall 4.7 4 12 66.7 75.0 blew 3.1 4 5 blow 3.7 4 8 66.7 75.0 drove 4.2 5 4 drive 4.5 5 1 75.0 80.0 rang 3.6 4 14 ring 4.2 4 11 66.7 75.0 drank 3.5 5 5 drink 4.8 5 3 80.0 80.0 ran 4.7 3 16 run 5.4 3 14 66.7 66.7 stole 2.4 5 7 steal 2.5 5 4 75.0 40.0 found 6.2 5 8 find 6.3 4 11 60.0 20.0 chose 3.5 5 6 choose 4.2 6 1 66.7 50.0 sprang 2.5 6 2 spring 4.2 6 5 80.0 83.3 rode 2.7 4 13 ride 3.5 4 14 66.7 75.0 sank 2.6 4 14 sink 3.3 4 13 75.0 75.0 swam 2.0 4 11 swim 3.2 4 5 75.0 75.0 broke 4.2 5 2 break 4.7 5 6 75.0 40.0 fought 3.6 6 3 fight 4.6 5 8 66.7 16.7 swore 1.9 5 7 swear 2.7 5 4 75.0 40.0 threw 3.9 5 3 throw 3.9 5 2 66.7 80.0 stuck 3.8 5 3 stick 4.0 5 5 75.0 80.0 began 5.9 5 3 begin 4.8 5 2 75.0 80.0 flew 3.1 4 8 fly 3.9 3 6 66.7 50.0 wrote 4.9 5 1 write 4.8 5 4 66.7 80.0 swung 3.1 5 3 swing 3.4 5 6 75.0 80.0 held 5.4 4 9 hold 5.1 4 10 75.0 75.0 Mean 3.7 4.5 7.8 4.0 4.4 7.9 70.0 63.8 SD 1.1 0.7 5.1 1.0 0.7 4.5 4.9 20.5 Phonological fairy 2.4 5 4 fair 4.5 4 4 75.0 80.0 belly 2.8 5 7 bell 3.7 4 13 75.0 80.0 corner 4.6 6 1 corn 3.2 4 10 80.0 66.7 master 3.9 6 7 mast 0.9 4 16 66.7 66.7 barn 2.3 4 12 bar 4.2 3 16 75.0 75.0 freak 1.6 5 3 free 5.3 4 3 75.0 60.0 keep 5.9 4 9 key 4.3 3 5 66.7 50.0 pitch 3.0 5 7 pit 2.6 3 18 66.7 60.0 bitter 3.6 6 8 bits 3.5 4 15 75.0 50.0 lawn 3.0 4 7 law 5.1 3 16 66.7 75.0 panel 3.0 5 1 pan 3.4 3 19 75.0 60.0 agent 3.8 5 0 age 5.5 3 9 40.0 60.0 plant 4.3 5 5 plan 4.6 4 3 80.0 80.0 seem 5.4 4 9 sea 5.1 3 10 66.7 50.0 blanket 2.8 7 0 blank 2.9 5 6 71.4 71.4 dear 4.8 4 15 dean 2.5 4 9 66.7 75.0 tank 3.0 4 12 rank 3.1 4 13 75.0 75.0 door 5.8 4 5 floor 5.1 5 2 50.0 40.0 match 4.0 5 7 mat 2.0 3 22 66.7 60.0 feet 5.4 4 8 fee 2.6 3 15 66.7 75.0 farm 4.2 4 6 far 6.2 3 17 75.0 75.0 early 5.8 5 1 earl 2.7 4 2 75.0 80.0 beef 2.8 4 6 bee 1.9 3 13 66.7 75.0 county 3.8 6 2 count 4.4 5 3 80.0 83.3 wink 1.2 4 11 sink 3.3 4 13 75.0 75.0 tent 3.6 4 15 ten 5.4 3 16 75.0 75.0 dollar 2.7 6 1 doll 2.9 4 11 60.0 66.7 army 4.7 4 2 arm 4.7 3 5 75.0 75.0 fancy 3.4 5 1 fan 2.4 3 16 60.0 60.0 party 5.9 5 6 part 6.2 4 17 80.0 80.0 start 5.4 5 4 star 4.0 4 7 80.0 80.0 home 6.2 4 10 some 7.6 4 7 66.7 75.0 pillow 2.6 6 2 pill 2.6 4 14 75.0 66.7 dragon 2.0 6 0 drag 2.9 4 7 66.7 66.7 Mean 3.8 4.9 5.7 3.9 3.7 10.9 70.3 68.9 SD 1.3 0.9 4.3 1.4 0.6 5.6 8.6 10.7 Semantic happy 4.9 5 2 sad 3.8 3 17 25.0 20.0 cat 3.7 3 22 dog 4.3 3 14 0.0 0.0 grass 4.4 5 7 green 5.1 5 3 50.0 40.0 dinner 4.5 6 2 lunch 4.4 5 6 20.0 16.7 boots 3.4 5 11 shoes 4.2 5 5 0.0 40.0 skirt 3.0 5 1 dress 4.4 5 5 0.0 0.0 toe 2.3 3 17 heel 2.5 4 7 0.0 25.0 cry 3.9 3 6 laugh 4.0 5 0 0.0 0.0 salt 3.8 4 4 pepper 1.9 6 1 0.0 0.0 gold 4.5 4 9 silver 4.0 6 0 20.0 16.7 pear 0.9 4 15 apple 2.9 5 2 0.0 0.0 cow 3.1 3 23 horse 4.4 5 5 0.0 20.0 jam 2.6 3 12 toast 2.7 5 3 0.0 0.0 queen 3.9 5 1 king 4.5 4 9 25.0 0.0 cherry 1.8 6 2 grape 0.7 5 8 0.0 0.0 chair 4.7 5 2 table 5.3 5 4 0.0 0.0 peace 4.5 5 3 war 5.8 3 15 0.0 0.0 hot 4.9 3 17 cold 5.2 4 10 0.0 25.0 summer 4.8 6 2 winter 4.4 6 2 40.0 33.3 rule 4.2 4 5 law 5.1 3 16 33.3 0.0 couch 2.2 5 6 sofa 3.0 4 3 0.0 20.0 below 4.8 5 0 above 5.4 5 1 25.0 0.0 mad 3.9 3 22 anger 4.0 5 1 0.0 0.0 south 5.3 5 2 north 5.0 5 2 25.0 60.0 west 5.3 4 14 east 4.9 4 10 50.0 50.0 bread 4.3 5 6 butter 3.3 6 10 25.0 16.7 forest 4.2 6 0 trees 4.8 5 4 16.7 33.3 sugar 4.0 5 0 sweet 3.8 5 5 0.0 20.0 doctor 4.9 6 0 nurse 3.5 5 2 0.0 0.0 jacket 3.5 6 2 coat 4.0 4 9 20.0 0.0 smooth 3.6 6 0 rough 3.8 5 5 0.0 0.0 white 6.0 5 3 black 5.8 5 4 0.0 0.0 rose 4.6 4 14 red 5.2 3 12 33.3 25.0 sour 2.4 4 11 lemon 2.6 5 1 0.0 0.0 Mean 3.9 4.6 7.1 4.1 4.6 5.9 12.0 13.6 SD 1.1 1.0 7.1 1.1 0.9 4.8 16.1 17.0 “−M + P + S” scald −0.8 5 4 scorch −0.1 6 0 40.0 33.3 scrape 1.6 6 1 scratch 2.4 7 0 60.0 57.1 screech 0.8 7 0 scream 2.6 6 1 80.0 57.1 shelve −0.6 6 0 shelf 2.6 5 2 75.0 66.7 shrivel −0.3 7 0 shrink 1.7 6 4 60.0 57.1 slither 0.1 7 0 slink −0.8 5 6 50.0 42.9 ghost 3.0 5 0 ghoul −0.4 5 0 50.0 60.0 groan 1.1 5 2 grumble 0.9 7 1 33.3 28.6 crinkle −1.1 7 2 wrinkle 0.0 7 1 83.3 85.7 loathe 0.7 6 0 loath −0.1 5 0 100.0 83.3 hotel 4.8 5 2 motel 1.9 5 4 80.0 80.0 nostril 0.6 7 0 nose 4.3 4 10 42.9 42.9 flutter 1.3 7 2 flurry 1.5 6 2 60.0 42.9 sneeze 0.3 6 0 snort 0.8 5 4 50.0 33.3 snarl 0.5 5 2 sneer 1.0 5 2 60.0 40.0 shovel 1.4 6 2 shove 1.4 5 4 75.0 83.3 shimmer 0.5 7 3 glimmer 0.7 7 2 80.0 71.4 converge 0.6 8 1 merge 1.2 5 3 50.0 50.0 plunge 2.0 6 0 plummet −0.2 7 0 50.0 42.9 flood 2.8 5 2 float 2.8 5 2 75.0 60.0 bustle 1.3 6 2 hustle 0.2 6 3 75.0 83.3 devil 3.3 5 0 evil 4.0 4 0 75.0 80.0 ham 2.6 3 17 spam 4.7 4 7 50.0 50.0 freeze 2.2 6 2 frost 2.4 5 1 50.0 33.3 mohair 0.0 6 0 hair 5.3 4 5 75.0 66.7 placard −0.6 7 0 card 3.8 4 12 66.7 57.1 elevator 2.1 8 0 escalator 0.3 9 0 62.5 11.1 Mean 1.1 6.1 1.6 1.7 5.5 2.8 63.3 55.6 SD 1.4 1.1 3.3 1.7 1.2 3.1 15.9 19.7

## Acknowledgments

A. K. was supported by a Postgraduate Scholarship from the Natural Sciences and Engineering Research Council (NSERC); M. F. J. was supported by an operating grant from NSERC and a New Investigator award from The Canadian Institutes for Health Research. Infrastructure support was provided by the Canada Foundation for Innovation. We thank Randy Lynn Newman and Katherine Freeman for assistance with ERP testing.

Reprint requests should be sent to Marc F. Joanisse, Department of Psychology, The University of Western Ontario, London, Ontario, Canada N6A 5C2, or via e-mail: marcj@uwo.ca.

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