Permanent and temporary phonological influences in ...

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Permanent and temporary phonological influences in Slovenian irregular verb production a

b

Teodor Petrič & Joseph Paul Stemberger a

Department of German Studies, University of Maribor, Maribor, Slovenia

b

Department of Linguistics, University of British Columbia, Vancouver, Canada Published online: 06 Nov 2013.

Click for updates To cite this article: Teodor Petrič & Joseph Paul Stemberger (2014) Permanent and temporary phonological influences in Slovenian irregular verb production, Language, Cognition and Neuroscience, 29:4, 470-482, DOI: 10.1080/01690965.2013.849812 To link to this article: http://dx.doi.org/10.1080/01690965.2013.849812

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Language, Cognition and Neuroscience, 2014 Vol. 29, No. 4, 470–482, http://dx.doi.org/10.1080/01690965.2013.849812

Permanent and temporary phonological influences in Slovenian irregular verb production Teodor Petriča and Joseph Paul Stembergerb* a

Department of German Studies, University of Maribor, Maribor, Slovenia; bDepartment of Linguistics, University of British Columbia, Vancouver, Canada

Downloaded by [93.103.195.45] at 06:21 24 March 2015

(Received 15 February 2013; accepted 23 September 2013) Lexical access in language production shows effects of permanent statistical properties of the lexicon, but also of temporary phonological influences of words produced in the same sentence. In this study of present-tense verbs in Slovene, conjoining an irregular and a regular form, where both verbs have the same base-final consonant, leads to a dramatic increase in the error rate, both over-regularisation of irregulars and over-irregularisation of regulars. A further degree of similarity (rhyming) affects only over-irregularisation of regular verbs. Another experiment explores output biases in purely phonological errors, to help clarify the nature of the interference during processing. The strong effect of temporary phonological influences on the processing of irregular forms provides new details about that process, but is less informative about the mechanisms for regulars. Keywords: language production; priming; irregular morphology; phonology–morphology interactions; Palatal Bias

In language production, words are never processed in a perfectly discrete fashion. There are always influences from other lexical items. Some of these influences derive from statistical properties of the lexicon; they are a permanent influence regardless of the context in which a word appears. Other influences arise from the context in which a word appears, with temporary facilitation or inhibition from words used previously or being prepared for production later in the same sentence. In this paper, we address temporary contextual and permanent non-contextual phonological influences on the over-regularisation of irregular verbs during language production in Slovene, a South Slavic language spoken primarily in Slovenia and surrounding countries. Every theory must have a mechanism for storing information that is idiosyncratic to a specific lexical item, but must also have a way to derive and generalise more general characteristics. Consider irregularity in morphology. Young children predominantly produce irregulars (e.g. Engl. broke, simple past-tense form of break) accurately from an early age, but sometimes overgeneralise the regular pattern (e.g. breaked). In order to be produced correctly, an irregular form must be learned well enough to overcome competition from the regular pattern, and this is achieved at an early age. Over-regularisations generally account for fewer than 10% of tokens even at the point of maximum error (e.g. Marcus et al., 1992), but occasionally occur even in adulthood (e.g. Bybee & Slobin, 1982; MacKay, 1976; Stemberger, 2004b). There is general agreement that irregular forms must be stored in

*Corresponding author. E-mail: [email protected] © 2013 Taylor & Francis

the lexicon and accessed during production. Processing is not discrete, however, because irregulars that are phonologically similar to other irregular forms have a lower error rate than those that are more unique: a permanent effect of the structure of the lexicon. Theories differ on how the regular pattern is learned. Some theories hold that learners extract rules and create regular forms by combining a base such as walk with an affix such as -ed (e.g. Clahsen, 1999; Pinker & Prince, 1988). Other theories propose that regulars are stored in the same way as irregulars, but that statistical generalisation across lexical entries (the permanent structure of the lexicon) leads to special properties for the regular pattern (e.g. Baayen, Milin, Filipović Ðurđević, Hendrix, & Marelli, 2011; Bybee, 2006; Rumelhart & McClelland, 1986). Both approaches agree that the regular pattern is learned better and generalises more readily than irregular patterns, and it is often difficult to distinguish between them empirically. Some permanent effects do not arise from similarity between stored past-tense forms, but derive from more general phonological characteristics that constitute PHONOLOGICAL OUTPUT BIASES. The mechanism behind such biases can be independent statements such as constraints (e.g. Prince & Smolensky, 2004) or can be generalisations across words in the lexicon (e.g. Bybee, 2006; Dell, Juliano, & Govindjee, 1993). Native speakers of a language are sensitive to phonotactic probability from an early age (e.g. Storkel, Armbrüster, & Hogan, 2006; Storkel & Rogers, 2000). Bernhardt and Stemberger


Language, Cognition and Neuroscience (1998) argue that high-frequency DEFAULT characteristics are most often learned earlier and overgeneralise to replace less well-learned lower-frequency NON-DEFAULT characteristics. As with irregular forms in morphology, lower-frequency non-default phonological features must be learned well enough to overcome competition from the high-frequency default features that are favoured by phonological output biases. Stemberger (1991, 1992b) explored such effects for purely phonological processing, and argued that there are two separate effects: (1) a frequency effect (a statistical bias for errors to involve replacement of lower-frequency phonemes with higher-frequency phonemes), and (2) a specific disadvantage for the highest-frequency default features (such as [Coronal] and [−continuant] in /t/ vs. [Labial] and/or [+continuant] in /p, f, s/) when the two sounds compete in language production (e.g. due to priming). One of the phonemes tends to win the competition: the phoneme of higher frequency, when the competition is between two lower-frequency non-default features; but the phoneme with lower-frequency nondefault features when the competition is with higherfrequency default features. Stemberger (1993), focusing on vowels, combines these two output biases under the term VOWEL DOMINANCE. Vowel dominance has a strong effect on irregular verb forms (Marchman, 1997; Stemberger, 1993, 2007): when the dominant vowel is in the irregular form, error rates are low; when the dominant vowel is in the base form, error rates are high. In addition, parallel to the hypersimilar families of irregular verbs, there are neighbourhood effects on the production of speech sounds, such that words with large numbers of friends in the neighbourhood have lower error rates than words with few friends (Dell & Gordon, 2003; Stemberger, 2004a; Vitevitch, 2002). These are permanent influences on processing, derived (either in processing or via learning) from the structure of the lexicon. These phonological effects arise whenever there is competition between outputs, which can also be of a temporary nature. If a speech sound appears in one word, it has a perseveratory (priming) effect on later sounds (e.g. Stemberger, 2004b). If two words are activated at the same time, because they are to be produced in the same phrase or sentence, there is interference between the sounds of the two words, partly driven by similarity between the interfering sounds (e.g., /t/ differs from /p/ only by place of articulation but from /f/ also by manner of articulation) and by similarity between the two interfering words (e.g. with more errors on initial consonants if the two words have the same vowel; Dell & Reich, 1981). Temporary phonological influences can also influence the accuracy of irregular morphological forms. Stemberger (2004b) had subjects convert sentences from past progressive aspect (e.g. The clown was spinning) into simple past (The clown spun). If the subject noun rhymed with

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the base form of the verb (e.g. ball-fall), the rate of overregularisation tripled, to 12% vs. non-rhyming 4%. If the subject noun had the same vowel as the base form of the irregular verb but did not rhyme, error rates increased oneand-a-half times (to 6%). If the irregular verb form contained the base (e.g. base put, past put), priming from the subject noun had no effect on error rates. Stemberger argued that phonological processing of other words in the sentence impacts on access of the irregular forms from the lexicon, in ways that either reinforce the irregular form (lowering the error rate), the base form (raising the error rate), or both forms (having no impact on the error rate). Stemberger (2008) had subjects produce sentences with conjoined verbs (e.g. The clown was grinning and spinning). Conjoining rhyming regular and irregular verbs dramatically raised error rates, especially for irregulars and especially for the second conjoined verb. Much of the focus on irregularity with verb forms in adult language production has been on English and other Germanic languages, and has focused especially on irregular past-tense and perfect forms that have a different vowel from the base form. In this paper, we focus on Slovene, a South Slavic language in which there are common irregular patterns also in the present tense (the most frequent tense), most often affecting consonants and in particular with palatoalveolar consonants (/č š ž/) in irregular forms vs. other places of articulation in base forms (/c t s z k g x/). We expect temporary phonological influences between conjoined verbs to increase the rate of both over-regularisation of irregulars and over-irregularisation of regulars. The ‘palatalization’ in these irregular forms allows an interesting link with previous research on phonological processing. Shattuck-Hufnagel and Klatt (1979) reported for English that high-frequency /s/ is more likely to be mispronounced as low-frequency /š/ than the reverse, based on a corpus of errors occurring in spontaneous speech. Levitt and Healy (1985) found no asymmetries (neither a Palatal Bias nor a frequency effect) using nonce words in the SLIP task, but it has since been replicated for real words in the SLIP task for English (Stemberger, 1991, 2004a), especially for words with a large number of friends in the neighbourhood; for English using imaging of the articulators (Pouplier & Goldstein, 2005); and for Russian using acoustic analysis (Kochetov & Radišič, 2009). We first address this phonological output bias for Slovene, and then consider how it interacts with the processing of irregular present-tense forms. Experiment 1: the Palatal Bias in phonological errors We address errors in language production that arise when dental and palatoalveolar consonants compete under conditions of priming. Error rates in this task are usually influenced by lexical frequency of the target word, by

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lexicality (whether the error creates a real word vs. a nonword), by the density of friends in the neighbourhood, by whether the two words have the same vs. a different vowel, and by phoneme frequency (or default status). Dell (1984) has shown that the majority of errors in this task are errors of production that occur after accurate perception of the presented orthographic form. In Slovene, there are three palatoalveolars (the voiceless affricate /č/, the voiceless fricative /š/, and the voiced fricative /ž/), corresponding to three dentals (the voiceless affricate /c/, the voiceless fricative /s/, and the voiced fricative /z/), with several other dentals that do not correspond to minimally different palatoalveolars (/t, d, n, l, r/). In terms of phoneme frequency, /s/ and /z/ are more frequent than /š/ and /ž/, but /č/ is more frequent than /c/ (Jakopin, 1999).

Method A SLIP experiment was carried out, involving word-initial singleton consonants. SLIP is an experimental task that involves priming of phonological elements which appear as competitors in a word pair that speakers must utter together (e.g. Baars, Motley, & MacKay, 1975; Stemberger, 1991). Two sample stimulus items are given in (Table 1). The first pair of words rhymes with the target words (to reinforce the other phonemes of the target words). The second and third word pairs prime for a particular initial consonant in the first word and a different initial consonant in the second word. These two consonants are reversed in the target pair. Subjects read the priming pairs silently. After the target pair, a prompt is presented as a cue to the subject to say aloud the last pair of words that was presented (the target word pair). The prompt may appear after as few as two word pairs, or as many as six word pairs; the subject never knows in advance whether a given pair of words will be cued or not, which forces the subject to process every trial sufficiently enough to speak it quickly. While unprimed errors do occur, priming increases the rate of errors involving the primed contrasts (Stemberger, 1992a). There were 28 target pairs of words that contrasted dental vs. palatoalveolar consonants (43.75% of cued trials), and 36 cued filler word pairs with other sorts of phoneme contrasts. Each word pair was presented for 903 Table 1.

Sample stimuli for the SLIP experiment in Experiment 1.

prime pairs

kup fen

plima proga

target pair Prompt

suh šef suš šeh šup sen ??????????

žito zofa žila zora zima žoga ??????????

ms, followed by 100 ms of blank screen. The prompt was presented after the target word pair for 600 ms, after which the word ‘nadaljujemo’ (‘we are moving on’) appeared for 500 ms, followed by 400 ms of blank screen; the subject’s response was recorded from the beginning of the prompt for 1500 ms. Then the next trial began. The two groups of target words (dental vs. palatoalveolar) had the same mean number of phonemes and letters. To the extent possible, we controlled for three factors of a lexical nature and one additional phonological factor. The two groups were balanced for target-word lexical frequency (mean and range), as measured using the GigaFida corpus of Slovene (Berginc & Šuster, 2009); words with an observed frequency at or above 10,000 tokens were counted as high frequency, and under 10,000 tokens were counted as low frequency. The two groups were balanced for the number of friends in the neighbourhood (i.e., the subset of neighbours that contained the same phoneme as the target word), since Stemberger (2004a) showed that the number of neighbours is predictive of error rates, but that overall neighbourhood density is not; words with 10 or more friends were counted as high density, and words with fewer than 10 friends as low density. We used as many words as possible where the error created a real word (e.g. target zima ‘winter’ to error žima ‘horsehair’), since this often increases the error rate in this task (Baars et al., 1975; Hartsuiker, Corley, & Martensen, 2005; Nooteboom, 2005); it was discovered only afterwards that almost all of the stimuli with realword errors were high-frequency words, meaning that this factor is nested within frequency and requires special handling for the statistics. In some word pairs, the vowels were the same in both words, and in others they were different; pairs with the same vowel were in the minority, but evenly distributed with respect to other factors. All words occurred only once, and any real words created by errors did not otherwise occur in the experiment. Since the error rates may be different in the first vs. the second word in the pair, we made use of two lists, differing only in terms of which of the two words in a pair is first; half of the subjects saw each list, determined by the order in which subjects appeared for the experiment. Since there is no theoretical interest in whether the error occurs in the first vs. second word in the pair, we do not address this factor further, noting that there are no confounds that could lead to apparent effects of other factors. Subjects’ oral responses were recorded on the computer, and the first author (a native speaker of Slovene) judged whether the pronunciation was correct vs. contained the primed error (vs. other unprimed errors that were not of interest and not analysed). Error rates involving primed errors were analysed using a Repeated Measures ANOVA for phoneme contrast (dental vs. palatoalveolar), lexicality of the error (real word vs. nonword), vowel (same or different), word frequency (high

Language, Cognition and Neuroscience vs. low), and density of friends in the neighbourhood (high vs. low). Subjects Fifty-six native speakers of Slovene, undergraduate students at the University of Maribor, took part in the experiment as part of the requirements for a course.


Results Lexicality requires special treatment, because it is nested within frequency. Restricting the ANOVA to just highfrequency target words, there were more errors that created real words than that created non-words, both across subjects (F1(1,55) = 7.10, MSE = .001258, p < .01) and across items (F2(1,23) = 6.83, MSE = .000434, p < .025). For high-frequency words, there were more errors on dentals than on palatoalveolars, marginally across subjects (F1(1,55) = 2.65, MSE = .001897, p = .109) but significantly across items (F2(1,23) = 5.86, MSE = .000372, p < .025). There was no significant interaction (F < 1). Lexical frequency and the number of friends in the neighbourhood showed evidence of having an effect, but not completely consistently. Frequency had a marginal effect, across both subjects and items (F1(1,55) = 2.56, MSE = .011510, p = .115; F2(1,35) = 3.02, MSE = .003140, p = .091), with a lower error rate on high frequency than on low-frequency words. Recall, however, that the error rate increased when the error created a real word, and that this inflated the error rate only on highfrequency words. If we eliminate items that created realword errors and analyse only the subset of stimuli where errors create non-words, frequency had a significant effect across both subjects and words (F1(1,55) = 21.38, MSE = .002271, p < 001; F2(1,29) = 4.69, MSE = .003140, p < .05). A greater density of friends in the neighbourhood led to a lower error rate, across subjects (F1(1,55) = 7.78, MSE = .005707, p < .0075) but not across words (F2 (1,35) = 1.46, MSE = .001520, ns); but again, if we analyse the subset of the data where errors created nonwords, the density of friends has a significant effect across both subjects and words (F1(1,55) = 6.00, MSE = .001992, p < .025; F2(1,29) = 4.21, MSE = .001520, p < .05). There was a marginal interaction between frequency and the number of friends, by subject (F1(1,55) = 3.04, MSE = .005554, p = .087) but not by word (F2 < 1); the number of friends had an effect primarily for lowfrequency words. (For the subset of stimuli where the error created a non-word, the interaction between frequency and friends was significant across subjects but not words: F1(1,55)5.34, MSE = .001670, p < .025; F2 < 1.) The density of friends had no further significant interactions with subject as random factor, but showed two

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marginal interactions with word as random factor: friends × consonant (F2 (1,35) = 2.61, MSE=.002720, p = .115), with a slightly larger difference between dentals and palatals when there were more friends, and friends × vowel (F2(1,35) = 2.58, MSE = .002682, p = .118), in which a shared vowel raises the error rate more when there is a low density of friends. There were more errors on dentals than on palatoalveolars, non-significantly across subjects (F1(1,55) = 1.90, MSE = .008495, ns) but marginally across words (F2(1,35) = 4.03, MSE = .004200, p = .053). As noted above, there were more errors on dentals than on palatoalveolars for just high-frequency words. For lowfrequency words, there are actually more errors on palatoalveolars than on dentals; if we test low-frequency words separately, this small difference is not significant across either subjects or words: F < 1. There was no main effect of shared vowel, with equal error rates on pairs with the same vowel (1.8% of trials) and with different vowels (1.5% of trials): F < 1, ns. There was a significant interaction between consonant type and vowel, across both subjects and items (F1(1,55) = 4.03, MSE = .007484 p < .05; F2(1,35) = 5.01, MSE = .005220, p < .05); there were more errors on dentals than on palatoalveolars when the two target words had the same vowel, but a slight non-significant difference with more errors on palatals than on dentals when the vowels were different. No other interactions approached significance. Discussion Before discussing the results, we would like to address the issue of whether the errors observed in Exp. 1 are errors of production (as we have assumed) or errors of perceptual encoding. Dell (1986) asked subjects to say after every trial whether it was correct or an error. Subjects rarely said something was an error when it was correct, and almost always said that the errors were errors. The only added wrinkle for Slovene is that the graphemes in the visual input are more similar than in English: the dentals are represented with the letters {s z c}, to which a haček is added to create the letters for the palatoalveolars {š ž č}. An anonymous reviewer suggested that there might be a perceptual tendency to add the haček from context to convert dentals into palatoalveolars, more than to lose the haček when it is absent in context to change palatoalveolars to dentals (a perceptual analogue of what Stemberger, 1991, termed ‘the addition bias’). We think that the details of our results, as discussed below, fit in well with the assumption that they are errors of production. If they are perceptual errors, many details (e.g., that the Palatal Bias is present for high-frequency words but not for lowfrequency words) do not have any obvious fit with theories of letter processing. There is one finding that is


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at odds with the letter-processing literature: that increased neighbourhood density (as reflected in the number of friends) decreased the error rate. As Dell and Gordon (2003) note, neighbourhood density invariably has a negative effect in perceptual tasks with real words, with larger neighbourhoods leading to more errors, but instead has a facilitatory effect in language production. We replicated (significantly, marginally or in subsets of the data) three effects that directly involve lexical processing. There was an effect of lexicality, whereby errors are more likely if they create real words than if they create non-words (Baars et al., 1975; Hartsuiker et al., 2005; Nooteboom, 2005); this is a lexical effect that reinforces the error consonant and makes errors more likely. There was an effect of the lexical frequency of the target word (Dell, 1990: fewer errors on high-frequency words than on low-frequency words), though this reached full levels of significance only when errors created non-words (due to a confound in the stimuli between frequency and the lexicality of the error). There was an effect of neighbourhood density (Vitevitch, 2002; Stemberger, 2004a: fewer errors on words with more friends), though not fully consistently across both subjects and words (except when errors created non-words). Lexical frequency and neighbourhood density are both lexical effects that relate to how strongly the target phoneme is supported by the lexicon; strong lexical support reinforces the target consonant and makes errors less likely. Lexical frequency showed an effect regardless of neighbourhood density, while neighbourhood density showed an effect primarily in low-frequency words. We failed to replicate fully the Palatal Bias that has been reported for English and Russian. We found evidence for the Palatal Bias only in interactions with other factors. (1) There was a Palatal Bias when the two words share the same vowel; when the two words have different vowels, the error rates are about equal (with a small difference towards more errors on palatoalveolars than on dentals). Even when the vowels are different, however, there is no phoneme-frequency effect, consistent with previous studies (Levitt & Healy, 1985; Stemberger, 2004a). (2) Stemberger (2004a) predicted a larger Palatal Bias in high-frequency and high-density words (but found the interaction only for neighbourhood density). We did find a marginal interaction for neighbourhood density (but only across words). However, there was a Palatal Bias on just high-frequency words (revealed by the analysis of highfrequency words only, for the real-word vs. non-word analysis) vs. a small non-significant difference in the opposite direction on low-frequency words. To fit our findings into the previous literature, we must first review why these effects arise in the first place. Stemberger (1991) posited that there are two effects: an output bias to produce high-frequency elements (arising from general frequency across words in the lexicon),

and a tendency to be more accurate on information that is more strongly stored in individual lexical items. Default features are supported by the output bias, are subject to a lower error rate, have fewer errors and consequently fewer learning trials, and tend to be stored less strongly in lexical items. Non-default features have higher error rates and consequently more learning trials, and tend to be stored more strongly in lexical entries. The output bias favours high-frequency dentals/alveolars. The strong-storage effect favours more strongly stored palatoalveolars. Because the effects make opposite predictions, quantitative results depend on the exact strength of each effect. If the output effect is stronger, there will be fewer errors on dentals/alveolars. If the two effects are equally strong, there will be equal error rates on dentals/alveolars and palatoalveolars. If the strong-storage effect is stronger, there will be fewer errors on palatoalveolars. Stemberger (2004a) argued that decreasing lexical support weakens the strong-storage effect, and decreases the effect size or even eliminates the Palatal Bias. Levitt and Healy (1985) used non-words (very weak storage), and found equal error rates. Stemberger (2004a) reported that for lowdensity words, which must rely more on themselves during processing, there was a high error rate with a small Palatal Bias; but for high-density words, which can rely more on joint processing with similar words, there was a low error rate with a large Palatal Bias. Stemberger (2004a) also predicted that the Palatal Bias should be larger for high-frequency words than for low-frequency words, but did not find a frequency effect in his data. In Exp. 1, we found a Palatal Bias for high-frequency words, but a non-significant difference in the opposite direction for low-frequency words. Lexical support in highfrequency words was enough to favour palatoalveolar consonants, but was weak enough in low-frequency word that the output bias towards high-frequency phonemes was slightly (but not significantly) stronger. If the error creates a real word, that also increases lexical support for the error, and this also led to a stronger Palatal Bias. The Shared-Vowel effect must be a lexically mediated effect. There is temporary interaction between the phonemes of two words, which leads to an increase in activation levels for both words (Dell, 1986), which then impacts on the processing of non-shared phonemes. The shared vowel should increase the size of lexical effects such as the strong-storage effect. Our data shows a Palatal Bias when the vowels are shared, with an increase in errors on dentals compared to the different-vowel pairs, and a decrease in errors on palatoalveolars compared to the different-vowel pairs (the predicted interaction). Competition between dentals and palatoalveolars in phonological processing in Slovene leads to a Palatal Bias when two words share the same vowel, and to neutralisation of the phoneme-frequency output bias when two words have different vowels.

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Table 2. Examples of regular verbs in Slovene. infinitive -l participle (past/future) imperative

delati delal delaj

Present tense


1st 2nd 3rd

kisati kisal kisaj

Singular

Plural

Singular

Plural

delam delaš dela

delamo delate delajo

kisam kisaš kisa

kisamo kisate kisajo

Our results are consistent with this perspective. All factors that increase lexical support for a given outcome lead to a greater error rate on dentals than on palatoalveolars (the Palatal Bias). When lexical support is weak, there is no Palatal Bias, but there is at best a weak non-significant tendency towards more errors on the lower-frequency palatoalveolars than on the higher-frequency dentals, showing a neutralisation of the otherwise expected phoneme frequency effect. There is both a strong-storage effect and a strong-output effect, which influence output in opposite directions when dentals compete with palatoalveolars. This provides some information about phonological processing as it would interact with irregular verbs in Slovene. In a phrase such as kisa in piše (‘makes something sour and writes’; where the first verb is regular and the second is irregular; see below), where the two words share the same vowel, we expect the phonological biases to favour the /š/, leading to more errors on regular kisati (to *kiše, an over-irregularisation error) than on irregular pisati (to *pisa, an over-regularisation error). But previous research on irregularity leads us to expect the opposite: that there will be more over-regularisation errors on irregular pisati than over-irregularisation errors on regular kisati. When we pit phonological processing effects against morphological processing effects, what is the result?

languages. Looking at different forms of the same base morpheme, we frequently find a palatoalveolar in some forms (/č, š, ž/) but a dental (/t, c, s, z/) or velar (/k, g, x/) in other forms. Among many morphological patterns, it appears as part of an irregular pattern in present-tense verb forms (ca. 12–14 reliably irregular verbs). The most frequent verb conjugation ends in the theme vowel /a/, which appears in all inflected forms. This is illustrated with delati ‘work’ and kisati ‘to make sour’ (Table 2). Note that the entire base (/dela/, /kisa/) is identical in all forms (plus other similar forms omitted for simplicity). Some verbs that have the theme vowel /a/ in the infinitive lose it in the present stem, and instead take the theme vowel /ε/, as illustrated with pisati ‘to write’ and klicati ‘to call’ (Table 3). Note that the irregular form piše has two points of difference from the base: a palatoalveolar consonant instead of a dental, and a different theme vowel. We can distinguish a simple error (which has just an error on the consonant or on the vowel, but not on both) from the most common morphological error (the full over-regularisation *pisa, with both differences). Similarly, an over-irregularisation of the regular form kisa involves both a change to a palatoalveolar consonant and the irregular theme vowel: *kiše. We now investigate the role of temporary phonological processing factors on the production of present-tense forms in Slovene.

Experiment 2: palatalisation in irregular present-tense verb forms

Method

Palatalisation is a standard phenomenon in the morphology of Slovene (Toporišič, 2004), as in other Slavic

A sentence-production task was used. Stemberger (2008) used a simple subject noun phrase (the NOUN) plus two

Table 3. Examples of irregular (palatalizing) verbs in Slovene. infinitive -l participle (past/future) imperative

pisati pisal piši

Present tense 1st 2nd 3rd

klicati klical kliči

Singular

Plural

Singular

pišem pišeš piše

pišemo pišete pišejo

kličem kličeš kliče

Plural kličemo kličete kličejo


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verbs presented in the past progressive with the conjunction and between them, e.g. The clown was grinning and spinning. Slovene is a pro-drop language that does not require an overt subject, so we did not use one; participants produce sentences of the form VERB-1 in VERB-2 (where in means ‘and’), in the third-person present form, e.g. kisa in piše ‘s/he makes (it) sour and writes’. (Most of the conjoined phrases are not very meaningful.) The task is to read silently a visually presented conjoined infinitival phrase (kisati in pisati) and then as quickly as possible produce it orally, converted into a conjoined present-tense sentence. We used as many reliably irregular verbs as possible (12), and matched them to reliably regular verbs. The infinitive forms had the same root-final consonant in half of the word pairs (e.g., both /s/), and different root-final consonants in the other half (e.g., /s/ and /r/). When the root-final consonant was the same, half the verb pairs also had the same vowel (rhyming) and the other half had different vowels (non-rhyming). The stimuli were matched as closely as possible for length in phonemes and letters and for lexical frequency, but because irregular forms are on-average more frequent than regular forms (as in English and other languages, e.g. MacKay, 1976), it was unbalanced: 75% of irregular verbs were of high frequency vs. 25% of regular verbs. Critical verbs appeared in two conditions: similar (in which a regular and an irregular verb are conjoined which share the same dental consonant at the end of the root) and dissimilar (where the verb is conjoined with a verb with no phonemes in common). In addition, the critical verb can be either the first or the second verb in the conjoined sentence. We used four different lists so that each critical verb could appear in four conditions: first vs. second in the sentence, in the similar vs. dissimilar condition. In dissimilar trials, critical verbs were conjoined with phonologically unrelated (and generally regular) verbs, containing consonants excluded from the main part of the study; these distractor verbs were occasionally in error, but are not analysed here. We also included several words where (ir)regularity is doubtful, because this varies across dialects or the verbs are becoming unfamiliar; the results on these verbs are not analysed here. Each trial had the following structure. A conjoined infinitive phrase (Verb-1 in Verb-2, where in means ‘and’) was presented centred on the screen. As soon as it appeared, subjects read it silently and then produced the same verbs in a conjoined present-tense phrase; sound recording on the computer was activated for 3200 ms, recording the subject’s oral response. After 2500 ms, the verbs were removed, with a blank screen for 1000 ms. Then the next trial began. Responses were transcribed by the first author (a native speaker of Slovene), and categorised into different sorts of errors. Here we analyse

Table 4. Examples of errors in experiment 2.

target (correct) full error consonant-only theme-vowel only

overregularization of irregular

over-irregularization of regular

piše pisa pise piša

kisa kiše kiša kise

only full and partial over-regularisations and over-irregularisations. Examples are given in (Table 4). The rates of full errors were analysed using Repeated Measures ANOVAs, with factors for regularity (regular vs. irregular), position in the sentence (Verb 1 vs. Verb 2), similarity (similar vs. dissimilar), rhyming (similar condition only), and lexical frequency (high vs. low). The rates of partial errors were too low to submit to a full statistical analysis. Subjects Fifty-six native speakers of Slovene, undergraduate students at the University of Maribor, took part in the experiment as part of the requirements for a course. Results All factors had a significant main effect, and there were several significant interactions. There was a significant effect of regularity, with more errors on irregulars (12.5% of trials) than on regulars (3.5% of trials): across subjects (F1(1,55) = 24.26, MSE = .0184, p < .001) and across words (F2(1,20) = 9.31, MSE = 4.350, p < .0075). There was a significant main effect of position, with a greater error rate on the second verb (9.2% of trials) than on the first verb (6.9% of trials): across subjects, (F1(1,1322) = 5.03, MSE = .0680, p < .025) and across items (F2(1,20) = 8.58, MSE = 1.549, p