Elsevier

Brain and Language

Volume 130, March 2014, Pages 33-41
Brain and Language

An ER-fMRI study of Russian inflectional morphology

https://doi.org/10.1016/j.bandl.2014.01.006Get rights and content

Highlights

  • We compare single vs. dual system models of inflectional morphology using fMRI.

  • Previous neuroimaging studies rely mostly on English and German, we use Russian.

  • This is the first fMRI study of Russian morphology, verbs and nouns are analyzed.

  • Our findings are more readily compatible with the single-system approach

  • We suggest a processing load explanation for observed activation patterns.

Abstract

The generation of regular and irregular past tense verbs has long been a testing ground for different models of inflection in the mental lexicon. Behavioral studies examined a variety of languages, but neuroimaging studies rely almost exclusively on English and German data. In our fMRI experiment, participants inflected Russian verbs and nouns of different types and corresponding nonce stimuli. Irregular real and nonce verbs activated inferior frontal and inferior parietal regions more than regular verbs did, while no areas were more activated in the opposite comparison. We explain this activation pattern by increasing processing load: a parametric contrast revealed that these regions are also more activated for nonce stimuli compared to real stimuli. A very similar pattern is found for nouns. Unlike most previously obtained results, our findings are more readily compatible with the single-system approach to inflection, which does not postulate a categorical difference between regular and irregular forms.

Introduction

Inflectional morphology is at the center of an important debate in cognitive science, concerning the general principles according to which the mental lexicon is organized. So-called “Dual-system” (DS) approach distinguishes regular and irregular morphological forms. The former are computed by rules, the latter are stored in the memory. In the alternative “Single-system” (SS) approach, all forms are generated and processed by a single integrated system.

Initially, English past tense morphology was the testing ground for both approaches. According to the “Words and Rules” model, a version of DS approach proposed by Pinker, 1991, Pinker, 1999, regular past tense forms are generated and processed by a symbolic rule that is part of the productive, combinatorial system of grammar. Irregular forms are learned by rote and stored in the lexicon, from where they can be retrieved through associative memory mechanisms. The DS approach was also advocated e.g. in (Marslen-Wilson and Tyler, 1997, Pinker and Prince, 1988, Ullman, 2004). On the contrary, a connectionist network model from (Rumelhart & McClelland, 1986) represents a single system without any symbolic rules. All past tense forms are generated and processed by associative mechanisms that take into account phonological similarity and token and type frequencies of different elements. The SS approach was further developed e.g. in (MacWhinney and Leinbach, 1991, McClelland and Patterson, 2002, Plunkett and Marchman, 1993). The range of data used to test SS and DS theories has been very diverse: behavioral and neurophysiological experiments where participants generated forms from various real and nonce verbs, language acquisition and language deficit studies, and computer simulations. The results have always been controversial.

However, English past tense morphology is exceptionally simple: there is only one productive class that includes the vast majority of verbs and a small number of irregular verbs. So various authors investigated verb and noun inflection in other languages where the situation is more complex. German, Icelandic, Norwegian, Italian, Spanish, Arabic and Hebrew were among them (e.g. Berent et al., 1999, Clahsen, 1999, Clahsen et al., 2002, Hahn and Nakisa, 2000, Orsolini et al., 1998, Orsolini and Marslen-Wilson, 1997, Plunkett and Nakisa, 1997, Ragnasdóttir et al., 1999). Studies on Russian are discussed in Section 1.3. The findings offered new challenges for both approaches, and some of them cannot be easily accounted for by either approach. We illustrate this on the example of Russian below.

Thus, widening the pool of languages was extremely important for the SS vs. DS debate. For this reason, it appears to be problematic that existing functional imaging studies of past tense generation rely only on English, German and, in one case, Spanish data (Beretta et al., 2003, de Diego-Balaguer et al., 2006, Desai et al., 2006, Dhond et al., 2003, Indefrey et al., 1997, Jaeger et al., 1996, Joanisse and Seidenberg, 2005, Oh et al., 2011, Sach et al., 2004, Sahin et al., 2006, Ullman et al., 1997a). In most studies, irregulars are associated with a larger number of activated regions, but the list of these regions, as well as proposed explanations differ greatly. There are also several electrophysiological studies dedicated to past tense formation in English and German (e.g. Lavric et al., 2001, Marslen-Wilson and Tyler, 1998, Münte et al., 1999, Newman et al., 1999, Newman et al., 2007), which show variable results.

The present paper aims to fill this gap. We conducted an fMRI study based on Russian language where participants were asked to generate present tense forms from different real and nonce (nonword) verbs and to pluralize real and nonce nouns. We tried to avoid numerous pitfalls identified by the critics of the previous studies. We used two tasks (inflecting verbs and nouns) in a random order, which minimizes the risk of priming and strategy effects, and large sets of stimuli matched for word frequency and phonological complexity.

Russian verbs have two stems: the present/future tense stem and the past tense stem. Correlation between them determines the verb class. Out of several existing approaches, we will rely on the one developed by Jakobson and his followers (Davidson et al., 1996, Jakobson, 1948, Townsend, 1975), according to which Russian has 11 verb classes and several so-called anomalous verbs. Ten classes are identified by their suffixes (verbal classifiers). The eleventh class has a zero suffix, and is subdivided into subclasses depending on the quality of the root-final consonant (Jakobson and Townsend counted them as 13 separate classes). It includes many conjugational patterns and contains well under 100 basic stems.

Conjugational patterns of different classes include truncations or additions of the final consonant or vowel and may also include stress shifts, suffix alternations, alternations of stem vowels and stem-final consonants. Russian has two conjugation types in the present and future tense, i.e. two different sets of endings, and to which one a verb belongs is determined by its class. Importantly, the verb class is often unrecoverable from a particular form of the verb. For example, čitát’ ‘to read’ belongs to the AJ class, and its 3Pl present tense form is čitá-j-ut (-j- suffix is added, first conjugation type). Pisát’ ‘to write’ belongs to the A class, and its 3Pl present tense form is píš-ut (-a- suffix is truncated, first conjugation type, final consonant alternation, stress shift). Drožát’ ‘to tremble’ belongs to the ZHA class, and its 3Pl present tense form is drož-át (-a- suffix is truncated, second conjugation type).

Thus, Russian verb system is very complex. And, crucially, there is no obvious division into regular and irregular verbs. Unlike in English, there is no single productive pattern that can be applied to any stem irrespective of its phonological characteristics. Five verb classes are productive, but dramatically differ in type frequency. The Grammatical Dictionary of the Russian Language (Zaliznyak, 1977) contains 27,409 verbs. We counted the number of verbs in these five classes: 11,735 in the AJ class, 6875 in the I class, 2815 in the OVA class, 1377 in the NU class and 638 in the EJ class.

Russian noun system is much less complex than Russian verb system. Nouns are inflected for number and case and are classified into different declensions depending on their gender and on the set of their number and case endings. There are three main declensions, the forth declension with adjectival endings, several exceptional nouns and a number of uninflected nouns. First and second declensions usually have a choice of two endings for a particular form depending on the last consonant of the stem (all stems in the third declension end in palatal or sibilant consonants and use one set of endings). In addition to that, inside every declension there are small groups of nouns with various irregularities: minor stem changes or unusual endings in some forms. The majority of Russian nouns do not change their stem.

Some endings are unique for a particular declension, but most of them are shared by two or even three main declensions. In particular, Nom.Pl forms, which we looked at in this study, can have the following endings: −i (used with palatal, sibilant and velar stems of masculine and feminine nouns in all three declensions), −y (used with the other stems of masculine and feminine nouns in the first and second declension), −ja (used with palatal, sibilant and velar stems of neuter and some masculine nouns in the first declension), −a (used with the other stems of neuter and some masculine nouns in the first declension), −e (used in a very small group of animate masculine nouns in the first declension).

The predictions of the SS and DS theories were tested in numerous experiments with Russian verbs (e.g. Chernigovskaya et al., 2007, Gor, 2003, Gor, 2010, Gor and Chernigovskaya, 2001, Gor and Chernigovskaya, 2003, Gor and Chernigovskaya, 2005, Gor and Jackson, 2013, Gor et al., 2009, Svistunova, 2008, Tkachenko and Chernigovskaya, 2010). Adult native speakers, L1 and L2 learners and subjects with various neurological and developmental deficits were examined. In the majority of these experiments, participants were provided with infinitives or past tense forms of real or nonce verbs and prompted to generate 1Sg and 3Pl present tense forms.

Healthy adult native speakers showed a strong tendency to overgeneralize the AJ class pattern (AJ class is the most frequent). In particular, they applied it to the nonce verbs ending in −ili (only two real verbs and their derivates have this conjugational pattern, all the others belong to the productive and highly frequent I class) and to the ones ending in −yli (no real verbs have this conjugational pattern). Thus, despite the fact that Russian has several productive and highly frequent classes, one conjugational pattern is used as the default one. This is in conflict with the SS theory.

Four-year-old children also heavily rely on the AJ class pattern. But gradually, other patterns become more active. For example, around the age of five children stop making mistakes with OVA class verbs and actively overgeneralize this pattern. Overgeneralizations that do not respect phonological properties of the stem are a hallmark of a rule in the DS approach, and several rules that have different potential to be overgeneralized depending on various frequency-related and phonological factors contradict its very essence. The generalizations made in the studies of English-speaking subjects with SLI (specific language impairment), aphasiac deficits and Alzheimer disease (e.g. Ullman et al., 1997b) that supported the DS approach also were not borne out in Russian. The group of authors working on Russian argues that Yang’s (2002) model relying on multiple rules of different status may be better suited to account for their findings. A similar model for Russian is developed in (Gor, 2003).

The data from most previous functional imaging studies were interpreted in favor of the DS theory (Beretta et al., 2003, Dhond et al., 2003, Indefrey et al., 1997, Jaeger et al., 1996, Oh et al., 2011, Sahin et al., 2006, Ullman et al., 1997a). De Diego-Balaguer et al. (2006) appeal both to DS and to SS approaches. In all these studies, regular and irregular verbs were associated with different sets of activated regions: Jaeger et al. (1996) compared past tense generation with a baseline of reading, while in all subsequent studies, direct regular vs. irregular comparisons were made. But these sets did not coincide across studies. One can only say that irregulars usually produced more extensive activation.

According to the version of the DS approach developed by Ullman (2004), memory-based processing of irregular verbs depends on medial-temporal and temporo-parietal regions, while rule-based processing of regular verbs depends on the basal ganglia, Broca’s area, and neighboring anterior regions. No other principally different model was proposed, although many authors disagree with Ullman in details. However, only two above-mentioned studies found more activation in Broca’s area for regulars than for irregulars (Dhond et al., 2003, Oh et al., 2011). In addition to that, increased left IFG activation for regulars was observed in an fMRI study where processing of spoken regular and irregular forms was compared in a same-different judgment task (Tyler, Stamatakis, Post, Randall, & Marslen-Wilson, 2005). Several other studies observed the reverse pattern: Broca’s area was more activated by irregulars (Beretta et al., 2003, de Diego-Balaguer et al., 2006, Sahin et al., 2006). Sahin et al. (2006) suggest that this can be explained by conflict monitoring between the regular rule and an irregular form or by inhibition of the regular rule application.

In several studies the SS approach was preferred. Sach et al. (2004) observed only frequency effects, but no difference between regular and irregular verbs. Joanisse and Seidenberg (2005) found greater bilateral IFG activation for regulars than for irregulars, but then demonstrated that irregulars that were phonologically similar to regulars (e.g., slept, fled, sold) produced the same level of activation as regulars did, and significantly more activation than irregulars that were not phonologically similar to regulars did (e.g., took, gave). They conclude that observed activation patterns are better predicted by phonological properties of stimuli than by their regularity.

Desai et al. (2006) found that, when word frequency and phonological complexity are controlled for, the ‘regular > irregular’ comparison revealed no activated regions, while the ‘irregular > regular’ comparison was associated with greater bilateral activation of the posterior IFG (BA 44), the precentral gyrus, the anterior insula, the intraparietal sulcus (IPS), the basal ganglia, as well as some other small foci of activation. The authors note that these areas are commonly associated with executive control and attentional processes and are also activated by regular past tense generation compared to reading. They conclude that the observed activation differences reflect greater processing load posed by irregulars, which rely on less frequent inflectional patterns than regular verbs and therefore have greater attentional and response selection demands.

In the present study, real and nonce verbs and nouns were used as stimuli (see Appendix). Verbs were visually presented in the infinitive form, and nouns were presented in the Nom.Sg form. Subjects were asked to generate aloud as fast as possible the 1Sg present tense form if they see a verb or the Nom.Pl form if they see a noun. All oral responses were recorded simultaneously with fMRI data acquisition. Their correctness was assessed offline. When a response was no longer appropriate to the target’s category, the corresponding trial was discarded in the subsequent fMRI analyses.

The first group of 35 verbs belonged to the AJ class, the second group contained 35 verbs from several small non-productive classes. For the sake of brevity, we will further call the latter verbs irregular. The AJ class is productive and the most frequent. So we reasoned that if any differences between these two groups were found, we could further compare them to other groups, and if not, no other differences would be expected because these are the two poles of the Russian verb system (i.e. in the latter case, we will not be able to further exploit the diversity of this system, but this will become clear only when we have the results of the present study, because no similar fMRI experiments on morphologically rich languages have been done before).

Verbs in these two groups were matched for frequency using The Frequency Dictionary of Modern Russian Language (Lyashevskaya & Sharoff, 2009) and for length (see Appendix). Only unprefixed imperfective verbs were used. We also created two sets of 35 nonce verbs. They mimicked the general characteristics of the corresponding real verb groups (length and phonological properties of the stem). However, we tried to avoid close resemblance to particular real words, because this would be an extra factor that is hard to control for.

Two groups of 35 nouns were matched with the verb groups for frequency and length. All nouns were masculine, belonged to the first declension and had the Nom.Pl form ending in −y. In one group, the last vowel of the stem is dropped in many forms including the Nom.Pl form: e.g. koster ‘fire’ – kostry. In the other group, the stem remains the same in all forms, as is standard for Russian nouns: e.g. šofer ‘driver’ – šofery. For the sake of brevity, we will further call the first group irregular, although this is a relatively minor irregularity. As we noted above, Russian nouns have less diverse inflectional paradigms than Russian verbs, so this was the only irregular feature we could find in the necessary frequency range. Our goal was to analyze whether the effects of regularity would be comparable in verbs and nouns. We also had two corresponding groups of 35 nonce nouns.

Let us note that vowels are dropped only in a subgroup of noun stems ending in particular vowel and consonant clusters (e.g. −er, −or, −el, −ol etc.). We chose stems with such clusters both for irregular and for regular nouns not to make the first group much more phonologically homogenous than the second. In the majority of cases whether the vowel is dropped can be predicted from the combination of consonants before this vowel and from the position of the stress. In case of nonce nouns, participants did not know what the intended stress was, so different Nom.Pl forms could be licitly derived from them.

In total, there were eight types of stimuli: regular verbs (RV), irregular verbs (IV), regular nonce verbs (RNV), irregular nonce verbs (INV), regular nouns (RN), irregular nouns (IN), regular nonce nouns (RNN) and irregular nonce nouns (INN). We analyze them below using Regularity and Lexicality factors (we looked at verbs and nouns separately rather than putting them together and treating word category as the third factor primarily because the type of irregularity we could find for nouns was very minor compared to what we had in case of verbs). 21 Subjects took part in our study, so in total we had 735 responses in each category.

Section snippets

Results and discussion

Let us start with behavioral results. Participants made fewer mistakes with regular verbs than with irregular verbs (22 out of 735 and 71 out of 735, or 3.0% and 9.7%, respectively) and fewer mistakes with nonce regular verbs than with nonce irregular verbs (96 out of 735 and 320 out of 735, or 13.1% and 45.7%, respectively). A two-way RM ANOVA revealed significant main effects of Lexicality (F(1, 20) = 83.23; p < 0.001; η2 = 0.81) and Regularity (F(1, 20) = 32.29; p < 0.001; η2 = 0.62), but no significant

Subjects

Subjects were 21 native speakers of Russian (13 women), 19–32 years of age, with no history of neurological or psychological disorders. All participants were right-handed, as assessed by the Edinburgh Handedness Inventory (Oldfield, 1971). Subjects were given no information about the specific purpose of the study. All subjects gave their written informed consent prior to the study and were paid for their participation. All procedures were in accordance with the declaration of Helsinki and were

Acknowledgment

The study was partially supported by the Grant #12-06-00706 from the Russian Foundation for Humanitarian Sciences (RFHR) and by the Grant #0.38.518.2013 from St. Petersburg State University.

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