Brain activity of regular and dyslexic readers while ... - Springer Link

Reading and Writing: An Interdisciplinary Journal 17: 707–737, 2004. 2004 Kluwer Academic Publishers. Printed in the Netherlands.

707

Brain activity of regular and dyslexic readers while reading Hebrew as compared to English sentences ZVIA BREZNITZ, REVITAL OREN and SHELLEY SHAUL Laboratory for Neurocognitive Research, Faculty of Education, University of Haifa, Israel

Abstract. The aim of the present study was to examine differences among ‘regular’ and dyslexic adult bilingual readers when processing reading and reading related skills in their first (L1 Hebrew) and second (L2 English) languages. Brain activity during reading Hebrew and English unexpected sentence endings was also studied. Behavioral and electrophysiological measures including event-related potentials (ERP) and low resolution electromagnetic tomography (LORETA) methodology were employed. Results indicated discrepancies in the processing profiles of dyslexic and regular bilingual readers in both first and second languages. In general, the amplitudes of the evoked potentials were higher and the latencies longer among dyslexic readers during processing of information in first and second languages (L1 and L2), but were more pronounced in English (L2). LORETA analysis indicated evidence that the source of brain activity measured by current density of brain activation is different when reading Hebrew as compared to English sentences mainly among dyslexics and not among regular readers. The data from the present study supports the ‘dominanat bilingual’ hypothesis for defining bilinguals. A discrepancy between achievement in performing various L1 and L2 tasks was consistent across groups. Both groups were better in there mother tongue, which was Hebrew as compared to English. Key words: Dyslexia, ERP, LORETA, Second language

Introduction It is widely accepted that poor readers with relative weakness in their first language (L1) are prone to similar difficulties in their second language (L2). In other words, second language reading disabilities do not exist in isolation; rather they occur concomitantly with native language reading difficulties (Ganschow, Sparks, Javorsky, Pohlman, & BishopMarbury, 1991). Cummins (1979) proposed the Linguistic Interdependence Hypothesis, which claims that cognitive academic language proficiency is transferred from one language to another. According to this hypothesis, there is a significant relationship between skills in the two

708

ZVIA BREZNITZ ET AL.

languages and therefore, low competence in L1 leads to low competence in L2 and high competence in L1 can predict a similarly high level of competence in L2 (Da Fontoura & Siegel, 1995). The available studies have supported this statement and have indicated a high correlation between reading skills in the individual’s two languages (Da Fontoura & Siegel, 1995). Even so, while a strong connection between L1 and L2 skills has been documented in studies of this kind over the years, difficulties with L1 are not usually stated explicitly as a cause of L2 learning problems. In their research study on college students with foreign language learning difficulties, Ganschow et al. (1991) formulated a theory, which attempts to explain the problems and variations in foreign language acquisition, the Foreign Language Linguistic Coding Differences Hypothesis (LCDH). This theory posits native language difficulties as the cause and predictor of foreign language difficulties. It shares many common views with Cummins’ Linguistic Interdependence Hypothesis. The LCDH is derived from earlier research on native language reading disabilities conducted by Vellutino and Scanlon (1986), which examined the role of the component parts or linguistic codes of language among good and poor readers. The linguistic codes include phonology, syntax, and semantics. According to the LCDH, difficulties with foreign language acquisition stem from deficiencies in one or more of these linguistic codes in the student’s native language (Schwartz, 1997). These deficiencies result from impairments in the native language, specifically from phonological processing deficits (Schwartz, 1997). Therefore, a central question in the present study pertains to determining whether the intensity of brain activity in L1 (Hebrew) and L2 (English) during reading of unexpected sentence endings among bilingual regular and dyslexic readers is similar or different. In the literature, individuals who understand and speak more than one language are considered to be bilingual. Researchers have suggested several classifications and definitions for distinguishing between the different types of bilinguals (Fabbro, 1999). For example, in a ‘subordinate bilingual’, one language serves as the mother tongue while the second serves as the mediator of the first. A ‘balanced bilingual’, for instance, is an individual who has mastered both languages to the same extent, whereas a ‘dominant bilingual’ is more fluent in one language than in the other (Fabbro, 1999). Such definitions have proven useful in identifying different groups of bilinguals in psychological and neurolinguistic studies (Vaid, 1986). Nonetheless, in many cases, defining and classifying bilinguals has proven very complex over

BRAIN ACTIVITY OF REGULAR AND DYSLEXIC READERS

709

the years due to the fact that people develop skills as needed for particular circumstances, and because an individual’s ability may differ significantly between the four basic skills of reading, writing, speaking, and listening. One important question that has puzzled researchers in the study of bilingualism has been the localization of languages in the bilingual brain, specifically whether the two different languages are localized in the same areas or in distinct areas of the brain (see Fabbro, 1999 for a comprehensive review). Several hypotheses have been proposed. The first hypothesis suggests that all languages known by bilinguals are localized in the same cerebral areas. The second, completely opposite hypothesis, states that each language is localized in a separate area of the brain. A third hypothesis proposed by neurologists considers that different languages in bilinguals are organized in the same cortical areas but in distinct neural circuits. Two prominent claims exist within this hypothesis. The first states that distinct cerebral cells subserve the different languages, whereas the second claim states that the same cells are active, yet in different combinations and interactions depending on the language involved (Fabbro, 1999). A fourth hypothesis, supported by many researchers, proposes the synthesis of the first three hypotheses: the first (same areas), the second (separate areas), and the third (within the same areas distinct neural circuits working independently for each language). In other words, language is organized partly in common areas and partly in specific and separate areas of the brain (Fabbro, 1999). Neuroscientific research on bilingualism, which has attempted to clarify whether the two languages of bilinguals are localized in shared or separate areas of the brain, has provided mixed results (Illes et al., 1999). Neuroimaging studies of bilingualism using positron emission tomography (PET) and functional magnetic resonance (fMRI) have demonstrated both different and identical representations of L1 and L2 in the brain. In a PET study conducted by Klein, Zatorre, Milner, and Meyer (1994) with highly fluent English–French bilinguals, subjects were requested to repeat aloud words presented auditorially in both languages. Results showed similar cortical localization of activation for both languages. In a second PET study conducted by Klein, Milner, Zatorre, Meyer, and Evans (1995), the previous findings were replicated when highly proficient English–French bilinguals were instructed to generate aloud synonyms of words presented to them auditorially in both languages. The studies presented above point to a shared cortical

710


system for semantic knowledge in the two languages of a bilingual (Illes et al., 1999). In a study conducted by Kim, Relkin, Lee, and Hirsch (1997) fMRI was applied to determine the spatial relationship between native and second languages in the cortex of fluent English–French bilinguals. Half of the subjects had been exposed to the two languages during infancy (‘early bilinguals’), the other half had been exposed to their second language in early adulthood (‘late bilinguals’). Subjects performed a silent sentence-generation task in both languages by internally ‘describing’ events that occurred during a specified period of the previous day. Findings showed that in the ‘early’ bilinguals the cortical localization of activity for the two languages was similar both in Broca’s and Wernicke’s area. In the ‘late’ bilinguals, however, while no differences were observed in Wernicke’s area, spatially distinct activation sites were evident for L1 vs. L2 in Broca’s area. The researchers concluded that age of language acquisition may be a significant factor in determining the functional organization of this area in the bilingual brain, with native and second languages less distinctly localized in early exposure to the second language. In a study conducted by Dehaene et al. (1997), both common and separate cortical activations for L1 and L2 were found in moderately fluent French-English bilinguals on a whole-language comprehension task. The two fMRI studies have revealed not only common activations for L1 and L2, but also separate activation patterns for L1 and L2. However, since both studies used whole-language comprehension or covert production tasks, it remains unclear whether the differences in L1 and L2 activations were due to semantic processes or to phonological and syntactic processes as well. Therefore, it is difficult to interpret the results in comparison with previous experiments, which examined cortical localization of activation for the semantic aspects of language per se. In a study conducted by Illes et al. (1999), researchers exploited the spatial resolution of fMRI to determine whether L1 and L2 have a unitary or separate cortical system for specifically semantic performance. Fluent bilinguals were tested on semantic and non-semantic judgments with words presented visually in both English and Spanish. In semantic decisions, subjects had to decide whether words were concrete or abstract in meaning. In non-semantic decisions, subjects had to decide whether words were printed in upper case or lower case letters. Results showed greater activation for semantic relative to non-semantic decisions in left and right frontal regions. Furthermore, the locations of


711

activations were similar for both languages, and no differences were found when semantic decisions for English words were compared directly with Spanish words. These results demonstrate a common neural system in the frontal lobe system for semantic processing of two languages in the bilingual brain, and are consistent with the PET studies mentioned earlier (Klein et al., 1994; Klein et al., 1995). Illes et al. (1999) attempted to resolve the conflicting experimental data found in the previous fMRI studies (Dehaene et al., 1997; Kim et al., 1997) by stating that both had examined moderately fluent bilinguals instead of highly proficient bilinguals as did the remaining studies. As shown by Perani et al. (1996), and Perani et al. (1998), proficiency clearly affected the cortical localization and processing of the second language. Less proficient bilinguals in Perani et al’s first study (1996) showed very different activation patterns in L1 and L2, whereas highly proficient late learners in the second study showed no differences between L1 and L2. According to the researchers, it is possible that the cortical localization of processes in L2 become increasingly similar to those in L1 with increasing proficiency in the second language, regardless of the age of acquisition (Illes et al., 1999). As for the findings in Kim et al.’s study (1997), greater separation was found for the two languages in Broca’s area of late bilinguals relative to early bilinguals. However, since proficiency was not a factor in the identification of the two groups, although fluent in their second language, it may very well have been the case that the late bilinguals were not as proficient in the second language as the early bilinguals (Illes et al., 1999). In Dehaene et al.’s study (1997), subjects were defined in the beginning as only moderately fluent in their second language, which may have accounted for their results. In addition, according to Illes et al. (1999), both Kim and Dehaenes’ studies used whole-language tasks that probably included syntactic processing, which may have led to differences in the cortical localization of activity for the two languages. Furthermore, a study conducted by Chee, Tan and Thiel (1999) lends support to Illes et al.’s hypothesis. In this study, highly proficient bilinguals, even ‘late bilingual’ subjects, showed indistinguishable activation patterns on a word-processing task in both their languages. By using an electrophysiological technique with event-related potential (ERP) methodology, Weber-Fox and Neville’s study (1996) revealed differences in the brain localization of L1 and L2 depending on the age of second language acquisition. In this study, subjects were adult Chinese/English bilinguals who were exposed to English at different points in their development. ERP’s were obtained as subjects read sentences in

712


English that included either semantic anomalies or syntactic violations. ERP’s showed that early and late acquirers of L2 processed semantic anomalies similarly, whereas significant differences were observed when they were asked to process grammatical anomalies. The amplitudes and distributions of the N400 component elicited in ‘early’ and ‘late’ bilinguals were not significantly different from the responses of English monolinguals. For instance, in English monolingual subjects, event-related brain potentials (ERP’s) exhibit greater activity in left posterior structures during the processing of the semantic components of language (nouns, verbs, adjectives) and greater activity in left frontal lobe structures during the processing of the syntactic components of language (articles, conjunctions, prepositions). Bilingual subjects displayed a similar pattern of localization for semantic anomalies, regardless of age of second language acquisition. In contrast, during syntactic processing, similar patterns of localization were found only in ‘early’ bilinguals. Even with short delays in language exposure, altered ERP’s were observed. With increased delays in language exposure, less activity in left frontal lobe structures was evident and greater changes in ERP asymmetries were found, with reduced specialization in left hemisphere language processing subsystems and increased right hemisphere involvement. Three additional studies of bilinguals have linked ERP changes to reading in L1 and L2 languages. Ardal, Donald, Meuter, Muldrew, and Luce (1990) found that the latency of the N400 latency of bilinguals to semantic incongruencies was longer than that of monolinguals. In addition, the N400 latency in response to incongruency in L2 was longer than that for L1. First-language ERP’s (French/English) were almost identical in the groups of bilinguals. No age of second language acquisition effects on N400 latencies or amplitudes were found. Kluender and Kutas (1993) also reported an overall N400 reduction for processing the bilinguals’ less efficient language. As can be seen most of the above evidence was obtained from studies carried out on languages based on Roman scripts. It is conceivable that some of the above results may be a consequence of this. In the present study, brain activity during reading of two distinct languages, Hebrew (L1) and English (L2), were studied among bilingual dyslexic and regular readers. Hebrew and English are two languages with different phonological, orthographic (scripts) and morphological structures (Ben-Dror, Bentin, & Frost, 1995; Ben-Dror, Frost, & Bentin, 1995; Bentin & Frost, 1995; Deutch & Rayner, 1999; Gronau & Frost, 1997; Levin & Landsmann, 1989; Ravid 1996; Shatil,


713

E, unpublished; Shimron, 1999). While the writing systems in both languages are alphabetical, Hebrew has several characteristics of special interest to reading researchers (Bentin & Frost, 1995). For example, it is read from right to left. In addition, its typographical architecture is based on a square-like structure. Moreover, it has a system of supplementary vowel symbols ranging from the absence of all vowel symbols to complete vowelization. In contrast to the irregular orthography of English, vowelized Hebrew falls within the regular orthography classification. Hebrew orthography also differs from English orthography in that each English grapheme represents a phoneme, while each Hebrew grapheme represents a syllable; in other words, Hebrew includes both consonant and vowel information (which remains even after the diacritics have been removed). Note that in the reading of vowelized Hebrew, the reader relies more on phonological information, and uses more bottom-up processing as in English (Shimron, 1999). When reading unvowelized Hebrew, however, the reader relies more on lexical-semanticcontextual information and thus, uses more top-down processing (Frost, 1994; Shimron, 1999). Acquiring accuracy in decoding may play a stronger role in English (Shatil, E, unpublished), while acquiring sufficient reading rate may play similar roles in both orthographies. These differences are also reflected in the impaired population in both orthographies. Preliminary data suggests that contrary to the distribution data on dyslexic subtypes in English (Wolf & Bowers, 1999), rate-deficit subtypes are twice as prevalent as phonology-deficit subtypes among Hebrew dyslexics (Lerner, Shaul, Berman, & Breznitz, 2001). It is also important to note that Hebrew morphology has a highly bound nature (Ravid, 1996). Hebrew conjugation is richer and more frequent than that found in the English language. For example, Hebrew verbs are conjugated according to gender, number, tense and person; adjectives are conjugated according to gender and number; and nouns are conjugated according to relation, such as location and ownership. This makes Hebrew a highly condensed language, with multiple sources of information that need to be derived from a single visual unit – the word. As indicated by the above, most studies carried out on the various Latin based languages have made the underlying assumption that the timing of acquisition and the experience of bilinguals with second language explains the differences in accuracy and fluency of word reading in L2. Shimron and Sivan (1994) were the first to examine orthography as a factor influencing both accuracy and rate. One of the important implications of such a study and its findings is that it indicates to some degree that rate of word reading is a flexible mechanism influenced by

714


the orthographic features of a language (Breznitz, 2001). Differences between scripts may indeed lead to differences in the rate of reading words and not only strategies of learning and critical ages of acquisition. The present study was designed in an attempt to verify whether Hebrew (L1) and English (L2) are processed in the same manner among bilingual regular readers, and whether a differentiation exists in this manner between bilingual dyslexic readers as compared to bilingual regular readers. In an attempt to trace brain activation during reading sentences in the two languages among regular and dyslexic readers different research methodologies were employed: behavioral, electrophysiological (ERP) and low resolution electromagnetic tomography (LORETA) (PascualMarqui, Michel & Lehmann, 1994). The ERP technique permits direct observation of information processing at different stages of information processing in terms of amplitude or latency variations. As compared to fMRI and PET methodologies the time resolution of ERP is higher (see Halgren, 1990). ERP components reflect the time course of sensory and cognitive processes with millisecond resolution that cannot be directly inferred from behavior or any other imaging technique. Nevertheless, the data obtained from behavioral and electrophysiological measures are complementary, as each provides information regarding the same cognitive activity. In different studies several ERP components have been identified, which appear to be characteristic of certain types of brain activity during cognitive processing. The components found in this study are P200 and P300. The P200 component is a positive-going waveform whose peak latency ranges from 150–275 msec. Oades, Dittmann-Balcar, and Zerbin (1997). P200 is thought to index mechanisms of feature detection (e.g., Luck & Hillyard, 1994), selective attention (e.g., Hackley, Woldorff, & Hillyard, 1990), and other early sensory stages of item encoding (Dunn, Dunn, Languis, & Andrews, 1998). The P300 component is a positive-going wave with a latency range of 280–600 msec (Dunn et al., 1998; Wilson, Swain, & Ullsperger, 1998). It has been suggested that P300 is a valid index of central information processing (Palmer, Nasman, & Wilson, 1994), and can be seen as an indicator of dynamic updating of information held in working memory (Fitzgerald & Picton, 1983), mental effort (Wilson et al., 1998) or as reflecting higher-order cognitive processes such as stimulus evaluation and categorization (Polich, 1987; Polich & Heine,1996).


715

In order to examine the localization source of electrical brain activation during L1 and L2 sentence processing, the current study used the LORETA software package (Pascual-Marqui et al., 1994). LORETA is based on multi-channel recordings (19 electrodes) taken from the skull. The program directly calculates current density distribution of brain volume at the P200 and P300 ERP components in 3-D Tailarach space. This method assumes that neighboring neurons are activated simultaneously and in a synchronized manner during stimulus processing (Llinas, 1988). In an attempt to detect the source of brain activity localization among dyslexic and regular readers during reading of Hebrew and English sentences, the program estimates the current source density distribution of brain activity at P200 and P300 epochs. The dense grid was on 2394 voxels at 7 mm special resolution (Pascual-Marqui et al., 1999).

Method Participants Thirty university students from middle-class backgrounds participated in this study: 15 dyslexic readers and 15 chronologically age-matched normal readers. All subjects were ‘subordinate’ bilinguals, who acquired Hebrew (L1) prior to English (L2). The level of listening comprehension and oral language fluency in L2 was assessed during a basic screening procedure (Oral Language Test for English as a Second Language, 1999). All subjects filled out a questionnaire regarding their language skills. Only those subjects who acquired English at the age of 8, spent at least 3 years in an English speaking country, and had mastered the academic level of L2 listening comprehension and oral language fluency skills were able to participate in the experiment. The two groups were matched on nonverbal IQ scores (Raven Standard Progressive Matrices, Raven, 1960), listening comprehension and oral language fluency in English (Oral language Test for English as a Second Language, 1999). The participants ranged in age from 23 to 29 years (mean age 25 years and 6 months, SD¼0.11), and each group contained 6 females and 9 males. Each participant was right-handed and displayed normal or corrected-to-normal vision in both eyes. None of the participants had a history of neurological or emotional disorders and all were screened for normal hearing. All were paid volunteers. The dyslexic readers were recruited through the University Student Support Service that

716


Table 1. Behavioral reading measures. Test

Decoding, Z-scores Hebrew words Decoding, Z-scores English words Decoding, Z-scores Hebrew pseudowords Decoding, Z-scores English pseudowords Decoding, Z-scores-connected text in Hebrew Decoding, Z-scores-connected text in English Reading time-connected text in Hebrew (in sec) Reading time-connected text in English (in sec) Comprehension connected text in Hebrew (out of 12) Comprehension connected text in English (out of 12) Phonological accuracy (out of 40) Parsing accuracy in Hebrew (out of 50) Parsing accuracy in English (out of 50) Parsing time in Hebrew (in sec) Parsing time in Hebrew (in sec) Dictation in Hebrew number of errors Dictation in English number of errors

Control

Dyslexic

Group Comparison

Mean

SD

Mean

SD

F (2,28)

0.81 0.83 0.82

0.41 0.51 0.62

)1.03 )1.01 )1.07

0.55 0.48 0.49

9.03** 9.11** 10.01**

0.84

0.38

)1.02

0.51

9.37**

1.34

0.79

).76

0.32

12.15**

1.49

0.86

).66

0.42

7.33**

86.62

16.46

127.13

32.01

12.61**

93.55

13.42

133.71

47.11

11.21**

9.69

1.44

9.01

1.61

NS

10.01

1.89

11.0

2.03

NS

35.35 45.26

3.19 2.54

23.22 45.53

12.87 2.25

13.63** NS

37.26

2.09

38.00

4.36

NS

167.91 191.03 0.2

5.23 12.11 0.4

276.84 318.09 9.72

23.16 26.81 3.05

8.23** 13.08** 6.55**

3.7

1.12

11.97

4.52

9.81**

P < 0.001**

aids students with learning disabilities; they had all been diagnosed as dyslexic in childhood and were classified as impaired readers by the Student Support Service (see Table 1).


717

Measures Behavioral baseline measures General ability IQ was assessed using the Raven Standard Progressive Matrices (Raven, 1960). Oral language test for English as a second language (OLTE, Haifa 1999) Part A: measured listening comprehension (scale of 1–12) and Part B: measured oral language fluency (scale of 1–9). Reading ability (Hebrew and English) Several tests were used to obtain estimates of reading accuracy, reading time and comprehension in Hebrew and English. The first set of tests provided measures of decoding accuracy for real words and pseudowords. The second set assessed reading time in context and evaluated reading comprehension. Decoding skills One Minute Tests (Shatil, E, unpublished) for Hebrew and English separately. This battery included two subtests in which subjects were asked to read lists as quickly and accurately as possible within the space of 1 minute. The first two lists contained 100 real words arranged in order of increasing length (1–5 syllables) and decreasing frequency (one for Hebrew words and one for English words), and the second two lists were comprised of 100 pseudowords (one for Hebrew and one for English) arranged in order of increasing length (1–5 syllables). Scores were based on the number of words/pseudowords read correctly. In order to obtain a comprehensive decoding score, Z-scores were first calculated for each of the tests (i.e., words and pseudowords for each language). Reading comprehension, accuracy and speed in context Reading performance for text was measured using 4 texts from the Reading Test section of the Israeli Psychometric SAT test (The Center for Psychometric Tests, 1994) containing two texts in Hebrew and two in English. Each text contained a short story comprised of 25 sentences (about 300 words each), which appeared in its entirety on the computer screen. The subject pressed a button upon beginning to read the story and again upon conclusion. The computer measured reading time for

718


each passage. When the subject had completed reading, the text was automatically erased from the screen and the first of 12 multiple-choice questions appeared. The subject selected an answer by pressing a number on the keyboard (1–4) corresponding to the answer chosen. Once each question was answered a new one appeared on the screen until all 12 had been completed. In each language, one story was read orally and the other silently. The experimenter recorded decoding errors during oral reading in order to obtain a measure of accuracy. Comprehension scores were based on the total number of correct answers for each text. Reading time scores were determined on the basis of mean reading time in each text. Word recognition skills Orthography (Hebrew and English) Two versions of Parsing Test (Breznitz, z, unpublished) one in Hebrew and one in English. Each test contained 50 rows of 4 words each. The words were presented as a continuous line of print (i.e., were not separated by blank spaces). The subject was asked to identify the words in each row by drawing a line to indicate where the spaces should be. Scores were based on performance accuracy and total performance time in each test. Dictation. Two passages of 50 words each, one in Hebrew and the other in English, were dictated to the subjects. Scores were based on the number of spelling errors. Phonology (in Hebrew). Phoneme Recognition Test for Words and Pseudowords (Ben-Dror, I., & Shani, M, unpublished). This test contains 2 sections, each containing 20 words. In the first section, the experimenter reads each word and indicates a syllable within the word. The subject must produce the word, which is obtained by omitting the specified syllable. In the second section, the experimenter reads each word aloud and specifies a phoneme located at the beginning, middle or end of the word. The subject must produce the pseudoword, which is obtained by omitting the designated phoneme. Scores are based on the total number of accurate responses and test performance time of the two subtests. Working memory (Hebrew) Working memory-opposites (Shani, M., & Ben Dror, M, unpublished) This test is comprised of a series of adjectives of approximately the same number of syllables in Hebrew, each of which has an opposite


719

(for instance, tall/short; big/small; black/white). The adjectives appeared in sets of two, and were presented in order of increasing length. The number of adjectives in each set ranged in succession from 2–8 adjectives, and each series of adjectives was read aloud, one at a time, by the examiner. When the examiner had completed the series, the subject was required to respond with the opposite of each adjective in the series, in the order in which the adjectives occurred. (For instance, the response to ‘‘tall-big-black’’ would be ‘‘short-small-white’’.) The examiner continued until the subject failed two consecutive adjective sequences within the same set. The test was not time-limited, and scores were based on the number of correct responses. Working memory-completion (Shani, M., & Ben Dror, M, unpublished.) This test contained sets of sentences in which the final word was missing. In this test, the examiner read each sentence aloud, and the subject filled in the missing word in the sentence. At the end of all of the sentences in a particular set, the subject was asked to recall the completed words in the order in which they appeared. Each set contained 2 series of sentences. The number of sentences in each series ranged, in ascending order, from 2–5. The examiner continued until the subject failed two consecutive series in the same set. The test was not time-limited, and scores were based on the number of correct responses. Timing measures Rate of retrieval from long term memory (Hebrew and English) Two word Fluency Tests (Breznitz, z, unpublished; designed on the basis of Lezak, 1993). one in Hebrew and one in English. This test assessed ability to retrieve words from long-term memory according to a specified criterion. Subjects were required to generate 3 separate lists of words based on their previous knowledge. In the first test, subjects wrote down words beginning with the letter ‘resh’ in Hebrew and ‘r’ in English (similar sounds), and in the second list, words beginning with the letter ‘shin’ in Hebrew and ‘sh’ in English. In the third test, the subject made a list of groceries in Hebrew and one in English. One minute was given for each word list in each language. A total fluency score for each language was derived from the sum of words generated in each of the three separate lists. Naming Test. This test consisted of five subtests containing 50 stimuli each. The first four subtests were Rapid Automatized Naming, or

720


RAN, tests (after Denckla & Rudel, 1976; Wolf, Bally & Morris, 1986). Each contained a single category of stimuli (script letters in Hebrew, script letters in English, object names in Hebrew and object names in English, respectively). The stimuli in each subtest were arranged randomly in a 10 5 matrix. The subject was required to name the stimuli in each subtest as quickly and accurately as possible. Speed and accuracy for each subtest were measured. Rate of processing 1. Digit-Symbol Test (WAIS, Wechsler, 1994). 2. Symbol Search (WAIS, Wechsler, 1994). ERP measures Hebrew and English sentence tasks with expected/unexpected endings for the electrophysiological experiment 120 Hebrew and 120 English sentences composed of four words each were presented separately to the subjects. Each test contained two types of sentences: 80 in which the last word was not related to the preceding text (unexpected) and 40 sentences in which the last word was related to the context (expected). Unexpected and expected sentences were distributed randomly among the two experimental conditions.

Instrumentation The sentences were presented on an IBM-PC terminal in white over a gray background on a computer display located 1.5 m in front of the subject. Subjects were asked to response immediately after stimulus occurrence by pressing a button on a joystick only for the unexpected sentences ending and to eliminate the other sentences endings. ISI between the sentences was 700 ms. Electroencephalogram (EEG)-brain atlas III Twenty-two channels of EEG were recorded using a Bio-Logic Brain Atlas III computer system with brain mapping capabilities. This system used a bandpass of 0.1–70 Hz interfaced with a 20-channel, 12-bit A/D converter. The EEGs were sampled at a rate of 250 Hz (dwell time¼ 4.0 ms) beginning 100 ms before stimulus onset. A full array of electrodes was placed according to the International 10/20 system


721

(Jasper, 1958) utilizing an Electro-cap (a nylon cap fitted over the head with 9 mm tin electrodes sewn within). Nineteen scalp electrodes were used corresponding to standard 10/20 system locations: PF1, PF2, F7, F3, FZ, F4, F8, T3, C3, CZ, C4, T4, T5, P300, PZ, P4, T6, O1, O2. All were referenced to an electrode on CVII (the seventh vertebra) and grounded to Fpz. In addition, one electrode was applied diagonally below the left eye to monitor eye movements. During data collection electrode impedances were kept below 5 K Ohms by first prepping scalp areas with a mildly abrasive cleanser (Omni-Prep) and then using an electrolyte gel (Electro-gel). Trial onset was marked on the Oz channel of EEG via a positive polarity 5 mV pulse delivered from an IBM-PC 486 computer. Signal averaging of the raw EEG data was performed off-line. EEG data was separated into discrete trials. After rejections of the trials containing eye movement, averages of the individual trials in each experiment were determined for each subject. Data analysis using the LORETA program (see Winterer et al., 2001 for details): Estimation of each subject’s current source of brain activity density distribution was measured for unexpected words in each of the L1 and L2 sentences at P200 component epoch, which was identified between 180– 280 msec post stimulus, and at P300 component epoch, which was identified 280–600 msec post stimulus. Group and language effects were calculated by Voxel t-tests analysis (Holmes, Ojemann, & Lettich, 1996). The calculations were based on absolute current density values the ERP component epochs of each subject for each language. Testing sessions Each subject participated in 3 testing sessions of about 1 hour each. All sessions took place at the Laboratory for Neurocognitive Research at the University of Haifa. In order to verify reading ability and group selection, the behavioral measures were administered first. The order of test presentation was randomized across subjects. When performing the behavioral tests, subjects sat in a quiet room. During the experimental tests in which electrophysiological data was collected, the subjects were seated in a sound-attenuated room in front of an IBM-PC computer screen. Experimental task presentation was counterbalanced. Subjects were connected to an Electro-cap (requiring about 30 minute of preparation) and were instructed to remain quiet and refrain from moving during the testing session. They were also told that it was important to avoid excessive eye movements, and to avoid

722


blinking as much as possible. ERP reaction time (RT) and accuracy (AC) were obtained for each subject for the unexpected sentences endings. Only single trials free from eye movements and associated with correct responses were averaged to obtain the ERP’s. Grand averages over conditions and subjects were then performed for each experiment for each of the 19 scalp electrodes. ERP peaks were first identified and then validated by a machine-scoring algorithm. Latencies were measured from stimulus onset. Amplitudes were measured relative to the mean voltage of each channel during the pre-stimulus baseline. In the computerized tasks, the computer program recorded measures of reaction time (measured from stimulus onset until button press response) and accuracy (percentage of correct responses). In all other behavioral, non-computerized tasks, reaction time was measured with a stopwatch, and accuracy computed by an objective experimenter.

Results Background behavioral measures RM-Analyses of Variance (Repeated Measures-MANOVAs) were conducted in order to determine group differences between the dyslexic and normal readers on each of the behavioral measures. The data revealed no significant differences between the two groups of subjects, neither in Hebrew nor in English, on measures of Raven Matrices IQ test, listening comprehension, connected text reading comprehension and accuracy in the Parsing test. A 2 2 2 (Group: dyslexics and controls Language: Hebrew and English Tasks: words and pseudowords) ANOVA yielded a significant group effect (F(4,26)¼ 66.74, P < .001), task effect (F(4,26)¼11.23, P < 0.001), but not effect of language or group language interaction. The dyslexics achieved significantly lower scores than the controls on the tests in both languages. For the two groups the number of Hebrew and English words per minutes read correctly were higher than the number of pseudowords (See Table 1 for mean and SD). A 2 2 (Groups) (Languages) ANOVA’s yielded a group effect but no effect of language and group language interaction on decoding errors, reading time of Hebrew and English connected texts, dictation errors, time performance on the parsing tests and word fluency tests in the two languages. The dyslexics were significantly less accurate


723

and slower than the controls in both languages: (F(2,28)¼8.99, P < .001), (F(2,28)¼10.23, P < .001), (F(2,28)¼6.11, P < .001), (F(2,28)¼9.13, P < .001) and (F(2,28)¼7.26, P < .001) respectively. One way Analyses of Variance (ANOVAs) indicates that the dyslexics achieved lower scores on STM, WM, Symbol Search, Digit Symbols and Phonological tests. They were also slower in performing the two RAN tests (See Tables 1 and 2 for these data).

Table 2. Behavioral cognitive and experimental measures. Test

Control Mean

Raven Matrices (raw scores) Listening comprehension in Hebrew (percent accuracy) Listening comprehension in English (percent accuracy) Level of English (1–6) Digit Span (standard scores) Working memory completion (out of 10) Working memory opposites (out of 10) RAN Letters naming, time (in sec) RAN Object naming, time (in sec) RAN Colors naming, time (in sec) WAIS Digit Symbols (percent accuracy) WAIS Symbol Search (speed) Total word production fluency in Hebrew Total word production fluency in English Hebrew sentences RT English sentences RT Hebrew sentences AC English sentences AC P < 0.001**

Dyslexic SD

Mean

SD

Group Comparison F (2,28)

52.1 100%

3.62 51.65 6.01 100%

3.81 NS 7.01 NS

100%

2.31 100%

5.89 NS

5.7 12.72 5.67

0.11 1.78 0.56

5.9 11.06 4.33

0.19 NS 1.56 6.38** 0.75 6.21**

6.89

0.83

4.95

20.78 29.70 34.77 100%

2.37 25.57 7.93 40.08 14.10 25.66 6.01 78%

0.91

5.47**

4.46 13.14** 8.10 12.53** 3.38 5.40** 8.65 7.63**

59.48 47.81

7.51 5.43

46.76 33.65

9.66 12.13** 6.43 9.12**

48.53

6.62

39.53

9.26

5.24**

972.80 111.88 1129.58 67.56 13.40** 1199.69 56.78 1267.80 71.98 11.84** 91.35 8.39 65.43 16.06 4.65* 90.64 10.26 76.93 14.88 7.88**

724


Experimental tasks RM-MANOVAs [group (dyslexics regular readers) language (Hebrew English)] were run separately for reaction time and response accuracy. Behavioral reaction time (RT) A significant main effect of group (F(2,28)¼18,96, P < .001) was found. Reaction times for the unexpected sentences endings were significantly longer among dyslexics than normal readers in the two languages. A language effect was also obtained (F(19,11)¼24,17, P < .001). RT’s were longer in the English sentences as compared to the Hebrew ones for both groups. Results indicated no significant group language interaction. Response accuracy (RC) Significant main effects of group (F(2,28)¼16,90, P < .001) and language (F(19,11)¼4,65, P < .04) were obtained. Response accuracy was significantly lower among dyslexics as compared to normal readers. However, accuracy regarding the English sentences was more pronounced in both groups. Results indicated no significant group language interaction (See Table 2 for these results). Electrophysiological measures In the Hebrew and the English tasks, two pronounced ERP components were identified for both groups of subjects: An early P200 component, and a later P300 component. Group (dyslexics and controls) Language (Hebrew and English) repeated measures RM-MANOVAs were carried out in order to evaluate between groups and between language differences in amplitudes and peak latencies of each component for each of the experimental tasks. It should be noted here that the results of the univariate analyses for amplitudes and latencies refer to the most prominent electrode in each experiment. As the latency location of a particular brain wave (ERP) typically varies at about 30 ms across individual electrodes, the electrode at which brain activity is at its highest amplitude for a specified wave was identified, and the latency for that electrode alone was reported. The results indicated that across tasks, the most prominent waveforms occurred at electrode CZ. Accordingly, Table 4 presents the means and standard deviations for ERP latencies among dyslexics and controls in each experiment at CZ.


725

Amplitudes A significant main effect of group (F(19,11)¼8.76, P < .001), main effect of language (F(19,11)¼11.26, P < .001] and group language interaction (F(19,11)¼7,26, P < .01) were only found for the P300 component. Amplitudes were significantly higher for the dyslexics as compared to the controls in L1 and L2. Amplitudes were higher in English as compared to Hebrew for the dyslexics group. The differences between amplitudes in English and in Hebrew mainly appeared among dyslexic readers. The amplitudes at CZ electrodes during reading Hebrew sentences were X¼11.10 (SD 4.22) for the dyslexics and X¼ 3.98 (SD 2.83) for the controls. In reading the English sentences X¼ 15.16 (SD 2.19) for the dyslexics and X¼4.03 (SD 1.33) for the controls. No further significant group differences were found for any of the other ERP components as compared to the control group. Latencies Table 3. presents the latency data for the P200 and P300 components on each of the experimental tasks. On average, dyslexic readers showed later latencies for the two components. P200. Results yielded a significant main effect of group, (F(19,11)¼ 8.99, P < .001). Latencies for the dyslexics were longer. Main effect of language (F(19,11)¼7.14, P < .001) and latencies occurred later in the English sentences. Furthermore, a significant group language interaction was also found (F(19,11)¼4.63, P < 0.01). This interaction was due to a larger difference between P200 latencies for the Hebrew and

Table 3. Experimental Measures: Latencies of P200 and P300 components at CZ. Test

Hebrew sentences unexpected

English sentences unexpected

P < 0.001**

Component

Control

Dyslexic

Mean

SD

Mean

SD

Group Comparison F (19,11)

P200

152.99

40.87

187.48

29.61

6.08**

P300

312.46

65.89

363.26

40.09

5.78**

P200

171.85

16.41

214.25

31.22

6.32**

P300

320.48

41.61

418.48

60.11

11.65**

726


Table 4. Description of brain region for the highest brain density at P200 (Figure 1) and P300 (figure 2) ERP’s latencies peaks during reading unexpected Hebrew and English sentences endings: Comparison of dyslexic and regular readers. Dyslexics P200 Hebrew: X, Y, Z = 46, 67, 8 Brodmann 39 Middle Temporal Gyrus Right Temporal Lobe.

Regular P200 Hebrew: X, Y, Z = 52, 67, 8 Brodmann 39 Middle Temporal Gyrus Left Temporal Lobe.

Dyslexics P200 English: X, Y, Z = 59, 39, 1 Brodmann 22 Middle Temporal Gyrus Left Temporal Lobe.

Regular P200 English: X, Y, Z = 52, 67, 8 Brodmann 39 Middle Temporal Gyrus Left Temporal Lobe.

Dyslexics P300 Hebrew: X, Y, Z = 46, 68, 8 Brodmann 39 Middle Temporal Gyrus Right Temporal Lobe.

Regular P200 Hebrew: X, Y, Z = 45, 67, 15 Brodmann 39 Middle Temporal Gyrus Left Temporal Lobe.

Dyslexics P300 English: X, Y, Z = 38, 52, 8 Brodmann 10 Anterior frontal Gyrus Left frontal Lobe.

Regular P300 English: X, Y, Z = 59, 32, 8 Brodmann 42 Superior Temporal Gyrus. Left Temporal Lobe.

Brain Coordinates: X = horizontal; Y = vertical; Z = depth

English sentences among the dyslexics as compared to the controls (see figure 1) mean and SD are presented in table 4. P300. Results yielded a significant main effect of group, (F(19,11)¼ 7.91, P < 0.001) and latencies for the dyslexics were longer. Main effect of language (F(19,11)¼3.39, P < 0.02) was also obtained, and latencies occurred later in the English sentences. Furthermore, a significant group · language interaction was also found (F(19,11)¼7.82, P < 0.001). This interaction was due to a larger difference between P300 latency of the two languages among dyslexics (see Figure 1) mean and SD are presented in Table 4. LORETA results The results are shown in figures 1(a–d) and 2 (a–d). LORETA source maps were created for the P200 and P300 components of each subject. Based on this, group average source maps were thereafter created


727

separately for the 12 dyslexic subjects and the 14 controls (See Figure 1–2). Each figure shows the averaged activity pattern in 3 slices based on the Talairach brain. Intensity of the color red represents the higher density of brain activation in the area. Figures 1 and 2 a through c and Table 4 show the brain density activation at P200 and P300 ERP components during reading of unexpected sentence endings. Figures 1, 2a and c relate to regular and dyslexic readers, respectively, regarding reading in Hebrew, and Figures 1, 2b and d relate to regular and dyslexic readers, respectively, when reading in English.

Discussion This study was carried out in an attempt to examine differences between bilingual regular and dyslexic readers matched on IQ when performing reading and reading related tasks in two distinct languages, Hebrew and English. In an attempt to examine the time, intensity and location of cerebral activation during reading in the two languages, behavioral ERP and LORETA measures were employed. The existing literature presents differing opinions regarding the definition of bilingualism. Researchers agree that the quality of processing the four basic skills comprising language; that is, reading, writing, speaking and listening, may indicate a person’s degree of proficiency in a certain language. The present study focused on dyslexic subjects, therefore, the decision regarding definition of proficiency in L1- Hebrew and L2 -English was by necessity based on only two skills: listening and spoken language effectiveness. Our data indicated that for the participants’ achievements on these measures reached about 100%. Moreover, no between groups’ differences were found in reading comprehension in both languages. It can therefore be said that based on these two skills the two groups were matched with a high degree of performance in English as a second language. For the regular group, the high degree of performance in English was also shown on the reading and writing tests. The dyslexic subjects diagnosed as developmental dyslexic in childhood continued to exhibit reading difficulties in Hebrew as L1, had lower working memory scores and slow speed of processing in adulthood. One of the questions in the present study was determining the extent to which dyslexia exists in English as L2. As data indicated the dyslexics were less accurate and slower on most of the reading and reading related tasks in Hebrew and English. Consistent differences between dyslexic and regular readers were found in both languages for decoding

728


quality and rate of reading word and pseudoword lists. Differences were also constant when reading text in context, and for orthographic, phonological and verbal fluency tasks. As indicated, the dyslexia phenomenon persists to the same extent among adult compensated dyslexic university students and exhibits similar characteristics in L1 and in L2.


729

Our results also indicated that good as well as poor readers were found to be proficient regarding aspects of spoken language and listening comprehension in Hebrew and in English, an advantage that still exists regarding reading in their native tongue. Based on this, it can be argued that our finding supports the hypothesis defining bilingualism as ‘‘bilingual dominant’’, (Fabbro, 1999). In other words, one language, usually the native language, is more effective and fluent than the other, which applies to regular as well as dyslexic readers. More support for this claim can be garnered from the fact that the subjects participating in the study were all adult university students who had been exposed for years to the Hebrew and English languages, and had reduced the within and between language variance as compared to the young subjects. This lead to stabilization of achievement skills in the two languages (see also Breznitz, 2001) suggesting that bilinguals can be characterized as ‘‘bilingual dominant’’. Furthermore, the present study examined brain activation during reading of Hebrew as compared to English sentences among the two groups of subjects. By analyzing the latencies and amplitudes of the ERP components, processing time and intensity of the various stages of information processing activation during reading could be observed. Moreover, the current density of brain activity could be traced using the LORETA program. As suggested (Pascual-Marqui et al., 1994), the location of the highest current density of brain activation can be seen as the cortical source of processing. The behavioral results from the experimental measures support the ‘‘dominant bilingual’’ hypothesis of bilingualism, at least with regard to reading time, among the two groups of subjects. For all subjects reading time of sentences with unexpected endings was longer in English

b

Figure 1. (a) (regular readers in Hebrew): As can be seen, activity is at its highest (3.2 lV) in the middle temporal gyrus of the left temporal lobe (Brodmann area 39). In addition, activity also appears, at reduced intensity (2.4 lV), in the right temporal lobe in the middle temporal gyrus (Brodmann area 37). (b) (dyslexic readers in Hebrew): The highest degree of activation (3.2 lV) was exhibited in the right temporal lobe in the middle temporal gyrus (Brodmann area 37). (c). (regular readers in English): The highest degree of activation (3.2 lV) is in the left temporal lobe in the middle temporal gyrus (Brodmann area 39). In addition, activity also appears, at reduced intensity (1.9 lV), in the right temporal lobe in the middle temporal gyrus (Brodmann area 37). (d) (dyslexic readers in English): the highest intensity (3.0 lV) is in the left temporal lobe in the middle temporal gyrus (Brodmann area 22), and at lV 2.7 in the right lobe in the Insula (Brodmann area 13). (see online version in colour)

730



731

than in Hebrew. Moreover, these results were more pronounced among dyslexic readers. Similar works studying brain activity during processing of unexpected sentences endings identified the N400 ERP component (e.g., Kluender & Kutas, 1993). However, in our study, two ERP components were identified in each subject within each group. The P200, which appeared between 180 and 280 msec, and P300, which appeared between 320 and 500 msec. Results indicate a higher P300 amplitude and longer P200 and P300 latencies among dyslexic as compared to regular readers when required to judge whether the last word in a sentence was semantically appropriate to the contents of the sentence. For the two groups of subjects, P200 and P300 latencies were longer and P300 amplitudes were higher when processing English sentences as compared to Hebrew. Contrary to the behavioral results, an interaction between groups and language was found for the latencies of P200 and P300 ERP components. The gap between the elicitation time of P200 and P300 latencies in Hebrew as compared to English was largest among the dyslexics. These results are due to later ERP latencies among dyslexics when reading English. English was not the subjects’ first language in either group, thus, the fact that cerebral processing in English was slower and more effortful than in Hebrew was not surprising. The cerebral processing slowness manifested in ERP latency length in English as compared to Hebrew was observed at the initial phase of stimuli processing (P200) that is, the stimuli classification stage, and continued into the stage of updating in working memory (P300). If we accept the hypothesis that amplitude height is evidence of cerebral effort, then the P200 and P300 amplitudes were highest among dyslexics. Thus, their English reading and semantic b

Figure 2. (a) (regular readers in Hebrew): Activity is at its highest (3.2 lV) in the middle temporal gyrus of the left temporal lobe (Brodmann area 32). (b) (dyslexic readers in Hebrew): The highest degree of activation (3.2 lV) was exhibited in the right temporal lobe in the middle temporal gyrus (Brodmann area 37). (c) (regular readers in English): Activity was at its highest (3.2 lV) in the left temporal lobe in the superior temporal gyrus (Brodmann area 42). In addition, activity also appears, at reduced intensity (1.9 lV), in the right temporal lobe in the middle temporal gyrus (Brodmann area 39). Moreover, activity appears in the left frontal lobe in the anterior frontal gyrus (Brodmann area 10) at 2.1 lV. (d) (dyslexic readers in English): the highest intensity (3.0 lV) is in the left frontal lobe in the anterior frontal gyrus (Brodmann area 10), and in the Anterior Cingulate Limbic Lobe (Brodmann area 32) at lV 2.2. (see online version in colour)

732


decision is more effortful than the same task in their mother tongue, and when compared to regular readers. We suggest that the ERP data can also be seen as further support for the ‘‘dominant bilingual’’ hypothesis in the two groups of readers. Another question arising from this study is whether there is a differentiation in the source of activation when reading Hebrew as compared to English, and whether the same pattern is observed in dyslexic and regular readers. In an attempt to answer this question, the present study utilized LORETA methodology (Pascual-Marqui et al., 1994), which enabled us to trace the location of the highest current density of brain activation during reading of unexpected sentence endings in L1 (Hebrew) and L2 (English). In the analysis we used the latency peaks of P200 and P300 ERP components. LORETA Figure 1 a–d shows the current density of brain activation between 180 and 280 ms (P200 ERP component’s peak). The P200 component has been conceptualized as being related to feature detection (e.g., Luck & Hillyard, 1994) and selective attention (e.g., Hackley & Valle-Inclan, 1998). These two processes relate to perception and discrimination at an early stage of stimulus processing. The data in the present study indicated that when processing both Hebrew and English sentences, the highest current density of brain activation in both groups of subjects appeared in the posterior part of the brain at Brodmann area 39 (left and right Angular Gyrus), which is linked to the processing of complex visual information and is considered to be an important cortical area of language (Geschwind, 1965). Moreover, additional activation in the right Insula (Brodmann area 13) was observed among dyslexics when processing English sentences. Our data revealed similar brain activity among regular readers when processing Hebrew and English unexpected sentences endings. The highest density when reading in both languages was observed in the left Angular Gyrus, and activation was also observed to a lesser degree in the right Angular Gyrus. This result can be interpreted as the normal course of Hebrew and English reading in the posterior and pariatel areas of the brain, which includes sequential (left side) as well as holistic (right side) processing. At the same time, impaired reading in the two languages presented a different picture. The highest current density among dyslexic readers during visual perception of Hebrew unexpected sentence ending was observed on the right side of the Angular Gyrus with much less activation on the left side. It is possible that for dyslexics, visual processing of alphabetic characters in Hebrew relies more on holistic processing with very limited sequential (left side activation) processing, which is necessary for reading. However, the highest current density among dyslexics when


733

reading English was on the left side of the Angular Gyrus and in the Insula. Various researchers have suggested that P300s reflect central activation of information processing (e.g., Palmer et al., 1994). It has also been suggested that P300 reflects updating in working memory (e.g., Donchin & Israel, 1980; Wolach & Pratt, 2001), cognitive resource allocation (Kramer, Strayer & Buckley, 1991; Polich, 1987; Polich & Heine, 1996), and mental effort (e.g., Wilson et al., 1998). Consequently it has been suggested that P300 latency can serve as a temporal measure of neural activity underlying the speed of attentional allocation and immediate memory operation (Cohen & Polich, 1997; Polich & Heine, 1996). LORETA Figure 2 a–d shows the P300 ERP component’s peak of brain current density activation. The data in the present study indicated that when processing sentences in both Hebrew and English in both groups of subjects, the highest density of brain activation appeared in various brain areas. For regular readers, the highest density when reading Hebrew was observed in the left Angular Gyrus. This can be seen as evidence of automatic processing in the sequential-visual language area of the brain. Brain activation in English among regular readers was mainly exhibited in the left Superior Temporal Gyrus, which is believed to detect and recognize elements of language. Somewhat reduced activation was also seen in the right Middle Temporal Gyrus and in the left and right Anterior Frontal Gyrus, which is possibly connected to high order cognitive processing. In contrast, for the dyslexics, the highest current density in Hebrew was observed in the right Angular Gyrus, which points once again to more holistic processing of the reading stimuli, and in the left Anterior Frontal Gyrus in English. It is conceivable that when dyslexics read English as L2, they require additional contextual information and exhibit activation in the area of high order cognitive processing. In sum, our behavioral and the ERP results from both groups support the ‘‘dominant bilingual’’ definition hypothesis. In addition, our results indicated that the localization of brain activation exhibited by brain current density in Hebrew and English is similar for regular readers but different for dyslexics. Moreover, the correlations between the Hebrew and English measures among regular readers ranged between r¼.78 P < .001 and r¼.86 P < .001. In contrast, the correlation within the dyslexic group ranged between r¼.41 and r¼.23 P < .01. Consequently, our results support the Linguistic Interdependence Hypothesis (Cummins, 1979), which suggests that competence in L1 relates to competence in L2 (Abu Rabia, 1997; Da Fontoura & Siegel, 1995) only among regular readers. Whereas among the dyslexics our data suggests that reading in Hebrew and English are less related and exhibit a differ-

734


ent localization source and pattern of brain activation. It is clear that more data is needed to support this claim.

References Abu-Rabia, S. (1997). Verbal and working-memory skills of bilingual Hebrew–English speaking children. International Journal of Psycholinguistics, 13, 25–40. Ardal, S., Donald, M.W., Meuter, R., Muldrew, S., & Luce, M. (1990). Brain responses to semantic incongruity in bilinguals. Brain and Language, 39, 187–205. Ben-Dror, I., Bentin, S., & Frost, R. (1995). Semantic, phonologic, and morphologic skills in reading disabled and normal children: Evidence from perception and production of spoken Hebrew. Reading Research Quarterly, 30, 876–893. Ben-Dror, I., Frost R. & Bentin S. (1995). Orthographic representation and phonemic segmentation in skilled readers: A cross-language comparison. Psychological Science, 6, 176–181. Ben Dror, I., & Shani, M. (1996). Phoneme recognition test for words and pseudowords. Unpublished Test. Bentin, S. & Frost, R. (1995). Morphological factors in visual word identification in Hebrew. In L. B. Feldman (Ed.), Morphological aspects of language processing (pp. 271–292). Hillsdale, NJ: Lawrence Erlbaum. Breznitz, Z. (1996). Word fluency test. Unpublished test. University of Haifa, Mt. Carmel, Israel. Breznitz, Z. (1997). Parsing tests. Unpublished tests. University of Haifa, Mt. Carmel, Israel. Breznitz, Z. (2001). The determinants of reading fluency: A comparison of dyslexic and average readers. In M. Wolf (Ed.), Dyslexia, fluency and the brain (pp. 245– 276). Cambridge, MA: York Press. The Center for Psychometric Tests (1994). Israeli psychometric scholastic aptitude test - Comprehension test. Jerusalem, Israel. Chee, M.W.L., Tan, E.W.L., & Thiel, T. (1999). Mandarin and English single word processing studied with functional magnetic resonance imaging. The Journal of Neuroscience, 19, 3050–3056. Cohen, J. & Polich, J. (1997). On the number of trials needed for P300. International Journal of Psychophysiology, 25, 249–255. Cummins, J. (1979). Linguistic interdependence and the educational development of bilingual children. Review of Educational Research, 49, 222–251. Da Fontoura, H. A. & Siegel, L. S. (1995). Reading, syntactic, and working memory skills of bilingual Portuguese-English Canadian children. Reading and Writing, 7, 139–153. Dehaene, S., Dupoux, E., Mehler, J., Cohen, L., Paulesu, E., Perani, D., van de Moortele, P. F., Lehericy, S. & Le Bihan, D. (1997). Anatomical variability in the cortical representation of first and second language. Neuroreport, 8(17), 3809– 3815. Denckla, M.B. & Rudel, R.G. (1976). Rapid automatized naming (R.A.N.): Dyslexia differentiated from other learning disabilities. Neuropsychologia, 14, 471–479. Deutsch, A. & Rayner, K. (1999). Initial fixation location effects in reading Hebrew words. Language and Cognitive Processes, 14, 393–421.


735

Donchin, E. & Israel, J. B. (1980). Event-related brain potential and psychological theory. In: H.H. Kornhuber and L. Deecke, L. (Eds.), Motivation, motor and sensory processes of the brain: Electrical potentials, behavior and clinical use (pp. 697– 715). Elsevier: Amsterdam, North Holland. Dunn, B. R., Dunn, D. A., Languis, M., & Andrews, D., (1998). The relation of ERP components to complex memory processing. Brain and Cognition, 36, 355–376. Fabbro, F. (1999). The Neurolinguistics of Bilingualism, U.K.: Psychology Press. Fitzgerald, P.G., & Picton, T.W. (1983). Event related potentials recorded during the discrimination of improbable stimuli. Biological Psychology, 17, 241–274. Frost, R. (1994). Prelexical and postlexical strategies in reading evidence from a deep and shallow orthography. Journal of Experimental Psychology: Learning, Memory and Cognition, 20, 116–129. Ganschow, L., Sparks, R.L., Javorsky, J., Pohlman, J., & Bishop-Marbury, A. (1991). Identifying native language difficulties among foreign language learners in college: A ‘‘foreign’’ language learning disability? Journal of Learning Disabilities, 24, 530–541. Geschwind, N. (1965). Disconnection syndromes in animals and man. Brain, 88, 237– 294. Gronau, N. & Frost, R. (1997). Prelexical phonologic computation in a deep orthography: Evidence from backward masking in Hebrew. Psychonomic Bulletin and Review, 4, 107–112. Hackley, S.A., Woldorff, M. & Hillyard, S. A. (1990). Cross-modal selective attention effects on retinal myogenic, brainstem and cerebral evoked potentials. Psychophysiology, 27, 195–208. Hackley, S.A. & Valle-Inclan, F. (1998). Automatic alerting does not speed late motoric processes in a reaction-time task. Nature, 391(6669), 786–788. Halgren, E. (1990). Insights from evoked potentials into the neuropsychological mechanisms of reading. In A.B. Scheibel & A.F. Wechsler (Eds.), Neurobiology of higher cognitive function. (pp. 103–150). NY: Guilford Press. Holmes, M.D., Ojemann, G.A. & Lettich, E. (1996). Neuronal activity in human right lateral temporal cortex related to visuospatial memory and perception. Brain Research, 711, 44–49. Illes, J., Francis, W.S., Desmond, J.E., Gabrieli, J.D.E., Glover, G.H., Pildrack, R., Lee, C.J., & Wagner, A.D. (1999). Convergent cortical representation of semantic processing in bilinguals. Brain and Language, 70, 347–363. Jasper, H.H. (1958). The ten-twenty electrode system of the International Federation. Electroencephalography and Clinical Neurophysiology, 10, 371–375. Kim, H.S., Relkin, N.R., Lee, K.M. & Hirsch, J. (1997). Distinct cortical areas associated with native and second languages. Nature, 388(6638), 171–174. Klein, D., Milner, B., Zatorre, R.J., Meyer, E. & Evans, A.C. (1995). The neural substrates underlying word generation: A bilingual functional-imaging study. Proceedings of the National Academy of Sciences USA, 92, 2899–2903. Klein, D., Zatorre, R.J., Milner, B. & Meyer, E. (1994). Left putaminal activation when speaking a second language: Evidence from PET. Neuroreport, 5, 2295– 2297. Kluender, R. & Kutas, M. (1993). Bridging the gap: Evidence of ERPs on the processing of unbound dependencies. Journal of Cognitive Neuroscience, 5, 196–214.

736


Kramer, A.F., Strayer, D.L. & Buckley, J. (1991). Task versus component consistency in the development of automatic processing: A psychophysiological assessment. Psychophysiology, 28, 425–437. Lerner, M., Shaul, S., Berman, L. & Breznitz, Z. (2001). The subtypes of dyslexia: A universal or language specific phenomenon. Poster presented at the International Dyslexia Association Conference, Washington D.C., (2001, June). Levin, I., & Landsmann, L.T. (1989). Becoming literate: Referential and phonetic strategies in early reading and writing. International Journal of Behavioral Development, 12, 369–384. Lezak, M.D. (1993). Newer contributions to the neuropsychological assessment of executive functions. Journal of Head Trauma Rehabilitation, 8, 24–31. Llinas, R.R. (1988). Neural transmission, squid giant synapse. In L.N. Irwin (Ed.), Comparative neuroscience and neurobiology. Readings from the ‘‘Encyclopedia of Neuroscience.’’ (pp. 87–89). Cambridge, MA: Birkhaeuser. Luck, S.J., & Hillyard, S. A., (1994). Electrophysiological correlates of feature analysis during visual search. Psychophysiology, 31, 291–308. Oades, R.D., Dittmann-Balcar, A. & Zerbin, D. (1997). Development and topography of auditory event-related potentials (ERPs): Mismatch and processing negativity in individuals 8–22 years of age. Psychophysiology, 34, 677–693. Oral language Test for English as a Second Language (1999). Unpublished test. University of Haifa, Mt. Carmel, Israel. Palmer, B., Nasman, V.T. & Wilson, G.F. (1994). Task detection difficulty: Effects on ERPs in a same-different letter classification task. Biological Psychology, 38, 199– 214. Pascual-Marqui, R.D., Michel C.M. & Lehmann, D. (1994). Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. International Journal of Psychophysiology, 18, 49–65. Pascual-Marqui, R.D., Lehmann, D., Koenig, T., Kochi, K., Merlo, M.C.G., Hell, D., & Koukkou, M. (1999). Low resolution brain electromagnetic tomography (LORETA) functional imaging in acute, neuroleptic-naive, first-episode, productive schizophrenia. Psychiatry Research: Neuroimaging, 90, 169–179. Perani, D., Dehaene, S., Grassi, F., Cohen, L., Cappa, S.F., Dupoux, E., Fazio, F. & Mehler, J. (1996). Brain processing of native and foreign languages. NeuroReport, 7, 2439–2444. Perani, D., Paulesu, E., Galles, N.S., Dupoux, E., Dehaene, S., Bettinardi, V., Cappa, S.F., Fazio, F. & Mehler, J. (1998). The bilingual brain: proficiency and age of acquisition of the second language. Brain, 121, 1841–1852. Polich, J. (1987). Task difficulty, probability, and inter-stimulus interval as determinants of P300 from auditory stimuli. Electroencephalography and Clinical Neurophysiology: Evoked Potentials, 68, 311–320. Polich, J. & Heine, M.R.D. (1996). P300 topography and modality effects from a single- stimulus paradigm. Psychopsysiology, 33, 747–752. Raven, J.C. (1960). Guide to the Standard Progressive Matrices. London: H.K. Lewis. Ravid, D. (1996). Accessing the mental lexicon: Evidence from incompatibility between representation of spoken and written morphology. Linguistics, 34, 1219–1246. Schwartz, R.L. (1997). Learning Disabilities and Foreign Language Learning: A Painful Collision. http://ldonline.com/ld_indepth/foreign_lang/painful_collision.html (April 2000). Shani, M. & Ben Dror, I. (1998). Working Memory Opposites. Unpublished Test.


737

Shatil, E. (1996). Predicting reading ability: Evidence for cognitive modularity. Unpublished doctoral dissertation. University of Haifa, Mt. Carmel, Israel. Shatil, E. (1997). One minute tests in Hebrew and English. Unpublished Tests. University of Haifa, Mt. Carmel, Israel. Shimron, J. (1999). The role of vowel signs in Hebrew: Beyond word recognition. Reading and Writing, 11, 301–319. Shimron, J. & Sivan, T. (1994). Reading proficiency and orthography: Evidence from Hebrew and English. Language Learning, 44, 5–27. Vaid, J. (1986). Language processing in bilinguals: Psycholinguistic and neuropsychological perspectives. Hillsdale, N.J.: Lawrence Erlbaum. Vellutino, F. & Scanlon, D. (1986). Linguistic coding and metalinguistic awareness: Their relationship to verbal and code acquisition in poor and normal readers. In: D. Yaden & S. Templeton (Eds.), Metalinguistic Awareness and Beginning Literacy. (pp.115–141). Portsmouth, NH: Heinemann. Wechsler, D. (1994). Wechsler intelligence scale for adults (WAIS-3). Winsor, UK: NFER. Weber-Fox, C.M. & Neville, H.J. (1996). Maturational constraints on functional specializations for language processing: ERP and behavioral evidence in bilingual speakers. Journal of Cognitive Neuroscience, 8, 231–256. Wilson, G.F., Swain, C.R., & Ullsperger, P. (1998). ERP components elicited in response to warning stimuli: The influence of task difficulty. Biological Psychology, 47, 137–158. Winterer, G., Mulert, C., Mientus, S., Gallinat, J., Schlattmann, P., Dorn, H., & Herrmann, W. M. (2001). P300 and LORETA: Comparison of normal subjects and schizophrenic patients. Brain Topography, 13, 299–313. Wolach, I., & Pratt, H. (2001). The mode of short-term memory encoding as indicated by event-related potentials in a memory scanning task with distractions. Clinical Neurophysiology, 112, 186–197. Wolf, M., Bally, H., & Morris, R. (1986). Automaticity, retrieval processes, and reading: A longitudinal study in average and impaired readers. Child Development, 57, 988–1000. Wolf, M., & Bowers, P. (1999). The double-deficit hypothesis for the developmental dyslexias. Journal of Educational Psychology, 91, 415–438. Address for correspondence: Dr Zvia Breznitz, Laboratory for Neurocognitive Research, Faculty of Education, University of Haifa, Mt. Carmel, Haifa, 31905, Isreal E-mail:[email protected]