Reading and cognitive abilities: Longitundinal studies of brain and behavior changes in young children
Molfese, Dennis L
This paper focuses on methods useful for identifying differences in the development of language reading abilities in children that rely on measures of brain responses and behavioral assessments. Findings from longitudinal and cross-sectional studies using brain and behavior measures are described, along with findings from research designed to influence changes in brain and behavioral responses through training. The findings show differences in event-related potentials (ERP) responses recorded at birth that are related to a child’s later performance on language and reading tasks. Such findings point to a strong biological influence on the development of language and reading skills. However, other findings show that the influence of biological factors on brain processing can be modified through learning. In fact, several studies show that even brief periods of stimulation and opportunities for learning can produce changes in the brain’s ERP repsonses. Such findings suggest that new approaches to evaluating the effectiveness of interventions might change the rate and likelihood of developmental changes.
The role of phonological processing in the development of language and reading abilities has received much research attention. Phonological processing refers to the ability to discriminate1 phonetic contrasts, and includes discrimination of speech sounds and categorical perception (voice-onset-time, place of articulation) as well as the ability to segment and manipulate phonemes and larger units. Some phonological skills important for analyzing the sound patterns in spoken words are present at or near birth and others develop in early infancy. Young infants discriminate between speech sounds that contain phonetic contrasts characteristic of their language environments, and young infants also appear to be sensitive to phonetic contrasts characteristic of other languages (Eilers, 1977; Eilers, Wilson, & Moore, 1977; Eimas, Siqueland, Jusczyk, & Vigorito, 1971; D. Molfese & V Molfese, 1979a, 1979b, 1980, 1985). This sensitivity changes in later infancy toward an increasing sensitivity to contrasts unique to the infant’s language environment, a change that appears to facilitate language acquisition. With further development, preschool children are able to segment spoken monosyllable words into onsets and rimes, and thus to play nursery rhyme games (Vellutino & Scanlon, 1987). As they continue to develop, children learn to segment polysyllabic words into syllables as they approach kindergarten age and monosyllabic words into phonemes around first grade (Liberman, Cooper, Shankweiler, & Studdert-Kennedy, 1967). Over the past decade a consensus of findings has emerged among researchers that phonological processing skills are fundamental to language development and to subsequent reading abilities (Brady, 1991; Fletcher, Foorman, Shaywitz, & Shaywitz, 1999; Wagner, Torgesen, & Rashotte, 1994).
Many of these studies of phonological processing have combined different methodologies in an effort to broadly evaluate the variety of factors that influence the development of language and reading abilities. This paper focuses on methods useful for identifying differences in the development of language and reading abilities in children that rely on measures of brain responses and behavioral assessments. Findings from these methods are described, along with findings from research designed to influence changes in brain and behavioral responses through training. Together, these findings show that even behaviors that appear to be strongly influenced by biological mechanisms can be influenced through training activities.
Brain-behavior relations have been a long-standing interest of researchers and theorists. This interest led to the development of methodologies for measuring brain responses, a useful and relatively new approach to studying developmental issues. Techniques such as electroencephalography (EEG), eventrelated potentials (ERPs), and brainstem evoked response (BSER) all share a common approach to cortical electrophysiology (scalp electrodes are used to detect electrical activity generated by neurons in the brain). A portion of this electrical signal passes through the brain and skull where it can be measured on the scalp. By positioning recording electrodes on the scalp’s surface, the minute electrical discharges (approximately 5 to 10 (mu)V, or millionths of a volt) can be detected, amplified, and analyzed. The end result is a read-out of the ongoing electrical discharges (in the form of continuous brainwaves) produced during activity (e.g., sleeping, listening to music, reading) across time.
The brain measure of interest in this paper is the eventrelated potential (ERP), a portion of ongoing EEG that is timelocked to the onset of a specific stimulus (see Figure 1). ERPs are characterized by a complex waveform that varies in amplitude and frequency, and its distribution across the scalp over time. ERPs are thought to reflect ongoing brain processing triggered by stimulus events. Fluctuations in the amplitude (height of the wave) or latency (delay from stimulus onset) of various positive and negative peaks within the ERP waveform occur at different points throughout its time course (Callaway, Tueting, & Koslow, 1978; Rockstroh, Elbert, Birbaumer, & Lutzenberger, 1982). It is these fluctuations in peaks at different time points and at different scalp recording sites (e.g., frontal, parietal, temporal) that serve as data in research studies. Research over the past 70 years demonstrated that the time-locked ERP can be used to effectively study both general and specific aspects of responses to eliciting events in the external as well as internal environment (D. Molfese, 1978a, 1978b). The ERP also is used to study an individual’s perceptions and decisions during tasks or following a learning situation (D. Molfese, 1983; D. Molfese & D. Molfese, 1996; Nelson & Salapatek, 1986; Ruchkin, Sutton, Munson, & Macar, 1981). Since the ERP technique does not require a planned and overt response from individuals from whom it is recorded, ERP techniques are particularly well suited for the neuropsychological study of the infant’s and the child’s early language development (D. Molfese, Freeman, & Palermo, 1975). One major advantage of the ERP is that it can provide very fine temporal information (one ms or less) regarding the brain’s response to an eliciting input such as a speech sound. In addition, it has some spatial resolution capabilities that provide a basis for speculations concerning the distribution of brain areas generating the ERPs that subserve functions such as language, reading, and other cognitive abilities.
ERP techniques have been used to investigate the development of language and reading abilities. Such abilities depend in part on the perception of speech sounds, an important basis for the development of both of these abilities. Particularly important has been the perception of different speech cues such as Place of Articulation (POA) and Voice-Onset-Time (VOT). VOT refers to the temporal relation between laryngeal pulsing and the onset of consonant release, and is an important cue for the discrimination of stop consonants such as b and p (Liberman et al., 1967). Thirty years of behavioral studies with infants (Eimas et al., 1971; Eilers, Gavin, & Wilson, 1980), children (Streeter, 1976), and adults (Lisker & Abramson, 1970) demonstrate the perception and discrimination of a wide range of voicing contrasts (such as ba, pa; ga, ka). In the case of either cue, VOT or POA, perception does not appear to be continuous but rather is categorical. Gradual physical changes in timing or formant frequencies produce no change in perception until a certain point is reached, at which time the listener indicates a different sound is perceived and labels it with a different name. For example, as indicated in Figure 2, listeners fail to detect differences in a speech sound although physical changes occur in the temporal delay of one component of that sound until a certain value is reached.
D. Molfese (1978 a, 1978b) and D. Molfese and V. Molfese (1979b) determined that categorical discrimination of VOT also could be assessed in adults as well as infants using ERP procedures (see also Simos, D. Molfese, & Brenden, 1997). Speech stimuli in this study varied in VOT across two different phonetic categories: ba NOT values of 0 and +20 ms) and pa NOT values of +40 and +60). These speech stimuli were presented to newborn infants and ERP responses were recorded from electrodes placed at frontal, temporal, and parietal scalp locations. As can be seen from Figure 3, marked differences in responses between the phonetic categorical boundaries of ba and pa were seen in ERP responses at parietal locations.
More recently, D. Molfese, V. Molfese, Key, and Kelley (in press) recorded ERPs from a group of 22 newborn infants using a high-density array electrode net that permits 128 electrodes to be placed on the scalp (see Figure 4). The ERPs were recorded in response to a subset of the speech syllables employed by D. Molfese (2000). Following artifact rejection, signal averaging, and baseline correction, 16,896 averaged auditory ERPs were submitted to a two-step analysis procedure. The first step involved the use of principal components analysis (PCA) followed by an overall MANOVA. Then, a set of univariate analyses of variance (ANOVAs) was conducted separately on the component scores calculated for each principal component. One region over the first 204 ms of the ERP discriminated between consonant sounds in these newborn infants, F(2,40) = 3.329, p
High-density array techniques also have been used to compare differences in ERP responses of infant and adult participants. Figures 5, which appears on the inside front cover of this journal, presents the averaged brain responses from a group of newborn infants and a group of adults to the speech sound ba. Although changes in responsiveness of different brain areas across time cannot be shown in a single set of pictures, the scalp displays of ERP activity do show gradient lines that reflect the differences in the responsiveness of brain areas to the speech sound at different points in time (30 ms, 100 ms, 200 ms, 540 ms, and 700 ms). The visual depiction of the ERPs across the 128 electrode sites and across these two age groups illustrate changes in the flow of electrical activity across brain regions. When viewed dynamically, the brain regions recorded from the newborn infants show responses that are more isolated within brain regions and do not show as much interaction between brain regions compared to adults. Newborn infant responses typically show a counter-clockwise sequence of positive and negative waves across the scalp that begins with an initial front– central positivity, followed in sequence by a negative left temporal response, then a left parietal-occipital positive response, and finally a right temporal negative response. As noted above, there is less flow of interaction between these brain regions during this sequence. In contrast, adult scalp recordings show much more flow of electrical activity across brain regions as indicated by current flows from one region to a second, and then back again. One can speculate that such differences in current flows across the scalp reflect greater levels in myelination, development of the corpus collosum, increasingly complex neuronal development, and interbrain region connections in adults than in newborn infants.
SPEECH PERCEPTION AND READING ABILITIES
The natural next step in this line of research investigated how speech perception may be related to language and reading abilities. Research by numerous groups have involved longitudinal studies of early speech perception abilities and subsequent language and reading skills (Leppanen et al., in press; Lyytinen et al., 2001). Our longitudinal research has tracked development from birth through eight years of age. These longitudinal studies show differences in newborn ERP components that are strongly predictive of performance on language tasks at three, five, and eight years of age (D. Molfese, 1989, 2000; D. Molfese & V. Molfese, 1985,1997). In the initial study in this sequence, D. Molfese and V Molfese (1985) noted that children showing slow development of language skills had produced neonatal ERPs responses to /bi/ and /gi/ speech stimuli that were highly similar. In contrast, children showing normal development of language skills had neonatal ERP responses differentiating /bi/ from /gi/. Group differences in effects were reflected in a large initial negative peak (N220: peak latency = 220 ms). A second peak (N630: peak latency = 630 ms) also discriminated between these two groups of children although it accounted for less variance. It can be assumed from these results that children who develop normal language skills were better able to discriminate between speech sounds at birth than children who develop poorer language skills. A subsequent report by D. Molfese & V. Molfese (1997) identified these same ERP regions as discriminating at birth between infants who five years later would have different levels of language skills.
Similar findings were reported when reading skills were the outcome measures under study. D. Molfese (2000) analyzed ERP data from a subset of 400 children participating in the longitudinal study for whom both brain and behavior data were available. Auditory ERPs were recorded from the left and right hemisphere frontal, temporal, and parietal scalp regions (linked ear references) of 48 of these infants within 36 hours of birth to a series of two consonant-vowel syllables, /bi/ and /gi/, and nonspeech homologues of these sounds (Tone-Onset-Time stimuli). These children were subsequently tested within two weeks of their eight-year birth date using these same ERP procedures. In addition, the Word Reading subtest from the Wide Range Achievement Test (WRAT-3) (Wilkinson, 1993) (mean=97.66, s.d.=12.6, range=50-126) was used to assess general reading performance while the Wechsler Intelligence Scale for Children – Third Edition (WISC-III) (Wechsler, 1991) was used to determined Full Scale IQ scores. Reading scores and IQ scores were used to separate the children into groups of 17 dyslexics, 7 poor readers, and 24 controls. Factors such as sex and socioeconomic (SES) were matched between the control and the two reading disabled groups.
Analysis procedures were used to determine whether neonatal ERP responses from these children could be used to discriminate between children grouped according to their reading skills at eight years of age (i.e., control, dyslexic, and poor readers). Six distinct ERP components, which included three amplitude and three latency measures, were identified: (1) the second large negative peak amplitude (N2) recorded at the right hemisphere frontal electrode site that was elicited in response to the /gi/ speech syllable; (2) the Ni amplitude change recorded at the right temporal hemisphere electrode site elicited in response to the /bi/ nonspeech syllable; (3) the second large positive peak amplitude (P2) elicited in response to the /bi/ speech syllable; (4) the first large negative peak latency (N1) to the speech syllable, /gi/, recorded at both the left hemisphere frontal and (5) parietal electrode sites, as well as at (6) the right temporal electrode. These six amplitude and latency measures were used in subsequent analyses to determine if neonatal ERP components were useful for classifying children by reading abilities. In one model, two significant canonical discriminant functions correctly classified 81.25% of the sample (39 of 48 children) according to reading performance at eight years of age (see table I). Six of seven poor readers were correctly classified (85.7%), as were 14 of 17 dyslexic children (82.4%), and 19 of 24 of control children (79.2%). These classifications are approximately two times greater than chance levels. If reading interventions designed to prevent reading disabilities were attempted shortly after birth on the basis of these data, 21 of 24 children in need of intervention could have been targeted to receive intervention, while only 5 of 24 children who did not require intervention would have received it. Thus, ERP measures taken shortly after birth demonstrate high accuracy (identifying nearly 88% of children in need of intervention) and generate relatively few false positives in predicting reading abilities at eight years of age.
The differences between reading groups in ERP waveforms are revealed through statistical analyses but are also obvious in visual inspection of the waveforms. Shown in Figures 6 and 7 are the group averaged ERP waveforms for this group of 48 children at different ages. Figure 6 shows ERP waveforms obtained at birth from these children grouped according to reading skills. Figure 7 shows waveforms of the same children also grouped according to reading skills, but these ERP waveforms were obtained when the children were eight years of age. Group differences in the waveforms are readily apparent in the latency of the waveforms and differences in component amplitudes across groups. The ERP effects that characterized the waveforms of the three groups include faster latencies and larger Ni amplitudes for the control children in comparison to the dyslexic and poor reading groups. In addition, larger N2 amplitudes were noted in the brain responses of the neonates who would later develop reading skills that fell into the dyslexic and poor reading groups, and a larger P2 amplitude characterized the poor reading group. Finally, one can note that the ERP waveform of the control group returns to baseline EEG activity levels earlier in time for both the neonatal brain responses as well as for the eight-year-old responses. Given the latency differences in the ERP components present at birth and also at eight-years of age for the different reading groups, one can speculate that differences in processing time are reflected with the longer latencies in low performing groups reflecting more processing time resources assigned to activities such as speech perception. The additional processing time resources also may mean that there are fewer resources to devote to other tasks. Thus, the processing differences in the ERP responses to speech sounds noted at birth may influence the development abilities that build on speech perception skills such as language and reading skills, with effects of differential use of processing resources persisting over at least an eight-year period.
We also obtained data from the parents of children participating in the longitudinal study to determine if differences found in the children’s ERP responses to speech sounds might also be reflected in parental responses. Parents of the child participants were asked to participate in ERP testing in which brain responses were obtained while the parents listened to the same speech sounds listened to by their children. In addition, parents completed the Shipley Scale to provide information about intelligence and completed the WRAT-3 Word Reading test. Although none of the parents’ reading scores were classifiable as dyslexic or poor readers, significant group differences were found in the ERP responses. Parents of children in the dyslexic/poor reading group could be discriminated from parents of the normal reading children using amplitude measures of the third ERP positive peak elicited in response to the speech sounds. These differences are shown in Figure 8. The parents of the children in the dyslexic/poor reading group showed higher amplitudes of the late positive peak component and a slower return to baseline.
The results outlined above repeatedly note the presence of group differences in ERP responses at birth that are related to a child’s later performance on language and reading tasks. Such findings point to a strong biological influence on the development of language and reading skills, as has been suggested by other researchers who have linked speech perception abilities in children to language and reading development (McBride-Chang, 1996; Burhanpurkar & Barron, 2001). However, evidence from other studies suggests that the influence of biological factors on brain processing can be modified through learning. In fact, several studies suggest that even brief periods of stimulation and opportunities for learning can produce changes in the brain’s ERP responses. Such possibilities could open up new approaches that might change the rate and likelihood of developmental changes. Just as brain responses can provide information concerning agerelated developmental changes, brain responses may also be useful in gauging the effectiveness of interventions designed to remediate learning disabilities or to influence developmental trajectories. Several studies have shown just these sorts of learning effects in children. For example, D. Molfese and D. Molfese (1996) investigated whether high school students receiving training on a task would show behavioral as well as ERP changes as a function of the training they received. In this study, 12 high school students participated in a pretraining/posttraining design that required them to form associations between nonsense words and random shapes. In the pretest, ERP responses and behavioral responses were obtained to nonsense names (e.g., PIVEK) and to nonsense shapes that were presented sequentially. Students were required to press buttons to indicate which words and pictures went together and ERP responses were recorded. During the pretest, all the materials were novel and students did not know which word and pictures matched, so their performance was expected to be at chance levels. Following this initial test, students received training for 15 minutes on which 20 words and pictures actually went together. No training was given for another group of 20 items. Finally, a post-training test was given in which ERP responses and behavioral responses were again obtained for the 20 items on the “learned” list and for the 20 items not learned. Figure 9 shows that during the pretest period, correct identification of names and shapes was at chance levels for the behavioral responses, but following training, names and shapes on the “learned” list were correctly identified nearly 100% of the time. Only chance performance was found for the names and shapes on the “not learned” list. These pretest/posttest differences were also reflected in the ERP responses (Figure 10). Before training, the ERPs elicited to both the “learned” and “not learned” lists were remarkably very similar. However, following the 15-minute training trial period, the ERP responses to the “learned” list differed markedly from those elicited in response on the “not learned” list. A large positive peak centered at approximately 400 ms occurred in response to the “learned” list (solid line) while no such peak is noted for the “not-learned” list (dashed line). Thus, the ERPs discriminated readily between the “learned” and “not-learned” lists, even after a relatively short training period.
Similar learning effects were found in a study with 14– month-old infants (D. Molfese, Morse, & Peters, 1990). In this study and before any training occurred, parents brought their 14 month old infants to the lab where the infants listened to series of nonsense words while they were handed wooden objects of different shapes and colors. Auditory ERPs were recorded to the nonsense words while the infants handled these different objects (Pretraining Test). For the next five days, mothers played with their infants at home using the same wooden objects for 10 minutes in the morning and in the afternoon. In the morning, mothers were instructed to name one object (e.g., a yellow stick) using one nonsense word (e.g., “bidu”) for five minutes, immediately after which they were to name the other object (e.g., a blue cylinder) using a different nonsense word (e.g., “gibu”). This activity was repeated in the afternoon except that the five-minute word-object training periods were reversed (i.e., blue gibu preceded the yellow bidu). During the naming periods, mothers provided their infants with a range of tactile and visual experiences with the object (e.g., placing the object in slots, floating them in water, putting them into containers) while they spoke the object’s name. This training sequence was changed from day to day across infants. Additionally, the names of the training objects differed among infants. After five days, the mothers and infants then returned to the laboratory for a posttraining test in which a series of 100 trials were presented to the infants. On some trials, the names and shapes matched (e.g., yellow stick and the sound “bidu”) while on other trials, the names and shapes did not match. Independent raters as well as the parents also verified the infants’ ability to link a name to an object, using a choice procedure.
Shown in Figure 11 are the pretraining and post-training test results as reflected in the ERP waveforms. As anticipated, the waveforms during the pretraining period are virtually identical for the match and mismatch word-object conditions. However, there were a number of changes reflected in ERP waveforms following the five training days. First, the overall waveshapes changed from one resembling a simple sine wave that contained one prominent negative peak that occurred approximately 225 ms post-sound onset to a larger ERP waveform that was characterized by two large negative deflections at 225 and 425 ms. Second, there was a marked overall increase in the amplitudes of the ERPs from the pretraining to the posttraining test. Finally, a closer inspection of the ERP waveforms indicates that ERPs elicited during the match and mismatch conditions differ at approximately 400 and 550 ms post-stimulus onset. These differences between the match and mismatch conditions, however, are not apparent in the pretraining ERP waveforms.
Both the study with the adolescents and the study with the infants show that ERPs can reflect training experiences even in situations where the training is as short as 15 minutes as was used with the high school students (D. Molfese & D. Molfese, 1996), or as long as a total of 50 minutes spread over five days as was used with the infants (D. Molfese, Morse, & Peters, 1990). These results are exciting because it might be anticipated that individual differences in rate of learning and training effectiveness might be gauged using changes in ERPs. Readily useable techniques and noninvasive procedures that characterize ERP methods might make this measurement technique particularly useful for this purpose.
As indicated in the review above, the ERP is very effective in reflecting the discrimination of speech sounds. Speech as well as nonspeech sounds elicit differences in the ERP waveform at different electrode sites (D. Molfese, 1978b; D. Molfese & V. Molfese, 1985). Such differences show consistencies across studies, and similar findings are noted across studies using different age populations and a range of electrode positions across the scalp. Differential ERP responses to speech sounds correlate highly with later language performance measures, and eventually, could provide the basis for the early identification of children at risk for developing language and language-related disabilities such as reading disabilities. If early identification in fact is possible, there will be a greatly increased opportunity to intervene much earlier in development than is currently possible. While these techniques are not currently in use for screening purposes it is hoped that further study may show that this technique is adaptable for such clinical applications. Given that the intervention effects clearly impact and enhance early brain development, one can readily imagine the benefits of interventions during the earliest periods of infancy that could potentially change the developmental outcomes of children at risk for cognitive disabilities before the overt behavioral indicators of the disability emerge.
The fact that ERPs in infancy (D. Molfese, Morse, & Peters, 1990) as well as in adolescence (D. Molfese & D. Molfese, 1996) correlate well with changes in behavior associated with even brief periods of learning suggest that such ERP measures might be used to monitor the effectiveness of intervention strategies. Failure to see changes in ERPs following some period of intervention could signal that the intervention is not effective in addressing remediation needs of the individual. Such information could be used to guide decisions to change intervention approaches in the hopes of increasing the positive impact on the child. In the past, clinicians sometimes invested considerable time in pursuing one intervention strategy, only to reach a point belatedly after a six- or nine-month period where they decided that the gains from that intervention had not met expectation. At that point, they may have decided to abandon this approach in favor of another intervention strategy. Unfortunately, they had already invested considerable time in the noneffective approach, and this time investment with minimal return was reflected in the disappointment of parents as well as in a further decline in the motivation of the child to invest further time. An alternate measure of intervention effectiveness that could monitor early success or failure could provide the clinician with the much-needed information to make decisions earlier to continue or switch interventions, thereby allowing them to make informed decisions in real time.
It is important to realize that the studies reported here have focused on children whose reading abilities and disabilities are grouped without consideration of subtypes of abilities. As there are more opportunities to use these ERP and behavioral techniques with children more specifically characterized by different types of cognitive strengths and weaknesses, it will be possible to learn more about how different regions within the waveforms, different scalp locations, and differences in amplitude and latency are related to general and specific cognitive skills. The combination of ERP and behavioral techniques has opened up an avenue with great promise for learning more about reading and cognitive abilities.
1The use of the term “discrimination” is intentional. Although discrimination is sometimes used synonymously with “detection,” the distinction sought here is that both behavioral and electrophysiological studies of speech perception have sought evidence that fine distinctions can be made between different stimuli composed of different combinations of consonant/vowel sounds and between speech sound when changes are made in format structure and frequency. In contrast, detection conveys that a stimulus was detected but may not convey that the individual responded to specific differences between stimuli.
Brady, S. (1991). The role of working memory in reading disability. In S. Brady & D. Shankweiler (Eds.), Phonological processes in literacy: A tribute to Isabelle Y. Liberman (pp. 129-161). Hillsdale, NJ: Lawrence Erlbaum.
Burhanpurkar, A., & Barron, R. (2001, April). Origins of phonological awareness skill in prereaders: Roles of language memory and proto-literacy. Poster presented at the biennial meeting of the Society for Research in Child Development, Minneapolis, Minnesota.
Callaway, C., Tueting, P., & Koslow, S. (1978). Event-related brain potentials and behavior. New York: Academic Press.
Eilers, R. (1977). Context-sensitive perception of naturally produced stop and fricative consonants by infants. Journal of the Acoustical Society of America, 61, 1321-1336. Eilers, R., Gavin, W., & Wilson, W. (1980). Linguistic experience and phonemic percep
tion in infancy: A cross-linguistic study. Child Development, 50,14-18.
Eilers, R., Wilson, W., & Moore, J. (1977). Developmental changes in speech discrimination in infants. Journal of Speech and Hearing Research, 20, 766-780.
Eimas, P., Siqueland, E., Jusczyk, P., & Vigorito, J. (1971). Speech perception in infants. Science, 171, 303-306.
Fletcher, J., Foorman, B., Shaywitz, S., & Shaywitz, B. (1999). Conceptual and methodological issues in dyslexia research: A lesson for developmental disorders. In H. Tager– Flusberg (Ed.), Neurodevelopmental disorders (pp. 271-305). Cambridge: MIT Press.
Lepp:nen, P. H. T., Richardson, U., Pihko, E., Eklund, K, Guttorm, T. K., Aro, M. & Lyytinen, H. (2002). Brain responses to changes in speech sound durations differ between infants with and without familial risk for dyslexia. Developmental Neuropsychology, 22, 407-22..
Liberman. A., Cooper, F., Shankweiler, D., & Studdert-Kennedy, M. (1967). Perception of the speech code. Psychological Review, 74,431-461.
Lisker, L., & Abramson, A. S. (1970). The voicing dimension: Some experiments in comparative phonetics. Proceedings of the 6th international congress of phonetic sciences (pp. 563-567). Prague: Academia.
Lyytinen, H., Ahonen, T., Eklund, K., Guttorm, T. K., Laakso, M-L., Leinonen, S., Leppanen, P. H. T., Lyytinen, P., Poikkeus, A-M., Puolakanaho, A., Richardson,
U., & Viholainen, H. (2001). Developmental pathways of children with and without familial risk for dyslexia during the first years of life. Developmental Neuropsychology, 20(2), 539-558.
McBride-Chang, C. (1996). Models of speech perception and phonological processing in reading. Child Development, 67, 1836-1856.
Molfese, D. (1978a). Left and right hemisphere involvement in speech perception: Electrophysiological correlates. Perceptual Psychophysiology, 23, 237-243.
Molfese, D. L. (1978b). Electrophysiological correlates of categorical speech perception in adults. Brain and Language, 5, 25-35.
Molfese, D. (1983). Event related potentials and language processes. In A. W. K. Gaillard & W. Ritter (Eds.), Tutorials in ERP research: Endogenous components (pp. 345-368). The Netherlands: North Holland Publishing Co.
Molfese, D. (1989). The use of auditory evoked responses recorded from newborns to predict later language skills. In N. Paul (Ed.), Research in infant assessment, 25, No. 6 (pp. 68-81). White Plains, NY: March of Dimes.
Molfese, D. (2000). Predicting dyslexia at 8 years of age using neonatal brain responses. Brain and Language, 72, 238-245.
Molfese, D., Freeman, R., & Palermo, D. (1975). The ontogeny of lateralization for speech and nonspeech stimuli. Brain and Language, 2, 356-368.
Molfese, D., & Molfese, D (1996, February). Brain responses discriminate learning from familiarity in adolescents. Paper presented at the International Neuropsychological Society meeting, Chicago, Illinois.
Molfese, D., & Molfese, V. (1979a). Hemisphere and stimulus differences as reflected in the cortical responses of newborn infants to speech stimuli. Developmental Psychology, 15, 505-511.
Molfese, D., & Molfese, V. (1979b). VOT distinctions in infants: Learned or innate. In H. A. Whitaker and H. Whitaker (Eds.), Studies in neurolinguistics ( Vol. 4, pp. 225-240). New York: Academic Press.
Molfese, D., & Molfese, V. (1980). Cortical responses of preterm infants to phonetic and nonphonetic speech stimuli. Developmental Psychology, 16, 574-581.
Molfese, D., & Molfese, V. (1985). Electrophysiological indices of auditory discrimination in newborn infants: The bases for predicting later language development. Infant Behavior and Development, 8, 197-211.
Molfese, D., & Molfese, V. (1997). Discrimination of language skills at five years of age using event related potentials recorded at birth. Developmental Neuropsychology, 13, 135-156.
Molfese, D., Molfese, V., Key, S., & Kelly, S. (in press). Event-related potentials: Their use in assessing, predicting, and augmenting language development. Developmental Neuropsychology.
Molfese, D., Morse, P., & Peters, C. (1990). Auditory evoked responses from infants to names for different objects: Cross modal processing as a basis for early language acquisition. Developmental Psychology, 26, 780-795.
Nelson, C., & Salapatek, P. (1986). Electrophysiological correlates of infant recognition memory. Child Development, 57,1483-1497.
Rockstroh, B., Elbert, T., Birbaumer, N., & Lutzenberger, W. (1982). Slow brain potentials and behavior. Baltimore: Urban-Schwarzenberg.
Ruchkin, D., Sutton, S., Munson, R., & Macar, F. (1981). P300 and feedback provided by the absence of the stimuli. Psychophysiology, 18, 271-282.
Simos, P. G., Molfese, D. L., & Brenden, R. A. (1997). Behavioral and electrophysiological indices of voicing cue discrimination: Laterality patterns and development. Brain and Language, 57,122-150.
Streeter, L. A. (1976). Language perception of two-month-old infants shows effects of both innate mechanisms and experience. Nature, 259, 39-41.
Vellutino, F., & Scanlon, D. (1987). Phonological coding: Phonological awareness, and reading ability: Evidence from a longitudinal and experimental study. MerrillPalmer Quarterly, 33,321-363.
Wagner, R., Torgesen, J., & Rashotte, C. (1994). Development of reading-related phonological processing abilities: New evidence of bidirectional causality from a latent variable longitudinal study. Developmental Psychology, 30, 73-87.
Wechsler, D (1991). Wechsler intelligence scale for children – third edition. New York: Psychological Corporation.
Wilkinson, G. (1993). The wide range achievement test – 3. Wilmington, DE: Jaslak Associates.
Dennis L. Molfese
Victoria . Molfese
University of Louisville
Address correspondence to: Dennis L. Molfese, Distinguished University Scholar, Chairman and Professor, Psychological and Brain Sciences, University of Louisville, Louisville, Kentucky 40292. Telephone: (502) 852-6775; fax: (502) 852-8904. e-mail: firstname.lastname@example.org.
Copyright International Dyslexia Association 2002
Provided by ProQuest Information and Learning Company. All rights Reserved