Reading phonologically means that children read words by converting letters into sounds. They could use grapheme–phoneme relations or intra-syllabic units, onset, and rime. There is little direct evidence that children who are learning to read rely on letter-sound relationships to help them read words. There is a great deal of evidence that they adopt a global strategy.

Authors: Usha Goswami & Peter Bryant

Source: Goswami, U. & Bryant, P. (2016). How children read words. Chapter 2 in a book Phonological Skills and Learning to Read. ISBN: 978-1-315-69506-8 (ebk).

This book chapter considers how children read words. Do they read words phonological or in some other way? Reading phonologically means that children read words by converting letters into sounds. They could use grapheme–phoneme relations or intra-syllabic units, onset, and rime. There is very little direct evidence that children who are learning to read do rely on letter-sound relationships to help them read words. There is a great deal of evidence that they adopt a global strategy.

Different ways to read words

• Children may read words phonologically or in some other way.
• Reading phonologically means that children read words by converting letters into sounds: using grapheme–phoneme relations or intra-syllabic units, or onset and rime.
• Although many English words could be read with the help of letter-sound relationships, not all can be read this way, because the English script is too unpredictable.
• Thus, there needs to be another way of reading.
• One approach is often called the visual (or global) strategy.
• It is possible to recognise particular familiar words as visual patterns.
• To read words as wholes without paying attention to the individual letters is to read in the same way as the Japanese read kanji characters.
• Children might initially learn to read words based on letter-sound relationships, and then much later they might come to recognise them as familiar wholes.
• Research has shown that beginning readers can read words without the help of phonology.
• Thus, even beginning readers can read whole words without analysing the grapheme–phoneme relations in those words; rather, it is possible to take quite easily to reading words as logograms.

Comparison between reading strategies

• It seems that better readers use more the grapheme–phoneme relations but weaker readers have problems with reading based on letter-sound relationships.

Conclusions

• There is little direct evidence that children who are learning to read rely on letter-sound relationships to help them read words.
• There is a great deal of evidence that these young children take easily and naturally to reading words in other ways: they adopt a global strategy, which means that they either recognise the word as a pattern or remember it as a sequence of letters.
• However, children depend heavily on letter-sound relations when they write words.

The authors review research about whether phonological awareness precedes or follows learning to read. They conclude that children become aware of phonemes as a result of learning to read. However, children are aware of syllables and detect rhymes and alliterations before they start to read.

Authors: Usha Goswami & Peter Bryant

Source: Goswami, U. & Bryant, P. (2016). Phonological awareness and reading. Chapter 1 in a book Phonological Skills and Learning to Read. ISBN: 978-1-315-69506-8 (ebk).

This book chapter is about phonological awareness and its relationship to reading. The authors review research about whether phonological awareness precedes or follows learning to read. They conclude that children become aware of phonemes as a result of learning to read. However, children are aware of syllables and detect rhymes and alliterations before they start to read.

Relationship between phonological awareness and reading

There are two possibilities:

1. Children learn how to divide words up into their constituent sounds because they are taught to do so when they learn to read.
2. Before children learn to read, they may build up phonological skills that then affect how well they learn to read.

Phonemes and other speech units

• The first and perhaps the most obvious approach is to break a word up into its syllables.
• The second approach involves much smaller phonological segments; this divides words into phonemes.
• A phoneme is the smallest unit of sound that can change the meaning of a word.
• The importance to the child of learning how to use the relationships between single letters and single phonemes (‘grapheme–phoneme correspondences’) has been widely recognised.
• The third approach is to divide words up into units that are larger than the single phoneme (units that themselves consist of two or more phonemes) but smaller than the syllable.
• It is usually possible to divide a syllable into two parts: an opening (or the onset) and an end (or the rime section).
• These are sometimes called intra-syllabic units.
• Words rhyme when they share common rimes.
• To know that there are categories of words that end with the same sound is a form of phonological awareness.
• Some studies show evidence that children become aware of phonemes as a result of being taught an alphabet.
• However, there are also other ways of becoming aware of phonemes.
• Young children stumble surprisingly badly when they have to make phonological judgements that depend on an explicit awareness of phonemes.
• However, there is evidence that children can delete a single phoneme in a word provided that this phoneme is the onset of the word.
• It has been found to be easier for young children to tap out the syllables rather than the phonemes of words.
• Overall, these results support the general idea that explicit knowledge about syllables precedes reading while an awareness of phonemes follows it.
• This evidence seems to support the notion that reading causes phonological awareness rather than the other way around.
• There is also evidence that children can detect rhyme and alliteration before they begin to read.

Conclusions and implications

Children’s progress in learning to read is probably the most important cause of awareness of phonemes. Children can easily judge whether words have the same onset and whether they have the same rime, and these are judgements that they can make some time before they learn to read. It is likely that the awareness of intra-syllabic units (which comes before learning to read) plays a causal role in children’s success in reading.

The effects of naming speed across languages and the nature of its relationship to reading are examined. The double-deficit hypothesis is also considered, in which students with both slow naming speed and low phonological awareness are hypothesised to be most at-risk of reading disability. Finally, the instructional literature regarding attempts to improve naming speed and use of naming speed as a predictor of response to intervention is reviewed.

Authors: John R. Kirby, George K. Georgiou, Rhonda Martinussen, & Rauno Parrila

Source: Kirby, J.R., Georgiou, G.K., Martinussen, R., & Parrila, R. (2010). Naming speed and reading: From prediction to instruction. Reading Research Quarterly, 45(3), 341–362, dx.doi.org/10.1598/RRQ.45.3.4

Current theoretical interpretations of naming speed and the research literature on its relation to reading are reviewed in this article. The effects of naming speed across languages and the nature of its relationship to reading are examined. The double-deficit hypothesis is also considered. This suggests that students with both slow naming speed and low phonological awareness are most at-risk of reading disability. Finally, the instructional literature regarding attempts to improve naming speed and use of naming speed as a predictor of response to intervention is reviewed. Naming speed is uniquely associated with a range of reading tasks across orthographies, and early identification would be improved by the inclusion of naming speed measures.

• Reading is complex, and it is not surprising that the factors contributing to reading success or failure are multiple.
• One of these factors is naming speed (or rapid automatised naming [RAN]), which refers to how quickly an individual can pronounce the names of a set of familiar stimuli.
• For example, a student may be shown a page of 50 colour patches presented in a semi-random order and asked to name them as quickly as possible.
• The four types of stimuli that have been used most often are colours, objects, digits, and letters.
• There is considerable evidence that naming speed is related to reading development (even after controlling other key variables) and slow naming speed is a characteristic of poor readers or those with dyslexia.
• Naming speed is one of many cognitive processes underlying skilled word recognition, which is extremely important for reading comprehension.
• The other key processes underlying skilled word reading include phonological awareness, phonetic decoding, orthographic processing, morphological processing, and vocabulary.

Why is naming speed related to reading?

• Naming speed and reading have so many common features that RAN has been characterised as a microcosm of reading. For example, both require eyes to move sequentially across the page, that the stimulus in fixation be encoded and can access its mental representation, and that the associated instructions for naming the stimulus be activated.
• Their differences are that reading does not always involve articulation (but naming speed does) and that reading usually involves the extraction or construction of meaning (but naming speed does not).
• One theory (e.g. Torgesen et al. 1994), hypothesises that naming speed tasks are related to reading through the more general construct of phonological processing because they measure the rate of access to stored phonological information in the long-term memory.
• Another theory (e.g. Bowers, 1995) suggests that naming speed is related to reading and is distinct from phonological awareness because it underlies or leads to orthographic processing. Orthographic processing occurs when groups of letters or entire words are processed as single units rather than as a sequence of grapheme–phoneme correspondences.
• Yet another view (e.g. Kail & Hall, 1994) states that naming speed is just one manifestation of general processing speed.
• These theoretical explanations are not mutually exclusive, and each may provide a part of the explanation for the close relationship between naming speed and reading.

Naming speed predicts performance on a variety of reading tasks

• Correlations between naming speed and word reading speed (or fluency) are generally higher than with word reading accuracy.
• The similarity of correlations of naming speed with real word and pseudoword challenges the orthographic interpretation of naming speed. By definition, pseudowords are not familiar words that could be recognised as orthographic units and should correlate less with RAN than words if orthographic interpretation applied.
• Timed measures of word or text reading are more strongly correlated with RAN than untimed reading measures (for example, reading accuracy and reading comprehension).
• Naming speed has survived many controls. It has been a significant predictor of reading after controlling statistically for verbal and nonverbal IQ, prior reading ability, attention deficit disorder, socioeconomic status, articulation rate, speed of processing, phonological short-term memory, phonological awareness, morphological awareness, and orthographic processing.
• The effect of naming speed is at least partly distinct from the effects of phonological awareness, orthographic processing, and processing speed, that affects the theoretical basis of RAN.
• The relationship between naming speed and reading may be curvilinear, stronger at lower levels and weaker at higher levels of reading ability.

Naming speed in different languages/orthographies

• Naming speed has been shown to be a strong concurrent and longitudinal predictor of reading ability in a wide array of languages/orthographies.
• It has been argued that naming speed is a stronger predictor of reading in orthographically consistent languages than in orthographically inconsistent languages. This may be because reading in consistent orthographies has been described in terms of reading speed measures (as opposed to reading accuracy measures in inconsistent orthographies). This gives an advantage to naming speed as a speeded measure itself, while consistent orthographies place less stress on phonological awareness and phonetic knowledge leaving more variance to be accounted for by naming speed.

Can naming speed be improved?

• In one study (Fugate, 1997), 1^st grade students in a letter-training (or comparison) group were compared. In the letter-training condition, each student received individual training and was provided with practice in letter naming (drill tasks in naming individual letters on flashcards). The letter-training group exhibited higher letter naming speed and oral reading fluency immediately post-test relative to the comparison group; however, there were no significant differences at follow-up.
• In another study (Conrad & Levy, 2011), Grade 1 or Grade 2 students were assigned to one of three groups: orthographic pattern training followed by letter naming training, letter naming training followed by orthographic training, or control (mathematics instruction). Letter naming speed improved only when the letter naming training followed the orthographic training; thus, promoting students’ orthographic awareness may help them to develop more efficient letter naming skills.
• The positive effect of an early literacy intervention programme on English-speaking kindergarten students’ phonemic awareness, letter naming speed, and word level reading skills has been identified (Nelson et al, 2005). The intervention programme comprised 25 lessons and targeted letter knowledge, phonemic awareness skills, understanding sentences, and rapid naming. Thus, a broad-based intervention that addresses emergent literacy skills (including naming speed training) can improve letter naming speed and reading skills in young at-risk students.
• Overall, studies suggest that naming speed is difficult to improve, and that students can improve in reading skills without accompanying improvements in naming speed.
• There is no evidence that improvement in phonological awareness improves naming speed.
• Several studies (e.g. Nelson et al, 2003) have demonstrated that slow naming speed is associated with a less positive response to reading instruction, independent of other characteristics such as behaviour and phonemic awareness.
• Students with slow naming speed may require more extensive instruction in word reading.
• Action video games can improve processing speed.

Implications

• Naming speed measures help to identify students with (or at risk of developing) serious reading difficulties or disabilities.
• It is important to be able to diagnose the source of the problems so that instruction can be tailored to address the specific difficulties.
• Naming speed is phonological, but it is also related to orthographic processing. In addition, it is related to general processing speed, but continues to predict reading after the latter is controlled. Other factors (such as working memory and other executive functions) may be involved.
• Current research indicates that students with slow naming speed are less likely to respond well to regular classroom and remedial instruction.
• It is not yet clear what form of remedial instruction students with slow naming speed require, although preliminary evidence suggests that multicomponent interventions are successful.

This review examines both rapid automatised naming (RAN) and reading fluency and how each has shaped our understanding of reading disabilities. The way automaticity that supports RAN affects reading across development, reading abilities, and languages is explored together with the biological bases of these processes. The contribution of collective studies of RAN and reading fluency to our goals of creating optimal assessments and interventions to help every child become a fluent, comprehending reader is also examined.

Authors: Elizabeth S. Norton & Maryanne Wolf

Source: Norton, E.S. & Wolf, M. (2012). Rapid automatised naming (RAN) and reading fluency: Implications for understanding and treatment of reading disabilities. Annual Review of Psychology, 63, 427–452, doi: 10.1146/annurev-psych-120710-100431

Fluent reading depends on a complex set of cognitive processes that need to work perfectly together. Rapid automatised naming (RAN) tasks provide insight into this system, acting as a microcosm of the processes involved in reading. This review examines both RAN and reading fluency and how each has shaped our understanding of reading disabilities. The way the automaticity that supports RAN affects reading across development, reading abilities, and languages is explored together with the biological bases of these processes. The contribution of collective studies of RAN and reading fluency to our goals of creating optimal assessments and interventions that help every child become a fluent, comprehending reader is also explored.

• To be a successful reader, it is necessary to rapidly integrate a vast circuit of brain areas with great accuracy and remarkable speed.
• This ‘reading circuit’ is composed of neural systems that support every level of language: phonology, morphology, syntax, and semantics – as well as visual and orthographic processes, working memory, attention, motor movements, and higher-level comprehension and cognition.
• When each of these components work smoothly with accuracy and speed, the reader develops what is called automaticity.
• As a cognitive process becomes automatic, it demands less conscious effort.
• The development of automaticity at all the lower levels of reading represents the great apex of development that provides us with the bridge to true reading with its capacity to direct cognitive resources to the deepest levels of thought and comprehension.
• It is imperative to comprehend the meaning of a text in order to go beyond what is on the page; in other words, to make connections to existing knowledge, to analyse the writer’s argument, and predict the next twist in the story.

History of research on reading disabilities

• Reading difficulties can be classified into two main types: developmental and acquired.
• The effects of developmental dyslexia begin in childhood and makes learning to read and developing reading skills difficult.
• Acquired reading difficulties (usually called alexia) often result from a brain trauma, such as an injury or stroke.
• In the late 1800s, physicians including Jules Dejerine and Adolf Kussmaul described patients who suffered brain injury with subsequent reading difficulty (despite intact language and vision) using the term ‘word-blindness’.
• John Hinshelwood and W. Pringle Morgan were among the first to describe ‘congenital word blindness’; that is, reading difficulty beginning in childhood that is not due to injury.
• Neurologist Samuel Orton developed a theory that inappropriate cerebral dominance accounted for the reversed letters and words sometimes observed in children with reading difficulties.
• He noted that many struggling readers he observed had average or above-average intellectual abilities, that perhaps as many as 10% of children might suffer from reading difficulties, and that reading difficulties were not likely due to a single brain abnormality.

History of RAN tasks

• In the 1960s, neurologist Norman Geschwind conceptualised the core deficit in alexia as a disconnection between the visual and verbal processes in the brain.
• He emphasised the importance of connectivity among brain regions (particularly association areas such as the angular gyrus) that act as a switchboard or relay station for different brain regions.
• The first RAN measure was based on an array of 50 coloured squares arranged in a grid with five rows, where each of five familiar colours was repeated in a random order.
• Geschwind did not believe that colour naming was an aspect of reading; rather, the neural processes supporting rapid serial colour naming might be similar to those involved in reading.
• Neurologist Martha Denckla (1972) discovered five boys who had dyslexia and were particularly slow and inconsistent in serial colour naming for their age, despite typical intelligence and colour vision.
• Together with Rita Rudel, Denckla created three other versions of the speeded serial naming test using objects, letters, and numbers as stimuli.
• They coined the term ‘rapid automatised naming’ to describe these tasks that were designed to measure the speed of naming familiar items.
• They found that RAN latencies were not related to how early certain stimuli were learned, but how ‘automatised’ the naming process was.
• Performance on RAN tasks differentiated children with reading difficulties from typical readers of the same age and from children with other, nonlanguage-based learning disabilities.

Toward a multi-componential view of reading and reading disability

• LaBerge and Samuels’ (1974) model of reading was one of the first to emphasise what we now know as ‘fluency’; the idea that successful reading depends on not only accuracy but automaticity of multiple cognitive and linguistic processes, requiring minimal conscious effort.
• Another possible core deficit associated with dyslexia is difficulty with phonological awareness (PA), which involves the explicit ability to identify and manipulate the sound units that comprise words.
• Isabelle Liberman (1971) promoted the idea that reading development depends on explicit awareness of the sounds of language and that possibly the greatest challenge facing young readers is learning to match the phonemes of speech with the graphemes that represent them in print.
• It is now generally agreed that PA is a crucial precursor to reading acquisition in alphabetic languages, and that many (if not most) children with dyslexia have PA deficits.
• We know that the reading circuit is intrinsically complex and that a lack of accuracy or automaticity at one of any number of levels can cause reading difficulties.
• Wolf and Bowers (1999) found that phonological awareness and RAN contributed separately to reading ability.
• They proposed the double deficit hypothesis (DDH) to demonstrate how children can be characterised in various subgroups according to their performance in each set of processes.
• According to this hypothesis, a deficit in either PA or naming speed can cause reading difficulties, with RAN deficits indicating weakness in one or more of the underlying fluency-related processes (and not simply a naming speed deficit).
• These deficits can co-occur, and children with a double deficit in PA and RAN characterise the most severely-impaired readers.
• Studies have suggested that 60% to 75% of individuals with reading or learning disabilities exhibit RAN deficits.

Characteristics and predictive value of RAN across development

• The measures that most consistently predict future reading difficulty in English are phonological processing/awareness, letter-name knowledge, and RAN.
• RAN-reading relationships are stronger in poor than in typical readers.
• Prediction in kindergarteners and prereaders. 5- and 6-year-olds often name the colour and object (nonalphanumeric) stimuli more quickly than letters and numbers (alphanumeric stimuli). With more practice and exposure, the alphanumeric stimuli become much more automatic and are named faster than nonalphanumeric stimuli. At this point, alphanumeric RAN becomes more strongly associated with reading ability. These differences underscore the importance of considering alphanumeric RAN separately from nonalphanumeric RAN stimuli.
• RAN and phonological processing tasks are valuable tools because both are excellent predictors of reading ability that can be assessed before children learn to read; thus, they can be used as easy indicators of risk for reading difficulties.
• In a large longitudinal study from kindergarten to 2^nd grade, RAN objects and PA predicted later outcomes on untimed passage comprehension in a similar way. However, RAN may have a stronger impact on timed reading measures (no timed measures were used in this study).
• Prediction in school-age and beyond. Longitudinal studies suggest that RAN scores measured in early school grades significantly predict later reading and spelling scores, and the predictive value of RAN seems to be stronger and more stable in poor readers than in typical readers. RAN seemed to be strongly related to decoding; however, it did not predict untimed reading comprehension measures in the later grades in typical or disabled readers. Unfortunately, the outcome measures did not include any timed reading or fluency tasks.
• RAN ability differences persist between young adults with and without dyslexia.
• A Dutch study found that the developmental trajectory of alphanumeric RAN reached an asymptote after age 16 but that RAN latencies for colours and objects continued to decrease through adolescence and adulthood.
• The correlations between alphanumeric RAN and reading are also significant through adulthood.

Cross-linguistic studies of RAN and fluency

• RAN and its relationship to reading have been studied in relation to many of the world’s languages, with findings following the general pattern of what is known about RAN in English: that RAN predicts reading (both concurrently and longitudinally) in both typically developing and reading-impaired populations.
• Alphabetic languages can be considered on a continuum based on the complexity of the mapping between sounds and letters (or phonology and orthography). The orthography of English is considered very deep (or opaque) because the correspondences from phonemes to graphemes are not consistent. Many other alphabetic languages such as German and Spanish have what is called a shallow or transparent orthography, where grapheme–phoneme correspondences are highly predictable. Learning sound-to-letter correspondences and decoding is more straightforward in these orthographically shallow languages.
• PA is important in early reading acquisition but as children essentially reach a ceiling in their ability to decode words accurately, a shift occurs in which the relationship between RAN and reading becomes much stronger. Children reading more transparent languages shift away from phonology earlier in schooling.
• Orthographic complexity affects the relationship of PA and reading ability; however, the relationship of RAN and reading is essentially consistent across languages.
• Nonalphabetic languages (such as Chinese and Japanese orthographies) are composed of thousands of characters that are essentially unrelated (or much less related) to phonemes. Phonological awareness is a weaker predictor of timed reading in Chinese. RAN is strongly correlated with reading in Chinese and accounts for additional variance after writing (orthographic) ability is controlled for.
• Overall, the differences in RAN across languages and orthographies are small in comparison with the number of similarities.

Contribution of neuroscience and genetics to understanding RAN and fluency

• Brain activation for reading-related tasks has been consistently identified in three main areas of the left hemisphere: the inferior frontal gyrus (IFG), the temporoparietal area, and the occipitotemporal area. For people with dyslexia relative to controls, the most consistent finding is an under-recruitment (hypoactivation) of the left temporoparietal and left occipitotemporal areas. Many individual studies have identified areas of the right frontal and temporal lobes that show greater activation in people with dyslexia relative to controls.
• There is some evidence that PA and RAN or fluency abilities may have separate neural substrates.
• Two studies (Misra et al., 2004; Christodoulou et al., 2011) found that for letter naming contrasted with fixation, the RAN task engaged the left inferior frontal gyrus, left posterior middle frontal gyrus, and bilateral inferior occipital areas.
• In one study (Christodoulou et al., 2011), adults with dyslexia had lower standardised RAN scores and lower in-scanner performance. The typical controls engaged several posterior areas in the occipital and parietal regions bilaterally more than the group with dyslexia, whereas the adults with dyslexia demonstrated greater activity than controls in a variety of bilateral temporal, motor, and left supramarginal gyrus (part of the temporoparietal area).
• From EEG research, it is known that different aspects of words are processed along a timeline. For example, initial visual processing occurs within the first 50 ms after a word is presented. Word-specific orthographic processing begins around 150 ms and executive and attention processes at about 200 ms, with phonological processes between 150 and 300 ms, followed by semantic and comprehension processes.
• The mismatch negativity (MMN) ERP component (a pre-attentive response to a difference within a series of auditory stimuli) has been studied as a possible correlate of automatic language processing. The MMN response is a significant predictor of reading outcomes (even better than a combination of behavioural assessments in children) and differs among infants with and without a family history of reading disability. Recently, it has been found that the MMN response in children was significantly correlated with RAN, timed single word reading, and timed connected text reading (but not with PA or untimed reading).
• In Magnetic Resonance Imaging (MRI), children with dyslexia showed smaller volumes of the pars triangularis area of the IFG bilaterally as well as an area of the right cerebellum. These anatomical measurements were also significantly correlated with RAN scores.
• It may be the case that extreme asymmetries of the planum temporale in either direction may induce risk for dyslexia.
• In a sample (Pernet et al., 2009), 100% of adults could be accurately classified as typical or dyslexic based on the volumes of the right cerebellar declive and left lentiform nucleus (part of the basal ganglia). The concept of a U-shaped curve, in which extreme values on either the high or low end can cause a disorder, could help explain conflicting findings in asymmetry.
• Because RAN and fluency depend on the speed and integration of multiple processes throughout the brain, the extent and quality of white matter pathways may play a substantial role in enhancing understanding of the biological basis of fluency-related processes.
• Heritability estimates for dyslexia range widely, from 0.3 to 0.7 (where a trait that was 100% determined by genetics would measure 1.0).
• Several researchers have reported a set of common genetic influences that affect PA, RAN, and reading (that is, they are all affected by some common genes) but that there are also separate genetic influences on PA and RAN.
• At least nine major candidate genes for susceptibility to dyslexia have been identified located on eight different chromosomes. Most of these are related to neuronal migration and axon growth in utero.

Implications of RAN and fluency for identifying reading difficulties, instruction, and intervention

• RAN tasks can be best used by educators and psychologists as part of a clinical assessment to identify a risk of reading and learning difficulties and as a measure of the development and efficiency of processes related to word retrieval and reading fluency.
• RAN tasks take only a few minutes to administer and require only modest training to administer and score. Using published normed measures, examiners can determine how a child’s RAN ability compares with what is typical for a given age or grade.
• A second important reason for assessing RAN and other fluency issues is that speed and automaticity are essential components of what it means to be a good reader, yet we tend to measure reading too often only in terms of accuracy.
• Children with phonological weaknesses who receive high-quality phonological interventions tend to improve both their PA skills and decoding ability. Although our best interventions can improve most reading and language variables, the RAN changes little from pre- to post-treatment, indicating that RAN taps a more basic index of processing.
• One technique that has been widely used as purported way to improve fluency is repeated reading. However, the entire approach of repeated reading measures yield changes in speed that may not be related to improvements in our sine qua non of reading, fluent comprehension.
• There are numerous programmes designed to address phonological decoding skills; however, few explicitly address multiple components of language (such as orthography, morphology, syntax, and semantics) with the goal of improving fluent comprehension.
• Children who received multi-componential interventions had significantly greater growth than other intervention groups on timed and untimed word and nonword reading and passage comprehension.
• The present review of the fluency research highlights the need for multi-componential interventions, especially for students with RAN or double deficits whose weaknesses are not adequately addressed by a phonological decoding programme.
• Successful intervention for reading disabilities depends on accurate assessment of a child’s profile in terms of both accuracy and speed across all levels of reading, from the sub-word to connected text. Multi-componential intervention programs that target phonology as well as multiple levels of language show the greatest promise in improving reading fluency.

Summary

• RAN measures act as a microcosm of the reading system providing an index of one’s abilities to integrate multiple neural processes.
• RAN and phonological awareness are both robust early predictors of reading ability, and one or both are often impaired in people with dyslexia. Longitudinal, cross-linguistic, genetic, and neuroimaging studies suggest that these two crucial reading-related processes should be considered distinct constructs rather than subcomponents of a single construct.
• It is advantageous to conceptualise fluent reading as a complex ability that depends on automaticity across all levels of cognitive and linguistic processing involved in reading, allowing time and thought to be devoted to comprehension.
• Multi-componential intervention programmes that target phonology as well as multiple levels of language show the greatest promise in improving reading fluency.

The present study examined why rapid automatised naming (RAN) is related to reading by manipulating one aspect of the RAN task at a time and by inspecting changes to the RAN-reading relation. The results of regression analyses indicated that seriality, access to phonological representations, and articulation play an important role in the RAN-reading relationship.

Authors: George Georgiou & Rauno Parrila

Source: Georgiou, G. & Parrila, R. (2020). What mechanism underlies the rapid automatized naming – reading relation? Journal of Experimental Child Psychology, 194, 104840. https://doi.org/10.1016/j.jecp.2020.104840

The present study examined why RAN is related to reading by manipulating one aspect of the RAN task at a time and by inspecting the changes to the RAN-reading relation. Accordingly, 136 Grade 2 English-speaking children and 121 university students were assessed on serial and discrete RAN, cancellation, yes/no naming, and oral and silent reading fluency. The results of regression analyses indicated that seriality, access to phonological representations, and articulation play an important role in the RAN-reading relationship. However, their effects were not equal for the two age groups or across the two reading outcomes.

• The ability of an individual to name as fast as possible highly familiar stimuli such as letters, digits, colours, and objects (RAN) is a strong predictor of reading.
• It has been shown that RAN continues to predict word reading after controlling for several proposed mediators.
• Pause time in RAN is more strongly related to word reading than articulation time.
• Serial RAN (when all stimuli are presented simultaneously in an array) produces stronger correlations with reading than discrete naming; however, discrete naming is a stronger correlate of discrete word reading than serial RAN.
• Serial RAN dominates the prediction of reading fluency over discrete naming and only RAN tasks requiring overt articulation correlate with reading fluency.
• RAN is related to reading because it requires serial processing and overt articulation of items accessed in long-term memory.

The study

The aim of the present study was to replicate and expand Georgiou et al.’s (2013) study with English-speaking second graders and university students.

Research questions:

1. Does seriality contribute to the RAN-reading relation?
2. Does set size contribute to the RAN-reading relation?
3. Does articulation contribute to the RAN-reading relation?

The data was gathered from 137 Grade 2 children and 121 university students from Canada. Serial RAN, discrete naming, cancellation, yes/no naming, and reading fluency of the participants were tested. Three versions of RAN were used: a) 5 letters repeated 10 times (5 × 10), b) 2 letters repeated 25 times (2 × 25), c) 25 letters repeated twice (25 × 2).

Findings

• The three serial RAN tasks correlated strongly with each other (r = 0.54–0.73 in Grade 2 and r = 0.78–0.84 in adults) and with oral reading fluency (r = −0.45– −0.56 in Grade 2 and r = −0.58– −0.63 in adults).
• In the regression analysis, where serial RAN (5 × 10) and discrete naming were analysed simultaneously, both naming tasks significantly predicted the reading outcomes in Grade 2 (betas ranged from −0.230 to −0.465), but only serial RAN significantly predicted the two reading outcomes in adults (betas were −0.595 and −0.313, respectively).
• When three types of RAN tests were analysed in the same model, both RAN (5 × 10) and RAN (25 × 2) accounted for unique variance in oral reading fluency in Grade 2; however, in adults, only RAN (25 × 2) predicted significantly oral reading fluency.
• When RAN (5 × 10), cancellation, and yes/no naming were analysed in the same model, RAN (5 × 10) was the only significant predictor of oral reading fluency in both groups (betas were −0.579 in Grade 2 and −0.520 in adults), and of silent reading fluency in Grade 2 (beta = −0.442).

Implications

• While both discrete naming and serial RAN predicted reading in Grade 2, only serial RAN predicted reading for adults. This implies that in early grades, children process words in reading fluency tasks one at a time (as in discrete naming). For this reason, discrete naming predicts reading. Serial naming is important irrespective of grade level because it involves processes specific to the sequential nature of the task (such as eye-movement control) and beyond the automaticity of name retrieval (tapped by discrete naming).
• This suggests that reading fluency requires both quick word recognition and efficient processing of words that appear in sequence.
• Both RAN (5 × 10) and RAN (25 × 2) accounted for unique variance in oral reading fluency in Grade 2, which may be expected given that most words in the reading tasks would be unknown to Grade 2 children and they would need quick access to phonological representations of graphemes to facilitate decoding.
• Neither cancellation nor yes/no naming predicted reading.
• RAN and the other measures accounted for a substantially lower degree of variance in silent reading fluency than in oral reading fluency, which suggests that articulation in both RAN and reading is partly responsible for their relation.

In this study, different views are examined concerning the relationships between working memory, long-term memory, and language processing. Thus, working memory is considered a gateway between sensory input and long-term memory (or rather a workspace), and working memory is considered not strictly tied to any particular cognitive system; rather, as drawing on the operation and storage capacities of a subset of components involved in language processing.

Authors: Fabienne Collette, Martial Van der Linden, & Martine Poncelet

Source: Collette, F.; Van der Linden, M.; Poncelet, M. (2000). Working memory, long-term memory, and language processing: Issues and future directions. Brain and Language, 71, 46–51.

In this study, different views are examined concerning the relationships between working memory, long-term memory, and language processing. Thus, working memory is considered a gateway between sensory input and long-term memory (or rather a workspace), and working memory is considered not strictly tied to any particular cognitive system; rather, as drawing on the operation and storage capacities of a subset of components involved in language processing.

• Working memory refers to a limited capacity system responsible for the temporary storage and processing of information while cognitive tasks are performed.
• According to the multicomponent model, working memory consists of a modality-free controlling central executive aided by two slave systems that ensure temporary maintenance of verbal and visuospatial information: the phonological loop and the visuospatial sketchpad.
• Working memory makes significant contributions to some aspects of language processing; namely, sentence comprehension, speech production, vocabulary acquisition, and reading.
• Working memory is considered a ‘gateway’ between sensory input and long-term memory, although this gateway perspective has been questioned because there are long-term memory effects in working memory tasks.
• Baddeley et al. (1998) postulated that two separate but interrelated short- and long-term phonological stores exist.
• The visually or auditorily presented verbal information is maintained in a phonological short-term store.
• The long-term phonological representations constitute a stable pattern corresponding to phonological structures that are frequently activated. Repeated presentations of phonological information will modify the representations in the long-term phonological system.
• Logie (1996) suggested that rather than working as a gateway between sensory input and long-term memory, working memory operates as a workspace.
• Accordingly, the storage components of working memory are not input buffers; rather, they serve as temporary buffers for the information that has yet to be processed or is about to be rehearsed overtly.
• Martin and Romani (1994) suggested that verbal working memory is not a specialised subsystem dedicated to short-term memory storage and separate from the language system. Rather, it draws on the operation and storage capacities of a subset of components involved in language processing.
• They concluded that the different levels of representation involved in memory span and language processing draw on specific resources, which may be conceptualised either as buffers specialised for particular types of representations or in terms of rate of decay that may differ for different levels of representation.
• These views clearly differ from Baddeley’s conception as they consider verbal short-term memory to be an integral part of the language system. For Baddeley, the working memory components are not strictly tied to any particular cognitive system.

Conclusion

There are neuroimaging studies that agree with the existence of two separate phonological stores, as postulated by Baddeley et al. (1998).

In this study, a meta-analytic approach was used to synthesise existing research. The results showed that overall classroom climate had small-to-medium positive associations with social competence, motivation and engagement, and academic achievement and small negative association with socioemotional distress and externalising behaviours.

Authors: Ming-Te Wang, Jessica L. Degol, Jamie Amemiya, Alyssa Parr, & Jiesi Guo

Source: Wang, M.-T., Degol, J.L., Amemiya, J., Parr, A., & Guo, J. (2020). Classroom climate and children’s academic and psychological wellbeing: A systematic review and meta-analysis. Developmental Review, 57, 100912. https://doi.org/10.1016/j.dr.2020.100912

In this study, a meta-analytic approach was used to synthesise existing research with the goal of determining a) the extent to which classroom climate as a multidimensional construct was associated with youth’s academic, behavioural, and socioemotional outcomes from kindergarten to high school; and b) whether the relations between classroom climate and youth’s outcomes differed by dimensions of classroom climate, study design, and child characteristics. Analysis included 61 studies. The results showed that overall classroom climate had small-to-medium positive associations with social competence, motivation, and engagement, and academic achievement and small negative association with socioemotional distress and externalising behaviours.

• Bronfenbrenner’s (1994; 2006) bioecological model posits that human development occurs within a set of interrelated contexts in which proximal processes mediate individual experiences, cognitions, emotions, and behaviours.
• It is through these proximal processes occurring between students and teachers that classroom climate provides the resources and opportunities for developing children and youth’s academic, socioemotional, and behavioural competencies.
• Classroom climate incorporates a multitude of dimensions, such as the organisation and structure of the classroom environment; pedagogical, disciplinary, and curriculum practices; and interpersonal relationships among students, peers, and teachers. These form a set of proximal processes that may mediate or moderate the influence of other contexts on children’s outcomes.
• There are at least three basic classroom components associated with student–teacher interactions: instructional support, socioemotional support, and classroom organisation and management.

The study

The present study used a meta-analytical approach to investigate the extent to which classroom climate was related to children’s academic, behavioural, and socioemotional outcomes. The study also examined whether the link between classroom climate and youth outcomes varied by classroom climate dimensions, grade level, study sample racial composition, family socioeconomic status, research methods, and study design.

Hypothesis:

1. Overall classroom climate is positively associated with youth’s social competence, motivation and engagement, and academic achievement, and negatively associated with socioemotional distress and externalising behaviours.

This meta-analysis consisted of 61 articles of which 34 examined instructional support, 42 socioemotional support, and 18 classroom organisation and management.

Implications

• Findings suggest that classroom contexts are associated with a wide range of developmental outcomes.
• Teachers and peers create opportunities for youths to engage in a variety of academic and social activities through instructional methods, classroom organisation, and the provision of socioemotional support.
• Students’ motivational beliefs are cultivated within the context of complex social and academic classroom networks, creating motivational orientations that either foster or undermine academic development.
• Classroom environments that meet students’ psychological needs are optimised for positive youth development.

Direct associations emerged between classroom-level adversity (CLA) and controlling instruction (positive), classroom management, and academic instruction (both negative). In addition, CLA was related to 1^st grade (G1) literacy (but not mathematics) directly and indirectly via classroom management (negatively) and controlling instruction (positively). There was a negative direct longitudinal association between CLA and 3^rd grade (G3) executive functioning, and indirect associations with G3 literacy and mathematics through G1 teaching practices and literacy.

Authors: Tashia Abry, Kristen L. Granger, Crystal I. Bryce, Michelle Taylor, Jodi Swanson, & Robert H. Bradley

Source: Abry, T., Granger, K. L., Bryce, C. I., Taylor, M., Swanson, J., & Bradley, R. H. (2018, May 24). First grade classroom-level adversity: Associations with teaching practices, academic skills, and executive functioning. School Psychology Quarterly. Advance online publication. http://dx.doi.org/10.1037/spq0000235

The authors examined direct and indirect associations between 1^st grade (G1) classroom-level adversity (CLA), G1 teaching practices, and student academic skills and executive functioning in G1 and 3^rd grade (G3). Direct associations emerged between CLA and controlling instruction (positive), classroom management, and academic instruction (both negative). In addition, CLA was related to G1 literacy (but not mathematics) directly and indirectly via classroom management (negatively) and controlling instruction (positively). There was a negative direct longitudinal association between CLA and G3 executive functioning, and indirect associations with G3 literacy and mathematics through G1 teaching practices and literacy.

• As part of young children’s microsystems, the early elementary classroom (including the peer context) plays a formative role in shaping academic and cognitive development.
• Collective characteristics of the students in a classroom group (such as academic skills, aggression, and internalising behaviours) have explained unique variations in developmental outcomes (beyond that explained by said characteristic) and other individual, family, and teacher factors at the student level.
• Theoretical propositions and limited empirical research indicate teaching practices as potential mechanisms that transmit the influence of collective student characteristics.
• Transactional and person-environment systems perspectives posit that learning and development stem from interactions and relationships between an individual and other actors within a system, such as those occurring within a classroom between and among individuals, their peers and the teacher(s).

The study

The present study examined G1 CLA, a classroom-level index of multiple child and family risk factors affecting the student group.

Research questions:

1. Is G1 CLA directly related to observed G1 teaching practices?
2. Is G1 CLA directly or indirectly associated with directly assessed student literacy and mathematic skills in both G1 and G3 and executive functioning in G3?

The study participants consisted of 1073 children. Teachers reported CLA. Teachers’ classroom management, controlling instruction, and amount of academic instruction were assessed by trained independent observers. Children’s literacy, mathematic skills, and executive functioning were directly assessed by trained research personnel.

Implications

• Higher CLA was directly negatively related to classroom management and academic instruction, and positively related to controlling instruction.
• CLA indirectly influenced students’ literacy and mathematics concurrently and longitudinally through influence on teaching practices.
• In the presence of the indirect associations, G1 CLA was directly and negatively related to G1 levels of student literacy and G3 levels of executive functioning.
• Teachers in higher-adversity classrooms were less effective in preventing disruption and maintaining and regaining order in their classrooms.
• The classroom context in the first years of schooling has a lasting influence on executive functioning beyond concurrent later-grade classroom milieus.
• G1 may function as a sensitive period for the formation of this important cognitive skill set.
• Both teachers and students may benefit from the purposeful diffusion of higher-adversity classroom compositions where possible.
• Teachers and students in higher-adversity classrooms may benefit more from investment compared to teachers working with more advantaged groups.

The aim of this study was to investigate which working memory and long-term memory components predict vocabulary learning. Short, phonologically native nonwords were learned best. Phonological sensitivity predicted learning of all nonword types (except short native nonwords), vocabulary predicted learning of only short native nonwords, and central attentional resources predicted learning of short nonwords (but not long nonwords).

Authors: Sergio Morra & Roberta Camba

Source: Morra, S. & Camba, R. (2009). Vocabulary learning in primary school children: Working memory and long-term memory components. Journal of Experimental Child Psychology, 104, 156–178. doi:10.1016/j.jecp.2009.03.007

The aim of this study was to investigate which working memory and long-term memory components predict vocabulary learning. A nonword learning paradigm was used in which children aged 8–10 years learned picture–nonword pairs. The nonwords varied in length and phonology. Short phonologically native nonwords were learned most effectively. Linear structural equation analyses demonstrated the influence of three constructs: phonological sensitivity, vocabulary knowledge, and central attentional resources on nonword learning. Phonological sensitivity predicted learning of all nonword types (except short native nonwords), vocabulary predicted learning of only short native nonwords, and central attentional resources predicted learning of short nonwords (but not long nonwords).

• It is universally recognised that vocabulary learning is a fundamental component of both the acquisition of native language and learning a foreign language.
• Baddeley and colleagues (1993; 1998) proposed that phonological short-term memory has an important role in constructing representations of the phonological form of new words in both native and foreign languages.
• They used terms such as ‘phonological memory skills’, ‘phonological loop abilities’, ‘phonological loop capacity’, and ‘phonological storage’ in an interchangeable way.
• Phonological memory is most commonly measured using forward digit span and nonword repetition.
• Baddeley and colleagues suggested that nonword repetition could be a relatively pure measure of phonological loop capacity.
• However, it has been suggested that nonword repetition involves various abilities, such as speech perception, phonological encoding, segmentation of speech sounds, and programming and executing articulatory instructions.
• Nonword repetition could depend on ‘phonological awareness’ (‘phonological sensitivity’).

Study

In this study, it is hypothesised that the construct of phonological memory could be deconstructed into three components: phonological sensitivity, attentional resources (M capacity), and rehearsal. The role of the predictors is likely to vary according to the different materials to be learned.

Participants were 161 elementary school children in Italy with a mean age of 9 years 8 months.

Conclusions and implications

The results of this study support the view that vocabulary learning cannot be accounted for by a single mechanism; instead, a cognitive system with several components is involved. The relative importance of these different components may vary depending on the characteristics of the words being learned. Consistent with the hypotheses, the predictor latent variables that represent the cognitive system involved in learning include both long-term memory and working memory components. Phonological memory can be decomposed into phonological sensitivity, rehearsal efficiency, and M capacity.

In this study, dual-process, latent growth models were used to examine patterns of bidirectional relations between letter knowledge and phonological awareness during preschool in a sample of 358 children. Letter-name knowledge and phonological awareness were bidirectionally related, where the initial level of each uniquely predicted growth in the other.

Authors: Mathew D. Lerner & Christopher J. Lonigan

Source: Lerner, M.D. & Lonigan, C.J. (2016). Bidirectional relations between phonological awareness and letter knowledge in preschool revisited: A growth curve analysis of the relation between two code-related skills. Journal of Experimental Child Psychology, 144, 166–183. http://dx.doi.org/10.1016/j.jecp.2015.09.023

In this study, dual-process, latent growth models were used to examine patterns of bidirectional relations between letter knowledge and phonological awareness during preschool in a sample of 358 children. Growth models were used to quantify the unique longitudinal relations between the initial level of each skill and growth in the other skill during the preschool year. Letter-name knowledge and phonological awareness were bidirectionally related, where the initial level of each uniquely predicted growth in the other. These findings extend the evidence of the relationship between letter knowledge and phonological awareness to supra-phonemic tasks, indicating that this bidirectional relation begins at an earlier point in the development of phonological awareness than previously reported.

• Proficient reading skill is a key determinant of quality of life.
• However, in 2013, only 34% of 4^th grade students and 27% of 8^th grade students demonstrated proficient reading skills.
• The developmental trajectory toward the level of reading skill a child will ultimately attain appears to be determined during the first years of formal education.
• Phonological awareness is one of the strongest predictors of reading skill.
• Phonological awareness appears to emerge in an ordered developmental progression.
• First, a rudimentary awareness of sounds within words is demonstrated by the ability to detect and manipulate larger units of sound.
• Next, a more sophisticated awareness of the sounds that compose spoken language is demonstrated by the ability to detect and manipulate smaller units of sound such as phonemes.
• Letter knowledge appears to be related to the development of phonological awareness.
• Vocabulary knowledge appears to influence the development of phonological awareness.

Study

The current study was designed to examine a) the possible bidirectional relation between letter knowledge and earlier emerging facets of phonological awareness, and b) the relationship between vocabulary size and growth in phonological awareness.

Hypotheses:

1. Children with higher initial levels of letter knowledge were expected to experience more growth in phonological awareness throughout the preschool year than that experienced by children with lower initial levels of letter knowledge.
2. Children with higher initial levels of phonological awareness were expected to experience more growth in letter knowledge throughout the preschool year than that experienced by children with lower initial levels of phonological awareness.
3. Children with higher levels of initial vocabulary were expected to experience more growth in phonological awareness throughout the preschool year than that experienced by children with lower initial levels of vocabulary.

Participants were 358 preschool children who were approximately 4 years of age at the start of the study.

Conclusions and implications

The results of this study demonstrated that growth in two important code-related skills—phonological awareness and letter knowledge—was partially dependent on the initial level of the other skill. There were bidirectional predictive relations between the initial status of each skill and growth of the other skill throughout the preschool year. Children with initially more letter-name knowledge experienced more growth in phonological awareness, and children with initially more phonological awareness experienced more growth in letter-name knowledge.