1 Semantic word representations in higher education students with dyslexia
Alisa Tillema (July 2015). Master Thesis Neurolinguistics, University of Groningen. Supervisor: dr. W. Tops.
Abstract
The current study examined verbal fluency and semantic word representations in students with dyslexia. Participants were first-year undergraduate students with (N=57) and without dyslexia (N=72). All participants were native speakers of Dutch. Students were tested for semantic or phonemic fluency. Semantic word representations were measured using different measures such as word frequency, Age of Acquisition (AoA) and Type-Token Ratio (TTR). Results showed evidence that lexical diversity of students with dyslexia was higher than that of students without dyslexia in a semantic fluency task. There were no differences found for phonemic fluency between both groups. There was also an effect of AoA for two categories. These findings indicated that students with dyslexia differed from students without dyslexia in semantic word representations.
Introduction
According to The Diagnostic and Statistical Manual of Mental Disorders Fifth edition or DSM-5 (American Psychiatric Association, 2013), dyslexia is an alternative term to refer to a pattern of specific learning difficulties characterized by problems with accurate or fluent word recognition, poor decoding, and poor spelling abilities. The DSM-5 considers dyslexia to be a type of neurodevelopment disorders (ND), namely a Specific Learning Disorder that disturbs the ability to learn or use specific academic skills, such as reading skills (speed, accuracy, and comprehension), writing skills (spelling, grammar and punctuation accuracy, and the organization of written expression), or arithmetic (calculation accuracy and fluency, memorization of arithmetic facts, number sense and accurate math reasoning), which are the foundation for other academic learning.
2 characterized by a persistent problem with the learning and/or effortless application of the reading and/or spelling of words (Dutch Dyslexia Foundation, 2008, p. 11). According to this definition dyslexia is a particular disorder in spelling and/or reading on word level.
Verbal memory deficits in young adults with dyslexia
Previous studies have shown that phonological deficits and problems with spelling and reading that characterize dyslexia persist into adulthood (o.a. Beaton, McDougall, &
Singleton, 1997; Shaywitz et al., 1999). More recent research showed that impairments in young adults with dyslexia are much broader than only a phonological deficit. Processes related to verbal memory, such as working memory and short- and long-term memory also seem to play an important role in young adults with dyslexia (Hatcher et al., 2002; Swanson and Hiesh, 2009; Chung et al., 2010; Callens et al., 2012).
Several studies into the cognitive profiles of students with dyslexia showed that besides phonological deficits, young adults with dyslexia also suffer from problems with verbal IQ, vocabulary, arithmetic, spelling, writing and specific cognitive processes as naming speed, short-term memory spans and verbal memory (Hatcher et al., 2002; Swanson and Hiesh, 2009; Chung et al., 2010). These problems with verbal memory seem to arise from problems with the long-term memory. Individuals with dyslexia have problems with recalling or retrieving verbal information from the long-term memory, either because of an additional weakness or because the verbal information has been processed less frequently (Callens, Tops and Brysbaert, 2012). In fact, it is known that implicit knowledge in long-term memory facilitates immediate recall and that less stored information is more demanding for working memory processes and therefore does not facilitates immediate recall (Baddeley, 2003). Poor retrieval of verbal information from long-term memory can result in word-finding difficulties. Some studies into dyslexia observed word-finding problems in individuals with dyslexia (a.o. Schwartz, 1999; Wolf & Bowers, 2000; Faust & Sharfstein-Friedman; 2003). Faust and Sharfstein-Friedman (2003) for example found that adolescents with dyslexia were slower and significantly less accurate in naming pictures of frequent objects than their peers. Just like Callens et al. (2012) the authors claimed that adolescents with dyslexia had additional
3 Semantic word representations
Szmalec, Loncke, Page and Duyk (2011) examined an underlying deficit in the long-term learning of serial-order information in adults with dyslexia. This effect is also known as the Hebb repetition learning effect and occurs when serial-order information from short-term memory gradually develops into a stable track in long term memory (Hebb, 1961). Hebb-learning implies different networks of word representations in the brain. When a part of the representation is stimulated, the entire network will be activated (Hebb, 1961). If
Hebb-learning is disturbed, recalling information from networks of word representation in long-term memory is more difficult (Szmalec, Duyck, Vandierendonck, Mata & Page, 2009). In the study of Szmalec et al. (2011), the Hebb effect was measured with a string of syllables. Remarkably, the adults with dyslexia knew which syllables were presented but failed to name the exact order in which the syllables were presented. With these findings the authors claimed that dyslexia is an impairment in serial-order learning (Hebb learning) and that this
impairment originates from problems with the abstract order representation. According to Szmalec et al. (2011) this impairment in Hebb learning does not only cause problems of just phonological processes, but also in learning new words. Following Page and Norris (2009), Szmalec and colleagues (2011) stated that a list of phonemes or syllables is likely to be learned as a single representation in memory that gets activated when the phonemes are presented in the correct order. The same applies for the learning of new words. An
impairment in learning the order of phonemes causes different semantic word representations in the brain and could explain why it is more difficult for individuals with dyslexia to learn or read new words than for typically developing peers. Hachmann, Bogaerts, Szmalec,
Woumans, Duyk and Job (2014) also examined serial ordering in adults with dyslexia. The authors assessed short-term recognition performances with verbal and nonverbal tests. The adults with dyslexia were as accurate as the control group for the verbal and nonverbal tests. However, the ability to recognize the serial order in which items were presented appeared to be affected in adults with dyslexia. Just like Szmalec et al. (2011) the authors claimed that dyslexia is characterized by a selective impairment in serial order for short-term memory and that this impairment leads to language problems, including impaired acquisition of word form representations.
4 any differences in Hebb learning in individuals with dyslexia. According to Steals and Van den Broeck (2015), their study had stronger power and the methodology they used was more precisely to detect Hebb learning than the methodology used in Szmalec et al (2011).
Therefore, Steals and Van den Broeck (2015) claimed that individuals with dyslexia were not impaired in Hebb learning and they did not differ in semantic representations of words.
In conclusion, individuals with dyslexia seem to have an additional impairment in recalling verbal information from long-term memory. However, there is not yet a consensus whether this impairment in recalling verbal information from the long-term memory results in different semantic word representations in individuals with dyslexia. Therefore, we want to examine semantic word representations further with a verbal fluency task. During a verbal fluency task, words must be constantly recalled from long-term memory using working memory processes (Daneman, 1990). The different types of words named in individuals with and without dyslexia in a verbal fluency task, will give more insight in the way words are learned and represented in the long-term memory for both groups.
Verbal fluency
Verbal fluency or the ability of word retrieval is described by Hirshorn and Thompson-Shill (2006) as the ability to produce a list of words within a category. This category can be semantic or phonemic. Multiple studies examined whether individuals with dyslexia differed in total number of words named within a semantic or phonemic category. For phonemic fluency applies that adults with dyslexia differ from adults without dyslexia in total words named, due to phonological deficits seen in dyslexia (a.o. Kinsbourne et al., 1991; Snowling et al., 1997; Hatcher et al., 2002). The findings about total number of words
5 semantically related items (horse, chicken, pig sheep) and then ‘switched’ to other clusters within the same category (elephant, tiger, lion) rather than producing random words. The terms ‘switching’ and ‘clustering’ they used, came from the research of Troyer, Moscovitch and Winocur (1997). Troyer et al. (1997) assumed that switching and clustering are two important components of fluency performance. First, clustering is accessing and using the mental lexicon by producing words within a semantic or phonemic category. Second, switching is a searching process and the ability to shift between clusters. The processes clustering and switching both correlated with semantic fluency, although in phonemic fluency, switching was more correlated than clustering. Troyer et al. (1997) mentioned that this might be the result of more subcategories in phonemic fluency than in semantic fluency, which makes switching more demanding for the phonemic fluency task than for the semantic fluency task.
To our knowledge no study in verbal fluency tasks examined the type of recalled words nor semantic word representations in individuals with and without dyslexia. Therefore, with a verbal fluency task examined semantic word representations in individuals with and without dyslexia by looking at Age of Acquisition (AoA), word frequency and Type-Token Ratio (TTR). Research showed that AoA and word frequency have an effect on how words are represented and processed in the brain (Steyvers & Tenenbaum, 2005; Lewis, Gerhand & Ellis, 2004). Moreover, AoA seems to have an effect on more language skills such as picture naming, word naming and episodic memory (Juhasz, 2005). AoA refers to differences in age at which specific words are acquired (Sailor, Zimmerman and Sanders, 2011). Steyvers and Tenenbaum (2005) claimed that words that are acquired in early age have a richer set of semantic connections than words that are acquired in later age. Also, AoA seems to have an effect on word frequency (Lewis et al., 2004). Lewis et al. (2004) found that words with a higher frequency and an early AoA have a long-term advantage over words that are acquired at a later age. These kinds of words were named more accurately and rapidly in healthy young adults, and thus more easily recalled from long-term memory than words with lower
frequency and later AoA. Studies in verbal fluency and effects of AoA and word frequency in other clinical groups showed that AoA had a greater effect on semantic fluency than
6 Because we are aware that AoA and word frequency have some limitations, as in (1) that they are not available for all words, (2) there exist different measuring sizes for word frequency and (3) that the norms of AoA and word frequency are based on written language and there are differences in spoken and written language (Ghyselinck et al., 2000, 2003; Moors et al., 2013; Keuleers et al., 2014; Brysbaert et al., 2014) we also examined the
TypeToken Ratio (TTR). Although TTR showed to be sensitive of sample size variations, it is proved to be a popular measure of lexical diversity in other clinical groups and gives insight in people’s vocabulary size (Miller, 1981; Owen & Leonard, 2002; Wright et al., 2003). The different types of words named (vocabulary) within a verbal fluency task may be influenced by the way the words are organized en represented in the brain. It is known that semantic representations of words are organized in categories in which related categories (for example zoo animals and farm animals) lay closer to each other than categories that are not related to each other (for example instruments and buildings) (Cukur et al., 2013). By looking at TTR within phonemic and semantic categories, we examined the different semantic word
representations in students with and without dyslexia.
This study
The aim of the present study is to examine semantic word representations in individuals with dyslexia. Following Szmalec et al. (2011), we hypothesize that there is a difference in semantic word representations of words between students with dyslexia and peer students without dyslexia. Individuals with dyslexia have difficulties recalling words from long-term memory, due to weak/different word representations in the brain (Faust &
7 Methods
Participants
Participants for the present study were first-year undergraduate students in higher education who participated in a longitudinal study about dyslexia in higher education (Callens et al., 2012). Callens et al. (2012) followed 200 students of whom 100 with a formal diagnosis of dyslexia throughout their bachelor years in higher education. The control group also
consisted of 100 students with no learning disorder, neurological or functional deficiencies. All participants were native speakers of Dutch and had normal or corrected-to normal vision. The students with dyslexia were diagnosed by trained diagnosticians conform the definition of the Dutch Dyslexia Foundation. Compared with peers, students with dyslexia had a
significant impairment in reading and/or spelling (< Pc 10). Despite of intensive reading and/or remedial teaching the impairment in reading and/or spelling persisted into their (young) adult life. Also, the impairment in reading and writing could not be attributed to external or individual factors, such as cultural background, intelligence or socio-economic status. Of the 200 students, 54 students with dyslexia (33 females) and 72 students without dyslexia (44 females) participated in this experiment. Four more Dutch-speaking first-year undergraduate students with dyslexia were recruited for the phonological verbal fluency tasks. These students were no participants in the study of Callens et al. (2012) but they met the same inclusion criteria. The students with dyslexia were matched with the control students for age and IQ (Table 1).
Table 1. Mean age and IQ for students with dyslexia and students without dyslexia Dyslexia Control Group
M (SD) M (SD) F p
Age 19.39 (0.76) 19.18 (0.62) 3.80 .053
IQ 106.91 (9.42) 110.23 (9.34) 3.13 .079
Note. M= mean; SD= standard deviation; F= test statistic; p <0.05
This study was approved by the Ethical committee of Ghent University. Students gave formal written consent and got a small financial compensation for their participation.
Materials
8 and writing skills, study skills and personality. For details about the full assessment we refer to the study of Callens et al. (2012)
For the present study, we focus on the measurements of verbal fluency tasks. We also administered an intelligence test, the Dutch version of the KAIT (Dekker, Dekker & Mulder, 2014) to match the dyslexia group with a control group. The KAIT consists of 10 subtests and categorizes two types of intelligence: a fluid intelligence (FIQ) and a crystallized intelligence (CIQ). The two intelligence scales together result in a total intelligence scale which is an indication of a person’s general intelligence.
Two verbal fluency tasks were administered, a semantic and phonemic fluency task. For the semantic fluency task, the students were asked to name as many words within four categories: (1) animals, (2) vegetables, (3) clothing and (4) means of transport. For the
phonemic fluency task, the students had to provide as many words as possible beginning with the letter /s/ and /n/. For both fluency tasks, there was a time limit of 60 seconds. The order of the fluency task was randomly over the students and groups. One student administered both the semantic fluency task and the phonemic fluency task.
Procedure
The fluency tasks were part of a large test battery administered during two sessions of each three hours. Halfway each session there was a break. Students could ask additional breaks if they wanted. The complete test battery of Callens et al. (2012) was divided into two parts. The order in both parts was fixed. According to an AB-design, the students started with part one or two. All tests were correctly administered according to the manuals guidelines. The test administrators were the two first authors of Callens et al. (2012) and a test
psychologist. Testing occurred in a quiet room.
All fluency tasks were audio recorded and issued later. For each student, the total number of words within the categories (tokens) and the number of unique words (types) were calculated. Next, TTR as a measure for lexical diversity was calculated, as well as AoA and word frequency. Word log transformed frequency and the norms of AoA and concreteness were selected from the database of the Dutch Lexicon Project if available. This database contains 44 million frequencies of Dutch words that appear in subtitles from movies and television. With a lexical decision tasks, Keuleers, Brysbaert and New (2010) validated the frequency of 14.000 monosyllabic and disyllabic Dutch words. Norms of AoA and
9 were considered as one type. Within the category means of transport, car brands were not scored, nor were semantic errors (words named that did not fit the category, e.g. potato within the category vegetables).
Analysis
First, we ran group comparisons for tokens within the semantic and phonemic categories, using a Mann-Whitney U test for each category. Next, we compared word frequency, AoA and TTR for both groups. Word frequency and AoA of the 25 most
mentioned words and word frequency and AoA of the 25 least mentioned words were selected for both groups and for each category. We assumed that the 25 most mentioned words would be less demanding for working memory processes than the 25 least mentioned words. The 25 most mentioned words are the words named by most students and thus words that come easily to one’s mind. The 25 least mentioned words were only named once by one student. These words are considered more difficult to retrieve or more demanding for working memory processes. Because not all variables were normally distributed, we used Mann-Whitney U tests to compare effects of word frequency and AoA for each category between groups. Last, type-token ratios were calculated for each category per group. Proportions for both groups were compared using a Z-test.
Results
Naming words within the semantic and phonemic categories
10 Table 2. Words named within the semantic and phonemic categories for both groups.
Dyslexia Control group
Variable N M Med N M Med U p
Semantic fluency Animals 37 22.81 22 51 25.63 24 754.5 0.11 Vegetables 37 13.43 13 51 13.65 14 878.0 0.58 Clothing 37 19.08 19 51 20.90 21 729.0 0.07 Transport 37 16.08 16 51 14.69 14 783.5 0.17 Phonemic fluency S-words 20 12.20 11 21 12.67 12 196.5 0.72 N-words 20 10.45 11 21 11.09 11 191.0 0.62
Note. N= number of subjects who participated in the test; M=Mean; Med= Median; U= Mann-Whitney U; P<0.05.
In table 2 it is shown that students with and without dyslexia did not differ in total words named for both the semantic and phonemic categories.
Word frequency and Age of Acquisition
For each category, Mann Whitney U-Test was used to determine the differences in word frequency and AoA for the 25 most mentioned and the 25 least mentioned words. Table 3 shows the effects of AoA for the 25 most and 25 least mentioned words.
Table 3. AoA of the 25 most mentioned words and the 25 least mentioned words for all categories for both groups.
Dyslexia Control group N Med N Med U p Semantic fluency Animals 25 5.08 25 4.78 305.5 0.89 (23) (9.02) (21) (9.37) (233.5) (0.85) Vegetables 25 7.08 25 6.90 312.0 0.99 (14) (8.55) (12) (11.0) (34.5) (0.01) Clothing 25 5.45 25 5.39 299.0 0.79 (18) (9.95) (20) (9.75) (154.5) (0.46) Transport 23 6.81 23 6.81 258.0 0.89 (12) (7.99) (8) (8.85) (45.5) (0.85) Phonemic fluency S-words 23 5.31 24 5.55 229.5 0.32 (18) (6.54) (21) (8.04) (114.5) (0.04) N-words 25 5.45 24 5.63 297.5 0.96 (22) (8.64) (21) (9.09) (201.0) (0.47)
11 As seen in table 3, students with dyslexia and students without dyslexia differ in AoA norms for the 25 least mentioned words for the categories vegetables and words beginning with /s/. Table 4 shows the effects of word frequency for the 25 most and 25 least mentioned words.
Table 4. Word frequency of the 25 most mentioned words and the 25 least mentioned words for all categories for both groups.
Dyslexia Control group N Med N Med U p Semantic fluency Animals 25 2.91 25 2.91 307.0 0.92 (25) (1.46) (23) (1.54) (261.5) (0.59) Vegetables 22 1.63 23 1.53 248.0 0.91 (14) (1.19) (11) (1.38) (69.0) (0.66) Clothing 25 2.53 25 2.50 282.0 0.55 (23) (1.04) (21) (1.04) (239.5) (0.96) Transport 24 1.79 23 1.90 254.5 0.65 (15) (1.11) (16) (1.08) (119.0) (0.97) Phonemic fluency S-words 24 3.18 25 2.89 268.5 0.53 (22) (2.13) (25) (2.28) (250.0) (0.59) N-words 25 3.64 25 3.76 301.0 0.83 (25) (1.95) (25) (1.87) (290.0) (0.66)
Note. N= mentioned words by students with or without dyslexia; Med= median; between brackets are the 25 least mentioned words; U= Mann Whitney U; P<0.05
Table 4 shows that there are no significant effects found for word frequency for the 25 most and 25 least mentioned words. Students with dyslexia and students without dyslexia do not differ in word frequency for all semantic and phonemic categories tested.
Lexical diversity
12 Table 5. Type-token ratio of both groups
TTR students with dyslexia TTR control group Z p Semantic fluency Animals .226 .187 1.95 .026 Vegetables .147 .114 1.68 .048 Clothing .199 .146 2.98 .001 Transport .198 .147 2.46 .007 Phonemic fluency S-words .691 .695 -.09 .539 N-words .511 .511 0 .500
Note. TTR= (number of types/number of tokens) * 100; Z=standard score; P<0.05.
Table 5 shows that the lexical diversity of students with dyslexia was significant larger than the lexical diversity of students without dyslexia for the semantic categories animals, vegetables, clothing and transport. The students did not differ in lexical diversity for the phonemic categories.
Discussion
Individuals with dyslexia seem to have difficulties recalling words from long-term memory (Faust & Sharfstein-Friedman, 2003; Callens et al., 2012). More specifically, Szmalec et al. (2011) and Hachmann et al. (2014) claimed that individuals with dyslexia differed from individuals without dyslexia in semantic word representations. However, because Steals and Van den Broeck (2015) failed to replicate the findings of Szamlec and colleagues (2011) and because there is no consensus yet on how words are represented in the brain, we decided to examine semantic word representations in young adults with dyslexia. More precisely, we looked at the effects of AoA and word frequency in phonemic and semantic fluency tasks. Besides that, we compared the lexical diversity of words recalled by both groups using the Type Token Ratio. To our knowledge this study was the first to focus on the semantic properties of the words recalled in verbal fluency tasks. Also our
methodology was for the first time applied for this purpose. The aim of this study was to get more insight in the way words are learned and represented in the long-term memory of individuals with and without dyslexia and to discover if there were differences between both groups.
13 categories. Kinsbourne et al. (1991) and Hatcher et al. (2002) also found that students with dyslexia did not show impairments in fast word retrieval within semantic categories, but in contrast to our findings, Kinsbourne et al. (1991) and Hatcher et al. (2002) showed that young adults with dyslexia performed worse in phonemic fluency tasks than typically developing controls. The authors claimed that these differences in phonemic fluency were caused by a phonological impairment which is often associated with dyslexia (Vellutino et al., 2004). Nevertheless, we have an alternative explanation for the fact that we did not find any differences between both groups. In fact, both groups found it more difficult to name words within phonemic categories rather than within semantic categories. This might be due to more possible subcategories (clusters) in phonemic fluency tasks than in semantic fluency tasks, which makes it more difficult to name words within phonemic categories than within semantic categories. For example within the semantic category animals, one can name pets, zoo animals, farm animals, reptiles etc., but within the phonemic category words beginning with /s/ or /n/, one can randomly name anything that starts with a /s/ or /n/ (from clothing to animals to vegetables etc). According to Troyer et al. (1997) and Hirshorn and Thompson-Shill (2006) clustering (accessing and using the mental lexicon by producing words within a semantic or phonemic category) is more correlated with semantic categories than phonemic categories in verbal fluency. On the other hand, switching (a search process and the ability to shift between clusters) might be more demanding for phonemic fluency tasks than for
semantic fluency tasks, which results in a lower number of words named within phonemic categories than within semantic categories for both groups.
In this study, semantic word representations were examined by looking at the effects of AoA and word frequency in words named by students with and without dyslexia. Because no differences were found, we decided to look at the 25 most and 25 least recalled words. Word frequency and AoA of the 25 most mentioned words and word frequency and AoA of the 25 least mentioned words were selected for both groups and for each category. There was only a significant effect for AoA within the 25 least mentioned names, more precisely for one semantic fluency category (vegetables) and for one phonemic category (words beginning with /s/). Traditional measures as AoA and word frequency that are often associated with semantic word representation failed to reveal significant differences between students with and without dyslexia. There are two possible explanations. First, this could mean that there is no
14 both groups. Indeed, norms for word frequency and AoA are not yet available for all words. Besides, there are different measuring sizes for word frequency and the norms of AoA. Word frequency is also often based on written language instead of spoken language which can make a big difference (Keuleers & Brysbaert, 2010).
To disentangle this doubt, we did a comparable comparison using the TTR,
considering this measure as a more suitable variable for lexical diversity. TTR is defined as the ratio of the total different words in a language sample to the total number of words in a sample and it is also proved to be a popular measure of lexical diversity in other clinical groups. Besides, it gives insight in people’s vocabulary size (Miller, 1981; Owen & Leonard, 2002; Wright et al., 2003). The different types of words named (vocabulary) within a verbal fluency task may be influenced by the way the words are organized en represented in the brain. It is known that semantic representations of words are organized in categories in which related categories (for example zoo animals and farm animals) lay closer to each other than categories that are not related to each other (for example instruments and buildings) (Ϛukur et al., 2013).
Comparison of TTR showed that students with dyslexia had a significant higher lexical diversity than students without dyslexia for all semantic categories tested. This means that students with dyslexia recalled a higher number of different words (types) than the control group for all semantic categories. These findings confirmed earlier findings from Szamlec et al. (2011) and Hachmann et al. (2014) that there is indeed a difference in semantic word representation between individuals with and without dyslexia. More precisely, these findings indicated that semantic word representations in students with dyslexia are differently organized than semantic word representations in students without dyslexia. Students with dyslexia named more different words (types) than their peers, which means that during a verbal fluency task, students with dyslexia used more different semantic word fields and less traditional and prototypical semantic word fields. For example: the words cow and horse are more traditional and prototypical than the words cockatoo and tapir. The students without dyslexia named more traditional and prototypical words. These are words that are strongly related to each other and words that have strong representations. Students with dyslexia named less traditional and prototypical words. Instead, they randomly named words within different semantic word fields. Nevertheless, students with dyslexia did not differ in lexical diversity for the phonemic categories from their peers. This also can be explained by
15 more difficult to name words within phonemic categories than within semantic categories. As explained earlier, this might be the result of more possible subcategories in phonemic
categories than in semantic categories (Troyer et al., 1997 and Hirshorn & Thompson-Shill, 2006 ).
We are aware that this study has a number of limitations. First, the traditional measures as AoA and word frequency can possibly not be suitable to reveal differences in semantic word representations between both groups. Norms for word frequency and AoA were not yet available for all words. Also, there are different measuring sizes for word frequency and the norms of AoA, and last word frequency is often based on written language instead of spoken language which can make a big difference (Keuleers & Brysbaert, 2010). Second, this study is a behavioral study and thus an indirect measurement of semantic word representations. Thereby, one have to realize that TTR is a good measurement for lexical diversity and indirect for semantic word representations, but sensitive to sample size variations.
For further research into semantic word representations in individuals with dyslexia, we suggest that ERP studies are a valuable complementary methodology. ERP studies would be a more exact and accurate measurement for semantic word representations and give more insight on how words are really learned and represented in the long-term memory for
individuals with and without dyslexia. Furthermore, a combination of online and offline tasks together may also lead to new insights. Last, it might be interesting to examine the first and last words named within semantic fluency tasks (for example the 10 first named words and the 10 last words named). In the current study, we examined how many times a word was named, but we did not look at the order in which words were named. The order of the words named and the characteristics of the words named (for example: the words belong or do not belong to the same categories/clusters) can give more insight in how words are represented and organized in the brain. Direct semantic representations and semantic connections will be examined this way.
Conclusion
16 students with dyslexia compared to students without dyslexia indicate and confirm earlier findings from Szmalec et al. (2011) and Hachmann et al. (2014) that students with dyslexia differ from students without dyslexia in the organization of semantic word representations. However, many questions remain about semantic word representations in students with dyslexia. Therefore, further studies into semantic word representations in students with dyslexia is needed.
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