Moreover, if family members are activated via orthography, then the activation of cross-language family members depends on the degree of orthographic overlap between cognate representations. In this case, effects of family size should then interact with cognate type. We further investigate to what extent the effects of cross-linguistic orthographic overlap are task sensitive. To do so, we examine how cross-language family size affects the response to two types of cognates with complete and non-complete form overlap and non-cognates in a lexical decision task and a language decision task.
In English lexical decision Experiment 1 , participants must decide if the input letter string is an English word or not. Because both readings of a cognate will become activated on the basis of the input letter string, a cognate facilitation effect should arise that is dependent on the degree of cross-linguistic orthographic overlap thus, it will be larger for identical cognates than for non-identical cognates.
Given the demands of the task, participants should base their response read-out primarily on the English lexical representation and English language membership of the word Dijkstra, There will be relatively little time for the Dutch orthographic reading of the cognate to activate its family members; as a result, the activation of cross-language family members is expected to proceed indirectly and especially via semantic co-activation.
This should lead to facilitatory family size effects for both identical and non-identical cognates, with relatively little difference between both types. In contrast, in English-Dutch language decision Experiment 2 , participants have to decide as quickly and accurately as possible whether a presented letter string is an English word or a Dutch word. In the case of a cognate, a response conflict is expected to arise, because of the formal overlap between cognate representations.
As a consequence, the response competition between the two readings of a cognate should result in a cognate inhibition effect cf. Dijkstra et al. In this paradigm, co-activation of cross-language family members might be expected to lead either to facilitatory effects because both families strengthen the activation of the target word via semantics or to inhibitory effects because of response competition and because both families reinforce English and Dutch language nodes.
Especially in this mixed-language paradigm, in which the orthography is important for making a correct decision about the language membership of a word, an interaction between family size and cognate type is expected. In all, we test the hypotheses that morphological family size is sensitive to cross-linguistic overlap and to task demands by including different item types identical and non-identical cognates, and non-cognates in two bilingual experiments: English lexical decision Experiment 1 and English-Dutch language decision Experiment 2.
Twenty-nine native speakers of Dutch, mainly students of the Radboud University Nijmegen mean age All participants had English as their second language, having learnt English at school from around the age of All had normal or corrected—to-normal vision. Participants were paid or received course credits for participating in the experiment.
The stimulus set consisted of items, half of which were English words and half were pseudo-words. Only word items with an English lemma frequency of at least one per million in the CELEX lexical database and a length between three and eight letters were selected. All word items were mono-morphemic words. For each item, the English family size values and the English lemma frequencies per million were extracted from the CELEX database and logarithmically transformed.
The English morphological family of a word in CELEX consists of the number of English morphological derivations and compounds of a given word not including inflections; for studies on inflectional family size effects, see Bertram et al. The experimental items were 90 Dutch-English cognates. Forty of these items were identical in form in Dutch and English identical cognates; e. The non-identical cognates were always presented in their English form. The degree of orthographical overlap was calculated by the Levenshtein distance measure.
For each cognate item, the Dutch family size values and the Dutch lemma frequencies per million were extracted from the CELEX database and logarithmically transformed.
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Similar to the English family size values in CELEX, the Dutch morphological family of a word consists of the number of Dutch morphological derivations and compounds of a given word not including inflections. Half of the identical and half of the non-identical cognates had a large family size in Dutch, while the other half of these cognates had a small Dutch family size.
Moreover, the non-identical cognates with large and small family size were matched on Levenshtein Distance. The experiment further included 90 English non-cognate words that were matched to the set of cognates on English Frequency, English Family Size, and Length, and 20 English filler words that were matched on Length to the cognates and non-cognates.
Finally, pseudo-words were added that were matched to the set of word items on Length. These pseudo-words could be orthographically and phonologically legal words in English. Table 1 presents the characteristics of the cognate and non-cognate items. The order of word and pseudo-word items was then pseudo-randomized with the restriction that no more than four words or pseudo-words were allowed to follow each other. A new pseudo-randomization was made for each participant. Participants performed an English visual lexical decision task.
The participants were seated at a table at a 60 cm distance from the computer screen. The visual stimuli were presented in white capital letters 24 points in font Arial in the middle of the screen on a dark gray background. Participants were tested individually in a soundproof room. They were asked to react as accurately and quickly as possible.
After ms the target stimulus was presented. The stimulus disappeared when the participant pressed a button, or when a time limit of ms was reached, and a new trial was started after an empty black screen of ms. The experiment was divided in two parts of equal length. The first part was preceded by 20 practice trials. After the practice trials, the participant could ask questions before continuing with the experimental trials.
The two parts each contained experimental trials. The proportion of items from each condition was the same in the two parts of the experiment. Each part began with three dummy trials to avoid lack of attention during the beginning of the two parts. The end of the first part was indicated by a pause screen. The experiment lasted for approximately 16 minutes. This task was used to obtain a general indication of their proficiency in English in terms of vocabulary knowledge.
Finally, participants were asked to fill out a language background questionnaire. The total session lasted approximately 30 minutes. Data cleaning was first carried out based on the error rate for participants and word items. After removal of these items, we were left with data points on the word items. RTs from incorrect responses or null responses were removed from the remaining data set 4. This resulted in a data set with data points. Inspection of the distribution of the response latencies revealed non-normality.
Response latencies were analyzed with a linear mixed effects model with subject and item as crossed random effects see, e. We considered the following predictors: one lexical variable that is known to affect response latencies is target word frequency. In the remainder of this experiment, we will use the term English Frequency to refer to the logarithmical transformation of SUBTLWF as a predictor of target word frequency. Moreover, because bilinguals are expected to be sensitive to non-target language word frequency, we considered the logarithmically transformed CELEX values per million for Dutch lemma frequency Dutch Frequency.
The English family size values were collinear with the values of the logarithmically transformed values of English Frequency and Dutch Family Size. To remove collinearity, we regressed English Family Size on English Frequency and Dutch Family Size and used the resulting residuals as new predictors of English family size uncontaminated by English frequency. Moreover, we added the predictor Total Family Size the sum of the Dutch and English family sizes to account for possible increased facilitation due to large amount of global activation in the lexicon produced by the family members.
Besides these predictors for target and non-target language family size and frequency, other predictors were considered that could affect lexical decision latencies. Finally, we included Trial the rank of the item in the experimental list as predictor to account for learning effects during the experiment. We performed a stepwise variable selection procedure in which non-significant predictors were removed to obtain the most parsimonious model. Moreover, for each significant predictor, it was evaluated whether inclusion of this predictor resulted in a better model i.
Next, potentially harmful outliers defined as data points with standardized residuals exceeding 2. We then fitted a new model with the same significant predictors to this trimmed data set. The standard deviation for residual error was 0. The relevant statistics and corresponding coefficients of the final model are reported in Table 2. The significant partial effects of the final model are visualized in Figure 1. In both Table 2 and Figure 1C , the two levels of Identical Cognate are specified as True and False : the former corresponding to the set of identical cognates, and the latter to the set of non-identical cognates and non-cognates.
TABLE 2. Coefficients of the main effects and interaction effects of the final model, together with the standard error, t -values and p -values in English lexical decision Experiment 1. Partial effects of the significant predictors on response latencies in English lexical decision Experiment 1. The analyses showed a facilitatory effect on response latencies for English Frequency , while non-target language Dutch Frequency had an inhibitory effect.
Moreover, the final model revealed a processing advantage for identical cognates in comparison to non-identical cognates and non-cognates. While models including either the predictors Cognate or Word Type also produced significant facilitation effects for cognates in comparison to non-cognates, with the latter predictor indicating the largest facilitation effects for identical cognates, Identical Cognate turned out to be a better predictor than either Cognate or Word Type , suggesting that it is maximal formal overlap with Dutch words that is most helpful in order to make an L2 lexical decision.
Dutch Family Size has a significant facilitatory main effect on response latency. However, the significant interaction between Dutch Family Size and OLD, shows that response latencies were slower when a word has a large Dutch Family Size and fewer close orthographic neighbors. However, when a word has more close orthographic neighbors, a large Dutch Family Size is beneficial to word processing. As predicted, in the English lexical decision task of Experiment 1, Dutch-English bilinguals were sensitive to the frequency of the English target words.
English words with a higher frequency led to faster responses than lower frequency words. The effect of English Family Size of the target words was not significant. This is not surprising, because this factor was controlled for in order to allow non-target language Dutch family size effects to arise. Importantly, statistical analyses revealed a significant effect of Identical Cognate. This predictor turned out to be a better predictor than both Cognate and Word Type. Responses to identical cognates were faster than to non-identical cognates and non-cognates. This result supports the distinction between identical cognates and non-identical cognates.
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This dissociation between the two cognate types is in line with the findings of Dijkstra et al. As the major mechanism underlying these findings, Dijkstra et al. This then resulted in differences in semantic co-activation. There was a significant facilitatory main effect of Dutch Family Size. The interaction revealed a processing disadvantage for words with a large Dutch family size and more distant English orthographic neighbors. Interestingly, no significant interaction was observed between Dutch Family Size and Identical Cognate.
A lack of a difference in the direction of the effect or the effect size for identical and non-identical cognates would follow if the family size effect is exclusively semantically driven. Therefore, although a morphological relationship links a target word to its family members, it seems that the effect of the activation of these family members itself is not dependent on the degree of formal overlap they share with the target word.
However, while this may be true for the present situation in which bilinguals processed words in a largely monolingual task context, formal overlap might affect the family size effect when there is an explicit bilingual task context. This would especially be the case for a language decision task in which bilinguals have to judge the language membership of presented words e. This issue is investigated in Experiment 2. Here Dutch-English bilinguals carried out a Dutch-English language decision task, in which they had to decide whether or not a presented word was English or Dutch.
There were no pseudo-words in this task. In this task, the two readings of a cognate are linked to a different response. For instance, in Dutch-English language decision, the English reading work of the cognate work is linked to an English response, while the Dutch reading werk is linked to a Dutch response. Making a language decision on a cognate should therefore result in response competition between the representations of a cognate and slow down target word processing.
The task dependency of processing form similar words was earlier observed for both interlingual homographs Dijkstra et al. Moreover, Dijkstra et al. As was hypothesized in the Introduction, the activation of morphological family members of a cognate in language decision may affect target word processing in two ways. First, given that morphological family members of a cognate share part of their semantics with the cognate, activation of both within-language and cross-language family members could lead to facilitation for cognates with a large family size.
This will then reduce the cognate inhibition effect. Alternatively, activated morphological families may inhibit word processing given that they are linked to cognate representations that are in response conflict. Because family members are assumed to strengthen the activation of the target word to which they are linked, cognates with a large family size could then strengthen response competition and increase the cognate inhibition effect.
Moreover, if language-specific information is necessary in order to resolve a response conflict, then family size effects might be sensitive to the degree of form overlap between cognate representations. If this is the case, stronger inhibitory effects of the family size of both languages are expected in identical cognates compared to non-identical cognates, because they activate less language-specific information. Forty-five students of Radboud University Nijmegen mean age They were all native speakers of Dutch, having English as their second language.
They were first exposed to English at school, approximately from the age of They were paid or received course credits for participating in the experiment. The stimulus set consisted of items. The set consisted of 72 Dutch-English noun cognates and 96 non-cognate items. The 72 cognate items were 24 form-identical Dutch-English cognates and 48 Dutch-English cognates that were not identical in form. The 96 non-cognate items were 48 English non-cognates and 48 Dutch non-cognates. Because of the change from an English lexical decision task in Experiment 1 to an English-Dutch language decision task in Experiment 2, Dutch non-cognates and non-identical cognates had to be added to the stimulus materials.
Further, 20 of the 90 cognates and 20 out of 90 non-cognates that were used in Experiment 1 lexical decision were also used in Experiment 2 language decision. In Experiment 1, in order to observe Dutch family size effects, English family size was controlled for. As we wanted to look at response competition between the Dutch and English and the contribution of their respective family sizes, we had to vary the English and Dutch family sizes; as a consequence, the item set of Experiment 1 was not completely suited for Experiment 2 4.
The 48 non-identical cognates were either presented in Dutch or English orthography. A participant was presented with only half of the non-identical cognates in their Dutch form and the other half in their English form. Thus, for each participant, half of the items were Dutch and half of the items were English 24 identical cognates, which could be both Dutch and English. In total, there were 72 Dutch words 24 Dutch non-identical cognates and 48 Dutch non-cognates and 72 English words 24 English non-identical cognates and 48 English non-cognates. Furthermore, the two sets of 24 language specific non-identical cognates of version 1 were matched on Length and their language specific bigram frequency with the non-identical cognates from the same language in version 2.
Finally, the identical cognates were matched on Length, English Frequency, and English Family Size to the set of 48 non-identical cognate items, but could not be matched on Dutch Family Size and Dutch Frequency. The identical cognates have a lower mean Dutch Frequency and are less productive in terms of morphological family members than Dutch non-identical cognates.
These non-cognate items only had a noun-reading. Table 3 presents the characteristics of the cognate and non-cognate stimuli. The experiment consisted of two item blocks. The presentation order of the items within each item block was randomized for each participant with the restriction that no more than three cognates or non-cognates followed each other directly. Participants performed an Dutch-English language decision task.
Participants were tested individually in a sound proof room. They were seated at a table at a 60 cm distance from the computer screen. Participants first read the English instructions. They were informed that some words in the experiment could belong to both Dutch and English. In those cases, they were free to choose whichever response they liked.
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It remained on the screen until the participant responded or until a maximum of ms passed by. The experiment was divided into two parts of equal length. After the practice trials, the participant could ask questions before continuing with the test trials. The two parts each contained 84 experimental trials, and each started with three dummy trials. All participants obtained a score of or higher, which qualified them as intermediately or highly proficient in English.
The experimental session lasted approximately 18 minutes. The data were first screened for high error rates of participants and items. The participant accuracy mean ranged between Due to the small proportion of errors, data of none of the participants had to be excluded. However, four participants were excluded based on their slow mean RTs more than 2 SDs from group RT mean on the task relative to the mean RTs of the other participants. These included two cognate items priest and thee and one non-cognate item poem.
Note that responses to identical cognates, which have an identical form in English and Dutch, could never result in errors, because both an English or a Dutch response is appropriate. Incorrect items and null responses were removed from the remaining data set. This resulted in a dataset of data points. Inspection of the distribution of the response latencies revealed non-normality, with outliers in both tails.
As in Experiment 1, the data were analyzed with a linear mixed effects model. We considered the same predictors as in Experiment 1. Response Language and Previous Language were added as variables. Response Language was defined as the value Dutch or English of the response given to the preceding word. Previous Language corresponded to the language membership of the preceding word Dutch, English, or in the case of identical cognates, both. Moreover, we added the predictor Total Family Size the sum of the Dutch and English family sizes to account for possible increased response conflict due to large amount of global activation in the lexicon produced by the family members.
The same procedure as in Experiment 1 was applied to obtain the final model. Both predictors had an inhibitory effect on response latencies when both were included in the same model or when included in a separate model with only one family size measure. Moreover, Total Family Size had an inhibitory effect. Further, the predictor Dutch Frequency produced an insignificant coefficient and was removed from the model.
The model with Identical Cognate resulted in the best fit of the data. The SD for residual error was 0. The relevant statistics and corresponding coefficients of the final model are reported in Table 4. The significant effects of the final model are visualized in Figure 2. In both Table 4 and Figures 2E,G , Identical Cognate has two levels: True and False : the former corresponding to the set of identical cognates, and the latter to the set of non-identical cognates and non-cognates.
TABLE 4. Coefficients of the main effects and interaction effects of the final model, together with the standard error, t -values and p -values in English-Dutch language decision Experiment 2. Partial effects of the significant predictors on response latencies in English-Dutch language decision Experiment 2. A significant facilitatory main effect of English Frequency was observed.
Further, Total Family Size had an inhibitory effect on word processing. Moreover, OLD had an overall inhibitory effect, showing that the more distant orthographic neighbors are in terms of orthographic similarity, the harder it is to make a language decision. The main effect of Response Language revealed slower response latencies when Dutch was chosen as response language including responses to Dutch identical cognates and Dutch non-cognate words. Moreover, we observed an interaction between Total Family Size and Response Language demonstrating faster RTs for words with a large combined family size when the response language was Dutch.
There was no significant main effect of Identical Cognate when multiple interactions were included in the model. Identical Cognate interacted significantly with Total Family Size and revealed more inhibition with an increasing number of Dutch and English family members for identical cognates than for the other stimuli. Finally, Identical Cognate interacted with Previous Language showing faster response latencies for non-identical cognates and non-cognates compared to identical cognates when the response language was English.
The possibility of a response strategy was considered in a model predicting the response language chosen by the participant English or Dutch on identical cognates only. The same predictors that were considered in the analysis of the complete data set were included. Again, all non-significant predictors were removed. The relevant statistics and corresponding coefficients of the final model are reported in Table 5.
The significant interaction of the final model is visualized in Figure 3. TABLE 5. Coefficients of the model predicting the choice for response language in identical cognates in Dutch-English language decision Experiment 2. In order to obtain a more fine-grained picture, we further looked at non-linear relationships involving family size and cognate status. We therefore also analyzed the data by means of a generalized additive mixed model GAMM 6. The parametric part of the model contained the predictor IRL specifying the four combinations of Identical Cognate and Response Language , while the non-parametric part included tensor product smooths for the interactions of IRL with English Frequency and Total Family Size, and smooth terms for item and the interaction of Trial by participant.
Table 6 presents the coefficients for the main effects and interaction effects of the GAMM, together with the standard error, t -value and p -value. Figure 4 visualizes these effects. The results of the GAMM refined the results of the earlier linear mixed effects model as follows. TABLE 6. EN in Table 6. For non-identical cognates and non-cognates in English, Frequency and Total Family Size were not predictive.
Both effects were now facilitatory. The final two panels of Figure 4 show a large variability in subjects and items. For subjects, the factor smooths show large differences between fast and slow subjects, plus considerable variation in how they proceeded through the experiment. A second GAMM analysis was performed to analyze the choice for response language upon seeing an identical cognate. The model included the predictor Total Family Size as well as smooth terms for RT , item, and the interaction of Trial by participant.
Table 7 presents the coefficients for the main effects and interaction effects of the model, together with the standard error, z -value, and p -value. The upper left panel indicates that, as RT increases, Dutch is more likely to be selected. For shorter response latencies, however, there is considerable uncertainty about the estimate, suggesting guessing behavior. The upper right panel shows that, with incomplete information about the time series of responses when only identical cognates are included in the analysis , most of the participant differences concern a language bias on the part of the participants, some preferring Dutch, others preferring English.
The lower left panel indicates that the item effects were fairly normal. Finally, the lower right panel presents the effect of Total Family Size. The greater the joint English-Dutch family size, the more likely Dutch was as the response category. TABLE 7.
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In sum, the model on response latencies reveals from the shifts in intercepts, that when dealing with an identical cognate, participants were faster to choose English and slower to choose Dutch. When they chose English, a large Total Family Size mostly coming from Dutch family size worked against this decision upper left panel of Figure 4. When they chose Dutch, a greater Frequency facilitated this response. For English, lexical distributional properties had no predictivity.
From the analysis of the language selected for response, we see that participants based their ultimate decision on semantics: the better integrated a word was in the lexical network, as evidenced by a large family size, the more likely a participant was to opt for Dutch. As family sizes in English are probably smaller than those for Dutch for these participants, using family size as a guide to language is a rational choice. Of course, using family size as a rationale for selecting Dutch words must give rise to longer decision latencies when actually a decision is made favoring English.
This is exactly what we see in the reaction time data upper left panel of Figure 4. We conclude that participants performing this language-decision task thus operate under two potentially conflicting sources of information. First, the orthography provides, for non-identical cognates and non-cognates, but distributionally also for identical cognates, a bias toward one or the other language. Second, the semantic activation of a word, gaged by its family size, does not allow a language decision.
Participants in this experiment chose to optimize their responses by taking a large family as evidence for their native language. For English, this slowed their responses. The aim of Experiment 2 was to tap into the task dependency of the family size effect for cognates. In this experiment, we applied a language decision task in which participants had to decide if a visually presented word was either English or Dutch. Because in this task participants have to distinguish the two readings of a word, response conflicts are expected to arise upon seeing a cognate and these conflicts should result in a cognate inhibition effect.
We hypothesized that activation of both target and non-target language family members should strengthen the activation of both representations and add to the response competition in cognates.
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As was shown in a linear mixed effects model and confirmed by a GAMM, there was a clear dissociation between identical cognates and non-identical cognates in terms of response latencies. Identical cognates were processed more slowly than non-identical cognates and non-cognates, though the main effect of Identical Cognate disappeared when multiple interactions with Identical Cognate were considered in the linear mixed effects model.
The inhibitory effect can be explained as follows. This will induce response competition for identical cognates. The response competition is attenuated in non-identical cognates, because these items contain orthographic cues that resolve the language ambiguity, resulting in no significant inhibition for these types of cognates compared to language specific non-cognates. Phoneme awareness was assessed through a phoneme deletion task. Assessment tasks Reading task: The reading assessment in Setswana and English consisted of a word reading task and a pseudoword reading task.
The reading task was constructed using the numbers ranging from 2 to 12, excluding 1, 8 and 11, and the pseudowords were derived from these number words, a method based on that of Aro and Wimmer see Appendix A. As stated by Aro and Wimmer , the English pseudowords can have different readings due to the irregular nature of the phoneme to grapheme mapping.
This was taken into account when assessing the children's responses. It can be seen from Appendix A that the Setswana number words may be relatively more difficult as they are longer and have more syllables than the English words. Each participant was administered two reading lists, a word and a pseudoword reading list Appendix A. Each word list comprised the same items repeated twice in pseudorandom order.
Two trials were administered; a total of 36 word and 36 pseudowords were presented to each child. Similar to Aro and Wimmer's study, the items of each list were printed on separate lines of text on one page. The children were instructed to read the lists as quickly as possible. Children were given practice items prior to the test items. The reading of the word and pseudoword lists was timed and incorrect reading responses were noted. When a child paused for more than 4 seconds on a particular item, the child was encouraged to go on to the next item. The children's reading times were measured in seconds.
Four different orders of the lists were prepared. All four reading tasks showed high internal consistency ranging from. Letter knowledge: For the Setswana letter knowledge task some of the letters of the alphabet had to be excluded as they do not occur naturally in Setswana, i. The child was shown lower-case letters printed randomly on a card and asked to name each one of them.
A response was considered correct if the child either named the letter or pronounced a syllable beginning with the phoneme corresponding to the target letter. Reliability Cronbach's Alpha was. The test items were adapted and translated into Setswana see Appendix B. Common everyday words that were familiar to children were selected. There were 8 items for beginning phoneme deletion and 8 for end phoneme deletion in each language, hence 16 items were administered for each language.
Practise trials were given to participants prior to the experimental trials to ensure that the children understood the task. The same procedures as outlined in the PAT manual were followed. The child's responses were recorded as either correct or incorrect. Results Table 3 shows the mean correct and percentage correct scores and standard deviations for letter knowledge, word reading, pseudoword reading and phoneme awareness in Setswana and English.
Original Research ARTICLE
It can be seen that the phoneme deletion task was difficult for children in both Setswana and English, but in particular for Setswana. In addition, performance in Setswana letter knowledge was noticeably low. A comparison of performance in the reading and language tasks in Setswana and English by the children was conducted using a series of dependent t-tests. The scores for letter knowledge in Setswana and English were made equivalent by using proportions.
The first prediction which explored whether performance in reading measures in Setswana, the first reading language, was better than in English, the second reading language, was only partially supported. The reading of words in Setswana and English was not significantly different. Based on previous research, it was further predicted that pseudoword reading would be more difficult than word reading in both Setswana and English. The difference was particularly marked in English.
Repeated measures analysis of variance was conducted in reading times for reading words and pseudowords in Setswana and English. A Bonferonni post hoc comparison set at. Word reading was significantly different from pseudoword reading for both Setswana and English. In addition, pseudoword reading times for Setswana and English were not significantly different. These results reflect greater difficulty in pseudoword reading than word reading in both languages.
English word reading Table 5 presents the raw correlations between age, reading for word and pseudoword reading, letter knowledge and phoneme awareness in English and Setswana. A number of patterns are evident in these correlations. As expected, there were significant correlations between the different measures with the exception of age and letter knowledge in Setswana with the other measures. Noticeably, there is a high correlation between word reading in Setswana and pseudoword reading in Setswana. There is also a strong correlation between word reading in Setswana and English. In each of the hierarchical regression analyses, age was entered at step 1.
As research indicates that letter knowledge is such a robust predictor of reading it was entered at step 2 after chronological age Foulin, Then phoneme awareness was entered in step 3 to reveal unique variance of the measure. Age and letter knowledge in Setswana did not account for a significant proportion of the unique variance. Hence, for pseudoword reading in English both letter knowledge and phoneme awareness are advantageous. Age did not account for a significant proportion of the unique variance. In each of the hierarchical regression analyses, age was entered at step 1, letter knowledge at step 2, and phoneme awareness at step 3.
In Table 9 it can be seen that English letter knowledge is a significant predictor of word and pseudoword reading in Setswana, but English phoneme deletion is not a predictor. Phoneme awareness in English was found to be a predictor of pseudoword reading only in English, but not for word or pseudoword reading in Setswana. Letter knowledge in Setswana was not found to be a predictor of reading in Setswana or English. However, phoneme awareness in Setswana was found to be a predictor of word and pseudoword reading in Setswana and pseudoword reading in English.
Discussion The current study investigated letter knowledge, phonological awareness and reading ability in children living in Botswana, who learn to read in both Setswana and English.
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The relationship between letter knowledge, phoneme awareness and reading ability in Setswana and English proved to be quite complex. For reading in Setswana, the first reading language, phoneme awareness and not letter knowledge was found to be a predictor of both word and pseudoword reading.
However, when reading English, the second reading language, letter knowledge rather than phoneme awareness was found to be the prominent factor in predicting word reading, but in the more demanding task of reading pseudowords that requires knowledge of grapheme- phoneme correspondence rules, both letter knowledge and phoneme awareness proved to be advantageous.
For cross-language transfer there was a complex, non-symmetrical interplay between first and second language reading skills. In relation to transfer of skills from English to Setswana, phoneme awareness in Setswana was found to be a predictor of pseudoword reading but not word reading in English.
An awareness of phonemes in the first language appears to facilitate pseudoword reading in the second language. In relation to transfer of skills from English to Setswana, letter knowledge in English was found to facilitate pseudoword reading and word reading in Setswana, whereas letter knowledge in Setswana does not predict reading in either Setswana or English. In addition, in everyday usage in Botswana, English sounds are used for the letters of the alphabet. It is only when children begin primary school that Setswana letter sounds are introduced. English and Setswana are significantly different in phonology, and the sounds of the language map onto the alphabetic orthography pose a major challenge for children learning to read in Botswana.
Mapping Setswana's complex phonology onto the Roman alphabet appears to be problematic for young children learning to read in Setswana, and the influence of letter knowledge in the second language, English, is apparent. A common error response to the phoneme deletion task was for participants to delete the whole syllable rather than a single phoneme from the word, e. This applied to both Setswana and English.
This suggests that the syllable may be a more prominent or easily accessed unit in Setswana than the phoneme, as has been found in other languages, for example French in comparison with English Bruck et al. Although the children already had oral language proficiency in Setswana, this did not automatically mean that the children would find all aspects of learning to read Setswana easier than learning to read English.
A much more complex picture emerged as English letter knowledge appears to be more accessible to the children than Setswana letter knowledge. However interestingly, awareness of the phonemes of Setswana, a language the children have been previously exposed to and are familiar with, plays an important role in both word and pseudoword reading in Setswana and word and pseudoword reading in English.
This illustrates the influence of prior exposure and experience with the first reading language and cross-language transference of skills from the first reading language to the second reading language. The implications of these results are that children can be trained in phonological awareness in Setswana, which will then facilitate reading in both Setswana and English. Similarly results also indicate that knowledge of letters of the English alphabet will also facilitate reading in both languages. These are potentially useful practical applications for helping children to learn to read.
Most of the children did not know any of the letters of the alphabet or only knew a few letters for both Setswana and English, thus, highlighting a more major problem faced by learners in schools in Botswana. Literacy acquisition is also exacerbated by the fact that the national language may not be their real 'mother tongue'. In order to become a proficient reader children need not only to 'crack the code' of how the sounds of the language map onto the particular orthography, but in addition need to be able to understand the language of the text. This is a particular problem for children learning to read English which is a language of the school in which they are not fluent.
According to Cummins , a threshold level of L2 proficiency is necessary for L1 reading skills and knowledge to transfer to L2 reading. Previous studies have also highlighted that a skill in one language can be transferred to another Gottardo et al. The current study is a preliminary study investigating phonological awareness and reading in children in Botswana, as the number of participants are small. However it does highlight some of the problems that learners are confronted with in learning two very different orthographies concurrently. A positive transfer of phonological and reading skills across languages was found, but also script-specific factors were seen to be important, for example there was interference between mapping of sounds of the languages onto the alphabet, in particular for Setswana.
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