University of Amsterdam
Research Report
Adult Second Language Acquisition
The Multilingual Advantage in
a Novel Language Word
Segmentation Task
Author:
Jonathan Krikeb
University of Amsterdam
Supervisors: Prof. Dr. Jaap M. J. Murre University of Amsterdam Dr. David A. Neville Donders Institute for Brain, Cognition, and Behaviour
Contents
1 Introduction 1
1.1 This Study . . . 1
2 Theoretical Background 2 2.1 The Multilingual Cognitive Advantage . . . 2
2.2 Working-Memory . . . 3
2.3 The Phonological Loop . . . 4
3 Methods 5 3.1 Participants . . . 5
3.2 The Main Task - Word Segmentation in a Novel Language . . 6
3.3 The Secondary Task - Word Recognition . . . 6
3.4 Memory Task - Reading Span . . . 6
3.5 Procedure . . . 8 3.6 Analyses . . . 8 4 Results 9 5 Discussion 15 6 Conclusions 20
List of Figures
1 Task 1 score, mono-multilingual boxpolot . . . 102 RSPAN score, mono-multilingual boxpolot . . . 10
3 Task 1 score divided to sentences and no. of languages . . . . 13
4 Task 1 score divided to sentences and RSPAN score . . . 14
5 RSPAN procedure . . . 26
6 Task 2 score, mono-multilingual boxpolot . . . 29
7 Task 1 score, multilingual spectrum boxpolot . . . 29
8 Task 2 score, multilingual spectrum boxpolot . . . 30
9 RSPAN score, multilingual spectrum boxpolot . . . 30
List of Tables
1 Repetition Chart . . . 72 Multilingualism of participants . . . 9
4 Linear regression for number of languages . . . 12
5 Languages . . . 27
6 recognition task words . . . 28
7 ANOVA table for L1 . . . 31
8 Variable names for linear regression analyses . . . 31
9 LM RSPAN . . . 32
10 LM mono-/multilingual . . . 32
11 LM education . . . 33
12 LM music 1 . . . 33
Abstract
This study set out to characterise the cognitive profile of a proficient adult foreign language learner. In an online design using the Neurotask platform, 47 participants (23 multilinguals) performed an auditory word-segmentation task in an existing novel language (Swahili). In addition, they were tested for word recognition, based on the first task; and the final test was a reading-span task to evaluate working-memory capacity. Results based on linear regression models suggest that increased number of languages, as well as a better working-memory capacity, improve performance in word-parsing. This is in line with theories connecting the phonological loop, a subset of working-memory, to better language acquisition skills. A surprising find of this research was that musical education provides a strong predictor of success in the parsing task. This has not been widely researched and would be an interesting topic of research for future studies.
Keywords: Adult SLA, multilingualism, working-memory, phonological loop, word parsing, reading-span, online
1
Introduction
Bilingualism is a common phenomenon in the world, more than half the pop-ulation speaks at least two languages (Bialystok et al., 2012). This, however, results mostly from need rather than desire as most bilinguals are a result of parents’ native tongues or immigration (Bialystok et al., 2012). These sce-narios differ substantially from learners who choose to learn a language later in life (also in the case of professional ”need”). What occurs in these situa-tions is that some people seem to have a certain advantage, an aptitude, for language learning that goes beyond the settings of place or specific language, while others seem incapable of picking it up at all (Biedro´n & Szczepaniak, 2009). It is a key question to ask what accounts for the success of some, as opposed to the failure of other, in the process of second language learning, and a question this paper will focus on.
Kormos & S´af´ar (2008) reflect on the work by Gardner (1985) who divided the variables relevant to second language acquisition (SLA) into ”affective, cognitive and personality-related individual differences” (p. 261). What is of interest to this study are the cognitive factors that include working-memory and (the vaguely defined) foreign language aptitude (Kormos & S´af´ar, 2008). Self-confidence and motivation, and a few other factors relating to the affec-tive and personality variables, are issues this paper will not delve into. Mem-ory in general, and working-memMem-ory (WM) in particular, is believed to play a major role in allowing foreign language learning (Biedro´n & Szczepaniak, 2012). Biedro´n & Szczepaniak (2009) and Biedro´n & Szczepaniak (2012) tried to examine individuals who are adept in language learning and con-struct a cognitive profile of these individuals. A major drawback of these studies is a lack of control group to function as a baseline.
division, as well as a continuous one - number of spoken languages - for com-parison. Additionally, the language tasks were all restricted to one domain, namely, auditory. Unlike many other studies (see Atkins & Baddeley, 1998, p. 538), in this study we used a real language’s short audio streams in order to simulate a real-life learning experience. A side-goal of this study was to achieve a high number of participants on the basis of the accessibility of the experiment. The general approach was to follow the findings of Biedro´n & Szczepaniak (2009, 2012) and attempt to characterise the cognitive profile of highly able language learners.
Using this design, we posed the following questions: are multilinguals better than monolinguals at word-parsing given little exposure to a novel language? Additionally, will multilinguals consciously recognise more words after a short exposure? And finally, does this word-parsing ability positively correlate with a larger WM capacity based on reading-span score?
2
Theoretical Background
2.1
The Multilingual Cognitive Advantage
Being bilingual, or multilingual, affects brain and cognition (Bialystok et al., 2012). In their conclusions, Bialystok & Craik (2010) discuss the find-ings showing that being bilingual is a component that positively correlates with a later onset of dementia. Following that, they raise the question of whether additional languages confer an even longer deferment of onset. In a later article, Bialystok et al. (2012) indeed refer to research (box 2, p. 247) that indicates that it is the case that multilinguals have a further advantage with postponing dementia onset, yet they remind us also that in most cases, multilinguals have made a choice to become such thus causation cannot be determined. This raises questions concerning their learning skills: are they innate or learned? Have polyglots managed to keep the window of learning open past the critical period?
A possible way to answer those questions can be provided by looking at the cognitive profile of bilinguals and examining whether it applies to multilinguals as well. For instance, improved task-switching and attention
control have been observed to be skills that bilinguals can apply in other domains beside language (Bialystok, 2008; Sorace, 2011). If these skills are developed by the need to switch between languages and filter-out what is irrelevant, then naturally multilinguals should be in possession of these skills. It is suggested that these are innate skills that apply to language learning in general, from the mother tongue and onwards in life, which remain open for people with an aptitude for language learning (Biedro´n & Szczepaniak, 2009). The sub-components of these skills are as follows: phonemic coding ability, grammatical sensitivity, inductive language learning ability, and associative memory (Biedro´n & Szczepaniak, 2009, p. 56).
One more aspect of learning to consider is a learning strategy. Ehrman & Oxford (1995) have suggested that with more experience at learning, i.e. more spoken languages, the strategy becomes more unconscious, rather than conscious, such as mnemonic techniques for instance. This suggests that a similar paradigm would be used by a learner of a second language (L2) as the one that has been used for the learning of the native language.
2.2
Working-Memory
It is generally accepted that human memory can be divided into several func-tional components: long-term, short-term, and working. Working-memory is a tool for the storage and manipulation of data that assists many cognitive tasks (Baddeley, 2003). It is classified separately from short-term memory, where information is held for short periods, of approximately 20 seconds, and then either discarded or committed further to long-term memory. WM capacity varies greatly between individuals as well as throughout a single individual’s lifetime. While the question whether WM is domain-specific or domain-general is still an open one, there is wide support for it being domain-general (Biedro´n & Szczepaniak, 2012).
had an advantage. In these studies, WM tasks showed no clear advantage for bilinguals. Despite this lack of evidence among bilinguals, apt language learners might be in possession of better WM as an assisting tool for learn-ing (Biedro´n & Szczepaniak, 2009). Immediate verbal memory span, verbal working-memory, and phonological short-term memory, have been identified as skills possessed by adept learners (Biedro´n & Szczepaniak, 2009, p. 57). Of particular interest to this paper is the discussion section of the paper by Biedro´n & Szczepaniak (2009), where they presented the results of their adept language learner: her memory, as well as her phonological abilities - distin-guishing and repeating phonemes of a foreign language - are both exception-ally good (Biedro´n & Szczepaniak, 2009, p. 66). This is further supported by their follow-up study where the accomplished multilinguals performed better in the memory tasks than the controls (Biedro´n & Szczepaniak, 2012).
It has been demonstrated that poor memory predicts poor language skills (Baddeley et al., 1998). This is in accordance with evidence relating WM ca-pacity in the native language (L1) and L2, showing strong correlation between the two, as well as reading comprehension in L2 (Harrington & Sawyer, 1992) and sentence comprehension (Miyake & Friedman, 1998), all of which suggest that WM ability is language independent (Mackey, 2006) and encouraging the idea that WM is domain-general. This relates also to a subcomponent of the WM, the phonological loop.
2.3
The Phonological Loop
The phonological loop, as a component of WM, stores information in phono-logical form for short periods and assists in rehearsal of this phonophono-logical information in order to preserve representations (Baddeley et al., 1998; Bad-deley, 2003; Biedro´n & Szczepaniak, 2012; Papagno & Vallar, 1995; Kaushan-skaya, 2012). Baddeley’s (2003) model divides WM into: the central-executive, the visuospatial sketchpad, and the phonological loop. The phonological loop theory states that the attention given to storage and rehearsal is directed by the central-executive function of the WM. Only through a developmental an-gle of study was the purpose of this component defined: it is a mechanism to facilitate the learning of new words during childhood (Baddeley et al., 1998).
However, Papagno & Vallar (1995) showed in their study that polyglots per-form better in terms of their verbal memory as compared to their control group based on auditory digit span and nonword repetition tasks (also repli-cated by Van Hell & Candia Mahn (1997)). This has been interpreted by Baddeley et al. (1998) to be an indication that a better phonological loop function is a sign for better language learning aptitude. Furthermore, accord-ing to Baddeley et al. (1998), also for average language users there is still the subvocal rehearsal mechanism, the second component of the phonological loop, that manifests only around the age of 7 and helps in further language maintenance and with second language acquisition (SLA).
Baddeley (2003) presented the role of the phonological loop in SLA by fol-lowing research that disrupted the phonological loop thus disrupting paired-associate learning in a foreign language, but not in the participants’ native language where semantic coding is dominant.
3
Methods
The experiment was hosted and run on the Neurotask online experiment plat-form (https://scripting.neurotask.com/) that is based on JavaScript (see appendix for the code).
3.1
Participants
Participants were recruited on the internet using e-mails and social media. Questions pertaining to their language knowledge were used to divide them into the two target groups (multilinguals = 24, monolinguals = 23) where late bilinguals were classified as monolinguals (only 4 pure monolinguals), and real (since infancy) bilinguals, like the rest of the multilinguals, were all speakers of 3 languages or more (maximum of 6). None were disqualified for prior
reading-span task, and an additional 5 participants failed to understand the reading-span task instructions and defaulted out of it. Therefore testing was done using the data from 47 participants. Participants’ ages ranged between 15 and 65 (mean = 30.06, SD = 12.46), they reside in 18 different countries and speak 14 different languages (see appendix, table 5).
3.2
The Main Task - Word Segmentation in a Novel
Language
The main task included 20 short sentences in Swahili that were recorded by a native speaker of the language from Kenya. A sample sentence is:
Nyumba yangu ni kijani which translates to: My house is green.
The participants could listen to every sentence only once and they were played in a fixed order. Immediately following listening, the participants were asked to type-in what they heard. Participants were instructed to separate the words using the space bar in order to evaluate their auditory segmentation skills (word-parsing). Moreover, the sentences had some repetition in them in order to allow learning (table 1).
3.3
The Secondary Task - Word Recognition
Following the main task, participants were given a recognition task. The task included ten Swahili words, played one at a time (see appendix for the words). After listening to each word participants were asked to indicate yes or no to whether they have heard this specific word in the previous 20 sentences or not. For instance, the word nyekundu (red) was included while the word chupa (bottle) was novel.
3.4
Memory Task - Reading Span
The third and final task was a reading-span task to measure working-memory capacity. The task was based on the work of Daneman & Carpenter (1980), Engle et al. (1999), as well as Unsworth et al. (2005), whose script was used
Swahili English Repetitions Mbwa Dog 8 Yangu/Wangu My 7 Paka Cat 6 Nyumba House 5 Nyekundu Red 5 Hukimbia Run/s 5 Kijani Green 4 Wake His 4 Gari Car 2 Mimi I 2 Samawati Blue 2 Wewe You 2 Table 1: Repeated words within the twenty sentences and number of repetitions.
as the basis for this experiment’s automated task. Despite the first two tasks being in the auditory domain, according to Turner & Engle (1989), a reading-span task should be indicative even across these domains.
In the task, the participants were given a sentence, for example: Dur-ing the final week of spaghetti, I felt like I was losDur-ing my mind. In the screen following the sentence the participants were given a choice between true or false, if the sentence was semantically valid or not. The sentences were grouped in inner-sets, increasing in size from 2 up to 7 sentences in a se-quence. Following each sequence of sentences and comprehension questions, the participants were asked to write down, in the correct order, the final word of each of the sentences (”mind” in the above example). The number of con-secutive sentences increased after the participant completed answering every
was recorded based on the highest level where they answered all the words correctly (see figure 5 in the appendix).
3.5
Procedure
Participants who logged into the experiment (https://scripting.neurotask .com/exp/d2NpKDnGQs) were greeted and presented with details concerning the experimental procedure and an informed consent page. Before the main task, demographic data was filled by the participants, including also educa-tion level, IQ score (if known to participant), past musical educaeduca-tion, and knowledge of Bantu languages - all factors with potential influence on the participant’s success in the tasks. Following that, a sound-check was made with a sample sentence. This check doubled as an understanding question for the task, since participants were asked to type-in what they heard and given feedback on it. After this one sentence practice, participants continued to the main task. Immediately after, a brief instruction page was presented, explaining the second (recognition) task, which followed. The final task, the reading-span memory test, was also preceded by a short training composed of two sets of two sentences with immediate feedback. The experiment ended with a score for the participants, a feedback page, address for complaints, and a final confounder-check question explicitly asking participants about their recognition of any of the Swahili words prior to the research and previous knowledge of the language.
3.6
Analyses
For the analysis, RStudio (version 0.98.1103) was used. The scores for the first task were defined as a variance (observed - expected) from the correct number of spaces in every sentence (n words - 1), and globally (sum of spaces). The second task had a score between 0 and 10 for the number of correctly recognised words. The third task, the reading span, was scored based on Engle et al. (1999) between 0 and 7. Based on the participants’ input, they were divided into mono- and late-bilinguals labelled as one group, the monolinguals, and the rest as multilinguals. This binary group division was used in addition to a discrete division where participants were divided
into a range from speaking one to six languages (see table 2). Initial testing # languages # participants 1 4 2 19 3 11 4 5 5 7 6 1 Total 47
Table 2: How many participants speaking how many languages
was made using a T-test (t.test function), followed by ANOVA (aov function) and regression analysis (lm function together with anova to compare the success of the models).
4
Results
Initial T-tests for the scores in the first task (variance from correct number of word-splits, figure 1), and the reading-span task (figure 2) both reveal no significance in terms of dividing the multilinguals from the monolinguals based on their scores in these tasks (task 1: t(937) = 0.56 and p = 0.57, task 3: t(44) = 0.43 and p = 0.67).
Figure 1: Task 1 score as function of binary division into mono- and multi-lingual. A score of 0 is ideal. Box composed of median, and 1st and 3rd quatiles. Whiskers are 1.5×IQR. Dots are outliers beyond that range.
Figure 2: Reading-span score as function of binary division into mono- and multi-lingual. Box composed of median, and 1st and 3rd quatiles. Whiskers are 1.5×IQR. Dots are outliers beyond that range.
T-test for task 2, the recognition task also yielded no significance based on the binary division (t(42) = 0.35, p-value = 0.73).
Further testing was performed using ANOVA. Based on the ANOVA, reading-span score is a significant predictor to the score in the first task (see table 3). Total number of spoken language as predictor reaches close to significance levels (p = 0.08). Possibly p-value will go below 0.05 if there was a larger sample of the number of participants who speak certain amount of languages (see table 2 . The word recognition task score has no predictive value (see table 3).
Predictor Df F p RSPAN 1, 938 6.73 0.0096 ** # of L 5, 934 1.94 0.08 Task 2 1, 938 0.04 0.84 Table 3: ANOVA scores. ’*’ p < 0.05; ’**’ p < 0.01
To get a better impression of the predictive value of the variables we constructed linear regression models. The binary function - mono-/multi-lingual - does not lead to prediction of success in the first task based on regression analysis (see appendix for models, tables 9 and 10). The discrete division into number of spoken languages shows significance as a predictor in regression analysis (see table 4 and figure 3). See appendix for full continuous and discrete analyses models, tables 12 and 13). Therefore, there is a main effect of number of spoken languages as predictor for task 1 score.
# L Estimate t value p 1 -0.39 -2.92 0.0036 ** 2 -0.34 -2.36 0.018 * 3 -0.33 -1.99 0.046 * 4 -0.44 -2.85 0.0044 ** 5 -0.34 -1.23 0.22 Table 4: The discrete division into num-ber of spoken languages by participants shows significance in linear regression analysis. ’*’ p < 0.05; ’**’ p < 0.01
Figure 3: Task 1 score as function of sentence separated into number of spoken languages. Every dot is a score of a user for the specific sentence, where 0 is a perfect score. The line is a regression line with standard error shading.
Models of increasing complexity including the reading-span score fit the data best with the number of spoken languages and reading-span as main effects (where both number of spoken languages and reading-span are clas-sified as a continuous variables) (β#L+RSPAN = -0.044, t(1, 937) = -2.59, p =
0.0096. See appendix for other RSPAN models, table 9. See also figure 4). Finally, among other potential predictors that were considered to offer al-ternative explanations to the data in the linear regression analysis, musical education as background seems to stand-out as it explains the data (task 1 score) best alongside the previous two discussed predictors (β#L+RSPAN+music
= 0.19, t(1, 936) = 2.47, p = 0.01). Also other order combinations of the same
Figure 4: Task 1 score as function of sentence separated into reading-span scores. Every dot is a score of a user for the specific sentence, where 0 is a perfect score. The line is a regression line with standard error shading.
5
Discussion
In this study, we used an online experiment to examine the difference be-tween mono- and multi-linguals in three tasks: word-segmentation in a novel language, word recognition in that same language, and a reading-span WM task. We found predictive values for reading-span, number of languages, and musical education, to success in a novel language word-parsing task.
Looking at the three hypotheses, the data offers satisfactory responses. First, looking at data from the first task, a division based on the data into monolingual and multilingual was not possible simply based on the T-tests. However, linear regression models show that a spectrum division along the number-of-spoken-languages curve does help explain the data and a future repetition study with a higher number of participants could confirm this. Thus, polyglots seem to be able to learn from examples how to segment words better than monolinguals.
Interestingly, in the second task, the recognition task, results showed no statistical significance when tested along the binary division nor the spectrum scale. Neither did it correlate with the reading-span scores in the linear regression analysis. This is surprising when considering figures 3 and 4 and the regression models derived from them indicating success in the first task predicted by WM capacity and number of spoken languages. Therefore we must discard our second hypothesis based on our data; monolinguals are as good as multilinguals in word recognition based on short exposure to novel language data. This could tie-in to theories of ”chunking” (Ellis, 2003). Ellis (2003) states that language learning is the result of the subconscious attention people give to certain collocations of morphemes, or even entire words. This study showed that in terms of recognition, there is a distribution of success that is independent of the number of spoken languages.
The results of the second task agree with linguistic theories stating that prior lexical knowledge is necessary for success in such a parsing task (Walley,
ditionally, this lack of significance is maintained when splitting participants over the multilingual spectrum (figure 8). Thus, all our participants, none of whom with prior knowledge of Swahili, succeeded equally in word recog-nition. However, this is not the case for real-time parsing, as the results of the first task indicate (see Figure 3). This does pose a challenge to the idea that vocabulary is a prerequisite. Our results also conflict with theories of categorical perception (Luo et al., 2006; Francis et al., 2003) that link se-mantic meaning directly to phonological input since our participants had no available semantic information. This would apply to results from the first, as well as the second, task.
As to the third hypothesis of this study, the data suggest that it falls in line with the literature connecting larger WM capacity and SLA skills (Biedro´n & Szczepaniak, 2009, 2012; Papagno & Vallar, 1995; Baddeley et al., 1998). The first task was exclusively in one domain, auditory, and therefore relied almost entirely on the phonological loop, which according to our data, correlates with WM capacity as it did in the aforementioned studies. An addition that this study makes to the previous research is our heterogeneous cohort of participants who do not necessarily have a passion for language learning - they do not have direct links to philology or linguistics which may be advantageous to learning as a factor by itself.
Since this study builds up on findings by Papagno & Vallar (1995), a challenge to their results also has implication for our analysis. Kaushan-skaya (2012) has challenged some of the findings of Papagno & Vallar (1995) in her paper. In her study phonologically-familiar, as well as unfamiliar, novel words both benefited from the bilingual advantage. In contrast, Papagno & Vallar (1995) found that polyglots and monolinguals performed similarly in learning familiar words. A problem with that challenge is that there is an underlying assumption that her bilingual participants indeed use the same cognitive mechanism as multilinguals (in the study by Papagno & Vallar (1995)), something that is not necessarily true. Furthermore, she addressed this in her discussion when referring to her past study finding no difference be-tween bilingual and monolingual phonological memory (Kaushanskaya, 2012, p. 483). Therefore, this study does not provide sufficient evidence to contra-dict our results.
Another challenge to the results of this study can be identified in the study made by Kormos & S´af´ar (2008). In their study, Kormos & S´af´ar (2008) tested WM capacity using backward digit span test and the phono-logical loop capacity using nonword repetition. They determined that WM and phonological loop are separate entities, due to lack of correlation between the two tasks’ performance results, as opposed to Baddeley et al.’s theory where the phonological loop is a subset of WM (1998; 2003). However, since they separated two tasks, digit-span, and nonword repetition, which are both classically WM tasks related to phonological loop, it makes placing of their results in respect to other studies difficult. Additionally, there is an assump-tion in the paper that an intensive L2 course simulates language learning in a natural setting, which is a problematic assumption that therefore complicates their drawn conclusions.
Kormos & S´af´ar’s (2008) findings also highlight a potential weakness of this study, namely, the reading-span task. The reading-span task was used in this study based on the work of Biedro´n & Szczepaniak (2012). To resolve this weakness, two approaches can be taken. One approach can follow the study by Atkins & Baddeley (1998). According to Atkins & Baddeley (1998), digit-span is the best test for the phonological loop. However, they also state that visual information is recoded phonologically, therefore the reading-span task should be just as accurate a measure of the phonological loop as the digit-span.
Another approach to take follows the line of reasoning presented by Kor-mos & S´af´ar (2008). According to this approach, reading-span is measuring WM capacity, as opposed to the phonological loop. Thus in our study there is a correlation of success in word segmentation, based on the first task, and WM, based on the reading-span task. The first task activates the phonologi-cal loop, while the latter relies on the visuospatial sketchpad which integrates visual information (Baddeley, 2003). In Baddeley’s (2003) model, these two
Our results do not entirely agree with those of Biedro´n & Szczepaniak (2012) who have demonstrated superior memory capacity, and WM specifi-cally, for their accomplished multilinguals. While both WM and the number of spoken languages predict success in the word-parsing task, the former does not predict the latter (nor vice versa). One explanation may attribute this to the fact that this study failed one of its side-goals: to get a higher num-ber of multilingual participants than Biedro´n & Szczepaniak (2012) (Biedro´n: n=28, this study: n=24). However, this study did have a heterogeneous, true random sample of multilinguals. Previous studies recruited their polyglots in biased manners. For example: a group restricted to Polish L1 speakers who all have some connection in their professional lives with language study (Biedro´n & Szczepaniak, 2012); students who may have never practised the foreign languages they learnt (see methods for experiment 1, Van Hell & Candia Mahn, 1997). Another explanation could be based on the findings of Kroll et al. (2002), that found a predictive value for reading-span for fluency in L2. Thus, differing levels of fluency among the cohort in this study would explain the lack of WM capacity prediction of learning skills (as represented by the number of spoken languages). Unfortunately, questions concerning level of fluency (to classify participants, as Biedro´n & Szczepaniak (2012) did in their study), and duration of familiarity with every language (age of arrival and length of residence in Slevc & Miyake (2006)), were not brought up due to time constraints (which is crucial in an online experiment with volunteer participants). Therefore, both of these are possible confounding variables that are beyond that scope of this study.
Another confounding factor to an experiment of this nature is IQ. Not enough participants had an IQ score to provide so we could not test the predicting effect of intelligence on success in the main task, even though it is believed to be a factor in the adept language learners’ toolbox (Biedro´n & Szczepaniak, 2009). To account for the lack of IQ score, we did collect the highest level of education accomplished by participants, yet this variable was not significant in accounting for the data.
One other possible confounding variable we did consider in this study is musical education. Our results mimic those found by Slevc & Miyake (2006) who find that musical skills may facilitate L2 phonological learning
even while other cognitive mechanisms decline due to age or illness. An interesting consideration here is that unlike WM capacity, where causation is impossible to determine, with musical education it would be rather easy to determine causality by a simple investigation of what commenced first, second language learning or music lessons.
In addition to the random sample of the population, another advantage of this study over past research is the realism of the design; a use of a real language, as well as the unfamiliarity of all the participants with the target language (Swahili) nor even its family of languages (Bantu), something that is very likely to give advantage to some participants over others (see discussion of Atkins & Baddeley, 1998). This design feature is an improvement to past studies that rely on nonword repetition tests to examine phonological WM.
A nonword repetition test might reflect the phonological loop capacity but not be very relevant to SLA if the nonwords conform to a given - known, as opposed to foreign - language. As Ellis & Sinclair (1996) mention in their discussion, the phonological skills measured in this test are relevant only for the relevant pieces of similar phonological characteristics between the native and the foreign language in question. Moreover, the process of ”equivalence classification” (Fledge (1987) in Ellis (1996), p. 95), where the cognitive system searches for similarities between new input and known language features, which is based in long term memory, is likely to cause interference in the form of lexical competition (Gaskell & Dumay, 2003, see also the model of Brown and Hulme (1996) in Baddeley (2003), p. 196). Gathercole (1995) solved some of these issues by splitting the nonwords into word-like, and less word-like, nonwords and indeed found an advantage in SLA amongst the children who could repeat the less world-like nonwords. The results from the current study build on Gathercole’s findings and extend them from nonwords to real words in a foreign language.
ac-cific vocabulary but rather syntactic categorisation of the words (see Valian, 1986). Furthermore, the incidental finding that music is an enabling factor in SLA is worthy of a deeper investigation since, as Slevc & Miyake (2006) noted, there is still not much research on the topic available.
6
Conclusions
In this study, we used three tasks to examine three hypotheses, of which two have been confirmed by our data: multilinguals were better able to segment words in the auditory domain when compared to monolinguals; WM capacity predicts success in an auditory parsing task. The third hypothesis was rejected as monolinguals and multilinguls recognised novel words with the same accuracy. An additional discovery made in this research is the predictive value of musical education to success in word-segmentation.
Acknowledgements
First, I would like to thank Prof. Jaap Murre for his supervision on the project and for the use of his Neurotask online testing platform. Additionally, I would like to thank David Neville for his support through this entire project, from the initial idea to the end. An enormous debt of gratitude goes to Dorcas Magai, who volunteered to translate and record the sentences needed for the experiment in Swahili.
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Appendix
Figure 5: The sequence of the automated reading-span task. Participants had to click through screens. This entire diagram depicts one inner-set of the task.
L1 L2 Additional Dutch Dutch Dutch English English English German French French Greek Polish German Hebrew Greek Hungraian Italian Spanish Korean Limburgish Mandarin Russian Spanish Table 5: The languages spoken by par-ticipants of the experiment including native(s) and additional languages
Swahili English Appeared Kimbia Run/s T Nyumba House T Nyekundu Red T Mimi I T Yake His F Kazi Work F Soma Read F Mti Tree F Chupa Bottle F Wao They F Table 6: The ten words for the sec-ond YES/NO recognition task
Figure 6: Number of correct answers in task 2 as function of binary division into mono- and multi-lingual. Box composed of median, and 1st and 3rd quatiles. Whiskers are 1.5×IQR. Dots are outliers beyond that range.
Figure 8: Number of correct answers in task 2 as function of levels of multilingual-ism. Box composed of median, and 1st and 3rd quatiles. Whiskers are 1.5×IQR. Dots are outliers beyond that range.
Figure 9: Reading-span score as function of levels of multilingualism. Box com-posed of median, and 1st and 3rd quatiles. Whiskers are 1.5×IQR. Dots are outliers beyond that range.
Effect Df F p Task 1 7, 932 1.23 0.28 RSPAN 7, 39 1.74 0.13 Table 7: ANOVA scores for L1 as predictor to scores.
Factor Code Description
Task 1 score S1 Variance in every sentence from the correct number of word-splits
Task 2 score Score2 Number of correct answers in the sec-ond task
Reading-span score RSPAN Score between 0 and 7 for the reading-span task
Number of spoken languages no Languages Number of spoken languages in addi-tion to native
Multilingual Polyglot Binary for Multilingual (> 2) or not Education level edu Highest accomplished level of
educa-tion
Age Age Age
Musical education music Binary for background including mu-sical education
Model 1 S1∼RSPAN
Model 2 S1∼RSPAN + Score2
Model 3 S1∼RSPAN + Score2 + Polyglot
Df RSS Sum of Sq F Pr(> F ) Model 1 938 1120.7
Model 2 937 1120.7 0.00094 0.0008 0.97 Model 3 936 1120.7 0.64 0.54 0.46 Table 9: Reading-span as predictor in linear regression mod-els. Reading-span is continuous.
Model 1 RSPAN∼Polyglot
Model 2 RSPAN∼Polyglot + Age
Model 3 RSPAN∼Polyglot + Age + Score2
Df RSS Sum of Sq F Pr(> F ) Model 1 45 208.24
Model 2 24 118.65 89.59 0.89 0.6 Model 3 23 109.41 9.24 1.94 0.18 Table 10: Mono-/multilingual and age as predictors lin-ear regression models.
The code of the experiment: // I n t r o d u c t i o n
i n s t r u c t i o n ( ” Thank you f o r p a r t i c i p a t i n g . T his e x p e r i m e n t i s i n t e n d e d t o t e s t working−memory i n r e l a t i o n t o l a n g u a g e l e a r n i n g . ” , ” Continue ” , ” I n t r o d u c t i o n ” ) ; i n s t r u c t i o n ( ” I t i s made up o f t h r e e s e c t i o n s : I n t h e f i r s t you w i l l be a s k e d t o t y p e down t h e s e n t e n c e s you h e a r . <br> I n t h e s e c o n d you w i l l be p r e s e n t e d w i t h a s e t o f y e s / no q u e s t i o n e s r e l a t e d t o t h e s e s e n t e n c e s . <br> And i n t h e t h i r d s e c t i o n you w i l l f o l l o w a memory t a s k . ” , ” Continue ” , ’ I n t r o d u c t i o n ’ ) ; i n s t r u c t i o n ( ” I t w i l l t a k e a p p r o x i m a t e l y 20 m i n u t e s t o c o m p l e t e and w i l l r e q u i r e your f u l l a t t e n t i o n . ” , ”
Model 1 S1∼edu
Model 2 S1∼edu + RSPAN
Model 3 S1∼edu + RSPAN + no Languages
Df RSS Sum of Sq F Pr(> F ) Model 1 935 1125.6
Model 2 934 1116 9.6 8.04 0.0047 ** Model 3 933 1114.8 1.21 1.02 0.31 Table 11: Education as predictor linear regression models. ’*’ p < 0.05; ’**’ p < 0.01
Model 1 S1∼music
Model 2 S1∼music + RSPAN
Model 3 S1∼music + RSPAN + no Languages
Df RSS Sum of Sq F Pr(> F ) Model 1 938 1123.7
Model 2 937 1115.7 8 6.75 0.0095 ** Model 3 932 1104 10.98 1.85 0.1 * Table 12: Music as predictor linear regression models. Number of languages is discrete and reading-span is con-tinuous. ’*’ p < 0.05; ’**’ p < 0.01
Continue ” , ’ I n t r o d u c t i o n ’ ) ;
i n s t r u c t i o n ( ” There w i l l be no p e r s o n a l d a t a c o l l e c t e d on you and a l l i n f o r m a t i o n you f i l l i n w i l l be used o n l y f o r s t a t i s t i c a l a n a l y s i s . ” , ” Continue ” , ”
I n t r o d u c t i o n ” ) ;
r a d i o (”<h2>I n f o r m e d Consent </h2>
I f you a g r e e t o p a r t i c i p a t e i n t h i s e x p e r i m e n t and a l l o w your d a t a t o be used anonymously c h e c k t h e ’ yes ’ b u t t o n .
Model 1 S1∼music
Model 2 S1∼music + RSPAN
Model 3 S1∼music + RSPAN + no Languages
Df RSS Sum of Sq F Pr(> F ) Model 1 938 1123.7
Model 2 937 1115.7 8 6.75 0.0095 ** Model 3 936 1111 4.67 3.94 0.047 * Table 13: Music as predictor linear regression models. Number of languages and reading-span are continuous. ’*’ p < 0.05; ’**’ p < 0.01 F u l l −c o n s e n t ’ ] , ” c o n s e n t ” ) ; i f ( r e s p o n s e . c o n s e n t == ’ F u l l −c o n s e n t ’ ) { i n s t r u c t i o n (”<h3>Goal o f t h e e x p e r i m e n t </h3> The g o a l o f t h i s e x p e r i m e n t i s t o examine t h e v a r i a n c e i n l a n g u a g e l e a r n i n g s k i l l s among d i f f e r e n t i n d i v i d u a l s . ” , ” Next ” , ” I n f o r m e d c o n s e n t ” ) ; i n s t r u c t i o n (”<h3>P r o c e d u r e o f t h e e x p e r i m e n t </ h3> As a p a r t i c i p a n t i n t h i s e x p e r i m e n t you w i l l be a s k e d t o t y p e i n t h e s e n t e n c e s t h a t you h e a r . You w i l l t h e n be a s k e d t o j u d g e t h e p r e s e n c e o f s p e c i f i c words i n t h e p r e v i o u s l y h e a r d s e n t e n c e s . Last , you w i l l p a r t i c i p a t e i n a working−memory t e s t . ” , ” Next ” , ” I n f o r m e d c o n s e n t ” ) ; i n s t r u c t i o n (”<h3>C o n f i d e n t i a l i t y o f your i n f o r m a t i o n </h3> A l l d a t a o f t h i s e x p e r i m e n t a r e c o n f i d e n t i a l and a r e a n a l y s e d anonymously . The d a t a o b t a i n e d i n t h i s
e x p e r i m e n t w i l l be made a v a i l a b l e t o o t h e r s o n l y i n anonymously encoded form . ” , ” Next ” , ” I n f o r m e d c o n s e n t ” ) ;
i n s t r u c t i o n (”<h3>V o l u n t a r i n e s s </h3>
This e x p e r i m e n t i s v o l u n t a r y . You can q u i t t h e e x p e r i m e n t a t any time , w i t h o u t any n e g a t i v e
c o n s e q u e n c e s .
A f t e r you have f i n i s h e d t h e e x p e r i m e n t you can s t i l l d e c i d e t o have t o have t h e d a t a t h a t i s g a t h e r e d on you
d u r i n g t h e e x p e r i m e n t removed from t h e d a t a b a s e .
When you q u i t t h e e x p e r i m e n t b e f o r e c o m p l e t i o n , o r d e c i d e you want t o have your d a t a
removed from t h e d a t a b a s e ,
you do not have t o p r o v i d e any e x p l a n a t i o n . ” , ” Next ” , ” I n f o r m e d c o n s e n t ” ) ; i n s t r u c t i o n (”<h3>Assurance </h3> Based on p r e v i o u s r e s e a r c h o f t h i s n a t u r e , none o r minimal d i s c o m f o r t i s e x p e c t e d f o r p a r t i c i p a n t s i n t h i s e x p e r i m e n t . ” , ” Next ” , ” I n f o r m e d c o n s e n t ” ) ; i n s t r u c t i o n (”<h3>Q u e s t i o n s </h3>
I f you have any o t h e r q u e s t i o n s r e g a r d i n g t h i s e x p e r i m e n t , p l e a s e do not h e s i t a t e t o
c o n t a c t t h e e x p e r i m e n t e r , J . Krikeb ( e m a i l : j o n a t h a n . k r i k e b @ s t u d e n t . auc . n l ) ” , ” Next ” , ” I n f o r m e d c o n s e n t ” ) ;
i n s t r u c t i o n (”<h3>Complaints </h3>
I f you have any s e r i o u s c o m p l a i n t s about t h i s e x p e r i m e n t you can c o n t a c t t h e member o f t h e E t h i c s Commission o f t h e department o f
P s y c h o l o g y o f t h e U n i v e r s i t y o f Amsterdam , Dr R .H. Phaf ( t e l : +31 20 5 2 5 6 8 4 1 , e m a i l : r .
about your l a n g u a g e and memory s k i l l s . ” , ” Continue ” , ” Feedback ” ) ; i n s t r u c t i o n ( ” A l l p a r t i c i p a n t s who c o m p l e t e t h e s t u d y may e n t e r a l o t t e r y f o r t h r e e 16GB USB d r i v e s . ” , ” Continue ” , ” L o t t e r y ” ) ; i n s t r u c t i o n ( ” This e x p e r i m e n t w i l l r e q u i r e you t o l i s t e n t o some a u d i o s e g m e n t s s o p l e a s e have your s p e a k e r s o r headphones r e a d y . ” , ” Next ” , ” Sound p r e p a r a t i o n ” ) ; i n p u t ( ” Choose a u s e r −name o r c o d e f o r y o u r s e l f s o you
w i l l be i d e n t i f a b l e i f you w i l l want your d a t a removed l a t e r . ” , ” username ” ) ;
// Demographics
i n s t r u c t i o n ( ” B e f o r e we s t a r t w i t h t h e t a s k , p l e a s e f i l l −i n some background i n f o r m a t i o n ” , ” Continue ” , ”
Background ” ) ;
i n p u t ( ”What i s your age ? ” , ” age ” ) ;
r a d i o ( ”What i s your g e n d e r ? ” , [ ’ Male ’ , ’ Female ’ , ’NA’ ] , ” s e x ” ) ; i n p u t ( ” Where do you c u r r e n t l y l i v e ? ( c i t y , c o u n t r y ) ” , ” home ” ) ; i n p u t ( ”What i s your c u r r e n t o c c u p a t i o n ? ” , ” j o b ” ) ; s e l e c t ( ”What i s t h e h i g h e s t l e v e l o f e d u c a t i o n you a t t e n d e d ? ” ,
[ ’ Primary s c h o o l ’ , ’ Middle −/J u n i o r high−s c h o o l ’ , ’ High−s c h o o l ’ , ’ V o c a t i o n a l e d u c a t i o n ’ , ” B a c h e l o r ’ s d e g r e e ” , ” Master ’ s d e g r e e ” , ”PhD ” , ” Other ” ] , ” edu ” ) ;
i n p u t ( ” Have you e v e r c o m p l e t e d an IQ t e s t ? i f so ,
p l e a s e f i l l i n your s c o r e , o t h e r w i s e e n t e r 0 ” , ” i q ” ) ; r a d i o ( ’ Do you have any m u s i c a l e d u c a t i o n ? ’ , [ ’ Yes ’ , ’ No
’ ] , ’ music ’ ) ;
// l a n g u a g e background
i n p u t ( ”What i s your n a t i v e l a n g u a g e ? ” , ” l a n g 1 ” ) ;
r a d i o ( ”Do you have a s e c o n d n a t i v e l a n g u a g e ? ( d i d you grow up abroad , o r w i t h p a r e n t s from d i f f e r e n t n a t i o n s ) ” , [ ’ Yes ’ , ’ No ’ ] , ” l a n g e x t r a ” ) ;
i f ( r e s p o n s e . l a n g e x t r a == ” Yes ” ) {
i n p u t ( ”What i s your s e c o n d n a t i v e l a n g u a g e ? ” , ” l a n g 2 ” ) ;
i n s t r u c t i o n ( ” The n e x t q u e s t i o n a p p l i e s o n l y t o
l a n g u a g e s you a c t u a l l y use , used , o r l i s t e n e d to , on a ( semi −) r e g u l a r b a s i s − l a n g u a g e s t h a t you t o o k a c l a s s i n but n e v e r use d a r e NOT r e l e v a n t . ” , ” Continue ” , ” P o l y g l o t ” ) ;
l a r g e i n p u t ( ” Which o t h e r l a n g u a g e s do you s p e a k ? ” , ” m u l t i l a n g ” ) ;
r a d i o ( ” Have you e v e r l e a r n t o r do you s p e a k any Bantu l a n g u a g e ? ” , [ ’ Yes ’ , ’ No ’ ] , ” e x c l u s i o n ” ) ; // S e c t i o n 1∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗ // t r i a l run i n s t r u c t i o n ( ” B e f o r e we s t a r t t h e main t a s k , you w i l l l i s t e n t o a t r i a l s e n t e n c e i n E n g l i s h .< br>Make s u r e your volume i s s e t −up c o r r e c t l y u s i n g t h i s t r i a l run . ” , ” Next ” , ” T r i a l run ” ) ;
i n s t r u c t i o n ( ” P l e a s e l i s t e n t o t h e f o l l o w i n g s e n t e n c e and t y p e i t i n . No p u n c t u a t i o n i s n e c e s s a r y but make
c e r t a i n t o s e p a r a t e t h e words u s i n g t h e SPACE BAR . ” , ”OK” , ” Sound t e s t ” ) ; v a r tryme = ’ Th is i s my house ’ , T = f a l s e ; // l o o p t o t e s t a u d i o sound . p r e l o a d ( ” t e s t 1 . mp3” ) ; sound . a w a i t ( ’ p r e l o a d i n g c o m p l e t e d ’ , sound ) ; w h i l e ( ! T) { p l a y ( ” t e s t 1 . mp3” ) ; r a d i o ( ” Have you h e a r d t h e s e n t e n c e c l e a r l y ? I f so , c l i c k ’ yes ’ t o c o n t i n u e , o t h e r w i s e c l i c k ’ no ’ t o p l a y a g a i n . ” , [ ’ Yes ’ , ’ No ’ ] , ” t ” ) ; T = ( r e s p o n s e . t == ’ Yes ’ ) ; } ; i n p u t ( ” Type t h e s e n t e n c e h e r e ” , ’ t r y s u b ’ ) ;
i n s t r u c t i o n ( ” You have typed t h e s e n t e n c e c o r r e c t l y . ” , ” Continue ” , ” Good j o b ” ) ; }
e l s e {
i n s t r u c t i o n ( ” Your s e n t e n c e d i f f e r s from what i t s h o u l d be . P l e a s e pay a t t e n t i o n . ” , ” Continue ” , ” A t t e n t i o n ” ) ; } // t h e main t a s k i n s t r u c t i o n ( ” I f you a r e c l e a r on t h e i n s t r u c t i o n s now and r e a d y t o s t a r t t h e n p l e a s e p r o c e e d w i t h t h e t e s t . <br>
The s e n t e n c e s w i l l be i n a l a n g u a g e you a r e not f a m i l i a r w i t h but you a r e r e q u e s t e d t o do a s you d i d w i t h t h e sample s e n t e n c e and w r i t e what you h e a r . <br>
Once more , p u n c t u a t i o n i s not n e c e s s a r y , however , t h e SEPARATION o f words w i t h t h e SPACE BAR i s i m p o r t a n t . ” , ” Next ” , ” S e c t i o n
1 ” ) ; // v a r i a b l e f o r t h e w r i t t e n down s e n t e n c e s v a r s e g m e n t s = [ ” Nyumba yangu n i k i j a n i ” , ”Paka k i j a n i hutembea ” , ”Kuna mbwa” , ”Mbwa n i k i j a n i ” ,
”Paka wangu na mbwa wangu hukimbia ” , ”Mbwa wake yuko nyumbani ” ,
”Paka huyu n i mwekundu ” ,
”Mbwa wake mwekundu hukimbia ” , ”Paka wangu na mbwa wake hucheza ” , ”Nyumba yangu nyekundu ” ,
” Gari l a k o n i samawati ” , ”Mimi huendesha g a r i l a n g u ” , ”Mimi hukimbia nyumbani ” , ”Wewe hukimbia na mbwa wako ” , ”Wewe unatazama j u a ” ,
”Paka wako mwekundu ” , ”Mbwa wake k i j a n i ” ,
”Nyumba yangu nyekundu ” ,
”Paka hukimbia ” ] ; // f o r t h e a n s w e r s v a r s 1 = [ ] ; v a r f i l e n a m e = [ ] ; f o r ( v a r i = 0 ; i <= s e g m e n t s . l e n g t h − 1 ; i ++) { f i l e n a m e = ” s ” + ( i +1) + ” . mp3 ” ; sound . p r e l o a d ( f i l e n a m e ) ; sound . a w a i t ( ’ p r e l o a d i n g c o m p l e t e d ’ , sound ) ; p l a y ( f i l e n a m e ) ; s 1 [ i ] = i n p u t ( ” Type t h e s e n t e n c e h e r e : ” , ” s 1 a n s ” ) ; w h i l e ( r e s p o n s e . s 1 a n s == ” ” ) { s 1 [ i ] = i n p u t ( ” You have l e f t i t b l a n k .< br>P l e a s e t y p e t h e s e n t e n c e h e r e : ” , ” s 1 a n s ” ) ; } ; } ; // S e c t i o n 2∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗ i n s t r u c t i o n ( ” You have j u s t c o m p l e t e d t h e f i r s t s e c t i o n .< br>
We w i l l now p r o c e e d t o s e c t i o n 2 where you w i l l l i s t e n t o 10 d i f f e r e n t words .< br>
For e a c h word p l e a s e c h o o s e ’ yes ’ i f you have h e a r d i t i n any o f t h e s e n t e n c e s o f t h e
p r e v i o u s s e c t i o n , o r ’ no ’ o t h e r w i s e . ” , ” Next ” , ” S e c t i o n 2 ” ) ;
// v a r i a b l e f o r t h e words
v a r words = [ ” Mti ” , ” Kimbia ” , ”Mimi ” , ” Kazi ” , ”Yake ” , ” Soma ” , ”Nyumba” , ”Chupa ” , ”Wao” , ”Nyekundu ” ] ;
v a r w o r d s b o o l = [ ’ No ’ , ’ Yes ’ , ’ Yes ’ , ’ No ’ , ’ No ’ , ’ No ’ , ’ Yes ’ , ’ No ’ , ’ No ’ , ’ Yes ’ ] ;
sound . a w a i t ( ’ p r e l o a d i n g c o m p l e t e d ’ , sound ) ; p l a y ( f i l e n a m e ) ;
s 2 [ i ] = r a d i o ( ” Have you h e a r d t h i s word b e f o r e ? ” , [ ’ Yes ’ , ’ No ’ ] , ” s 2 a n s ” ) ; i f ( s 2 [ i ] == w o r d s b o o l [ i ] ) { s c o r e L a n g ++; } ; } ; l o g ( s c o r e L a n g , ” WordScore ” ) ; // S e c t i o n 3∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗ i n s t r u c t i o n ( ” You have j u s t c o m p l e t e d t h e s e c o n d s e c t i o n .< br>
We w i l l now p r o c e e d t o s e c t i o n 3 where your working−memory w i l l be t e s t e d . ” , ” Next ” , ” S e c t i o n 3 ” ) ;
// T r i a l r e a d i n g span
i n s t r u c t i o n ( ” B e f o r e we b e g i n t h e t a s k you w i l l do a s h o r t p r a c t i c e .< br>
Read t h e f o l l o w i n g E n g l i s h s e n t e n c e s and answer ’ t r u e ’ f o r e a c h one i f i t makes s e n s e , o r ’ f a l s e ’ o t h e r w i s e .< br> A f t e r a p a i r o f s e n t e n c e s , you w i l l have t o r e c a l l t h e l a s t word o f both s e n t e n c e s . ” , ” Next ” , ” P r a c t i c e ” ) ; v a r p r a c t i c e S e n t e n c e s = [ ” Andy was s t o p p e d by t h e p o l i c e m a n b e c a u s e he c r o s s e d t h e y e l l o w heaven . ” ,
” During w i n t e r you can g e t a room a t t h e beach f o r a v e r y low r a t e . ” ,
” P e o p l e i n our town a r e more g i v i n g and c h e e r f u l a t C h r i s t m a s t i m e . ” , ” During t h e week o f f i n a l s p a g h e t t i , I f e l t l i k e I was l o s i n g my mind . ” , ” A f t e r f i n a l exams a r e over , we ’ l l be a b l e t o t a k e a w e l l −d e s e r v e d r e s t . ” , ” A f t e r a hard day a t t h e o f f i c e , B i l l o f t e n s t o p s a t t h e c l u b t o r e l a x . ” ,
”No m a t t e r how much we t a l k t o him , he i s n e v e r g o i n g t o change . ” ,
”The p r o s e c u t o r ’ s d i s h was l o s t b e c a u s e i t was not b a s e d on f a c t . ” ,
” Every now and t h e n I c a t c h m y s e l f swimming b l a n k l y a t t h e w a l l . ” ,
”We were f i f t y l a w n s out a t s e a b e f o r e we l o s t s i g h t o f l a n d . ” , ” Throughout t h e e n t i r e o r d e a l , t h e h o s t a g e s n e v e r a p p e a r e d t o l o s e hope . ” , ” Paul i s a f r a i d o f h e i g h t s and r e f u s e s t o f l y on a p l a n e . ” , ”The young p e n c i l k e p t h i s e y e s c l o s e d u n t i l he was t o l d t o l o o k . ” ,
”Most p e o p l e who l a u g h a r e c o n c e r n e d about c o n t r o l l i n g t h e i r w e i g h t . ” ,
”When L o r i s h o p s s h e a l w a y s l o o k s f o r t h e l o w e s t f l o o d . ” ] ;
v a r p r a c t i c e S e n t e n c e s C o r r e c t = [ ” FALSE” , ”TRUE” , ”TRUE ” , ”FALSE” , ”TRUE” , ”TRUE” , ”TRUE” , ”FALSE” ,
”FALSE” , ”FALSE” , ”TRUE” , ”TRUE” , ”FALSE” , ” FALSE” , ”FALSE ” ] ;
v a r memory = ” ” ;
v a r correctMemory = [ ” heaven ” , ” r a t e ” , ” t i m e ” , ” mind ” , ” r e s t ” , ” r e l a x ” , ” change ” , ” f a c t ” , ” w a l l ” , ” l a n d ” , ” hope ” , ” p l a n e ” , ” l o o k ” , ” w e i g h t ” , ” f l o o d ” ] ; v a r s c o r e = 0 ; f o r ( v a r i = 0 ; i <= 3 ; i +=2){ f o r ( v a r j = i ; j <= ( i +1) ; j ++){ t e x t ( p r a c t i c e S e n t e n c e s [ j ] ) ; a w a i t ( ’ c l i c k ’ ) ; r a d i o ( ” Did t h e s e n t e n c e make s e n s e ? ” , [ ” TRUE” , ”FALSE ” ] , ” c o m p r e h e n d P r a c t i c e ” ) ; i f ( r e s p o n s e . c o m p r e h e n d P r a c t i c e == p r a c t i c e S e n t e n c e s C o r r e c t [ j ] ) { t e x t ( ” C o r r e c t ” ) ; a w a i t ( ” c l i c k ” ) ; } e l s e {
have j u s t s e e n i n t h e c o r r e c t o r d e r . ” ) ; a w a i t ( ” c l i c k ” ) ; f o r ( v a r j = i ; j <= ( i +1) ; j ++){ i n p u t ( ” S e n t e n c e ” + ( j +1) + ” : ” , ” memoPractice ” ) ; memory = r e s p o n s e . memoPractice ; memory = memory . toLowerCase ( ) ;
i f ( memory == correctMemory [ j ] ) { s c o r e ++; } ; } ; t e x t ( ” You s c o r e d ” + s c o r e + ” out o f ” + 2 ) ; a w a i t ( ” c l i c k ” ) ; s c o r e =0; } ;
// Real r e a d i n g span . Test s t o p s when s u b j e c t s f a i l a l l t h r e e s e t s o f a c e r t a i n l e v e l . i n s t r u c t i o n ( ” The p r a c t i c e i s c o m p l e t e .< br> P l e a s e be aware t h a t i n t h e t e s t t h e number o f s e n t e n c e s w i l l i n c r e a s e a s you p r o g r e s s . When you a r e p r e s e n t e d w i t h a s e t o f q u e s t i o n marks ( ? ? ? ) , p l e a s e w r i t e down t h e s e n t e n c e − f i n a l words you remember i n t h e c o r r e c t
o r d e r a s you d i d i n t h e p r a c t i c e .
P r e s s n e x t when you a r e r e a d y t o s t a r t . ” , ” Next ” , ” Reading span ” ) ;
v a r p r o b l e m S e n t e n c e s = [ ” When I g e t up i n t h e morning , t h e f i r s t t h i n g I do i s f e e d my dog . ” ,
” A f t e r y e l l i n g a t t h e game , I knew I would have a t a l l v o i c e . ” ,
”Mary was a s k e d t o s t o p a t t h e new m a l l t o p i c k up s e v e r a l i t e m s . ” ,
”When i t i s c o l d , my mother a l w a y s makes me wear a cap on my head . ” ,
” A l l p a r e n t s hope t h e i r l i s t w i l l grow up t o be i n t e l l i g e n t . ” ,
”When John and Amy moved t o Canada , t h e i r w ish had a huge g a r a g e s a l e . ” ,
” I n t h e f a l l , my g i f t and I l o v e t o work t o g e t h e r i n t h e yard . ” ,
made a t e r r i b l e plum . ” ,
” Unaware o f t h e hunter , t h e d e e r wandered i n t o h i s s h o t g u n r a n g e . ” ,
” S i n c e i t was t h e l a s t game , i t was hard t o c o p e w i t h t h e l o s s . ” ,
” Because s h e g e t s t o k n i f e e a r l y , Amy u s u a l l y g e t s a good p a r k i n g s p o t . ” ,
”The o n l y f u r n i t u r e S t e v e had i n h i s f i r s t bowl was h i s waterbed . ” ,
” L a s t year , Mike was g i v e n d e t e n t i o n f o r r u n n i n g i n t h e h a l l . ” ,
”The huge c l o u d s c o v e r e d t h e morning s l i d e and t h e r a i n began t o f a l l . ” ,
” A f t e r one d a t e I knew t h a t Linda ’ s s i s t e r s i m p l y was not my t y p e . ” ,
” Jason b r o k e h i s arm when he f e l l from t h e t r e e o n t o t h e ground . ” ,
”Most p e o p l e a g r e e t h a t Monday i s t h e w o r s t s t i c k o f t h e week . ” ,
”On warm sunny a f t e r n o o n s , I l i k e t o walk i n t h e park . ” ,
”With i n t e n s e d e t e r m i n a t i o n he overcame a l l o b s t a c l e s and won t h e r a c e . ” ,
”A p e r s o n s h o u l d n e v e r be d i s c r i m i n a t e d a g a i n s t b a s e d on h i s r a c e . ” ,
”My mother has a l w a y s t o l d me t h a t i t i s not p o l i t e t o s h i n e . ” ,
”The lemonade p l a y e r s d e c i d e d t o p l a y two out o f t h r e e s e t s . ” ,
” R a i s i n g c h i l d r e n r e q u i r e s a l o t o f d u s t and t h e a b i l i t y t o be f i r m . ” ,
”The g a t h e r i n g crowd t u r n e d t o l o o k when t h e y h e a r d t h e gun s h o t . ” ,
”As s oon a s I g e t done t a k i n g t h i s envy I am g o i n g t o go home . ” ,
” I t o l d t h e c l a s s t h a t t h e y would g e t a s u r p r i s e i f t h e y were o r a n g e . ” ,
” Jim was s o t i r e d o f s t u d y i n g , he c o u l d not r e a d a n o t h e r page . ” ,
” Although Joe i s s a r c a s t i c a t t i m e s , he can a l s o be v e r y s w e e t . ” ,
” C a r o l w i l l a s k h e r s n e a k e r how much t h e f l i g h t t o Mexico w i l l c o s t . ” ,
”The s u g a r c o u l d not b e l i e v e he was b e i n g o f f e r e d su ch a g r e a t d e a l . ” , ” I t o o k my l i t t l e p u r p l e t o t h e i c e cream s t o r e t o g e t a c o n e . ” , ” K r i s t e n dropped h e r p a r e n t s o f f a t t h e l o v e f o r t h e i r a n n u a l v a c a t i o n . ” , ”The f i r e f i g h t e r s s o u r t h e k i t t e n t h a t was t r a p p e d i n t h e b i g oak t r e e . ” ,
” P e t e r and Jack r u i n e d t h e f a m i l y carwash when t h e y burned t h e t u r k e y . ” ,
” Martha went t o t h e c o n c e r t , but a t e t o b r i n g a t h i c k s w e a t e r . ” ,
” S a r a wanted h e r mother t o r e a d h e r a window b e f o r e g o i n g t o s l e e p . ” ,
”Our dog Sammy l i k e s t o g r e e t new p e o p l e by j o y f u l on them . ” ,
”Wendy went t o c h e c k h e r m a i l but a l l s h e r e c e i v e d were c a t s . ” ,
” R e a l i z i n g t h a t s h e was l a t e , J u l i a r u s h e d t o p i c k up h e r c h i l d from s p e a k e r . ” ,
” Paul l i k e s t o c r y l o n g d i s t a n c e s i n t h e park n e a r h i s h o u s e . ” ,
”The s i c k boy had t o s t a y home from s c h o o l b e c a u s e he had a phone . ” ,
”The j u d g e gave t h e boy community s w e a t f o r s t e a l i n g t h e candy bar . ” ,
”Women f a l l i n jump w i t h t h e i r i n f a n t s a t f i r s t s i g h t o r even s o o n e r . ” ,
” Jason ’ s f a m i l y l i k e s t o v i s i t him i n A t l a n t a d u r i n g t h e c h e r r y e v e r y y e a r . ” ,
”The d o c t o r t o l d my aunt t h a t s h e would f e e l b e t t e r a f t e r g e t t i n g happy . ” ,
out h i s r e p o r t l a s t n i g h t . ” ,
” Nick ’ s hockey team won t h e i r f i n a l game t h i s p a s t weekend a t t h e s h o e s . ” ,
”My mother and f a t h e r have a l w a y s wanted t o l i v e n e a r t h e cup . ” ,
”The prom was o n l y t h r e e days away , but n e i t h e r g i r l had a d r e s s y e t . ” , ”The c h i l d r e n e n t e r e d i n a t a l e n t c o n t e s t t o win a t r i p t o D i s n e y World . ” , ”They were w o r r i e d t h a t a l l o f t h e i r l u g g a g e would not f i t i n t h e c a r . ” , ”The s e v e n t h g r a d e r s had t o b u i l d a v o l c a n o f o r t h e i r s c i e n c e c l a s s . ” ,
”The c o l l e g e s t u d e n t s went t o New York i n March and i t snowed . ” ,
” She had t o c a n c e l t h e appointment b e c a u s e s h e c a u g h t t h e f l u y e s t e r d a y . ” ,
”Doug h e l p e d h i s f a m i l y d i g i n t h e i r backyard f o r t h e i r new swimming p o o l . ” ,
”The dogs were v e r y e x c i t e d about g o i n g f o r a walk i n t h e park . ” ,
” I n t h e s p r i n g , t h e l a r g e b i r d f e e d e r o u t s i d e my window a t t r a c t s many b i r d s . ” ,
” B e f o r e K a t i e l e f t f o r t h e c i t y , s h e t o o k a s e l f −d e f e n s e c l a s s a t t h e gym . ” ,
”Mary was e x c i t e d about h e r new f u r n i t u r e t h a t s h e had bought on s a l e . ” ,
”The c l a s s d i d not t h i n k t h e p r o f e s s o r ’ s l e c t u r e on h i s t o r y was v e r y i n t e r e s t i n g . ” , ” Jane f o r g o t t o b r i n g h e r u m b r e l l a and g o t wet
i n t h e r a i n . ” ,
”Dan walked around t h e s t r e e t s p o s t i n g s i g n s and l o o k i n g f o r h i s l o s t puppy . ” ,
”The c o u p l e d e c i d e d t h a t t h e y wanted t o have a p i c n i c i n t h e park . ” ,
” Harry p l a n s t o p l a y a l o t o f g o l f when he r e t i r e s from h i s j o b . ” , ” H i s s t e r e o was p l a y i n g s o l o u d t h a t he blew out t h e s p e a k e r s . ” , ” I t was a c l e a r n i g h t , and we c o u l d s e e t h e s t a r s i n t h e sky . ” ,
”At t h e p a r t y , Randy g o t out t h e camera t o t a k e some p i c t u r e s . ” ,
” C a t h e r i n e d r e s s e d up a s a s c a r y w i t c h f o r t h e H a l l o w e e n p e n c i l on F r i d a y . ” ,
” S p r i n g i s h e r f a v o r i t e t i m e o f y e a r b e c a u s e f l o w e r s b e g i n t o bloom . ” ,
”Even though s h e was i n t r o u b l e , s h e managed t o go t o t h e d i c e and shop . ” ,
” A f t e r b e i n g i l l , Suzy hoped t o c a t c h up on h e r work o v e r t h e weekend . ” ,
”He wrecked h i s c a r b e c a u s e he was g o i n g t o o f a s t i n t h e r a i n . ” ,
”The t o r n a d o came out o f nowhere and d e s t r o y e d our r a i s i n . ” ,
” John wants t o be a f o o t b a l l p l a y e r when he g e t s o l d e r . ” ,
”The boys knew t h e y would have t o h u r r y t o make i t t o t h e a p p l e on t i m e . ” ] ;
v a r p r o b l e m S e n t e n c e s C o r r e c t = [ ”TRUE” , ”FALSE” , ”TRUE” , ” TRUE” , ”FALSE” , ”FALSE” , ”FALSE” , ”FALSE” , ”TRUE” , ”TRUE ” , ”FALSE” , ”FALSE” , ”TRUE” , ”FALSE” , ”TRUE” , ”TRUE ” , ”FALSE” , ”TRUE” , ”TRUE” , ”TRUE”
, ”FALSE” , ”FALSE” , ”FALSE” , ”TRUE” , ”FALSE” , ” TRUE” , ”TRUE” , ”FALSE” , ”FALSE” , ”TRUE” , ” TRUE” , ”FALSE” , ”FALSE” , ”FALSE” , ”FALSE” , ” FALSE” , ”FALSE” , ”FALSE” , ”FALSE” , ”FALSE” , ”FALSE” , ”FALSE”
, ”FALSE” , ”FALSE” , ”FALSE” , ”FALSE” , ”FALSE” , ”FALSE” , ”FALSE” , ”FALSE” , ”FALSE” , ”TRUE” , ”TRUE” , ”TRUE” , ”TRUE” , ”TRUE” , ”TRUE” , ” TRUE” , ”TRUE” , ”TRUE” , ”TRUE” , ”TRUE” , ”TRUE
” , ”TRUE” , ”TRUE”
, ”TRUE” , ”TRUE” , ”TRUE” , ”TRUE” , ”TRUE” , ”TRUE ” , ”TRUE” , ”TRUE” , ”FALSE” , ”TRUE” , ”FALSE” ,
