Yim & Rudoy (2012)
Frost et al. (2013)
Kempe & Brooks (2009)
Dabrowska & Street (2005)
The first article we have chosen by Yim & Rudoy (2012) studies the implicit statistical learning (ISL) and language skills of bilingual children. According to the researchers, ISL is an innate ability to “learn new information in which patterns and rules are embedded” in an incidental manner. They suggest that “the ability to learn sequential visual information is significantly correlated with language performance in children.” One of their hypotheses is that “bilinguals’ life experience with linguistic systemising will influence their statistical learning” (Yim & Rudoy, 2012).
They qualify that claim by introducing mediating factors that cause ISL abilities to vary among individuals. For example, they cite “early life experience” as a factor that determines ISL abilities, stating that the earlier infants are exposed to two or more languages, the more sophisticated their ISL skills are. In contrast, auditory deprivation, as in the cause for children with cochlear implants, may stunt the development of ISL development.
In their experiment, participants were presented with either non-namable visual shapes (Fig. 1) or nine pure-tone sounds i.e. sounds that could not be named. In the visual test, participants were shown two sets of three shapes. The former portion of the test was a ‘training phase’ whilst the latter portion was the ‘test’. Based on a training phase, participants had to decide which set looked more familiar during the test.
In the auditory test, three sounds were administered to the participants to form a continuous stream of sounds in a semi-random order. The former portion of the presentation was the ‘training phase’, and the latter was the ‘test session’. Participants were then asked to identify which sounds from the test session were similar to the training phase, as in the visual task.
Both tasks were designed to measure participants’ ability to pick out patterns amidst streams of information, whether visual or auditory.
The research question asked was “whether bilingual children would outperform monolingual children on implicit statistical learning” (Yim & Rudoy, 2012). From the tests, results suggested that no statistical difference between groups was found, either on the visual task or the auditory task. Thus, there was no group difference in ISL.
The second important finding was that bilinguals and monolinguals had different methods in predicting auditory and visual statistical learning skills. This finding helped to support the idea of age being an important variable in ISL and that even though there is no significant predictor for visual statistical learning in children, there seems to exist a significant predictor for auditory statistical learning in children. In general, the study has found that linguistic experience does not influence the learning of statistical regularity in children. However, auditory implicit learning is related to language performance, as mentioned above.
In the next study by Frost et al. (2013), the research question “what predicts successful literacy acquisition in a second language?” was asked. Similar to the findings in Yim & Rudoy’s article, this paper considers the acquisition of language to be the implicit assimilation of statistical properties of a linguistic environment. Throughout the course of the paper, Frost and his team answered the question of whether individual differences in statistical learning predict individual differences in SLA, bringing in factors that may affect the process of acquiring a second language (L2), as well as any relevant trends that may account for the variability in SLA in individuals. In general, it has been suggested that first language (L1) linguistic capacities appear to be reasonable predictors of the success in the acquisition of an L2. In addition, the level of literacy achieved in one’s L1 seems to also be a good determiner of the ability to acquire literacy in a foreign language.
Besides an individual’s statistical learning skills, an external factor that weighs in on SLA would be the degree of similarity and dissimilarity between statistical properties of the L1 and L2. Little else was mentioned in this paper, however, about why or how this is so.
In the experiment, it was hypothesised that statistical learning ability would correlate to learning to read in a new language that is characterized by a novel set of statistical regularities. They compared relative success in the Visual-Statistical Learning (VSL) task with relative success in Hebrew reading tasks, predicting that relative success in learning a series of random visual shapes would predict the speed and success of learning a new language.
American students were recruited for this experiment. They were administered a VSL task and this task examined participants’ ability to detect the patterns embedded in a continuous stream of visual shapes. Scores were collated for that task, then participants were administered three reading tasks in Hebrew to test for speed of decoding, naming of unpointed words and whether there would be a priming effect between modalities.
In the end, it was found that participants who scored well in the VSL task also scored well on two out of three of the Hebrew reading tasks, exclusive of morphological priming. From the findings, they conclude that VSL only correlates to learning the structural properties of Hebrew and not the semantic properties. In the end, they attribute any inconsistencies to factors like a small sample size and motivation among other factors.
Whilst children or younger individuals are often the main focus when studying language acquisition, we have also included a study that delves into the individual differences in adult SLA. The study by Kempe & Brooks (2009) provides a broad look at various factors of SLA. It introduces elements of psycholinguistics and brings in concepts on working memory, phonological short-term memory (PSTM), non-verbal intelligence, IQ and metalinguistic awareness. It also suggests the role of prior experience with other languages in SLA, stating that “individuals [consistently] try to transfer their knowledge from a previously learned language to the new language” (Kempe & Brooks, 2011).
Participants in this study were required to learn a small set of nouns from Russian, to listen and repeat short phrases, identify the referents, and then to produce short statements on their own, all without explicit teaching. Afterwards, participants were expected to make generalisations of the grammar, i.e. case-marking, as well as retention of vocabulary. Performance in the new L2 was then linked to various multiple cognitive abilities and it was said to predict performance in SLA as well.
While prior language experience can be beneficial, it has been shown that it benefits can only be applied under specific conditions. For example, an L1 speaker of Spanish or Italian, that is, languages with a fairly transparent gender-marking on nouns, would be able to apply this knowledge when acquiring a language like Russian (Kempe & Brooks, 2008). This has to do with the similarities between case markings which aids in the acquisition of another language.
Lastly, we reviewed Dabrowska & Street (2005)’s study on the comprehension of passive sentences by native and non-native English speakers. They wanted to see if a greater depth in experience with a particular construction would predict one’s proficiency at the language. It was suggested that proficiency of a language “is merely a function of the amount of exposure”, a proposal which contradicts claims in other studies. Such a claim presents us with the possibility that SLA could be enhanced with the intensive and diligent studying and practise of the language, given the proper motivation and instruction.
In the study of English passives, four groups of participants were involved in this study: graduates (native and non-native) who have had at least 15 years of English education, and non-graduates (native, with no formal English education after secondary school, and non-native, who are currently studying English). Participants were then asked to listen and identify the ‘do-er’ (i.e. agent) of the sentence.
Passives were chosen as for the experiment as they do not tax the working memory since it does not involve embedding. Furthermore, while passives are part of English’s ‘core’ grammar, experience with the structure differs significantly between speakers of the language, with passives found more often in formal written text. As such, academics and intellectuals are more likely, as compared to the layman, to have encountered and have been exposed to such sentence structures due to the very nature of their jobs.
Examples of a full passive sentence: (i) Passive and plausible: The man was bitten by the dog, (ii) Passive and implausible: The dog was bitten by the man.
Results showed that graduate speakers performed better than their non-graduate counterparts. Surprisingly, however, for the non-graduates, non-natives performed better than the natives.
The reasons why non-graduate non-natives performed better than the non-graduate natives could be that there may be a continuum of proficiency with passive sentences, where those with formal schooling do better than those without; and that perhaps the non-graduate natives might have misunderstood the task. But both explanations do not seem to be adequate in explaining this linguistic phenomenon, which will have to be substantiated with additional research adopting other methodology methods.