Friday, November 11, 2016
Newport, E. L. (2016). Statistical language learning: Computational, maturational, and linguistic constraints. Language and Cognition, 8, (447-461).
Is the ability to use language innate, or is it learned? Do children and adults differ in how they learn language? Why do certain patterns consistently occur in many languages across the world? For example, number words (‘two’) usually come before the noun they’re counting as in “two cars”, in English, and “deux voitures”, in French. To answer these questions, we can look to the ways in which humans learn. Our system of language is essentially a series of patterns; from what makes a word, to how sentences are formed. Humans are quite good at learning these patterns, and the ability to learn these patterns in language is known as statistical language learning. In this paper, Newport described research over the past 20 years showing that statistical language learning can explain many of the patterns we observe when humans start to learn language, even across different languages of the world.
Language learners show an extraordinary ability to learn language through statistical language learning. However, when the input is variable or inconsistent, language learning across children and adults look quite different. Researchers have examined this phenomenon in the lab by comparing how children and adults learn artificial languages. These artificial languages contain a consistent grammatical rule that applies to most cases, but this rule is violated some of the time. Adults learn the language exactly as they heard it, learning both the rules and the violations. Young children, on the other hand, learn the rule but not the exceptions, and generalize that rule across the language. This difference in how adults and children learn a novel language is important for understanding language acquisition.
The finding of children learning and generalizing the consistent patterns in a language has been shown in a number of experimental studies and real-world settings. But why does children’s language learning look different from adults? One explanation is that children are biased towards learning the consistent patterns because of how they learn. Children are able to process less information than adults; because of this, it is easier for children to learn and use a consistent pattern that they can apply to many cases than it is to learn inconsistent patterns, or many exceptions to a rule. This tendency to adopt the easy-to-learn patterns may explain why certain patterns exist in many languages across the world. Examining how young children learn language tells us a lot about language acquisition, and why certain patterns exist across many languages of the world.
Blogger: Nicolette Noonan; Nicolette is a Psychology Ph.D. student, supervised by Drs. Lisa Archibald and Marc Joanisse
Tuesday, November 1, 2016
Howard-Jones, P.A. (2016). Neuroscience and education: mythsand messages. Nature Reviews Neuroscience,15. 817-824.
Neuromyths, misconceptions of scientific knowledge about the brain, have persisted in the field of education for several decades. For example, studies conducted in five European countries 1,2,3,4 have demonstrated that 93-97% of teachers believe that students learn better in their “preferred” learning style, 71-91% agree that differences in left or right brain dominance are related to individual differences in learning, and 60-88% believe that integration of neural function across the brain’s hemispheres can be improved by brief coordination exercises. Misconceptions like these can lead to ineffective teaching approaches and may influence teachers’ opinions on teaching methods and management of learning disorders. In the current paper, the author discusses the origins of neuromyths and highlights the importance of understanding neuromyths in order to bridge the gap between neuroscience and education.
Although neuromyths often have some underlying scientific origin, spreading of misinterpretations of these scientific facts can lead to persistent distortions. Often, these misinterpretations are a result of cultural differences between neuroscientists and educators: scientific findings may only be published in neuroscience journals and may be communicated using jargon or terms that are defined differently in education and neuroscience. Neuromyths may also be related to biases towards approaches that are less costly and time-consuming to implement in the classroom, particularly when educational programs are marketed with an underlying scientific basis to boost their credibility.
In recent years collaboration between the fields of neuroscience and education has become more widespread, and scientific insights have begun to inform educational practice. However, many of the biases and conditions underlying the development of neuromyths have continued to influence public misconceptions about the brain. The author calls for more interdisciplinary research and communication between neuroscientists and educators. To work towards bridging the gap between the fields, neuroscientists need to collaborate with educational experts to ensure that neuroscientific insights are accessible, relevant, and can be easily and effectively implemented in the classroom.
1. Dekker, S., Lee, N. C., Howard-Jones, P. & Jolles, J. (2012) Neuromyths in education: prevalence and predictors of misconceptions among teachers. Frontiers in psychology, 3, 429-429.
2. Deligiannidi, K. & Howard-Jones, P. (2015). The neuroscience literacy of teachers in Greece. Procedia – Social and Behavioural Sciences, 174. 3909-3915.
3. Karakus, O. & Howard-Jones, P. (2015). Primary and secondary school teachers’ knowledge and misconceptions about the brain in Turkey. Procedia – Social and Behavioural Sciences, 174. 1933-1940.
4. Pei, X., Zhang, S., Liu, X., Jin, Y. & Howard-Jones, P. (2015). Teachers’ understanding about the brain in East China. Procedia – Social and Behavioural Sciences, 174. 3680-3688.
Blogger: Alex Cross is completing a combined MClSc and PhD in speech language pathology. Her work focusing on reading will be part of both the Language and Working Memory and the Language, Reading, and Cognitive Neuroscience labs.