Statistical
learning is a term used to describe the discovery of patterns within incoming
information, and has been widely applied to language learning. Language is
composed of many different, and learnable, patterns. For example, knowing that
the two syllables “ba” and “nan” will be followed by “a”, to make the word “banana”. Or, learning the pattern “is verb-ing”, such as “is running”, “is hopping” or “is skipping”. Infants are sensitive to these patterns when learning language,
and considerable research over the last two decades has highlighted statistical
learning as one of the primary ways in which humans acquire language.
This paper
distinguished between two types of patterns, or statistics, learned via
statistical learning. The first are conditional statistics, which refer to the
likelihood of two or more elements co-occurring, such as syllables or words.
Conditional statistics are useful because they go beyond the simple frequency
of co-occurrence. For example, given the phrase “the dog”, you know they are two distinct words because of the low
conditional relationship between them; Although “the dog” is a frequent phrase, the conditional relationship between
“the” and “dog” is low because the word “the”
occurs in combination with many other words. The other type of statistic is
known as a distributional statistic, which refers to how the variability of items
stored in memory shapes what is subsequently stored within memory. For
instance, if a newly encountered item matches something that is already stored
in memory, it won’t be stored as a unique item. However, if this item is distinct
from what is already stored in memory, it will be stored as a unique item. Learning
the distributional regularities within a language plays an important role in
learning the phonemes within your native language, and other patterns across
language.
The
statistical learning of conditional and distributional statistics are natural
extensions of processes we know to exist in memory more generally. These memory
processes include recognizing familiar items already stored in memory, gradually
forgetting items stored in memory if they are not re-encountered, and
interference between similar items stored in memory. Looking at statistical
learning from the perspective of memory has a couple of important implications.
First, it connects statistical learning, and thereby language learning, to
other types of learning. Second, it gives and explanation as to why the ability
to learn a language tends to decline with age. If statistical learning is
linked to memory processes, and memory changes with age, changes in memory over
the lifespan may help explain why language learning tends to decline with age. Exploring
the connection between statistical learning and memory is important for
theories of language acquisition because it connects language acquisition with
what we know about human cognition.
Blogger: Nicolette is a Psychology Ph.D.
student, supervised by Dr.s Lisa Archibald and Marc Joanisse
This comment has been removed by a blog administrator.
ReplyDelete