More Naturalistic Cross-situational Word Learning

نویسندگان

  • George Kachergis
  • Chen Yu
چکیده

Previous research has found that people can use word-object co-occurrences from ambiguous situations to learn word meanings (e.g., Yu & Smith, 2007). However, most studies of cross-situational learning present an equal number of words and objects, which may simplify the problem by, for example, encouraging learners to use assumptions such as each word going with one object. This paper presents several conditions in which the number of words and objects do not match: either additional objects appear at random, or objects appear sometimes without their intended words. These manipulations do generally hurt learning in comparison to balanced conditions, but people still learn a significant proportion of word-object pairings. The results are explored in terms of statistics of the training trials—including contextual diversity and context familiarity—and with the uncertaintyand familiarity-biased associative model. Parametric differences between conditions hint at hidden cognitive constructs.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

The Pursuit of Word Meanings.

We evaluate here the performance of four models of cross-situational word learning: two global models, which extract and retain multiple referential alternatives from each word occurrence; and two local models, which extract just a single referent from each occurrence. One of these local models, dubbed Pursuit, uses an associative learning mechanism to estimate word-referent probability but pur...

متن کامل

The Interplay of Cross-Situational Word Learning and Sentence-Level Constraints

A variety of mechanisms contribute to word learning. Learners can track co-occurring words and referents across situations in a bottom-up manner (cross-situational word learning, CSWL). Equally, they can exploit sentential contexts, relying on top-down information such as verb-argument relations and world knowledge, offering immediate constraints on meaning (word learning based on sentence-leve...

متن کامل

Statistical Cross-Situational Learning to Build Word-to-World Mappings

There are an infinite number of possible word-to-world pairings in naturalistic learning environments. Previous proposals to solve this mapping problem focus on linguistic, social, representational constraints at a single moment. This paper investigates a cross-situational learning strategy based on computing distributional statistics across words, across referents, and most importantly across ...

متن کامل

Rapid word learning under uncertainty via cross-situational statistics.

There are an infinite number of possible word-to-word pairings in naturalistic learning environments. Previous proposals to solve this mapping problem have focused on linguistic, social, representational, and attentional constraints at a single moment. This article discusses a cross-situational learning strategy based on computing distributional statistics across words, across referents, and, m...

متن کامل

2.5-year-olds use cross-situational consistency to learn verbs under referential uncertainty.

Recent evidence shows that children can use cross-situational statistics to learn new object labels under referential ambiguity (e.g., Smith & Yu, 2008). Such evidence has been interpreted as support for proposals that statistical information about word-referent co-occurrence plays a powerful role in word learning. But object labels represent only a fraction of the vocabulary children acquire, ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2013