Children’s preference for HAS and LOCATED relations: A word learning bias for noun–noun compounds

The present study investigates children’s bias when interpreting novel noun–noun compounds (e.g. kig donka) that refer to combinations of novel objects (kig and donka). More specifically, it investigates children’s understanding of modifier–head relations of the compounds and their preference for HAS or LOCATED relations (e.g. a donka that HAS a kig or a donka that is LOCATED near a kig) rather than a FOR relation (e.g. a donka that is used FOR kigs). In a forced-choice paradigm, two- and three-year-olds preferred interpretations with HAS/LOCATED relations, while five-year-olds and adults showed no preference for either interpretation. We discuss possible explanations\ud for this preference and its relation to another word learning bias that is based on perceptual features of the referent objects, i.e. the shape bias. We argue that children initially focus on a perceptual stability rather than a pure conceptual stability when interpreting the meaning of nouns

Storytelling with objects to explore digital archives

Finding media in archives is difficult while storytelling with photos can be fun and supports memory retrieval. Could the search for media become a natural part of the storytelling experience? This study investigates spatial interactions with objects as a means to encode information for retrieval while being embedded in the story flow. An experiment is carried out in which participants watch a short video and re-tell the story using cards each of which shows a character or object occurring in the video. Participants arrange the cards when telling the story. It is analyzed what information interactions with cards carry and how this information relates to the language of storytelling. Most participants align interactions with objects with the sentences of the story while some arrange the cards corresponding to the video scene. Spatial interactions with objects can carry information on their own or complemented by language

Minimal model of associative learning for cross-situational lexicon acquisition

An explanation for the acquisition of word-object mappings is the associative learning in a cross-situational scenario. Here we present analytical results of the performance of a simple associative learning algorithm for acquiring a one-to-one mapping between $N$ objects and $N$ words based solely on the co-occurrence between objects and words. In particular, a learning trial in our learning scenario consists of the presentation of $C + 1 < N$ objects together with a target word, which refers to one of the objects in the context. We find that the learning times are distributed exponentially and the learning rates are given by $\ln{[\frac{N(N-1)}{C + (N-1)^{2}}]}$ in the case the $N$ target words are sampled randomly and by $\frac{1}{N} \ln [\frac{N-1}{C}]$ in the case they follow a deterministic presentation sequence. This learning performance is much superior to those exhibited by humans and more realistic learning algorithms in cross-situational experiments. We show that introduction of discrimination limitations using Weber's law and forgetting reduce the performance of the associative algorithm to the human level

Kidz in the \u27Hood: Syntactic Bootstrapping and the Mental Lexicon

This paper explores the limits of syntactic bootstrapping and demonstrates that the use of syntactic structure to build verb meanings is constrained to operate only within \u27frame neighborhoods,\u27 i.e., complement types that antecedently share formal and interpretive features. The results suggest that inferences over change in number of arguments are easier than inferences over change in type of arguments. This kind of finding establishes the limits within which the \u27syntactic bootstrapping\u27 paradigm for verb learning can operate, and also has implications for whether we should think about the architecture of the lexicon in projectionist or constructionist terms

Human Simulations of Vocabulary Learning

The work reported here experimentally investigates a striking generalization about vocabulary acquisition: Noun learning is superior to verb learning in the earliest moments of child language development. The dominant explanation of this phenomenon in the literature invokes differing conceptual requirements for items in these lexical categories: Verbs are cognitively more complex than nouns and so their acquisition must await certain mental developments in the infant. In the present work, we investigate an alternative hypothesis; namely, that it is the information requirements of verb learning, not the conceptual requirements, that crucially determine the acquisition order. Efficient verb learning requires access to structural features of the exposure language and thus cannot take place until a scaffolding of noun knowledge enables the acquisition of clause-level syntax. More generally, we experimentally investigate the hypothesis that vocabulary acquisition takes place via an incremental constraint-satisfaction procedure that bootstraps itself into successively more sophisticated linguistic representations which, in turn, enable new kinds of vocabulary learning. If the experimental subjects were young children, it would be difficult to distinguish between this information-centered hypothesis and the conceptual change hypothesis. Therefore the experimental learners are adults. The items to be “acquired” in the experiments were the 24 most frequent nouns and 24 most frequent verbs from a sample of maternal speech to 18-24-month old infants. The various experiments ask about the kinds of information that will support identification of these words as they occur in mother-to-child discourse. In Experiment 1, subjects were required to identify the words from observing several extralinguistic contexts for their use (silent videos in which mothers are seen uttering the “mystery word” several times to the infants, with each such use cued by a beep or a nonsense word). The findings under these conditions mimicked the known learning trajectory for infants at the inception of speech and comprehension: Nouns are learned far more efficiently than verbs. Experiment 2 showed that the Experiment 1 results are best understood as concreteness differences that are correlated with lexical class membership in the common useage of mothers to young children. Experiment 3 presented (different) subject groups with 24 verbs under varying information Conditions; namely: (1) extralinguistic information; (2) noun-co-occurrence information; (3) both (1) and (2); (4) syntactic-frame information but with nouns and verbs represented by nonsense words; (5) both (2) and (4); (6) both (1) and (5). Each Condition led to greater identification success than the preceding Condition. Moreover, not only the number but the type of verb that was efficiently learned was different under the different information conditions. We discuss these results as consistent with the incremental construction of a highly lexicalized grammar by cognitively and pragmatically sophisticated human infants, but inconsistent with a procedure in which lexical acquisition is independent of and antecedent to syntax acquisition

Goldilocks Forgetting in Cross-Situational Learning

Given that there is referential uncertainty (noise) when learning words, to what extent can forgetting filter some of that noise out, and be an aid to learning? Using a Cross Situational Learning model we find a U-shaped function of errors indicative of a "Goldilocks" zone of forgetting: an optimum store-loss ratio that is neither too aggressive nor too weak, but just the right amount to produce better learning outcomes. Forgetting acts as a high-pass filter that actively deletes (part of) the referential ambiguity noise, retains intended referents, and effectively amplifies the signal. The model achieves this performance without incorporating any specific cognitive biases of the type proposed in the constraints and principles account, and without any prescribed developmental changes in the underlying learning mechanism. Instead we interpret the model performance as more of a by-product of exposure to input, where the associative strengths in the lexicon grow as a function of linguistic experience in combination with memory limitations. The result adds a mechanistic explanation for the experimental evidence on spaced learning and, more generally, advocates integrating domain-general aspects of cognition, such as memory, into the language acquisition process

Is China The Next Bollywood?

For those English language lovers, the word “synecdoche” (pronounced si-NEK-də-kee) is a figure of speech in which a word is used to represent the whole of something else. Thus, the city of Hollywood, California represents, figuratively, the whole of the film industry. For the Indian film industry, there is the cleverly devised “Bollywood,” an obvious play on the California city, with the B for Bombay in lieu of the H in Hollywood. Now that China looks to expand its already burgeoning film industry, perhaps the next figure of speech in the movie world will be Sinewood (pronounced SEE-nay-wood) from the prefix “sino” which refers to China or the Chinese people, and a definite play on the word “cine,” which means film. This post was originally published on the Cardozo Arts & Entertainment Law Journal website on December 8, 2016. The original post can be accessed via the Archived Link button above

Acquiring and processing verb argument structure : distributional learning in a miniature language

Adult knowledge of a language involves correctly balancing lexically-based and more language-general patterns. For example, verb argument structures may sometimes readily generalize to new verbs, yet with particular verbs may resist generalization. From the perspective of acquisition, this creates significant learnability problems, with some researchers claiming a crucial role for verb semantics in the determination of when generalization may and may not occur. Similarly, there has been debate regarding how verb-specific and more generalized constraints interact in sentence processing and on the role of semantics in this process. The current work explores these issues using artificial language learning. In three experiments using languages without semantic cues to verb distribution, we demonstrate that learners can acquire both verb-specific and verb-general patterns, based on distributional information in the linguistic input regarding each of the verbs as well as across the language as a whole. As with natural languages, these factors are shown to affect production, judgments and real-time processing. We demonstrate that learners apply a rational procedure in determining their usage of these different input statistics and conclude by suggesting that a Bayesian perspective on statistical learning may be an appropriate framework for capturing our findings