Research suggests that second language words are learned faster when they are similar in phonological structure or accent to the words of an individual's first language. Many major theories suggest this happens because of differences in frequency of exposure and context between first and second language words. Here, we examine the independent…

Recent semantic space models learn vector representations for word meanings by observing statistical redundancies across a text corpus. A word's meaning is represented as a point in a high-dimensional semantic space, and semantic similarity between words is quantified by a function of their spatial proximity (typically the cosine of the angle…

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…

Several recent studies have explored the applicability of the preferential attachment principle to account for vocabulary growth. According to this principle, network growth can be described by a process in which existing nodes recruit new nodes with a probability that is an increasing function of their connectivity within the existing network.…

Humans are remarkably sensitive to the statistical structure of language. However, different mechanisms have been proposed to account for such statistical sensitivities. The present study compared adult learning of syntax and the ability of two models of statistical learning to simulate human performance: Simple Recurrent Networks, which learn by…

This dissertation uses the processing of anaphoric relations to probe how linguistic information is encoded in and retrieved from memory during real-time sentence comprehension. More specifically, the dissertation attempts to resolve a tension between the demands of a linguistic processor implemented in a general-purpose cognitive architecture and…

We present a neurocomputational model with self-organizing maps that accounts for the emergence of taxonomic responding and fast mapping in early word learning, as well as a rapid increase in the rate of acquisition of words observed in late infancy. The quality and efficiency of generalization of word-object associations is directly related to…

This paper describes a set of simulations which explore the way different features of lexical organisation affect the probability of finding a pair of associated words in a set of five randomly selected words. The simulation is equivalent to giving Ss a set of five words and asking if they can identify a pair of associated words among them. The…