Measuring Chinese character recognition ability is essential in research on character learning among learners of Chinese as a second language. Three methods are typically used to evaluate character recognition competence by investigating the following properties of a given character: pronunciation, meaning, and pronunciation and meaning. However, no study has explored the similar or dissimilar outcomes that these three measurements might yield. The current study examined this issue by testing 162 CSL learners with various L1 backgrounds and Chinese proficiency levels. Participants' (...) performance in character recognition measured using a phonological method, a semantic method, and a PS method was compared, which led to two major findings. In terms of similarity, participants' performance in character recognition and the influence of L1 background and Chinese proficiency level on character recognition was similar across the three methods. As for differences, the semantic method could yield a character recognition test with better quality than the other two methods, and the three methods yielded different best fitting models and showed different predictions for Chinese proficiency across different L1 groups. Theoretical and practical implications of these findings are proposed. (shrink)

Cross-situational word learning, like any statistical learning problem, involves tracking the regularities in the environment. However, the information that learners pick up from these regularities is dependent on their learning mechanism. This article investigates the role of one type of mechanism in statistical word learning: competition. Competitive mechanisms would allow learners to find the signal in noisy input and would help to explain the speed with which learners succeed in statistical learning tasks. Because cross-situational word learning provides information at multiple (...) scales—both within and across trials/situations—learners could implement competition at either or both of these scales. A series of four experiments demonstrate that cross-situational learning involves competition at both levels of scale, and that these mechanisms interact to support rapid learning. The impact of both of these mechanisms is considered from the perspective of a process-level understanding of cross-situational learning. (shrink)

Most words in natural languages are polysemous; that is, they have related but different meanings in different contexts. This one‐to‐many mapping of form to meaning presents a challenge to understanding how word meanings are learned, represented, and processed. Previous work has focused on solutions in which multiple static semantic representations are linked to a single word form, which fails to capture important generalizations about how polysemous words are used; in particular, the graded nature of polysemous senses, and the flexibility and (...) regularity of polysemy use. We provide a novel view of how polysemous words are represented and processed, focusing on how meaning is modulated by context. Our theory is implemented within a recurrent neural network that learns distributional information through exposure to a large and representative corpus of English. Clusters of meaning emerge from how the model processes individual word forms. In keeping with distributional theories of semantics, we suggest word meanings are generalized from contexts of different word tokens, with polysemy emerging as multiple clusters of contextually modulated meanings. We validate our results against a human‐annotated corpus of polysemy focusing on the gradedness, flexibility, and regularity of polysemous sense individuation, as well as behavioral findings of offline sense relatedness ratings and online sentence processing. The results provide novel insights into how polysemy emerges from contextual processing of word meaning from both a theoretical and computational point of view. (shrink)

The Visual World Paradigm (VWP) presents listeners with a challenging problem: They must integrate two disparate signals, the spoken language and the visual context, in support of action (e.g., complex movements of the eyes across a scene). We present Impulse Processing, a dynamical systems approach to incremental eye movements in the visual world that suggests a framework for integrating language, vision, and action generally. Our approach assumes that impulses driven by the language and the visual context impinge minutely on a (...) dynamical landscape of attractors corresponding to the potential eye-movement behaviors of the system. We test three unique predictions of our approach in an empirical study in the VWP, and describe an implementation in an artificial neural network. We discuss the Impulse Processing framework in relation to other models of the VWP. (shrink)

Two experiments investigated subliminal semantic and form priming in a word-completion task. Visual gap-words with a dominant and a subordinate solution were preceded by form-related or by semantically related words, which were briefly presented and sandwich-masked. Priming of the subordinate solution was assessed in Experiment 1, relative to a neutral condition. Both solutions were primed in Experiment 2. In the absence of conscious prime recognition, both semantic and form primes reliably increased the probability with which the primed solution was given. (...) With our variant of fragment-completion, response priming can be ruled out as explanation. Moreover, effects were already present at first presentation, excluding an interpretation in terms of partial awareness due to massive repetition. The data demonstrate automatic activation at both form and semantic levels in the absence of conscious awareness. (shrink)

Journal Name: Cognitive Linguistics Issue: Ahead of print.

Lexical ambiguity—the phenomenon of a single word having multiple, distinguishable senses—is pervasive in language. Both the degree of ambiguity of a word (roughly, its number of senses) and the relatedness of those senses have been found to have widespread effects on language acquisition and processing. Recently, distributional approaches to semantics, in which a word's meaning is determined by its contexts, have led to successful research quantifying the degree of ambiguity, but these measures have not distinguished between the ambiguity of words (...) with multiple related senses versus multiple unrelated meanings. In this work, we present the first assessment of whether distributional meaning representations can capture the ambiguity structure of a word, including both the number and relatedness of senses. On a very large sample of English words, we find that some, but not all, distributional semantic representations that we test exhibit detectable differences between sets of monosemes (unambiguous words; N = 964), polysemes (with multiple related senses; N = 4,096), and homonyms (with multiple unrelated senses; N = 355). Our findings begin to answer open questions from earlier work regarding whether distributional semantic representations of words, which successfully capture various semantic relationships, also reflect fine‐grained aspects of meaning structure that influence human behavior. Our findings emphasize the importance of measuring whether proposed lexical representations capture such distinctions: In addition to standard benchmarks that test the similarity structure of distributional semantic models, we need to also consider whether they have cognitively plausible ambiguity structure. (shrink)