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Cross-situational learning is a mechanism for learning the meaning of words across multiple exposures, despite exposure-by-exposure uncertainty as to the word's true meaning. We present experimental evidence showing that humans learn words effectively using cross-situational learning, even at high levels of referential uncertainty. Both overall success(More)
This article investigates the problem of how language learners decipher what words mean. In many recent models of language evolution, agents are provided with innate meanings a priori and explicitly transfer them to each other as part of the communication process. By contrast, I investigate how successful communication systems can emerge without innate or(More)
We present a mathematical model of cross-situational learning , in which we quantify the learnability of words and vocabularies. We find that high levels of uncertainty are not an impediment to learning single words or whole vocabulary systems, as long as the level of uncertainty is somewhat lower than the total number of meanings in the system. We further(More)
This paper investigates the development of experience-based meaning creation and explores the problem of establishing successful communication systems in a population of agents. The aim of the work is to investigate how such systems can develop, without reliance on phenomena not found in actual human language learning, such as the explicit transmission of(More)
Cross-situational learning is a mechanism for learning the meaning of words across multiple exposures, despite exposure-by-exposure uncertainty as to a word's true meaning. Doubts have been expressed regarding the plausibility of cross-situational learning as a mechanism for learning human-scale lexicons in reasonable timescales under the levels of(More)
Language is a symbolic, culturally transmitted system of communication, which is learnt through the inference of meaning. In this paper, I describe the importance of meaning inference, not only in language acquisition, but also in developing a unified explanation for language change and evolution. Using an agent-based computational model of meaning creation(More)
Cross-situational learning allows word learning despite exposure-by-exposure uncertainty about a word's meaning, by combining information across exposures to a word. A number of experimental studies demonstrate that humans are capable of cross-situational learning. The strongest claims here are made by Yu and Smith (2007), who provide experimental data(More)
A central question for ecologists is the extent to which anthropogenic disturbances (e.g. tourism) might impact wildlife and affect the systems under study. From a research perspective, identifying the effects of human disturbance caused by research-related activities is crucial in order to understand and account for potential biases and derive appropriate(More)