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In recent years, Bayesian models have become increasingly popular as a way of understanding human cognition. Ideal learner Bayesian models assume that cognition can be usefully understood as optimal behavior under uncertainty, a hypothesis that has been supported by a number of modeling studies across various domains (e.g., Griffiths and Tenenbaum,(More)
This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents(More)
2 Abstract The induction problems facing language learners have played a central role in debates about the types of learning biases that exist in the human brain. Many linguists have argued that some of the learning biases necessary to solve these language induction problems must be both innate and language-specific (i.e., the Universal Grammar (UG)(More)
I completely agree with Ambridge, Pine, and Lieven (AP&L) that anyone proposing a learning-strategy component needs to demonstrate precisely how that component helps solve the language acquisition task. To this end, I discuss how computational modeling is a tool well suited to doing exactly this, and that it has the added benefit of highlighting hidden(More)
Subtle social information is available in text such as a speaker’s emotional state, intentions, and attitude, but current information extraction systems are unable to extract this information at the level that humans can. We describe a methodology for creating databases of messages annotated with social information based on interactive games between humans(More)
Language acquisition research is oen concerned with questions of what, when, and how – what children know, when they know it, and how they learn it. eoretical research traditionally yields the what – the knowledge that children attain. For instance, this includes how many vowel phonemes the language has, how the plural is formed, and if the verb comes(More)
The frequent occurrence of divergences|structural diier-ences between languages|presents a great challenge for statistical word-level alignment. In this paper, we introduce DUSTer, a method for systematically identifying common divergence types and transforming an English sentence structure to bear a closer resemblance to that of another language. Our(More)
We use historical change to explore whether children filter their input for language learning. Although others (e.g., Rohde & Plaut, 1999) have proposed filtering based on string length, we explore two types of filters that assume richer linguistic structure. One presupposes that linguistic utterances are structurally highly ambiguous and focuses learning(More)
We describe a new supervised machine learning approach for detecting authorship deception, a specific type of authorship attribution task particularly relevant for cybercrime forensic investigations, and demonstrate its validity on two case studies drawn from realistic online data sets. The core of our approach involves identifying uncharacteristic behavior(More)