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Johns, Brendan T.; Mewhort, Douglas J. K.; Jones, Michael N. – Cognitive Science, 2019
Distributional models of semantics learn word meanings from contextual co-occurrence patterns across a large sample of natural language. Early models, such as LSA and HAL (Landauer & Dumais, 1997; Lund & Burgess, 1996), counted co-occurrence events; later models, such as BEAGLE (Jones & Mewhort, 2007), replaced counting co-occurrences…
Descriptors: Semantics, Learning Processes, Models, Prediction
Hofmann, Markus J.; Biemann, Chris; Westbury, Chris; Murusidze, Mariam; Conrad, Markus; Jacobs, Arthur M. – Cognitive Science, 2018
What determines human ratings of association? We planned this paper as a test for association strength (AS) that is derived from the log likelihood that two words co-occur significantly more often together in sentences than is expected from their single word frequencies. We also investigated the moderately correlated interactions of word…
Descriptors: Prediction, Correlation, Word Frequency, Emotional Response
Çöltekin, Çagri – Cognitive Science, 2017
This study investigates a strategy based on predictability of consecutive sub-lexical units in learning to segment a continuous speech stream into lexical units using computational modeling and simulations. Lexical segmentation is one of the early challenges during language acquisition, and it has been studied extensively through psycholinguistic…
Descriptors: Speech Communication, Phonemes, Prediction, Computational Linguistics
Montag, Jessica L.; Jones, Michael N.; Smith, Linda B. – Cognitive Science, 2018
The words in children's language learning environments are strongly predictive of cognitive development and school achievement. But how do we measure language environments and do so at the scale of the many words that children hear day in, day out? The quantity and quality of words in a child's input are typically measured in terms of total amount…
Descriptors: Language Acquisition, Vocabulary Development, Linguistic Input, Prediction
Lau, Jey Han; Clark, Alexander; Lappin, Shalom – Cognitive Science, 2017
The question of whether humans represent grammatical knowledge as a binary condition on membership in a set of well-formed sentences, or as a probabilistic property has been the subject of debate among linguists, psychologists, and cognitive scientists for many decades. Acceptability judgments present a serious problem for both classical binary…
Descriptors: Grammar, Probability, Sentences, Language Research
Ridgeway, Karl; Mozer, Michael C.; Bowles, Anita R. – Cognitive Science, 2017
We explore the nature of forgetting in a corpus of 125,000 students learning Spanish using the Rosetta Stone® foreign-language instruction software across 48 lessons. Students are tested on a lesson after its initial study and are then retested after a variable time lag. We observe forgetting consistent with power function decay at a rate that…
Descriptors: Computational Linguistics, Second Language Learning, Second Language Instruction, Computer Software
Reilly, Jamie; Hung, Jinyi; Westbury, Chris – Cognitive Science, 2017
Arbitrary symbolism is a linguistic doctrine that predicts an orthogonal relationship between word forms and their corresponding meanings. Recent corpora analyses have demonstrated violations of arbitrary symbolism with respect to concreteness, a variable characterizing the sensorimotor salience of a word. In addition to qualitative semantic…
Descriptors: Computational Linguistics, Semantics, Word Recognition, Auditory Perception