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Kumar, Abhilasha A.; Balota, David A.; Steyvers, Mark – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2020
We examined 3 different network models of representing semantic knowledge (5,018-word directed and undirected step distance networks, and an association-correlation network) to predict lexical priming effects. In Experiment 1, participants made semantic relatedness judgments for word pairs with varying path lengths. Response latencies for…
Descriptors: Semantics, Networks, Correlation, Semitic Languages
Jones, Michael N.; Gruenenfelder, Thomas M.; Recchia, Gabriel – Grantee Submission, 2017
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…
Descriptors: Semantics, Computational Linguistics, Spatial Ability, Proximity
Gruenenfelder, Thomas M.; Recchia, Gabriel; Rubin, Tim; Jones, Michael N. – Cognitive Science, 2016
We compared the ability of three different contextual models of lexical semantic memory (BEAGLE, Latent Semantic Analysis, and the Topic model) and of a simple associative model (POC) to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network…
Descriptors: Memory, Semantics, Associative Learning, Networks
Hamrick, Phillip – Language Learning, 2014
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…
Descriptors: Second Language Learning, Role, Syntax, Computational Linguistics
Steyvers, Mark; Tenenbaum, Joshua B. – Cognitive Science, 2005
We present statistical analyses of the large-scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small-world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of…
Descriptors: Semantics, Internet, Associative Learning, Statistical Analysis
Samsonovich, Alexei V.; Ascoli, Giorgio A. – Learning & Memory, 2005
The goal of this work is to extend the theoretical understanding of the relationship between hippocampal spatial and memory functions to the level of neurophysiological mechanisms underlying spatial navigation and episodic memory retrieval. The proposed unifying theory describes both phenomena within a unique framework, as based on one and the…
Descriptors: Associative Learning, Neurological Organization, Networks, Physiology

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