S0109 · landauer_dumais_1997_lsa · registry identity
Latent Semantic Analysis and word acquisition
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Resolution status: resolved
Landauer, Dumais · 2008 · Scholarpedia · journal-article
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2006 · The Knowledge Deficit
Landauer's computer modeling which successfully simulates human word-learning rates through probability computation from context.
direct Thomas Landauer's computer model demonstrates that language acquisition can be achieved by gauging probabilities from both the presence and the absence of words in context.
Warrant (implicit): A computational model that successfully replicates the output of a human cognitive process provides a valid explanation of the internal mechanisms humans use to perform that process.
could fail ifThe 'black box' problem, where multiple different algorithms (some purely mathematical and non-human) can produce the same external results as human cognition.
Finding demonstrating that vocabulary acquisition is accelerated by familiar contexts, supporting domain-based instruction.
direct Word learning is significantly accelerated when it occurs within a familiar context.
Warrant (implicit): Cognitive processes identified in word-learning studies can be optimized in a classroom setting by aligning the curriculum structure with those cognitive mechanisms.
could fail ifAccelerated word learning in a familiar context may lead to narrow, domain-specific vocabularies that do not generalize to other subjects.
2016 · Why Knowledge Matters
Research proposing Latent Semantic Analysis (LSA) as a computational theory for how humans acquire and represent knowledge from context, though still struggling with the 'unstated context' problem in machine translation.
related The inability of computers to accurately translate language, even after decades of development, serves as a theoretical proof of their limitations in primary education.
Latent Semantic Analysis theory showing that word learning occurs four times faster in knowledge-rich contexts.
direct Word learning occurs up to four times faster when students are systematically becoming familiar with new knowledge domains compared to isolated word study.
Warrant (implicit): Computational models of semantic association can accurately predict the rate of biological brain acquisition of vocabulary in classroom settings.
could fail ifThe LSA model measures lexical co-occurrence patterns in large corpora, which may overestimate actual human retention when students face cognitive load or varying levels of prior interest in a domain.
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Landauer and Dumais (1997)T. K. Landauer and S. T. Dumais, 'A Solution to Plato’s Problem: The Latent Semantic Analysis Theory of the Acquisition, Induction, and Representation of Knowledge,' Psychological Review 104 (1997): 211–240.not specified in text (referred to as 'recent finding')