Cognitive Science 41 (S4):677-705 (2017)
AbstractBy the time they reach early adulthood, English speakers are familiar with the meaning of thousands of words. In the last decades, computational simulations known as distributional semantic models have demonstrated that it is possible to induce word meaning representations solely from word co-occurrence statistics extracted from a large amount of text. However, while these models learn in batch mode from large corpora, human word learning proceeds incrementally after minimal exposure to new words. In this study, we run a set of experiments investigating whether minimal distributional evidence from very short passages suffices to trigger successful word learning in subjects, testing their linguistic and visual intuitions about the concepts associated with new words. After confirming that subjects are indeed very efficient distributional learners even from small amounts of evidence, we test a DSM on the same multimodal task, finding that it behaves in a remarkable human-like way. We conclude that DSMs provide a convincing computational account of word learning even at the early stages in which a word is first encountered, and the way they build meaning representations can offer new insights into human language acquisition.
Similar books and articles
A Probabilistic Computational Model of Cross-Situational Word Learning.Afsaneh Fazly, Afra Alishahi & Suzanne Stevenson - 2010 - Cognitive Science 34 (6):1017-1063.
Précis of How Children Learn the Meanings of Words.Paul Bloom - 2001 - Behavioral and Brain Sciences 24 (6):1095-1103.
Associability: A Study of the Properties of Associative Ratings and the Role of Association in Word-Word Learning.Richard Kammann - 1968 - Journal of Experimental Psychology 78 (4p2):1.
Retrieval Dynamics and Retention in Cross‐Situational Statistical Word Learning.Haley A. Vlach & Catherine M. Sandhofer - 2014 - Cognitive Science 38 (4):757-774.
iMinerva: A Mathematical Model of Distributional Statistical Learning.Erik D. Thiessen & Philip I. Pavlik - 2013 - Cognitive Science 37 (2):310-343.
Detailed Behavioral Analysis as a Window Into Cross-Situational Word Learning.Sumarga H. Suanda & Laura L. Namy - 2012 - Cognitive Science 36 (3):545-559.
Cross‐Situational Learning of Minimal Word Pairs.Paola Escudero, Karen E. Mulak & Haley A. Vlach - 2016 - Cognitive Science 40 (2):455-465.
The Interplay of Cross‐Situational Word Learning and Sentence‐Level Constraints.Judith Koehne & Matthew W. Crocker - 2015 - Cognitive Science 39 (5):849-889.
Cross-Situational Learning: An Experimental Study of Word-Learning Mechanisms.Kenny Smith, Andrew D. M. Smith & Richard A. Blythe - 2011 - Cognitive Science 35 (3):480-498.
Effects of Visual Information on Adults' and Infants' Auditory Statistical Learning.Erik D. Thiessen - 2010 - Cognitive Science 34 (6):1093-1106.
Cross-Situational and Supervised Learning in the Emergence of Communication.Jose Fernando Fontanari & Angelo Cangelosi - 2011 - Interaction Studies 12 (1):119-133.
The Semantics of Prosody: Acoustic and Perceptual Evidence of Prosodic Correlates to Word Meaning.Lynne C. Nygaard, Debora S. Herold & Laura L. Namy - 2009 - Cognitive Science 33 (1):127-146.
Good Intentions and Bad Words.Frank C. Keil - 2001 - Behavioral and Brain Sciences 24 (6):1110-1111.
Using Variability to Guide Dimensional Weighting: Associative Mechanisms in Early Word Learning.Keith S. Apfelbaum & Bob McMurray - 2011 - Cognitive Science 35 (6):1105-1138.
Looking in the Wrong Direction Correlates With More Accurate Word Learning.Stanka A. Fitneva & Morten H. Christiansen - 2011 - Cognitive Science 35 (2):367-380.
Added to PP
Historical graph of downloads
Citations of this work
Grounding the Neurobiology of Language in First Principles: The Necessity of Non-Language-Centric Explanations for Language Comprehension.Uri Hasson, Giovanna Egidi, Marco Marelli & Roel M. Willems - 2018 - Cognition 180 (C):135-157.
Symbol Grounding Without Direct Experience: Do Words Inherit Sensorimotor Activation From Purely Linguistic Context?Fritz Günther, Carolin Dudschig & Barbara Kaup - 2018 - Cognitive Science 42 (S2):336-374.
Mining a Crowdsourced Dictionary to Understand Consistency and Preference in Word Meanings.Brendan T. Johns - 2019 - Frontiers in Psychology 10.
Compounding as Abstract Operation in Semantic Space: Investigating Relational Effects Through a Large-Scale, Data-Driven Computational Model.Marco Marelli, Christina L. Gagné & Thomas L. Spalding - 2017 - Cognition 166:207-224.
Cross‐Situational Word Learning With Multimodal Neural Networks.Wai Keen Vong & Brenden M. Lake - 2022 - Cognitive Science 46 (4).
References found in this work
Building Machines That Learn and Think Like People.Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum & Samuel J. Gershman - 2017 - Behavioral and Brain Sciences 40.
A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge.Thomas K. Landauer & Susan T. Dumais - 1997 - Psychological Review 104 (2):211-240.
Human Simulations of Vocabulary Learning.Jane Gillette, Henry Gleitman, Lila Gleitman & Anne Lederer - 1999 - Cognition 73 (2):135-176.
An Amorphous Model for Morphological Processing in Visual Comprehension Based on Naive Discriminative Learning.R. Harald Baayen, Petar Milin, Dusica Filipović Đurđević, Peter Hendrix & Marco Marelli - 2011 - Psychological Review 118 (3):438-481.
Learning to Use Words: Event-Related Potentials Index Single-Shot Contextual Word Learning.Arielle Borovsky, Marta Kutas & Jeff Elman - 2010 - Cognition 116 (2):289-296.