From Words to Behaviour via Semantic Networks

Distributional models of word meaning assign vector representations to words based on word co-occurrence patterns in text. In the context of semantic memory research, our work aims to assess whether distributional models can better fit human data when the structure but also the processing of semantic information is taken into account. We develop a model that embeds text-semantics as well as spreading of activation. Starting from a standard textual model of semantics, we allow activation to spread throughout the semantic network, and record the activation of each word, as a function of time.

[1]  W. Maki,et al.  Latent structure in measures of associative, semantic, and thematic knowledge , 2008, Psychonomic bulletin & review.

[2]  M. Steyvers.  Combining Feature Norms and Text Data with Topic Models , 2022 .

[3]  Gert Storms,et al.  Word associations: Network and semantic properties , 2008, Behavior research methods.

[4]  Mark Steyvers,et al.  Topics in semantic representation. , 2007, Psychological review.

[5]  Lidia Suárez,et al.  Observing neighborhood effects without neighbors , 2011, Psychonomic bulletin & review.

[6]  R. Ratcliff.  Modeling response signal and response time data , 2006, Cognitive Psychology.

[7]  Marc Brysbaert,et al.  How useful are corpus-based methods for extrapolating psycholinguistic variables? , 2015, Quarterly journal of experimental psychology.

[8]  Max Coltheart,et al.  Access to the internal lexicon , 1977 .

[9]  Ramon Ferrer i Cancho,et al.  The small world of human language , 2001, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[10]  D. Mirman,et al.  Competition and cooperation among similar representations: toward a unified account of facilitative and inhibitory effects of lexical neighbors. , 2012, Psychological review.

[11]  T. Rogers,et al.  Semantic diversity: A measure of semantic ambiguity based on variability in the contextual usage of words , 2012, Behavior Research Methods.

[12]  Markus J. Hofmann,et al.  Now you see it, now you don't: on emotion, context, and the algorithmic prediction of human imageability judgments , 2013, Front. Psychol..

[13]  Marc Brysbaert,et al.  Subtlex-UK: A New and Improved Word Frequency Database for British English , 2014, Quarterly journal of experimental psychology.

[14]  Nikolaus Kriegeskorte,et al.  Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .

[15]  Christian Bentz,et al.  A Quantitative Empirical Analysis of the Abstract/Concrete Distinction , 2014, Cogn. Sci..

[16]  Michael N. Jones,et al.  Redundancy in Perceptual and Linguistic Experience: Comparing Feature-Based and Distributional Models of Semantic Representation , 2010, Top. Cogn. Sci..

[17]  Mark J. Huff,et al.  An Abundance of Riches: Cross-Task Comparisons of Semantic Richness Effects in Visual Word Recognition , 2012, Front. Hum. Neurosci..

[18]  Stefan Richter,et al.  Centrality Indices , 2004, Network Analysis.

[19]  Michael N. Jones,et al.  Graph-Theoretic Properties of Networks Based on Word Association Norms: Implications for Models of Lexical Semantic Memory , 2016, Cogn. Sci..

[20]  Joshua B. Tenenbaum,et al.  The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth , 2001, Cogn. Sci..

[21]  Richard M. Shiffrin,et al.  Word Association Spaces for Predicting Semantic Similarity Effects in Episodic Memory. , 2005 .

[22]  M. Brysbaert,et al.  Age-of-acquisition ratings for 30,000 English words , 2012, Behavior research methods.

[23]  M. Louwerse.  A Case for Symbol Interdependency 1 Symbolic or Embodied representations : A Case for Symbol Interdependency , 2008 .

[24]  Elia Bruni,et al.  Multimodal Distributional Semantics , 2014, J. Artif. Intell. Res..

[25]  Nick Chater,et al.  Networks in Cognitive Science , 2013, Trends in Cognitive Sciences.

[26]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[27]  Rebecca Treiman,et al.  The English Lexicon Project , 2007, Behavior research methods.

[28]  Geoffrey Leech,et al.  CLAWS4: The Tagging of the British National Corpus , 1994, COLING.

[29]  Michael N. Jones,et al.  The semantic richness of abstract concepts , 2012, Front. Hum. Neurosci..

[30]  Daniel Mirman,et al.  Attractor Dynamics and Semantic Neighborhood Density: Processing Is Slowed by near Neighbors and Speeded by Distant Neighbors We Thank Ann Kulikowski for Her Help with Data Collection And , 2022 .

[31]  Lawrence W. Barsalou,et al.  Language and simulation in conceptual processing , 2008 .

[32]  Petr Sojka,et al.  Software Framework for Topic Modelling with Large Corpora , 2010 .

[33]  Max M. Louwerse,et al.  Symbol Interdependency in Symbolic and Embodied Cognition , 2011, Top. Cogn. Sci..

[34]  Georgiana Dinu,et al.  Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors , 2014, ACL.

[35]  D. Balota,et al.  Moving beyond Coltheart’s N: A new measure of orthographic similarity , 2008, Psychonomic bulletin & review.

[36]  Marc Brysbaert,et al.  The British Lexicon Project: Lexical decision data for 28,730 monosyllabic and disyllabic English words , 2011, Behavior Research Methods.

[37]  Amy Beth Warriner,et al.  Norms of valence, arousal, and dominance for 13,915 English lemmas , 2013, Behavior Research Methods.

[38]  Gabriella Vigliocco,et al.  Integrating experiential and distributional data to learn semantic representations. , 2009, Psychological review.

[39]  Felix Hill,et al.  Multi-Modal Models for Concrete and Abstract Concept Meaning , 2014, TACL.

[40]  Kirsty Kitto,et al.  How activation, entanglement, and searching a semantic network contribute to event memory , 2013, Memory & cognition.

[41]  Marc W. Howard,et al.  Constructing Semantic Representations From a Gradually Changing Representation of Temporal Context , 2011, Top. Cogn. Sci..

[42]  Amy Beth Warriner,et al.  Concreteness ratings for 40 thousand generally known English word lemmas , 2014, Behavior research methods.

[43]  Thomas A. Schreiber,et al.  The University of South Florida free association, rhyme, and word fragment norms , 2004, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[44]  John R. Anderson.  A spreading activation theory of memory. , 1983 .

[45]  J. Tenenbaum,et al.  Probabilistic models of cognition: exploring representations and inductive biases , 2010, Trends in Cognitive Sciences.

[46]  Akira Utsumi,et al.  A Complex Network Approach to Distributional Semantic Models , 2015, PloS one.

[47]  N. Kriegeskorte,et al.  Author ' s personal copy Representational geometry : integrating cognition , computation , and the brain , 2013 .

[48]  H. Stadthagen-González,et al.  The Bristol norms for age of acquisition, imageability, and familiarity , 2006, Behavior research methods.

[49]  Thomas T. Hills,et al.  Optimal foraging in semantic memory. , 2012, Psychological review.

[50]  P. Hoffman,et al.  Opposing Effects of Semantic Diversity in Lexical and Semantic Relatedness Decisions , 2015, Journal of experimental psychology. Human perception and performance.

[51]  Felix Hill,et al.  Learning Abstract Concept Embeddings from Multi-Modal Data: Since You Probably Can’t See What I Mean , 2014, EMNLP.

[52]  Xu Xu,et al.  Content Differences for Abstract and Concrete Concept , 2005, Cogn. Sci..

[53]  Brendan T. Johns,et al.  The role of semantic diversity in lexical organization. , 2012, Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale.

[54]  R. Logie,et al.  Age-of-acquisition, imagery, concreteness, familiarity, and ambiguity measures for 1,944 words , 1980 .

[55]  Ian S. Hargreaves,et al.  Get rich quick: The signal to respond procedure reveals the time course of semantic richness effects during visual word recognition , 2014, Cognition.

[56]  James L. McClelland,et al.  You shall know an object by the company it keeps: An investigation of semantic representations derived from object co-occurrence in visual scenes , 2015, Neuropsychologia.

[57]  Stavroula Kousta,et al.  Toward a theory of semantic representation , 2009, Language and Cognition.

[58]  James L. McClelland,et al.  Letting structure emerge: connectionist and dynamical systems approaches to cognition , 2010, Trends in Cognitive Sciences.

[59]  R. Shillcock,et al.  Rethinking the Word Frequency Effect: The Neglected Role of Distributional Information in Lexical Processing , 2001, Language and speech.

[60]  Curt Burgess,et al.  Characterizing semantic space: Neighborhood effects in word recognition , 2001, Psychonomic bulletin & review.

[61]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..

[62]  James L. McClelland,et al.  Semantic Cognition: A Parallel Distributed Processing Approach , 2004 .

[63]  Carina Silberer,et al.  Learning Grounded Meaning Representations with Autoencoders , 2014, ACL.

[64]  Douglas L. Nelson,et al.  Is the Concreteness Effect a Result of Underlying Network Interconnectivity , 1999 .

[65]  David P Vinson,et al.  Semantic feature production norms for a large set of objects and events , 2008, Behavior research methods.

[66]  Diane Pecher,et al.  Abstract concepts: sensory-motor grounding, metaphors, and beyond , 2011 .

[67]  Mark S. Seidenberg,et al.  Semantic feature production norms for a large set of living and nonliving things , 2005, Behavior research methods.