Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-05T14:01:57.373Z Has data issue: false hasContentIssue false
  • English
  • Français

Effects of iconicity in lexical decision

Part of: Iconicity

Published online by Cambridge University Press:  28 October 2019

DAVID M. SIDHU ,
GABRIELLA VIGLIOCCO  and
PENNY M. PEXMAN
DAVID M. SIDHU
Affiliation:
University of Calgary
GABRIELLA VIGLIOCCO
Affiliation:
University College London
PENNY M. PEXMAN
Affiliation:
University of Calgary
Get access Rights & Permissions [Opens in a new window]

Abstract

In contrast to arbitrariness, a recent perspective is that words contain both arbitrary and iconic aspects. We investigated iconicity in word recognition, and the possibility that iconic words have special links between phonological and semantic features that may facilitate their processing. In Experiment 1, participants completed a lexical decision task (“Is this letter string a word?”) including words varying in their iconicity. Notably, we manipulated stimulus presentation conditions such that the items were visually degraded for half of the participants; this manipulation has been shown to increase reliance on phonology. Responses to words higher in iconicity were faster and more accurate, but this did not interact with condition. In Experiment 2 we explicitly directed participants’ attention to phonology by using a phonological lexical decision task (“Does this letter string sound like a word?”). Responses to words that were higher in iconicity were once again faster. These results demonstrate facilitatory effects of iconicity in lexical processing, thus showing that the benefits of iconic mappings extend beyond those reported for language learning and those argued for language evolution.

Keywords

iconicityword recognitionsound symbolismlexical decision
Type
Article
Information
Language and Cognition , Volume 12 , Issue 1: Special Issue on Iconicity , March 2020 , pp. 164 - 181
Copyright
Copyright © UK Cognitive Linguistics Association 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

The authors thank Mark Dingemanse and Darin Flynn for helpful correspondence about matters related to this work. The authors also thank Kristen Deschamps and Stella Heo for assistance in running the experiments.

References

references

Baayen, H., Bates, D., Kliegle, R. & Vasishth, S. (2015). RePsychLing: data sets from psychology and linguistics experiments. R package version 0.0.4.Google Scholar
Balota, D. A., Paul, S. & Spieler, D. H. (1999). Attentional control of lexical processing pathways during word recognition and reading. In Garrod, S. & Pickering, M. (eds.), Language processing , (pp. 15–57). East Sussex: Psychology Press.Google Scholar
Balota, D. A., Yap, M. J., Hutchison, K. A., Cortese, M. J., Kessler, B., Loftis, B., … & Treiman, R. (2007). The English Lexicon Project. Behavior Research Methods 39, 445–459.CrossRefGoogle ScholarPubMed
Bates, D., Kliegl, R., Vasishth, S. & Baayen, H. (2015). Parsimonious mixed models. arXiv:1506.04967 [stat]. Retrieved from <http://arxiv.org/abs/1506.04967>.Google Scholar
Bates, D., Maechler, M., Bolker, B. & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67, 1–48.CrossRefGoogle Scholar
Brysbaert, M. & New, B. (2009). Moving beyond Kučera and Francis: a critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behavior Research Methods 41, 977–990.CrossRefGoogle ScholarPubMed
Brysbaert, M., Warriner, A. B. & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods 46, 904–911.CrossRefGoogle ScholarPubMed
Coltheart, M., Rastle, K., Perry, C., Langdon, R. & Ziegler, J. (2001). DRC: a dual route cascaded model of visual word recognition and reading aloud. Psychological Review 108, 204–256.CrossRefGoogle ScholarPubMed
Dingemanse, M., Blasi, D. E., Lupyan, G., Christiansen, M. H. & Monaghan, P. (2015). Arbitrariness, iconicity, and systematicity in language. Trends in Cognitive Sciences 19, 603–615.CrossRefGoogle ScholarPubMed
Hargreaves, I. S. & Pexman, P. M. (2012). Does richness lose its luster? Effects of extensive practice on semantic richness in visual word recognition. Frontiers in Human Neuroscience 6: 234. https://doi.org/10.3389/fnhum.2012.00234CrossRefGoogle ScholarPubMed
Harm, M. W. & Seidenberg, M. S. (2004). Computing the meanings of words in reading: cooperative division of labor between visual and phonological processes. Psychological Review 111, 662–720.CrossRefGoogle ScholarPubMed
Hino, Y. & Lupker, S. J. (1996). Effects of polysemy in lexical decision and naming: an alternative to lexical access accounts. Journal of Experimental Psychology: Human Perception and Performance 22, 1331–1356.Google Scholar
Hockett, C. (1963). The problem of universals in language. In Greenberg, J. (ed.), Universals of language (pp. 1–22). Cambridge, MA: MIT Press.Google Scholar
Imai, M. & Kita, S. (2014). The sound symbolism bootstrapping hypothesis for language acquisition and language evolution. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 369. http://doi.org/10.1098/rstb.2013.0298CrossRefGoogle ScholarPubMed
Imai, M., Kita, S., Nagumo, M. & Okada, H. (2008). Sound symbolism facilitates early verb learning. Cognition 109, 54–65.CrossRefGoogle ScholarPubMed
Jaeger, B. (2017). r2glmm: computes R squared for mixed (multilevel) models. R package version 0.1.2. https://CRAN.R-project.org/package=r2glmmGoogle Scholar
Kuperman, V., Stadthagen-Gonzalez, H. & Brysbaert, M. (2012). Age-of-acquisition ratings for 30,000 English words. Behavior Research Methods 44, 978–990.CrossRefGoogle ScholarPubMed
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. (2017). lmerTest package: tests in linear mixed effects models. Journal of Statistical Software 82, 1–26.CrossRefGoogle Scholar
Laing, C. E., Viham, M. & Portnoy, T. K. (2017). How salient are onomatopoeia in the early input? A prosodic analysis of infant-directed speech. Journal of Child Language 44 , 1117–1139.CrossRefGoogle ScholarPubMed
Lockwood, G. & Dingemanse, M. (2015). Iconicity in the lab: a review of behavioral, developmental, and neuroimaging research into sound-symbolism. Frontiers in Psychology 6. https://doi.org/10.3389/fpsyg.2015.01246Google ScholarPubMed
Lockwood, G., Hagoort, P. & Dingemanse, M. (2016). How iconicity helps people learn new words: neural correlates and individual differences in sound-symbolic bootstrapping. Collabra 2, 1–15.CrossRefGoogle Scholar
Lockwood, G. & Tuomainen, J. (2015). Ideophones in Japanese modulate the P2 and late positive complex responses. Frontiers in Psychology 6. https://doi.org/10.3389/fpsyg.2015.00933CrossRefGoogle ScholarPubMed
Lupyan, G. & Winter, B. (2018). Language is more abstract than you think, or, why aren’t languages more iconic? Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1752). https://doi.org/10.1098/rstb.2017.0137CrossRefGoogle ScholarPubMed
Meteyard, L., Stoppard, E., Snudden, D., Cappa, S. F. & Vigliocco, G. (2015). When semantics aids phonology: a processing advantage for iconic word forms in aphasia. Neuropsychologia 76, 264–275.CrossRefGoogle ScholarPubMed
Newman, S. S. (1933). Further experiments in phonetic symbolism. American Journal of Psychology 45, 53–75.CrossRefGoogle Scholar
Peeters, D. (2016). Processing consequences of onomatopoeic iconicity in spoken language comprehension. In 38th Annual Meeting of the Cognitive Science Society (CogSci 2016) (pp. 1632–1647). Cognitive Science Society. https://pure.mpg.de/rest/items/item_2304565_7/component/file_2327422/contentGoogle Scholar
Perlman, M., Dale, R. & Lupyan, G. (2015). Iconicity can ground the creation of vocal symbols. Royal Society Open Science 2. https://doi.org/10.1098/rsos.150152CrossRefGoogle ScholarPubMed
Perlman, M. & Lupyan, G. (2018). People can create iconic vocalizations to communicate various meanings to naïve listeners. Scientific Reports 8. https://www.nature.com/articles/s41598-018-20961-6CrossRefGoogle ScholarPubMed
Perniss, P., Lu, J. C., Morgan, G. & Vigliocco, G. (2018). Mapping language to the world: the role of iconicity in the sign language input. Developmental Science 21. https://doi.org/10.1111/desc.12551CrossRefGoogle ScholarPubMed
Perniss, P., Thompson, R. & Vigliocco, G. (2010). Iconicity as a general property of language: evidence from spoken and signed languages. Frontiers in Psychology 1. https://doi.org/10.3389/fpsyg.2010.00227CrossRefGoogle ScholarPubMed
Perniss, P. & Vigliocco, G. (2014). The bridge of iconicity: from a world of experience to the experience of language. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 369. https://doi.org/10.1098/rstb.2013.0300Google Scholar
Perry, L. K., Perlman, M. & Lupyan, G. (2015). Iconicity in English and Spanish and its relation to lexical category and age of acquisition. PLoS ONE 10. https://doi.org/10.1371/journal.pone.0137147CrossRefGoogle ScholarPubMed
Perry, L. K., Perlman, M., Winter, B., Massaro, D. W. & Lupyan, G. (2017). Iconicity in the speech of children and adults. Developmental Science 21. https://doi.org/10.1111/desc.12572Google ScholarPubMed
Pexman, P. M., Heard, A., Lloyd, E. & Yap, M. J. (2017). The Calgary semantic decision project: concrete/abstract decision data for 10,000 English words. Behavior Research Methods 49(2), 407–417.CrossRefGoogle ScholarPubMed
Pexman, P. M., Lupker, S. J. & Jared, D. (2001). Homophone effects in lexical decision. Journal of Experimental Psychology: Learning, Memory, & Cognition 27, 139–156.Google ScholarPubMed
Pexman, P. M., Lupker, S. J. & Reggin, L. D. (2002). Phonological effects in visual word recognition: investigating the impact of feedback activation. Journal of Experimental Psychology: Learning, Memory, & Cognition 28 , 572–584.Google ScholarPubMed
R Core Team (2018). R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Online <https://www.R-project.org/>..>Google Scholar
Sapir, E. (1929). A study in phonetic symbolism. Journal of Experimental Psychology 12, 225–239.CrossRefGoogle Scholar
Sidhu, D. M. & Pexman, P. M. (2018a). Lonely sensational icons: semantic neighbourhood density, sensory experience and iconicity. Language, Cognition and Neuroscience 33, 25–31.CrossRefGoogle Scholar
Singmann, H., Bolker, B. & Westfall, J. (2015). afex: analysis of factorial experiments. R package version 0.23-0. https://cran.r-project.org/web/packages/afex/afex.pdfGoogle Scholar
Vigliocco, G. & Kita, S. (2006). Language-specific properties of the lexicon: implications for learning and processing. Language and Cognitive Processes 21, 790–816.CrossRefGoogle Scholar
Winter, B., Perlman, M., Perry, L. K. & Lupyan, G. (2017). Which words are most iconic? Interaction Studies 18, 443–464.CrossRefGoogle Scholar
Yap, M. J. (2007). Visual word recognition: explorations of megastudies, multisyllabic words, and individual differences. Unpublished doctoral dissertation, Washington University in St Louis, St Louis, MO.Google Scholar
Yap, M. J., Lim, G. Y. & Pexman, P. M. (2015). Semantic richness effects in lexical decision: the role of feedback. Memory & Cognition 43, 1148–1167.CrossRefGoogle Scholar
Yarkoni, T., Balota, D. & Yap, M. (2008). Moving beyond Coltheart’s N: a new measure of orthographic similarity. Psychonomic Bulletin & Review 15, 971–979.CrossRefGoogle ScholarPubMed
Supplementary material: File

Sidhu et al. supplementary material

Sidhu et al. supplementary material

Download Sidhu et al. supplementary material(File)
File 68.6 KB