Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-28T16:58:21.384Z Has data issue: false hasContentIssue false
  • English
  • Français

Quantifying semantic animacy: How much are words alive?

Published online by Cambridge University Press:  17 March 2016

JELENA RADANOVIĆ ,
CHRIS WESTBURY  and
PETAR MILIN
JELENA RADANOVIĆ*
Affiliation:
University of Novi Sad
CHRIS WESTBURY
Affiliation:
University of Alberta
PETAR MILIN
Affiliation:
University of Novi Sad and Eberhard Karls University Tübingen
*
ADDRESS FOR CORRESPONDENCE Jelena Radanović, Department of Psychology, Faculty of Philosophy, Dr Zorana Đinđića 2, Novi Sad 21000, Serbia. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The main goal of this study, which comprised two experimental tasks and three normative studies, was to describe the underlying distribution of semantic animacy, with the focus on Serbian and English. Animacy was measured using three normative techniques. The cognitive effects of obtained measures were tested in two experiments conducted in both Serbian and English: a visual lexical decision task and a semantic categorization task. Results suggest that semantic animacy is a graded property. A high correlation between Serbian and English measures suggests that semantic animacy might be language independent, most likely because of its biological grounding. As for its behavioral correlates, animacy does not affect lexical decision times but it does codetermine the categorization speed: the category decision gradually slows as a function of the degree of animacy. These results were consistent across two languages under research scrutiny. We thus conclude that animacy is a continuous aspect of meaning.

Type
Articles
Information
Applied Psycholinguistics , Volume 37 , Issue 6 , November 2016 , pp. 1477 - 1499
Check for updates
Copyright
Copyright © Cambridge University Press 2016 

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.)

References

REFERENCES

Aissen, J. (2003). Differential object marking: Iconicity vs. economy. Natural Language & Linguistic Theory, 21, 435–483.Google Scholar
Baayen, R. H. (2008). Analyzing linguistic data: A practical introduction to statistics using R. Cambridge: Cambridge University Press.Google Scholar
Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59, 390–412.CrossRefGoogle Scholar
Baayen, R. H., Feldman, L. B., & Schreuder, R. (2006). Morphological influences on the recognition of monosyllabic monomorphemic words. Journal of Memory and Language, 53, 496–512.Google Scholar
Baayen, R. H., & Milin, P. (2010). Analyzing reaction times. International Journal of Psychological Research, 3, 12–28.Google Scholar
Baayen, R. H., Milin, P., Filipović-Đurđević, D., Hendrix, P., & Marelli, M. (2011). An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychological Review, 118, 438–481.CrossRefGoogle ScholarPubMed
Balota, D. A., Cortese, M. J., Sergent-Marshall, S. D., Spieler, D. H., & Yap, M. (2004). Visual word recognition of single-syllable words. Journal of Experimental Psychology: General, 133, 283–316.CrossRefGoogle ScholarPubMed
Branigan, B. H., Pickering, J. M., & Tanaka, M. (2008). Contributions of animacy to grammatical function assignment and word order production. Lingua, 118, 172–189.Google Scholar
Bresnan, J., Cueni, A., Nikitina, T., & Baayen, R. H. (2007). Predicting the dative alternation. In Bouma, G., Kraemer, I., & Zwarts, J. (Eds.), Cognitive foundations of interpretation (pp. 69–94). Amsterdam: Royal Netherlands Academy of Science.Google Scholar
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
Cappa, S. F., Perani, D., Schnur, T., Tettamanti, M., & Fazio, F. (1998). The effects of semantic category and knowledge type on lexical-semantic access: A PET study. NeuroImage, 8, 350–359.Google Scholar
Caramazza, A., & Shelton, J. R. (1998). Domain-specific knowledge systems in the brain: The animate-inanimate distinction. Journal of Cognitive Neuroscience, 10, 1–34.Google Scholar
Chao, L. L., Weisberg, J., & Martin, A. (2002). Experience-dependent modulation of category-related cortical activity. Cerebral Cortex, 12, 545–551.Google Scholar
Dahl, Ö., & Fraurud, K. (1996). Animacy in grammar and discourse. In Fretheim, T. & Gundel, J. K. (Eds.), Reference and referent accessibility (pp. 47–64). Amsterdam: John Benjamins.CrossRefGoogle Scholar
De Renzi, E., & Lucchelli, F. (1994). Are semantic systems separately represented in the brain? The case of living category impairment. Cortex, 30, 3–25.CrossRefGoogle ScholarPubMed
Devlin, J. T., Russell, R. P., Davis, M. H., Price, C. J., Moss, H. E., Fadili, M. J., et al. (2002). Is there an anatomical basis for category-specificity? Semantic memory studies in PET and fMRI. Neuropsychologia, 40, 54–75.Google Scholar
Dixon, R. M. W. (1979). Ergativity. Language, 55, 59–138.CrossRefGoogle Scholar
Dunteman, G. (1989). Principal component analysis. Newbury Park, CA: Sage. Google Scholar
Dye, M., Milin, P., Futrell, R., & Ramscar, M. (in press). A functional theory of gender paradigms. In Kiefer, F., Blevins, J. P., & Bartos, H. (Eds.), Morphological paradigms and functions. Leiden: Brill.Google Scholar
Farah, M. J., Meyer, M. M., & McMullen, P. A. (1996). The living/nonliving dissociation is not an artifact: Giving an a priori implausible hypothesis a strong test. Cognitive Neuropsychology, 13, 137–154.Google Scholar
Forster, K. I., & Forster, J. C. (2003). DMDX: A windows display program with millisecond accuracy. Behavior Research Methods, Instruments, and Computers, 35, 116–124.Google Scholar
Frawley, W. (1992). Linguistic semantics. New York: Routledge.Google Scholar
Grabowski, T. J., Damasio, H., & Damasio, A. R. (1998). Premotor and prefrontal correlates of category-related lexical retrieval. NeuroImage, 7, 232–243.Google Scholar
Hay, J. B., & Baayen, R. H. (2005). Shifting paradigms: Gradient structure in morphology. Trends in Cognitive Sciences, 9, 342–348.CrossRefGoogle ScholarPubMed
Ilić, O., Ković, V., & Styles, S. J. (2013). In the absence of animacy: Superordinate category structure affects subordinate label verification. PLOS ONE, 8, e83282.CrossRefGoogle ScholarPubMed
Inagaki, K., & Hatano, G. (2003). Conceptual and linguistic factors in inductive projection: How do young children recognize commonalities between animals and plants? In Gentner, D. & Goldin-Meadow, S. (Eds.), Language in mind: Advances in the study of language and thought (pp. 313–333). Cambridge, MA: MIT Press.Google Scholar
Ishai, A., Ungerleider, L. G., Martin, A., Schouten, J. L., & Haxby, J. V. (1999). Distributed representation of objects in the human ventral visual pathway. Proceedings of the National Academy of Sciences, 96, 9379–9384.Google Scholar
Jolliffe, I. (2002). Principal component analysis. New York: Wiley.Google Scholar
Kadhila, N. (2005). NSSC Biology Module 1. Cambridge: Cambridge University Press.Google Scholar
Keuleers, E., & Brysbaert, M. (2010). Wuggy: A multilingual pseudoword generator. Behavior Research Methods, 42, 627–633.CrossRefGoogle ScholarPubMed
Klenin, E. (2014). Belebtheit, personalität/Animacy, personhood. In Kempgen, S., Kosta, P., Berger, T., & Gutschmidt, K. (Eds.), Die slavischen Sprachen/The Slavic Languages. Halbband 2 (Vol. 32, pp. 152–161). Chicago: Walter de Gruyter.Google Scholar
LimeSurvey Project Team/Carsten Schmitz. (2012). LimeSurvey: An Open Source survey tool [Computer software]. Retrieved from http://www.limesurvey.org Google Scholar
Love, B. C., Medin, D. L., & Gureckis, T. M. (2004). SUSTAIN: A network model of category learning. Psychological Review, 111, 309–332.Google Scholar
Martin, A., Wiggs, C. L., Ungerleider, L. G., & Haxby, J. V. (1996). Neural correlates of category-specific knowledge. Nature, 379, 649–652.Google Scholar
Moore, C. J., & Price, C. J. (1999). A functional neuroimaging study of the variables that generate category-specific object processing differences. Brain, 122, 943–962.Google Scholar
Mummery, C. J., Patterson, K., Hodges, J. R., & Price, C. J. (1998). Functional neuroanatomy of the semantic system: Divisible by what? Journal of Cognitive Neuroscience, 10, 766–777.Google Scholar
Nosofsky, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39–61.CrossRefGoogle ScholarPubMed
Perani, D., Cappa, S. F., Bettinardi, V., Bressi, S., Gorno-Tempini, M., Matarrese, M., et al. (1995). Different neural systems for the recognition of animals and man-made tools. NeuroReport, 6, 1637–1641.Google Scholar
Perani, D., Schnur, T., Tettamanti, M., Gorno, M., Cappa, S. F., & Fazio, F. (1999). Word and picture matching: A PET study of semantic category effects. Neuropsychologia, 37, 293–306.CrossRefGoogle ScholarPubMed
Pilgrim, L. K., Fadili, J., Fletcher, P., & Tyler, L. K. (2002). Overcoming confounds of stimulus blocking: An event-related fMRI design of semantic processing. NeuroImage, 16, 713–723.Google Scholar
Radanović, J., & Milin, P. (2011). Morpho-semantic properties of Serbian nouns: Animacy and gender pairs. Psihologija, 44, 343–366.Google Scholar
R Core Team. (2013). R: A language and environment for statistical computing [Computer software]. Vienna: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org/ Google Scholar
Rosenbach, A. (2007). Animacy and grammatical variation—Findings from English genitive variation. Lingua, 118, 151–171.Google Scholar
Sacchett, C., & Humphreys, G. W. (1992). Calling a squirrel a squirrel but a canoe a wigwam: A category-specific deficit for artefactual objects and body parts. Cognitive Neuropsychology, 9, 73–86.Google Scholar
Schütze, C. T., & Sprouse, J. (2014). Judgment data. In Podesva, R. J. & Sharma, D. (Eds.), Research methods in linguistics (pp. 27–50). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Silverstein, M. (1976). Hierarchy of features and ergativity. In Dixon, R. M. W. (Ed.), Grammatical categories in Australian languages (pp. 112–171). Canberra: Australian Institute of Aboriginal Studies.Google Scholar
Tyler, L., & Moss, H. (2001). Towards a distributed account of conceptual knowledge. Trends in Cognitive Sciences, 5, 244–252.Google Scholar
Tyler, L., Russell, R., Fadili, J., & Moss, H. (2001). The neural representation of nouns and verbs: PET studies. Brain, 124, 1619–1634.Google Scholar
Tyler, L. K., Bright, P., Dick, E., Tavares, P., Pilgrim, L., Fletcher, P., et al. (2003). Do semantic categories activate distinct cortical regions? Evidence for a distributed neural semantic system. Cognitive Neuropsychology, 20, 541–559.Google Scholar
Tyler, L. K., Moss, H. E., Durrant-Peatfield, M. R., & Levy, J. P. (2000). Conceptual structure and the structure of concepts: A distributed account of category-specific deficits. Brain and Language, 75, 195–231.Google Scholar
Tyler, L. K., Stamatakis, E. A., Dick, E., Bright, P., Fletcher, P., & Moss, H. (2003). Objects and their actions: Evidence for a neurally distributed semantic system. Neuroimage, 18, 542–557.Google Scholar
Vigliocco, G., Vinson, D. P., Lewis, W., & Garrett, M. F. (2004). Representing the meanings of object and action words: The featural and unitary semantic space hypothesis. Cognitive Psychology, 48, 422–488.Google Scholar
Warrington, E. K., & McCarthy, R. (1987). Categories of knowledge: Further fractionation and an attempted integration. Brain, 110, 1273–1296.CrossRefGoogle Scholar
Westbury, C. (2007). ACTUATE: Assessing cases: The University of Alberta Testing Environment [Computer software]. Retrieved from http://www.psych.ualberta.ca/~westburylab/downloads/actuate.download.html Google Scholar
Wood, S. N. (2006). Generalized additive models: An introduction with R. Boca Raton, FL: Chapman & Hall/CRC Press.CrossRefGoogle Scholar
Yamamoto, M. (1999). Animacy and reference: A cognitive approach to corpus linguistics. Amsterdam: John Benjamins.Google Scholar
Zaenen, A., Carletta, J., Garretson, G., Bresnan, J., Koontz-Garboden, A., Nikitina, T., et al. (2004). Animacy encoding in English: Why and how. In Byron, D. & Webber, B. (Eds.), Proceedings of the 2004 ACL Workshop on Discourse Annotation (pp. 118–125). East Stroudsburg, PA: Association for Computational Linguistics.Google Scholar