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In the years, we have developed theoretical and computational proposals of how humans represent the meaning of words in their mind/brain.
In other modelling work, we have assessed the fit of information theoretical measures such as surprisal to reading (behavioural and EEG data, Fernandez-Monsalve et al., 2012; Frank et al., 2013; 2015). We are now extending this work to multimodal communication (Zheng, et al., in prep).
Having explicit models of how humans represent meaning has allowed us to tackle a number of other long-standing issues such as: (i) linguistic relativity (our work has shown surprisingly limited domains in which effects of linguistic relativity are observed, Vigliocco et al., 2005; Iwasaki et al., 2010). (ii) the representation of nouns and verbs in the brain (our work provides behavioural and imaging evidence in favour of semantic, rather than syntactic, neural organisation, e.g., Iwasaki et al., 2008; Siri et al., 2008; Vigliocco et al., 2005; Vigliocco et al., 2011). (iii) representational differences between concrete and abstract words (Rotaru et al., in prep.)
- Key References
1) Theoretical papers
Andrews, M., Frank, S. & Vigliocco, G. (2014). Reconciling embodied and distributional accounts of meaning and language. Topics in Cognitive Science, 6, 359-370.
Kousta, S..-T., Vigliocco, G., Vinson, D.P., Andrews, M. & Del Campo, E. (2011). The representation of abstract words: Why emotion matters. Journal of Experimental Psychology: General, 140, 14-34.
Meteyard, L., Rodriguez, S., Bahrami, B., & Vigliocco, G. (2012). Coming of age: A review of embodiment and the neuroscience of semantics. Cortex, 48, 788-804.
Vigliocco, G., Meteyard, L., Andrews, M., & Kousta, S.-T. (2009). Toward a Theory of Semantic Representation. Language and Cognition 1, 219-247.
2. Computational Papers
Andrews, M. & Vigliocco, G. (2010). Learning semantic representations with hidden Markov topic models. Topics in Cognitive Science, 2, 101-113.
Andrews, M., Vigliocco, G. & Vinson, D.P. (2009). The role of attributional and distributional information in learning semantic representation. Psychological Review, 116, 463-498.
Fernandez Monsalve, I., Frank, S.L., & Vigliocco, G. (2012). Lexical Surprisal as a general predictor of reading time. Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (pp. 398-408). Avignon, France: Association for Computational Linguistics
Frank, S.L., Otten, L. Galli, G. & Vigliocco, G. (2015). The ERP response to the amount of information conveyed by words in sentences. Brain and Language. 140, 1-11.
Frank, S. L., & Vigliocco, G. (2011). Sentence comprehension as mental simulation: an information-theoretic perspective. Information, 2, 672–696.
Frank, S.L., Fernandez Monsalve, I., Thompson, R.L., Vigliocco, G. (2013). Reading-time data for evaluating broad-coverage models of English sentence processing. Behavioural Research Methods. 4, 1182-1190. doi:10.3758/s13428-012-0313-y
Rotaru, A., Frank, S.F., Vigliocco, G. (2016). Concreteness, imageability and semantic network structure. In Proceeding of the Cognitive Science Society Meeting.
