Article published In: The Mental Lexicon
Vol. 15:2 (2020) ► pp.161–188
The role of affective meaning, semantic associates, and orthographic neighbours in modulating the N400 in single words
Available under the Creative Commons Attribution-NonCommercial (CC BY-NC) 4.0 license.
For any use beyond this license, please contact the publisher at rights@benjamins.nl.
Published online: 6 November 2020
https://doi.org/10.1075/ml.19021.blo
https://doi.org/10.1075/ml.19021.blo
Abstract
The N400 has been seen to be larger for concrete than abstract words, and for pseudowords than real words. Using a word
vector analysis to calculate semantic associates (SA), as well as ratings for emotional arousal (EA), and a measure of orthographic
neighbourhood (ON), the present study investigated the relation between these factors and N400 amplitudes during a lexical decision task
using Swedish word stimuli. Four noun categories differing in concreteness: specific (squirrel), general
(animal) emotional (happiness) and abstract (tendency) were compared with pseudowords
(danalod). Results showed that N400 amplitudes increased in the order emotional < abstract < general < specific
< pseudoword. A regression analysis showed that the amplitude of the N400 decreased the more semantic associates a word had and the
higher the rating for emotional arousal it had. The N400 also increased the more orthographic neighbours a word had. Results provide support
for the hierarchical organisation of concrete words assumed in lexical semantics. They also demonstrate how affective information
facilitates meaning processing.
Article outline
- Introduction
- The N and lexical properties
- Concrete words, imageability, and semantic specificity
- Semantic neighbourhood
- The present study
- Aims
- Predictions
- Method
- Participants
- Stimuli
- Procedure
- EEG recordings
- Data analysis
- Analysis of semantic neighbourhood: Semantic associates (SA)
- Analysis of orthographic neighbourhood
- Results
- Behavioural results: Lexical decision (LD)
- Regression with continuous measures
- ERP results
- N (300–500 ms time-window)
- Imageability-matched specific/general words
- Regression with continuous measures
- Behavioural results: Lexical decision (LD)
- Discussion
- N and RT differences between the test word categories
- Semantic associates as predictors of N in real words
- Role of affective meaning in modulating the N
- The N for abstract words and language processing
- Behavioural results
- Conclusion
- Acknowledgements
- Notes
References
References (68)
Barber, H. A., Otten, L. J., Kousta, S.-T., & Vigliocco, G. (2013). Concreteness in word processing: ERP and behavioral effects in a lexical decision task. Brain and Language 1251, 47–53.
Blomberg, F. (2016). Concreteness, specificity, and emotional content in Swedish nouns. Neurocognitive studies of word meaning. (Doctoral dissertation). Retrieved from: [URL]
Blomberg, F., & Öberg, C. (2015). Swedish and English word ratings of imageability, familiarity and age of acquisition are highly correlated. Nordic Journal of Linguistics 381, 351–364.
Blomberg, F., Roll, M., Lindgren, M., Brännström, K. J., & Horne, M. (2015). Emotional arousal and lexical specificity modulate response times differently depending on ear of presentation in a dichotic listening task. The Mental Lexicon 101, 221–246.
Bojanowski, P., Grave, E., Joulin, A. & Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics 51, 135–146.
Collobert, R. & Weston, J. (2008). A unified architecture for natural language processing: Deep neural networks with multitask learning. Proceedings of the 25th International Conference on Machine Learning. ACM, 160–167.
Coltheart, M. (1981). The MRC psycholinguistic database. The Quarterly Journal of Experimental Psychology Section A 331, 497–505.
Crutch, S. J., & Warrington, E. K. (2005). Abstract and concrete concepts have structurally different representational frameworks. Brain 1281, 615–627.
(2010). The differential dependence of abstract and concrete words upon associative and similarity-based information: Complementary semantic interference and facilitation effects. Cognitive Neuropsychology 271, 46–71.
Crutch, S. J., Connell, S., & Warrington, E. K. (2009). The different representational frameworks underpinning abstract and concrete knowledge: Evidence from odd-one-out judgements. The Quarterly Journal of Experimental Psychology 621, 1377–1390.
Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods 134(1), 9–21.
Dove, G. (2016). Three symbol ungrounding problems: Abstract concepts and the future of embodied cognition. Psychonomic Bulletin & Review 231, 1–13.
Dreyer, F. R., Frey, D., Arana, S., von Saldern, S., Picht, T., Vajkoczy, P. & Pulvermüller, F. (2015). Is the motor system necessary for processing action and abstract emotion words? Evidence from focal brain lesions. Frontiers in Psychology 61, 1–17.
Ejerhed, E., Källgren, G., Wennstedt, O., & Åström, M. (1992). The linguistic annotation system of the Stockholm-Umeå corpus project. Report no. 33, Department of General Linguistics, Umeå University.
Firth, J. R. (1957). A synopsis of linguistic theory 1930–1955. Studies in Linguistic Analysis: 1–32. Reprinted in F. R. Palmer, (Ed.) (1968). Selected papers of J. R. Firth 1952–1959. London: Longman.
Gilhooly, K. J., & Logie, R. H. (1980). Age-of-acquisition, imagery, concreteness, familiarity, and ambiguity measures for 1,944 words. Behavior Research Methods and Instrumentation 121, 395–427.
Grondin, R., Lupker, S. J., & McRae, K. (2009). Shared features dominate semantic richness effects for concrete concepts. Journal of Memory and Language 601, 1–19.
Gullick, M. M., Mitra, P., & Coch, D. (2013). Imagining the truth and the moon: An electrophysiological study of abstract and concrete word processing. Psychophysiology 501, 431–440.
Holcomb, P. J., Kounios, J., Anderson, J. E., & West, W. C. (1999). Dual-coding, context availability, and concreteness effects in sentence comprehension: An electrophysiological investigation. Journal of Experimental Psychology: Learning, Memory and Cognition 251, 721–742.
Holcomb, P. J., Grainger, J., & O’Rourke. (2002). An electrophysiological study of the effects of orthographic neighbourhood size on printed word perception. Journal of Cognitive Neuroscience, 141, 938–950.
Jasper, H. H. (1958). Report of the committee on methods of clinical examination in electroencephalography. Electroencephalography and Clinical Neurophysiology 101, 370–375.
Jung, T.-P., Makeig, S., Humphries, C., Lee, T.-W., McKeown, M. J., Iragui, V., & Sejnowski, T. J. (2000). Removing electroencephalographic artifacts by blind source separation. Psychophysiology 371, 163–178.
Kanske, P., & Kotz, S. A. (2007). Concreteness in emotional words: ERP evidence from a hemifield study. Brain Research 11481, 138–148.
Kounios, J., & Holcomb, P. J. (1992). Structure and process in semantic memory: Evidence from event-related brain potentials and reaction times. Journal of Experimental Psychology: General, 1211, 459–479.
(1994). Concreteness effects in semantic processing: ERP evidence supporting dual-coding theory. Journal of Experimental Psychology: Learning, Memory, and Cognition 201, 804–823.
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 1401, 14–34.
Kutas, M., & Federmeier, K. D. (2000). Electrophysiology reveals semantic memory use in language comprehension. Trends in Cognitive Sciences 41, 463–470.
(2011). Thirty years and counting: finding meaning in the N component of the Event-Related Brain Potential (ERP). Annual Review of Psychology 621, 621–647.
Kutas, M., & Hillyard, S. A. (1980). Reading senseless sentences: Brain potentials reflect semantic incongruity. Science 2071, 203–205.
Laszlo, S., & Federmeier, L. (2009). A beautiful day in the neighborhood. An event-related potential study of lexical relationships and prediction in context. Journal of Memory and Language 631, 326–338.
Laszlo, S. & Federmeier, L. (2011). The N as a snapshot of interactive processing: evidence from regression analyses of orthographic neighbor and lexical associate effects. Psychophysiology 481, 176–186.
Laszlo, S., & Federmeier, L. (2014). Never seem to find the time: evaluating the physiological time course of visual word recognition with regression analysis of single-item event-related potentials. Language, Cognition and Neuroscience 291, 642–661.
Lau, E. F., Phillips, C., & Poeppel, D. (2008). A cortical network for semantics: (de)constructing the N
. Nature Reviews Neuroscience 91, 920–933.
Levy, O., Goldberg, Y., & Dagan, I. (2015). Improving distributional similarity with lessons learned from word embeddings. Transactions of the Association for Computational Linguistics 31, 211–225.
Lund, K., & Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instruments, & Computers 281, 203–208.
Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
Mårtensson, F., Roll, M., Apt, P., & Horne, M. (2011). Modelling the meaning of words: neural correlates of abstract and concrete noun processing, Acta Neurobiologiae Experimentalis 711, 455–478.
Mårtensson, F., Roll, M., Lindgren, M., Apt, P., & Horne, M. (2014). Sensory-specific anomic aphasia following left occipital lesions: Data from free oral descriptions of concrete word meanings. Neurocase 201, 192–207.
Nittono, H., Suehiro, M., & Hori, T. (2002). Word imageability and N in an incidental memory paradigm. International Journal of Psychophysiology 441, 1–11.
Oldfield, R. C. (1971). The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia 91, 97–113.
(2010). Dual coding theory and the mental lexicon. The Mental Lexicon 51, 205–230.
Paivio, A., Yuille, J. C., & Madigan, S. A. (1968). Concreteness, imagery, and meaningfulness values for 925 nouns. Journal of Experimental Psychology 76, Pt.2),1–25.
Pexman, P. M., Holyk, G. G., & Monfils, M. H. (2003). Number-of-features effects and semantic processing. Memory and Cognition 311, 842–855.
Pulvermüller, F. (2013). How neurons make meaning: brain mechanisms for embodied and abstract-symbolic semantics. Trends in Cognitive Sciences 171, 458–470.
Rabovsky, M., & McRae, K. (2014). Simulating the N ERP component as semantic network error: Insights from a feature-based connectionist attractor model of word meaning. Cognition 1321, 68–89.
Recchia, G., & Jones, M. N. (2012). The semantic richness of abstract concepts. Frontiers in Human Neuroscience 61, 315.
Rohde, D. L. T., Gonnerman, L. M., & Plaut, D. C. (2006). An improved model of semantic similarity based on lexical co-occurrence. Communications of the ACM 81, 627–633.
Roll, M., Mårtensson, F., Sikström, S., Apt, P., Arnling-Bååth, R., & Horne, M. (2012). Atypical associations to abstract words in Broca’s aphasia. Cortex 481, 1068–1072.
Rosch, E., Mervis, C. B., Gray, W. D., Johnson, D. M., & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive Psychology 81, 382–439.
Rugg, M. D. (1985). The effects of semantic priming and word repetition on Event-Related Potentials. Psychophysiology 221, 642–647.
Sabsevitz, D. S., Medler, D. A., Seidenberg, M., & Binder, J. R. (2005). Modulation of the semantic system by word imageability. NeuroImage 271, 188–200.
Shaoul, C. & Westbury, C. (2006). Word frequency effects in high-dimensional co-occurrence models: A new approach. Behaviour Research Methods 381, 190–195.
Siakaluk, P., Newcombe, P., Duffels, B., Li, E., Sidhu, D., Yap, M., & Pexman, P. (2016). Effects of emotional experience in lexical decision. Frontiers in Psychology 71:1157.
Szewczyk, J. & Schriefers, H. (2018). The N as an index of lexical preactivation and its implications for prediction in language comprehension. Language, Cognition and Neuroscience 331, 665–686.
Welcome, S. E., Paivio, A., McRae, K., & Joanisse, M. F. (2011). An electrophysiological study of task demands on concreteness effects: evidence for dual coding theory. Experimental Brain Research 2121, 347–358.
West, W. C., & Holcomb, P. J. (2000). Imaginal, semantic, and surface-level processing of concrete and abstract words: an electrophysiological investigation. Journal of Cognitive Neuroscience 121, 1024–1037.
Westbury, C., & Moroschan, G. (2009). Imageability x phonology interactions during lexical access: Effects of modality, phonological neighbourhood, and phonological processing efficiency. The Mental Lexicon 41, 115–145.
Westbury, C., Shaol, C., Hollis, G., Smithson, L., Briesemeister, B., Hofmann, M., & Jacobs, A. (2013). Now you see it, now you don’t: on emotion, context, and the algorithmic prediction of human imageability judgments. Frontiers in Psychology 41:991.
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