Volume 15, Issue 2
  • ISSN 1871-1340
  • E-ISSN: 1871-1375



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: (), () () and () were compared with (). Results showed that N400 amplitudes increased in the order < < < < . 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.

Available under the CC BY-NC 4.0 license.

Article metrics loading...

Loading full text...

Full text loading...



  1. Andersen, G.
    (2011) Leksikalsk database for svensk. Oslo: Technical report, Nasjonalbiblioteket.
    [Google Scholar]
  2. Ariel, M.
    (1990) Accessing noun-phrase antecedents. London: Routledge.
    [Google Scholar]
  3. 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 Language125, 47–53. doi:  10.1016/j.bandl.2013.01.005
    https://doi.org/10.1016/j.bandl.2013.01.005 [Google Scholar]
  4. Blomberg, F.
    (2016) Concreteness, specificity, and emotional content in Swedish nouns. Neurocognitive studies of word meaning. (Doctoral dissertation). Retrieved from: https://lup.lub.lu.se/search/publication/4a492bdf-2d91-4955-a820-1143d470d624
  5. Blomberg, F. , & Öberg, C.
    (2015) Swedish and English word ratings of imageability, familiarity and age of acquisition are highly correlated. Nordic Journal of Linguistics38, 351–364. doi:  10.1017/S0332586515000220
    https://doi.org/10.1017/S0332586515000220 [Google Scholar]
  6. 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 Lexicon10, 221–246. doi:  10.1075/ml.10.2.03blo
    https://doi.org/10.1075/ml.10.2.03blo [Google Scholar]
  7. Bojanowski, P. , Grave, E. , Joulin, A. & Mikolov, T.
    (2017) Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics5, 135–146. 10.1162/tacl_a_00051
    https://doi.org/10.1162/tacl_a_00051 [Google Scholar]
  8. 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.
    [Google Scholar]
  9. Coltheart, M.
    (1981) The MRC psycholinguistic database. The Quarterly Journal of Experimental Psychology Section A33, 497–505. doi:  10.1080/14640748108400805
    https://doi.org/10.1080/14640748108400805 [Google Scholar]
  10. Cruse, A.
    (1986) Lexical semantics. Cambridge: Cambridge University Press.
    [Google Scholar]
  11. Crutch, S. J. , & Warrington, E. K.
    (2005) Abstract and concrete concepts have structurally different representational frameworks. Brain128, 615–627. doi:  10.1093/brain/awh349
    https://doi.org/10.1093/brain/awh349 [Google Scholar]
  12. (2010) The differential dependence of abstract and concrete words upon associative and similarity-based information: Complementary semantic interference and facilitation effects. Cognitive Neuropsychology27, 46–71. doi:  10.1080/02643294.2010.491359
    https://doi.org/10.1080/02643294.2010.491359 [Google Scholar]
  13. 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 Psychology62, 1377–1390. doi:  10.1080/17470210802483834
    https://doi.org/10.1080/17470210802483834 [Google Scholar]
  14. Delorme, A. , & Makeig, S.
    (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods134(1), 9–21. doi:  10.1016/j.jneumeth.2003.10.009
    https://doi.org/10.1016/j.jneumeth.2003.10.009 [Google Scholar]
  15. Dove, G.
    (2016) Three symbol ungrounding problems: Abstract concepts and the future of embodied cognition. Psychonomic Bulletin & Review23, 1–13. doi:  10.3758/s13423‑015‑0825‑4
    https://doi.org/10.3758/s13423-015-0825-4 [Google Scholar]
  16. 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 Psychology6, 1–17. doi:  10.3389/fpsyg.2015.01661
    https://doi.org/10.3389/fpsyg.2015.01661 [Google Scholar]
  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.
    [Google Scholar]
  18. 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.
    [Google Scholar]
  19. Gilhooly, K. J. , & Logie, R. H.
    (1980) Age-of-acquisition, imagery, concreteness, familiarity, and ambiguity measures for 1,944 words. Behavior Research Methods and Instrumentation12, 395–427. doi:  10.3758/BF03201693
    https://doi.org/10.3758/BF03201693 [Google Scholar]
  20. Grondin, R. , Lupker, S. J. , & McRae, K.
    (2009) Shared features dominate semantic richness effects for concrete concepts. Journal of Memory and Language60, 1–19. doi:  10.1016/j.jml.2008.09.001
    https://doi.org/10.1016/j.jml.2008.09.001 [Google Scholar]
  21. Gullick, M. M. , Mitra, P. , & Coch, D.
    (2013) Imagining the truth and the moon: An electrophysiological study of abstract and concrete word processing. Psychophysiology50, 431–440. doi:  10.1111/psyp.12033
    https://doi.org/10.1111/psyp.12033 [Google Scholar]
  22. Harris, Z.
    (1954) Distributional structure. Word10, 146–162. doi:  10.1080/00437956.1954.11659520
    https://doi.org/10.1080/00437956.1954.11659520 [Google Scholar]
  23. 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 Cognition25, 721–742. doi:  10.1037/0278‑7393.25.3.721
    https://doi.org/10.1037/0278-7393.25.3.721 [Google Scholar]
  24. 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, 14, 938–950. doi:  10.1162/089892902760191153
    https://doi.org/10.1162/089892902760191153 [Google Scholar]
  25. Jasper, H. H.
    (1958) Report of the committee on methods of clinical examination in electroencephalography. Electroencephalography and Clinical Neurophysiology10, 370–375. doi:  10.1016/0013‑4694(58)90053‑1
    https://doi.org/10.1016/0013-4694(58)90053-1 [Google Scholar]
  26. 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. Psychophysiology37, 163–178. doi:  10.1111/1469‑8986.3720163
    https://doi.org/10.1111/1469-8986.3720163 [Google Scholar]
  27. Kanske, P. , & Kotz, S. A.
    (2007) Concreteness in emotional words: ERP evidence from a hemifield study. Brain Research1148, 138–148. doi:  10.1016/j.brainres.2007.02.044
    https://doi.org/10.1016/j.brainres.2007.02.044 [Google Scholar]
  28. 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, 121, 459–479. doi:  10.1037/0096‑3445.121.4.459
    https://doi.org/10.1037/0096-3445.121.4.459 [Google Scholar]
  29. (1994) Concreteness effects in semantic processing: ERP evidence supporting dual-coding theory. Journal of Experimental Psychology: Learning, Memory, and Cognition20, 804–823. doi:  10.1037/0278‑7393.20.4.804
    https://doi.org/10.1037/0278-7393.20.4.804 [Google Scholar]
  30. 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: General140, 14–34. doi:  10.1037/a0021446
    https://doi.org/10.1037/a0021446 [Google Scholar]
  31. Kutas, M. , & Federmeier, K. D.
    (2000) Electrophysiology reveals semantic memory use in language comprehension. Trends in Cognitive Sciences4, 463–470. doi:  10.1016/S1364‑6613(00)01560‑6
    https://doi.org/10.1016/S1364-6613(00)01560-6 [Google Scholar]
  32. (2011) Thirty years and counting: finding meaning in the N400 component of the Event-Related Brain Potential (ERP). Annual Review of Psychology62, 621–647. doi:  10.1146/annurev.psych.093008.131123
    https://doi.org/10.1146/annurev.psych.093008.131123 [Google Scholar]
  33. Kutas, M. , & Hillyard, S. A.
    (1980) Reading senseless sentences: Brain potentials reflect semantic incongruity. Science207, 203–205. doi:  10.1126/science.7350657
    https://doi.org/10.1126/science.7350657 [Google Scholar]
  34. 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 Language63, 326–338. doi:  10.1016/j.jml.2009.06.004
    https://doi.org/10.1016/j.jml.2009.06.004 [Google Scholar]
  35. Laszlo, S. & Federmeier, L.
    (2011) The N400 as a snapshot of interactive processing: evidence from regression analyses of orthographic neighbor and lexical associate effects. Psychophysiology48, 176–186. doi:  10.1111/j.1469‑8986.2010.01058.x
    https://doi.org/10.1111/j.1469-8986.2010.01058.x [Google Scholar]
  36. 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 Neuroscience29, 642–661. doi:  10.1080/01690965.2013.866259
    https://doi.org/10.1080/01690965.2013.866259 [Google Scholar]
  37. Lau, E. F. , Phillips, C. , & Poeppel, D.
    (2008) A cortical network for semantics: (de)constructing the N400 . Nature Reviews Neuroscience9, 920–933. doi:  10.1038/nrn2532
    https://doi.org/10.1038/nrn2532 [Google Scholar]
  38. Levy, O. , Goldberg, Y. , & Dagan, I.
    (2015) Improving distributional similarity with lessons learned from word embeddings. Transactions of the Association for Computational Linguistics3, 211–225. doi:  10.1162/tacl_a_00134
    https://doi.org/10.1162/tacl_a_00134 [Google Scholar]
  39. Lund, K. , & Burgess, C.
    (1996) Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instruments, & Computers28, 203–208. doi:  10.3758/BF03204766
    https://doi.org/10.3758/BF03204766 [Google Scholar]
  40. Mikolov, T. , Chen, K. , Corrado, G. & Dean, J.
    (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
    [Google Scholar]
  41. Miller, G. A. , & Fellbaum, C.
    (1991) Semantic networks of English. Cognition41, 197–229. doi:  10.1016/0010‑0277(91)90036‑4
    https://doi.org/10.1016/0010-0277(91)90036-4 [Google Scholar]
  42. Mårtensson, F. , Roll, M. , Apt, P. , & Horne, M.
    (2011) Modelling the meaning of words: neural correlates of abstract and concrete noun processing, Acta Neurobiologiae Experimentalis71, 455–478.
    [Google Scholar]
  43. 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. Neurocase20, 192–207. doi:  10.1080/13554794.2012.741258
    https://doi.org/10.1080/13554794.2012.741258 [Google Scholar]
  44. Nittono, H. , Suehiro, M. , & Hori, T.
    (2002) Word imageability and N400 in an incidental memory paradigm. International Journal of Psychophysiology44, 1–11. doi:  10.1016/S0167‑8760(02)00002‑8
    https://doi.org/10.1016/S0167-8760(02)00002-8 [Google Scholar]
  45. Oldfield, R. C.
    (1971) The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia9, 97–113. doi:  10.1016/0028‑3932(71)90067‑4
    https://doi.org/10.1016/0028-3932(71)90067-4 [Google Scholar]
  46. Paivio, A.
    (1990) Mental Representations. Oxford: Oxford UP. 10.1093/acprof:oso/9780195066661.001.0001
    https://doi.org/10.1093/acprof:oso/9780195066661.001.0001 [Google Scholar]
  47. (2010) Dual coding theory and the mental lexicon. The Mental Lexicon5, 205–230. doi:  10.1075/ml.5.2.04pai
    https://doi.org/10.1075/ml.5.2.04pai [Google Scholar]
  48. Paivio, A. , Yuille, J. C. , & Madigan, S. A.
    (1968) Concreteness, imagery, and meaningfulness values for 925 nouns. Journal of Experimental Psychology76, Pt.2),1–25. doi:  10.1037/h0025327
    https://doi.org/10.1037/h0025327 [Google Scholar]
  49. Pexman, P. M. , Holyk, G. G. , & Monfils, M. H.
    (2003) Number-of-features effects and semantic processing. Memory and Cognition31, 842–855. doi:  10.3758/BF03196439
    https://doi.org/10.3758/BF03196439 [Google Scholar]
  50. Pulvermüller, F.
    (2013) How neurons make meaning: brain mechanisms for embodied and abstract-symbolic semantics. Trends in Cognitive Sciences17, 458–470. doi:  10.1016/j.tics.2013.06.004
    https://doi.org/10.1016/j.tics.2013.06.004 [Google Scholar]
  51. Rabovsky, M. , & McRae, K.
    (2014) Simulating the N400 ERP component as semantic network error: Insights from a feature-based connectionist attractor model of word meaning. Cognition132, 68–89. doi:  10.1016/j.cognition.2014.03.010
    https://doi.org/10.1016/j.cognition.2014.03.010 [Google Scholar]
  52. Recchia, G. , & Jones, M. N.
    (2012) The semantic richness of abstract concepts. Frontiers in Human Neuroscience6, 315. doi:  10.3389/fnhum.2012.00315
    https://doi.org/10.3389/fnhum.2012.00315 [Google Scholar]
  53. 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 ACM8, 627–633.
    [Google Scholar]
  54. Roll, M. , Mårtensson, F. , Sikström, S. , Apt, P. , Arnling-Bååth, R. , & Horne, M.
    (2012) Atypical associations to abstract words in Broca’s aphasia. Cortex48, 1068–1072. doi:  10.1016/j.cortex.2011.11.009
    https://doi.org/10.1016/j.cortex.2011.11.009 [Google Scholar]
  55. Rosch, E. , & Lloyd, B. B.
    (1978) Cognition and categorization. New Jersey: Lawrence Erlbaum.
    [Google Scholar]
  56. Rosch, E. , Mervis, C. B. , Gray, W. D. , Johnson, D. M. , & Boyes-Braem, P.
    (1976) Basic objects in natural categories. Cognitive Psychology8, 382–439. doi:  10.1016/0010‑0285(76)90013‑X
    https://doi.org/10.1016/0010-0285(76)90013-X [Google Scholar]
  57. Rugg, M. D.
    (1985) The effects of semantic priming and word repetition on Event-Related Potentials. Psychophysiology22, 642–647. doi:  10.1111/j.1469‑8986.1985.tb01661.x
    https://doi.org/10.1111/j.1469-8986.1985.tb01661.x [Google Scholar]
  58. Sabsevitz, D. S. , Medler, D. A. , Seidenberg, M. , & Binder, J. R.
    (2005) Modulation of the semantic system by word imageability. NeuroImage27, 188–200. doi:  10.1016/j.neuroimage.2005.04.012
    https://doi.org/10.1016/j.neuroimage.2005.04.012 [Google Scholar]
  59. Sahlgren, M.
    (2008) The distributional hypothesis. Rivista di Linguistica20 (1), 33–53.
    [Google Scholar]
  60. Shaoul, C. & Westbury, C.
    (2006) Word frequency effects in high-dimensional co-occurrence models: A new approach. Behaviour Research Methods38, 190–195. 10.3758/BF03192768
    https://doi.org/10.3758/BF03192768 [Google Scholar]
  61. Siakaluk, P. , Newcombe, P. , Duffels, B. , Li, E. , Sidhu, D. , Yap, M. , & Pexman, P.
    (2016) Effects of emotional experience in lexical decision. Frontiers in Psychology7:1157. doi:  10.3389/fpsyg.2016.01157
    https://doi.org/10.3389/fpsyg.2016.01157 [Google Scholar]
  62. Szewczyk, J. & Schriefers, H.
    (2018) The N400 as an index of lexical preactivation and its implications for prediction in language comprehension. Language, Cognition and Neuroscience33, 665–686. doi:  10.1080/23273798.2017.1401101
    https://doi.org/10.1080/23273798.2017.1401101 [Google Scholar]
  63. 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 Research212, 347–358. doi:  10.1007/s00221‑011‑2734‑8
    https://doi.org/10.1007/s00221-011-2734-8 [Google Scholar]
  64. West, W. C. , & Holcomb, P. J.
    (2000) Imaginal, semantic, and surface-level processing of concrete and abstract words: an electrophysiological investigation. Journal of Cognitive Neuroscience12, 1024–1037. doi:  10.1162/08989290051137558
    https://doi.org/10.1162/08989290051137558 [Google Scholar]
  65. Westbury, C. , & Moroschan, G.
    (2009) Imageability x phonology interactions during lexical access: Effects of modality, phonological neighbourhood, and phonological processing efficiency. The Mental Lexicon4, 115–145. doi:  10.1075/ml.4.1.05wes
    https://doi.org/10.1075/ml.4.1.05wes [Google Scholar]
  66. 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 Psychology4:991. doi:  10.3389/fpsyg.2013.00991
    https://doi.org/10.3389/fpsyg.2013.00991 [Google Scholar]
  67. Westbury, C. , Cribben, I. , & Cummine, J.
    (2016) Imaging imageability: behavioral effects and neural correlates of its interaction with affect and context. Frontiers in Human Neuroscience10:346. doi:  10.3389/fnhum.2016.00346
    https://doi.org/10.3389/fnhum.2016.00346 [Google Scholar]
  68. Zuccon, G. , Koopman, B. , Bruza, P. , & Azzopardi, L.
    (2015) Integrating and evaluating neural word embeddings in information retrieval. InProceedings ADCS ’15, 1–8. doi:  10.1145/2838931.2838936
    https://doi.org/10.1145/2838931.2838936 [Google Scholar]

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error