Behavioural and EEG evidence for inter-individual variability in late encoding stages of word production

References

• Alario, F.-X., Costa, A., & Caramazza, A. (2002). Frequency effects in noun phrase production: Implications for models of lexical access. Language and Cognitive Processes, 17(3), 299–319. https://doi.org/10.1080/01690960143000236
   Web of Science ®Google Scholar
• Aristei, S., Melinger, A., & Abdel Rahman, R. (2011). Electrophysiological chronometry of semantic context effects in language production. Journal of Cognitive Neuroscience, 23(7), 1567–1586. https://doi.org/10.1162/jocn.2010.21474
   PubMed Web of Science ®Google Scholar
• Balota, D. A., & Yap, M. J. (2011). Moving beyond the mean in studies of mental chronometry: The power of response time distributional analyses. Current Directions in Psychological Science, 20(3), 160–166. doi:10.1177/0963721411408885
   Web of Science ®Google Scholar
• Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01
   Web of Science ®Google Scholar
• Blackford, T., Holcomb, P. J., Grainger, J., & Kuperberg, G. R. (2012). A funny thing happened on the way to articulation: N400 attenuation despite behavioral interference in picture naming. Cognition, 123(1), 84–99. doi:10.1016/j.cognition.2011.12.007
   PubMed Web of Science ®Google Scholar
• Boersma, P., & Weenink, D. (2014). Praat: Doing phonetics by computer (5.4.01) [Computer software]. https://www.fon.hum.uva.nl/praat/
   Google Scholar
• Bürki, A. (2017a). Differences in processing times for distractors and pictures modulate the influence of distractors in picture–word interference tasks. Language, Cognition and Neuroscience, 32(6), 709–723. https://doi.org/10.1080/23273798.2016.1267783
   Web of Science ®Google Scholar
• Bürki, A. (2017b). Electrophysiological characterization of facilitation and interference in the picture-word interference paradigm. Psychophysiology, 54(9), 1370–1392. https://doi.org/10.1111/psyp.12885
   PubMed Web of Science ®Google Scholar
• Bürki, A., Cheneval, P. P., & Laganaro, M. (2015). Do speakers have access to a mental syllabary? ERP comparison of high frequency and novel syllable production. Brain and Language, 150, 90–102. https://doi.org/10.1016/j.bandl.2015.08.006
   PubMed Web of Science ®Google Scholar
• Bürki, A., Elbuy, S., Madec, S., & Vasishth, S. (2020). What did we learn from forty years of research on semantic interference? A Bayesian meta-analysis. Journal of Memory and Language, 114, 104125. https://doi.org/10.1016/j.jml.2020.104125
   Web of Science ®Google Scholar
• Bürki, A., & Madec, S. (2022). Picture-word interference in language production studies: Exploring the roles of attention and processing times. Journal of Experimental Psychology: Learning, Memory & Cognition. https://doi.org/10.1037/xlm0001098
   Google Scholar
• Cai, X., Ouyang, M., Yin, Y., & Zhang, Q. (2020). The effect of time pressure and semantic relatedness in spoken word production: A topographic ERP study. Behavioural Brain Research, 387, 112587. doi:10.1016/j.bbr.2020.112587.
   PubMed Web of Science ®Google Scholar
• Champely, S. (2018). Paireddata: Paired data analysis. R Package Version, 1(1), https://rdrr.io/cran/PairedData/
   Google Scholar
• Chaumon, M., Bishop, D. V. M., & Busch, N. A. (2015). A practical guide to the selection of independent components of the electroencephalogram for artifact correction. Journal of Neuroscience Methods, 250, 47–63. https://doi.org/10.1016/j.jneumeth.2015.02.025
   PubMed Web of Science ®Google Scholar
• Costa, A., Strijkers, K., Martin, C. D., & Thierry, G. (2009). The time course of word retrieval revealed by event-related brain potentials during overt speech. http://www.pnas.org/lens/pnas/106/50/21442
   Google Scholar
• Damian, M. F., & Martin, R. C. (1999). Semantic and phonological codes interact in single word production. Journal of Experimental Psychology. Learning, Memory, and Cognition, 25(2), 345–361. https://doi.org/10.1037/0278-7393.25.2.345
   PubMed Web of Science ®Google Scholar
• Dell, G. S. (2013). Cascading and feedback in interactive models of production: A reflection of forward modeling? The Behavioral and Brain Sciences, 36(4), 351–352. https://doi.org/10.1017/S0140525X12002531
   PubMed Web of Science ®Google Scholar
• Dell’Acqua, R., Sessa, P., Peressotti, F., Mulatti, C., Navarrete, E., & Grainger, J. (2010). ERP evidence for ultra-fast semantic processing in the picture–word interference paradigm. Frontiers in Psychology, 1. https://doi.org/10.3389/fpsyg.2010.00177.
   Google Scholar
• 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. https://doi.org/10.1016/j.jneumeth.2003.10.009
   PubMed Web of Science ®Google Scholar
• de Zubicaray, G. I., McMahon, K. L., Eastburn, M. M., & Wilson, S. J. (2002). Orthographic/phonological facilitation of naming responses in the picture-word task: An event-related fMRI study using overt vocal responding. NeuroImage, 16(4), 1084–1093. https://doi.org/10.1006/nimg.2002.1135
   PubMed Web of Science ®Google Scholar
• Dirani, J., & Pylkkänen, L. (2020). Lexical Access in Naming and Reading: Spatiotemporal Localization of Semantic Facilitation and Interference Using MEG.. Neurobiology of Language, 1(2), 185-207. https://doi.org/10.1162/nol_a_00008.
   Google Scholar
• Duñabeitia, J. A., Crepaldi, D., Meyer, A. S., New, B., Pliatsikas, C., Smolka, E., & Brysbaert, M. (2018). Multipic: A standardized set of 750 drawings with norms for six European languages. The Quarterly Journal of Experimental Psychology, 71(4), 808–816. https://doi.org/10.1080/17470218.2017.1310261.
   PubMed Web of Science ®Google Scholar
• Eriksen, B. A., & Eriksen, C. W. (1974). Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception & Psychophysics, 16(1), 143–149. doi:10.3758/BF03203267
   Web of Science ®Google Scholar
• Ferreira, V. S., & Pashler, H. (2002). Central bottleneck influences on the processing stages of word production. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28(6), 1187. https://doi.org/10.1037/0278-7393.28.6.1187
   PubMed Web of Science ®Google Scholar
• Fuhrmeister, P., Madec, S., & Bürki, A. (in preparation). Cognitive skills and inter-individual variability in word production.
   Google Scholar
• Ganushchak, L. Y., Christoffels, I. K., & Schiller, N. O. (2011). The Use of electroencephalography in language production research: A review. Frontiers in Psychology, 2. https://doi.org/10.3389/fpsyg.2011.00208
   Google Scholar
• Garrod, S., & Pickering, M. J. (2007). Automaticity of language production in monologue and dialogue. In Automaticity and control in language processing (pp. S. 19–38). Psychology Press. https://doi.org/10.4324/9780203968512.
   Google Scholar
• Goldstein, H. (1987). Multilevel covariance component models. Biometrika, 74(2), 430–431. https://doi.org/10.1093/biomet/74.2.430
   Web of Science ®Google Scholar
• Green, P., & MacLeod, C. J. (2016). Simr: An R package for power analysis of generalized linear mixed models by simulation. Methods in Ecology and Evolution, 7(4). 493–498. https://doi.org/10.1111/2041-210X.12504
   Web of Science ®Google Scholar
• Greenham, S. L., Stelmack, R. M., & Campbell, K. B. (2000). Effects of attention and semantic relation on event-related potentials in a picture-word naming task. Biological Psychology, 55(2), 79–104. doi:10.1016/S0301-0511(00)00070-3
   PubMed Web of Science ®Google Scholar
• Hartsuiker, R. J., & Moors, A. (2017). On the automaticity of language processing. https://doi.org/10.1037/15969-010.
   Google Scholar
• Heister, J., Würzner, K.-M., Bubenzer, J., Pohl, E., Hanneforth, T., Geyken, A., & Kliegl, R. (2011). dlexDB–eine lexikalische datenbank für die psychologische und linguistische forschung. Psychologische Rundschau, 62(1), 10–20. https://doi.org/10.1026/0033-3042/a000029.
   Web of Science ®Google Scholar
• Hirschfeld, G., Jansma, B., Bölte, J., & Zwitserlood, P. (2008). Interference and facilitation in overt speech production investigated with event-related potentials. Neuroreport, 19(12), 1227–1230. https://doi.org/10.1097/WNR.0b013e328309ecd1
   PubMed Web of Science ®Google Scholar
• Indefrey, P. (2011). The spatial and temporal signatures of word production components: A critical update. Frontiers in Psychology, 2. https://doi.org/10.3389/fpsyg.2011.00255
   Web of Science ®Google Scholar
• Indefrey, P., & Levelt, W. J. M. (2004). The spatial and temporal signatures of word production components. Cognition, 92(1–2), 101–144. https://doi.org/10.1016/j.cognition.2002.06.001
   PubMed Web of Science ®Google Scholar
• Jongman, S. R., Roelofs, A., & Meyer, A. S. (2015). Sustained attention in language production: An individual differences investigation. The Quarterly Journal of Experimental Psychology, 68(4), 710–730. doi:10.1080/17470218.2014.964736
   PubMed Web of Science ®Google Scholar
• Krott, A., Medaglia, M. T., & Porcaro, C. (2019). Early and late effects of semantic distractors on electroencephalographic responses during overt picture naming. Frontiers in Psychology, 10, 696. https://doi.org/10.3389/fpsyg.2019.00696
   PubMed Web of Science ®Google Scholar
• Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmertest package: Tests in linear mixed effects models (lmer objects of lme4 package). https://doi.org/10.18637/JSS.V082.I13.
   Google Scholar
• Laganaro, M. (2016). Dynamics of word production and processing speed. Language. Cognition and Neuroscience, 31(4), 463–464.
   Google Scholar
• Laganaro, M., & Alario, F.-X. (2006). On the locus of the syllable frequency effect in speech production. Journal of Memory and Language, 55(2), 178–196. https://doi.org/10.1016/j.jml.2006.05.001
   Web of Science ®Google Scholar
• Laganaro, M., Morand, S., & Schnider, A. (2009). Time course of evoked-potential changes in different forms of anomia in aphasia. Journal of Cognitive Neuroscience, 21(8), 1499–1510. https://doi.org/10.1162/jocn.2009.21117
   PubMed Web of Science ®Google Scholar
• Laganaro, M., Python, G., & Toepel, U. (2013). Dynamics of phonological-phonetic encoding in word production: Evidence from diverging ERPs between stroke patients and controls. Brain and Language, 126(2), 123–132. https://doi.org/10.1016/j.bandl.2013.03.004
   PubMed Web of Science ®Google Scholar
• Laganaro, M., Valente, A., & Perret, C. (2012). Time course of word production in fast and slow speakers: A high density ERP topographic study. NeuroImage, 59(4), 3881–3888. https://doi.org/10.1016/j.neuroimage.2011.10.082
   PubMed Web of Science ®Google Scholar
• Lancheros, M., Jouen, A.-L., & Laganaro, M. (2020). Neural dynamics of speech and non-speech motor planning. Brain and Language, 203, 104742. https://doi.org/10.1016/j.bandl.2020.104742
   PubMed Web of Science ®Google Scholar
• Levelt, W. J. (1999). Models of word production. Trends in Cognitive Sciences, 3(6), 223–232. https://doi.org/10.1016/S1364-6613(99)01319-4
   PubMed Web of Science ®Google Scholar
• Levelt, W. J., Roelofs, A., & Meyer, A. S. (1999). A theory of lexical access in speech production. Behavioral and Brain Sciences, 22(1), 1–38. doi:10.1017/S0140525X99001776
   PubMed Web of Science ®Google Scholar
• Lorenz, A., Zwitserlood, P., Bürki, A., Regel, S., Ouyang, G., & Abdel Rahman, R. (2021). Morphological facilitation and semantic interference in compound production: An ERP study. Cognition, 209, 104518. https://doi.org/10.1016/j.cognition.2020.104518
   PubMed Web of Science ®Google Scholar
• Lupker, S. J. (1979). The semantic nature of response competition in the picture-word interference task. Memory & Cognition, 7(6), 485–495. https://doi.org/10.3758/BF03198265
   Web of Science ®Google Scholar
• Mahon, B. Z., Costa, A., Peterson, R., Vargas, K. A., & Caramazza, A. (2007). Lexical selection is not by competition: A reinterpretation of semantic interference and facilitation effects in the picture-word interference paradigm. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33(3), 503–535. https://doi.org/10.1037/0278-7393.33.3.503
   PubMed Web of Science ®Google Scholar
• Marmolejo-Ramos, F., Cousineau, D., Benites, L., & Maehara, R. (2015). On the efficacy of procedures to normalize Ex-Gaussian distributions. Frontiers in Psychology, 5, 1548. doi:10.3389/fpsyg.2014.01548
   PubMed Web of Science ®Google Scholar
• Mensen, A., & Khatami, R. (2013). Advanced EEG analysis using threshold-free cluster-enhancement and non-parametric statistics. NeuroImage, 67, 111–118. https://doi.org/10.1016/j.neuroimage.2012.10.027
   PubMed Web of Science ®Google Scholar
• Mensen, A., Marshall, W., & Tononi, G. (2017). EEG differentiation analysis and stimulus set meaningfulness. Frontiers in Psychology, 8, 1748. https://doi.org/10.3389/fpsyg.2017.01748
   PubMed Web of Science ®Google Scholar
• Meyer, A., Wheeldon, L., & Krott, A. (2007). Automaticity and control in language processing. Psychology Press. https://shorturl.at/fBOST
   Google Scholar
• Miozzo, M., Pulvermüller, F., & Hauk, O. (2015). Early Parallel Activation of Semantics and Phonology in picture naming: Evidence from a Multiple Linear Regression MEG study. Cerebral Cortex, 25(10), 3343–3355. https://doi.org/10.1093/cercor/bhu137
   PubMed Web of Science ®Google Scholar
• Mognon, A., Jovicich, J., Bruzzone, L., & Buiatti, M. (2011). Adjust: An automatic EEG artifact detector based on the joint use of spatial and temporal features. Psychophysiology, 48(2), 229–240. doi:10.1111/j.1469-8986.2010.01061.x
   PubMed Web of Science ®Google Scholar
• Morey, R. D. (2008). Confidence intervals from normalized data: A correction to Cousineau (2005). Tutorials in Quantitative Methods for Psychology, 4(2), 61–64. Scopus. https://doi.org/10.20982/tqmp.04.2.p061.
   Google Scholar
• Munding, D., Dubarry, A.-S., & Alario, F.-X. (2015). On the cortical dynamics of word production: A review of the MEG evidence. Language, Cognition and Neuroscience, 0(0), 1–22. https://doi.org/10.1080/23273798.2015.1071857
   Google Scholar
• Pernet, C. R., Chauveau, N., Gaspar, C., & Rousselet, G. A. (2011). LIMO eeg: A toolbox for hierarchical LInear MOdeling of ElectroEncephaloGraphic data. Computational Intelligence and Neuroscience, 2011(2011), 831409.
   PubMedGoogle Scholar
• Pernet, C. R., Latinus, M., Nichols, T. E., & Rousselet, G. A. (2015). Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields: A simulation study. Journal of Neuroscience Methods, 250, 85–93. https://doi.org/10.1016/j.jneumeth.2014.08.003
   PubMed Web of Science ®Google Scholar
• Perret, C., & Bonin, P. (2019). Which variables should be controlled for to investigate picture naming in adults? A Bayesian meta-analysis. Behavior Research Methods, 51(6), 2533–2545. https://doi.org/10.3758/s13428-018-1100-1
   PubMed Web of Science ®Google Scholar
• Peterson, R. R., & Savoy, P. (1998). Lexical selection and phonological encoding during language production: Evidence for cascaded processing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24(3), 539–557. https://doi.org/10.1037/0278-7393.24.3.539
   Web of Science ®Google Scholar
• Piai, V., Roelofs, A., Acheson, D. J., & Takashima, A. (2013). Attention for speaking: Neural substrates of general and specific mechanisms for monitoring and control. Frontiers in Human Neuroscience, 7), doi:10.3389/fnhum.2013.00832
   PubMedGoogle Scholar
• Piai, V., Roelofs, A., & van der Meij, R. (2012). Event-related potentials and oscillatory brain responses associated with semantic and stroop-like interference effects in overt naming. Brain Research, 1450, 87–101. https://doi.org/10.1016/j.brainres.2012.02.050
   PubMed Web of Science ®Google Scholar
• Posnansky, C. J., & Rayner, K. (1977). Visual-feature and response components in a picture-word interference task with beginning and skilled readers. Journal of Experimental Child Psychology, 24(3), 440–460. https://doi.org/10.1016/0022-0965(77)90090-X
   PubMed Web of Science ®Google Scholar
• Rabovsky, M., Schad, D., & Abdel Rahman, R. (2021). Semantic richness and density effects on language production: Electrophysiological and behavioral evidence. Journal of Experimental Psychology: Learning, Memory, and Cognition, 47(3), 508. https://doi.org/10.1037/xlm0000940
   PubMed Web of Science ®Google Scholar
• Rahman, R. A., & Aristei, S. (2010). Now you see it… and now again: Semantic interference reflects lexical competition in speech production with and without articulation. Psychonomic Bulletin and Review, 17(5), 657–661.
   PubMed Web of Science ®Google Scholar
• Rayner, K., & Posnansky, C. (1978). Stages of processing in word identification. Journal of Experimental Psychology: General, 107(1), 64–80. https://doi.org/10.1037/0096-3445.107.1.64
   PubMed Web of Science ®Google Scholar
• R core team. (2021). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org
   Google Scholar
• Riès, S. K. (2016). Serial versus parallel neurobiological processes in language production: Comment on munding, dubarry, and alario, 2015. Language, Cognition and Neuroscience, 31(4), 476–479. https://doi.org/10.1080/23273798.2015.1117644
   Web of Science ®Google Scholar
• Roelofs, A. (2008). Tracing attention and the activation flow in spoken word planning using eye movements. Journal of Experimental Psychology: Learning, Memory, and Cognition, 34(2), 353. https://doi.org/10.1037/0278-7393.34.2.353
   PubMed Web of Science ®Google Scholar
• Roelofs, A., Piai, V., Garrido Rodriguez, G., & Chwilla, D. J. (2016). Electrophysiology of cross-language interference and facilitation in picture naming. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior, 76, 1–16. https://doi.org/10.1016/j.cortex.2015.12.003
   PubMed Web of Science ®Google Scholar
• Rose, S. B., Aristei, S., Melinger, A., & Abdel Rahman, R. (2019). The closer they are, the more they interfere: Semantic similarity of word distractors increases competition in language production. Journal of Experimental Psychology: Learning, Memory, and Cognition, 45(4), 753–763. https://doi.org/10.1037/xlm0000592
   PubMed Web of Science ®Google Scholar
• Rosinski, R. R. (1977). Picture-word interference is semantically based. Child Development, 48(2), 643–647. https://doi.org/10.2307/1128667
   Web of Science ®Google Scholar
• Sassenhagen, J., & Draschkow, D. (2019). Cluster-based permutation tests of MEG/EEG data do not establish significance of effect latency or location. Psychophysiology, 56(6), e13335. https://doi.org/10.1111/psyp.13335
   PubMed Web of Science ®Google Scholar
• Schad, D. J., Vasishth, S., Hohenstein, S., & Kliegl, R. (2020). How to capitalize on a priori contrasts in linear (mixed) models: A tutorial. Journal of Memory and Language, 110, 104038. https://doi.org/10.1016/j.jml.2019.104038
   Web of Science ®Google Scholar
• Schriefers, H., Meyer, A. S., & Levelt, W. J. M. (1990). Exploring the time course of lexical access in language production: Picture-word interference studies. Journal of Memory and Language, 29(1), 86–102. https://doi.org/10.1016/0749-596X(90)90011-N
   Web of Science ®Google Scholar
• Schuhmann, T., Schiller, N. O., Goebel, R., & Sack, A. T. (2009). The temporal characteristics of functional activation in broca’s area during overt picture naming. cortex, 45(9), 1111–1116. https://doi.org/10.1016/j.cortex.2008.10.013
   PubMed Web of Science ®Google Scholar
• Shao, Z., Roelofs, A., Acheson, D. J., & Meyer, A. S. (2014). Electrophysiological evidence that inhibition supports lexical selection in picture naming. brain Research, 1586, 130–142. https://doi.org/10.1016/j.brainres.2014.07.009
   PubMed Web of Science ®Google Scholar
• Shao, Z., Roelofs, A., & Meyer, A. S. (2012). Sources of individual differences in the speed of naming objects and actions: The contribution of executive control. The Quarterly Journal of Experimental Psychology, 65(10), 1927–1944. doi:10.1080/17470218.2012.670252
   PubMed Web of Science ®Google Scholar
• Shitova, N., Roelofs, A., Coughler, C., & Schriefers, H. (2017). P3 event-related brain potential reflects allocation and use of central processing capacity in language production. Neuropsychologia, 106, 138–145. https://doi.org/10.1016/j.neuropsychologia.2017.09.024
   PubMed Web of Science ®Google Scholar
• Smith, S. M., & Nichols, T. E. (2009). Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage, 44(1), 83–98. https://doi.org/10.1016/j.neuroimage.2008.03.061
   PubMed Web of Science ®Google Scholar
• Starreveld, P. A., & La Heij, W. (1996). Time-course analysis of semantic and orthographic context effects in picture naming. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22(4), 896–918. https://doi.org/10.1037/0278-7393.22.4.896
   Web of Science ®Google Scholar
• Strijkers, K., Costa, A., & Thierry, G. (2010). Tracking lexical access in speech production: Electrophysiological correlates of word frequency and cognate effects. Cerebral Cortex, 20(4), 912–928. https://doi.org/10.1093/cercor/bhp153
   PubMed Web of Science ®Google Scholar
• Valente, A., Bürki, A., & Laganaro, M. (2014). Erp correlates of word production predictors in picture naming: A trial by trial multiple regression analysis from stimulus onset to response. Frontiers in Neuroscience, 8), https://doi.org/10.3389/fnins.2014.00390
   Google Scholar
• Van Schie, H. T., Wijers, A. A., Kellenbach, M. L., & Stowe, L. A. (2003). An event-related potential investigation of the relationship between semantic and perceptual levels of representation. Brain and Language, 86(2), 300–325.
   PubMed Web of Science ®Google Scholar
• Wickham, H. (2009). Ggplot: Using the grammar of graphics with R. Springer. https://doi.org/10.1007/978-0-387-98141-3_3.
   Google Scholar
• Wong, A. W.-K., Chiu, H.-C., Wang, J., Cao, J., Wong, S.-S., & Chen, H.-C. (2017). An early locus of associative and categorical context effects in speech production: Evidence from an ERP study using the picture–word interference paradigm. Language, Cognition and Neuroscience, 32(10), 1305–1319. https://doi.org/10.1080/23273798.2017.1355060
   Web of Science ®Google Scholar