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Interactions of stimulus quality and semantic context on N400 in visual word recognition

Published online by Cambridge University Press:  04 January 2022

Yong Zhang Open the ORCID record for Yong Zhang [Opens in a new window] ,
Min Xie ,
Youguo Chen ,
Rongmin Xiong ,
Change Yue ,
Shuqiong Wu ,
Feng Ji  and
Quanhong Wang
Yong Zhang*
Affiliation:
School of Foreign Languages, Southwest University of Political Science and Law, Chongqing401120, China
Min Xie
Affiliation:
Faculty of Psychology, Southwest University, Chongqing400715, China Key Laboratory of Cognition and Personality (SWU), MoE, Chongqing400715, China
Youguo Chen
Affiliation:
Faculty of Psychology, Southwest University, Chongqing400715, China Key Laboratory of Cognition and Personality (SWU), MoE, Chongqing400715, China
Rongmin Xiong
Affiliation:
School of Foreign Languages, Southwest University of Political Science and Law, Chongqing401120, China
Change Yue
Affiliation:
School of Foreign Languages, Southwest University of Political Science and Law, Chongqing401120, China
Shuqiong Wu
Affiliation:
Center for Linguistic, Literary and Cultural Studies, Sichuan International Studies University, Chongqing400031, China
Feng Ji
Affiliation:
Chongqing Institute of Foreign Studies, Chongqing401120, China
Quanhong Wang*
Affiliation:
Faculty of Psychology, Southwest University, Chongqing400715, China Key Laboratory of Cognition and Personality (SWU), MoE, Chongqing400715, China
*
*Corresponding authors. Emails: zhangyong@swupl.edu.cn; quanhong177@yahoo.com
*Corresponding authors. Emails: zhangyong@swupl.edu.cn; quanhong177@yahoo.com
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Abstract

The joint effects of stimulus quality and semantic context in visual word recognition were examined with event-related potential (ERP) recordings. In one-character Chinese word recognition, we manipulated stimulus quality at two degradation levels (highly vs. slightly degraded) and semantic context at two priming levels (semantically related vs. unrelated). In a prime–target–probe trial flow, ERPs were recorded to the target character which was presented in either high or slight degradation and which was preceded by either a semantically related or unrelated prime character. The target character was then followed by a probe character which was either identical to or different from the target character. Subjects were instructed to make target–probe matching judgments. The ERP results demonstrated a degradation by priming interaction, with larger N400 semantic priming effects for slightly degraded targets. Moreover, the degradation effects were observed on the P200, N250, and N400. These findings provided evidence for the cascaded model of visual word recognition such that the visual processing cascaded into the semantic stage and thus interacted on the N400 amplitude. The results were compared to an earlier study with a null ERP degradation by priming interaction. The ramifications of these results for models of visual word recognition are discussed.

Keywords

visual word recognitionstimulus qualitysemantic contextcascaded modelN400
Type
Original Article
Information
Applied Psycholinguistics , Volume 43 , Issue 2 , March 2022 , pp. 389 - 410
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Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

Aschenbrenner, A. J., & Yap, M. J. (2019). The influence of relatedness proportion on the joint relationship among word frequency, stimulus quality, and semantic priming in the lexical decision task. Quarterly Journal of Experimental Psychology, 72(10), 2452–2461. doi: 10.1177/1747021819845317.CrossRefGoogle ScholarPubMed
Balota, D. A., Yap, M. J., Cortese, M. J., & Watson, J. M. (2008). Beyond mean response latency: Response time distributional analyses of semantic priming. Journal of Memory and Language, 59(4), 495–523. doi: 10.1016/j.jml.2007.10.004.CrossRefGoogle Scholar
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2014). Fitting Linear Mixed-Effects Models Using lme4. Statistics & Computing, arXiv:1406(1), 133–199. doi: 10.1007/0-387-22747-4_4.CrossRefGoogle Scholar
Becker, C. A. (1979). Semantic context and word frequency effects in visual word recognition. Journal of Experimental Psychology-human Perception and Performance, 5(5), 252–259. doi: 10.1037//0096-1523.5.2.252 CrossRefGoogle ScholarPubMed
Becker, C. A., & Killion, T. H. (1977). Interaction of visual and cognitive effects in word recognition. Journal of Experimental Psychology-human Perception and Performance, 3(3), 389–401. doi: 10.1037/0096-1523.3.3.389.CrossRefGoogle Scholar
Beijing Language Institute (1986). Beijing Language Institute Press.Google Scholar
Besner, D., & Roberts, M. A. (2003). Reading nonwords aloud: results requiring change in the dual route cascaded model. Psychonomic Bulletin & Review, 10(2), 398–404. doi: 10.3758/BF03196498.CrossRefGoogle ScholarPubMed
Besner, D., & Smith, M. C. (1992). Models of visual word recognition: When obscuring the stimulus yields a clearer view. Journal of Experimental Psychology-Learning Memory and Cognition, 18(3), 468–482. doi: 10.1037/0278-7393.18.3.468.CrossRefGoogle Scholar
Borowsky, R., & Besner, D. (1993). Visual word recognition: a multistage activation model. Journal of Experimental Psychology-Learning Memory and Cognition, 19(19), 813–840. doi: 10.1037/0278-7393.19.4.813.CrossRefGoogle ScholarPubMed
Brown, M., & Besner, D. (2002). Semantic priming: On the role of awareness in visual word recognition in the absence of an expectancy. Consciousness and Cognition, 11(3), 402–422. doi: 10.1016/S1053-8100(02)00008-9.CrossRefGoogle ScholarPubMed
Carreiras, M., Armstrong, B. C., Perea, M., & Frost, R. (2014). The what, when, where, and how of visual word recognition. Trends in Cognitive Sciences, 18(2), 90–98. doi: 10.1016/j.tics.2013.11.005.CrossRefGoogle ScholarPubMed
Carreiras, M., Gillon-Dowens, M., Vergara, M., & Perea, M. (2009). Are vowels and consonants processed differently? event-related potential evidence with a delayed letter paradigm. Journal of Cognitive Neuroscice, 21(2), 275–288. doi: 10.1162/jocn.2008.21023 CrossRefGoogle ScholarPubMed
Carreiras, M., Vergara, M., & Perea, M. (2007). ERP correlates of transposed-letter similarity effects: are consonants processed differently from vowels? Neuroscience Letters, 419(3), 219–224. doi: 10.1016/j.neulet.2007.04.053.CrossRefGoogle ScholarPubMed
Chen, Y., Davis, M. H., Pulvermüller, F., & Hauk, O. (2015). Early visual word processing is flexible: Evidence from spatiotemporal brain dynamics. Journal of Cognitive Neuroscience, 27(9), 1738–1751. doi: 10.1162/jocn_a_00815.CrossRefGoogle ScholarPubMed
Chetail, F., Colin, C., & Content, A. (2012). Electrophysiological markers of syllable frequency during written word recognition in French. Neuropsychologia, 50(14), 3429–3439. doi: 10.1016/j.neuropsychologia.2012.09.044.CrossRefGoogle ScholarPubMed
Cohen, J. (1988). Statistical power ANALYSIS for the Behavioral sciences. Lawrence Erlbaum Associates.Google Scholar
Coltheart, M., & Curtis, B. (1993). Models of reading aloud: dual-route and parallel distributed processing approaches. Psychological Review, 100(4), 589–608. doi: 10.1037/0033-295X.100.4.589.CrossRefGoogle Scholar
Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001). DRC: a dual route cascaded model of visual word recognition and reading aloud. Psychological Review, 108(1), 204–256. doi: 10.1037/0033-295X.108.1.204.CrossRefGoogle ScholarPubMed
Davis, C. J. (2012). Developing a universal model of reading necessitates cracking the orthographic code. Behavioral & Brain Sciences, 35(5), 283–284. doi: 10.1017/S0140525X12000039.CrossRefGoogle ScholarPubMed
Deacon, D., Dynowska, A., Ritter, W., & Grose-Fifer, J. (2004). Repetition and semantic priming of nonwords: Implications for theories of N400 and word recognition. Psychophysiology, 41(1), 60–74. doi: 10.1111/1469-8986.00120.CrossRefGoogle ScholarPubMed
Deutsch, A., Frost, R., Pollatsek, A., & Rayner, K. (2005). Morphological parafoveal preview benefit effects in reading: Evidence from Hebrew. Language & Cognitive Processes, 20(1–2), 341–371. doi: 10.1080/01690960444000115.CrossRefGoogle Scholar
Duñabeitia, J., Dimitropoulou, M., Grainger, J., & Hernández, J. (2012). Differential sensitivity of letters, numbers, and symbols to character transpositions. Journal of Cognitive Neuroscience, 24(7), 1610–1624. doi: 10.1162/jocn_a_00180 CrossRefGoogle ScholarPubMed
Duñabeitia, J., Perea, M., & Carreiras, M. (2009). There is no clam with coats in the calm coast : Delimiting the transposed-letter priming effect Quarterly Journal of Experimental Psychology, 62(10), 1930–1947. doi: 10.1080/17470210802696070.CrossRefGoogle ScholarPubMed
Faísca, L., Reis, A., & Araújo, S. (2019). Early brain sensitivity to word frequency and lexicality during reading aloud and implicit reading. Frontiers in Psychology, 10, 1–13. doi: 10.3389/fpsyg.2019.00830.CrossRefGoogle ScholarPubMed
Ferguson, R., Robidoux, S., & Besner, D. (2009). Reading aloud: Evidence for contextual control over lexical activation. Journal of Experimental Psychology-human Perception and Performance, 35(2), 499–507. doi: 10.1037/a0013162.CrossRefGoogle ScholarPubMed
Frost, R. (2012). A universal approach to modeling visual word recognition and reading: Not only possible, but also inevitable. Behavioral & Brain Sciences, 35(5), 263–329. doi: 10.1017/S0140525X12000635.CrossRefGoogle ScholarPubMed
Grainger, J., & Holcomb, P. J. (2009). Watching the word go by: On the time-course of component processes in visual word recognition. Language and Linguistics Compass, 3(1), 128–156.CrossRefGoogle ScholarPubMed
Heilbron, M., Richter, D., Ekman, M., Hagoort, P., & Lange, F. P. D. (2020). Word contexts enhance the neural representation of individual letters in early visual cortex. Nature Communications, 11(1), 321–331. doi: 10.1038/s41467-019-13996-4.CrossRefGoogle ScholarPubMed
Holcomb, P. J. (1993). Semantic priming and stimulus degradation: implications for the role of the N400 in language processing. Psychophysiology, 30(1), 47–61. doi: 10.1111/j.1469-8986.1993.tb03204.x CrossRefGoogle ScholarPubMed
Holcomb, P. J., & Grainger, J. (2006). On the time course of visual word recognition: an event-related potential investigation using masked repetition priming. Journal of Cognitive Neuroscience, 18(10), 1631–1643. doi: 10.1162/jocn.2006.18.10.1631 CrossRefGoogle ScholarPubMed
Holcomb, P. J., & Grainger, J. (2007). Exploring the temporal dynamics of visual word recognition in the masked repetition priming paradigm using event-related potentials. Brain Research, 1180(8), 39–58. doi: 10.1016/j.brainres.2007.06.110.CrossRefGoogle ScholarPubMed
Hsu, C.-H., Tsai, J.-L., Lee, C.-Y., & Tzeng, O. J.-L. (2009). Orthographic combinability and phonological consistency effects in reading Chinese phonograms: an event-related potential study. Brain and Language, 108(1), 56–66. doi: 10.1016/j.bandl.2008.09.002.CrossRefGoogle ScholarPubMed
Jobard, G., Crivello, F., & Tzourio-Mazoyer, N. (2003). Evaluation of the dual route theory of reading: a metaanalysis of 35 neuroimaging studies. Neuroimage, 20(2), 693–712. doi: 10.1016/S1053-8119(03)00343-4 CrossRefGoogle ScholarPubMed
Kong, L., Zhang, B., Zhang, J. X., & Kang, C. (2012). P200 can be modulated by orthography alone in reading Chinese words. Neuroscience Letters, 529(2), 161–165. doi: 10.1016/j.neulet.2012.09.028.CrossRefGoogle ScholarPubMed
Kong, L., Zhang, J. X., Kang, C., Du, Y., Zhang, B., & Wang, S. (2010). P200 and phonological processing in Chinese word recognition. Neuroscience Letters, 473(1), 37–41. doi: 10.1016/j.neulet.2010.02.014.CrossRefGoogle ScholarPubMed
Kutas, M., & Federmeier, K. D. (2011). Thirty years and counting: Finding meaning in the N400 component of the event related brain potential (ERP). Annual Review of Psychology, 62(1), 621–647. doi: 10.1146/annurev.psych.093008.131123.CrossRefGoogle Scholar
Kutas, M., & Hillyard, S. A. (1980). Reading senseless sentences: brain potentials reflect semantic incongruity. Science, 207(4427), 203–205. doi: 10.1126/science.7350657.CrossRefGoogle ScholarPubMed
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest package: Tests in linear mixed effects models. Journal of Statistical Software, 82, 1–26. doi: 10.18637/jss.v082.i13.CrossRefGoogle Scholar
Kwon, Y., Lee, Y., & Nam, K. (2011). The different P200 effects of phonological and orthographic syllable frequency in visual word recognition in Korean. Neuroscience Letters, 501(2), 117–121. doi: 10.1016/j.neulet.2011.06.060.CrossRefGoogle ScholarPubMed
Levy, J., Pernet, C., Treserras, S., Boulanouar, K., Berry, I., Aubry, F., Demonet, J. F., & Celsis, P. (2008). Piecemeal recruitment of left-lateralized brain areas during reading: A spatio-functional account. Neuroimage, 43(3), 581–591. doi: 10.1016/j.neuroimage.2008.08.008.CrossRefGoogle ScholarPubMed
Liu, Y., Perfetti, C. A., & Hart, L. (2003). ERP evidence for the time course of graphic, phonological, and semantic information in Chinese meaning and pronunciation decisions. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29(6), 1231–1247. doi: 10.1037/0278-7393.29.6.1231.Google ScholarPubMed
Malone, H. E., Nicholl, H., & Coyne, I. (2016). Fundamentals of estimating sample size. Nurse Research, 23(5), 21–25. doi: 10.7748/nr.23.5.21.s5.CrossRefGoogle ScholarPubMed
Masson, M. E. J., & Kliegl, R. (2013). Modulation of additive and interactive effects in lexical decision by trial history. Journal of Experimental Psychology-Learning Memory and Cognition, 39(3), 898–914. doi: 10.1037/a0029180.CrossRefGoogle ScholarPubMed
Mcclelland, J. L. (1979). On the time relations of mental processes: An examination of systems of processes in cascade. Psychological Review, 86(4), 287–330. doi: 10.1.1.228.2731.CrossRefGoogle Scholar
Mcclelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception (Part I). Psychological Review, 88(5), 375–407. doi: 10.1.1.298.4480.CrossRefGoogle Scholar
Meyer, D. E., Schvaneveldt, R. W., & Ruddy, M. G. (1975). Loci of Contextual Effects on Visual Word Recognition. In Rabbitt, P. (Ed.), Attention & Performance. London, England: Academic Press.Google Scholar
O’Malley, S., & Besner, D. (2008). Reading aloud: qualitative differences in the relation between stimulus quality and word frequency as a function of context. Journal of Experimental Psychology-Learning Memory and Cognition, 34(6), 1400–1411. doi: 10.1037/a0013084.CrossRefGoogle Scholar
O’Malley, S., Reynolds, M. G., & Besner, D. (2007). Qualitative differences between the joint effects of stimulus quality and word frequency in reading aloud and lexical decision: extensions to Yap and Balota (2007). Journal of Experimental Psychology-Learning Memory and Cognition, 33(2), 451–458. doi: 10.1037/0278-7393.33.2.451.CrossRefGoogle Scholar
Perry, C., Ziegler, J. C., & Zorzi, M. (2007). Nested incremental modeling in the development of computational theories: the CDP+ model of reading aloud. Psychological Review, 114(2), 273–315. doi: 10.1037/0033-295X.114.2.273.CrossRefGoogle ScholarPubMed
Petit, J., Grainger, J., Midgley, K., & Holcomb, P. (2005). On the time-course of processing in letter perception: A masked priming ERP investigation. Psychonomic Bulletin and Review, (13), 674–681. doi: 10.3758/BF03193980.Google Scholar
Price, C. J., & Devlin, J. T. (2011). The Interactive Account of ventral occipitotemporal contributions to reading. Trends in Cognitive Sciences, 15(6), 246–253. doi: 10.1016/j.tics.2011.04.001.CrossRefGoogle Scholar
R Core Team (2018). R: A Language and Environment for Statistical Computing. doi:10.1890/0012-9658(2002)083[3097:CFHIWS]2.0.CO;2.CrossRefGoogle Scholar
Rastle, K., & Brysbaert, M. (2006). Masked phonological priming effects in English: Are they real? Do they matter? Cognitive Psychology, 53(2), 97–145. doi: 10.1016/j.cogpsych.2006.01.002.CrossRefGoogle ScholarPubMed
Rayner, K., Foorman, B. R., Perfetti, C. A., Pesetsky, D., & Seidenberg, M. S. (2010). How Psychological Science Informs the Teaching of Reading. Psychological Science in the Public Interest, 2(2), 31–74. doi: 10.1111/1529-1006.00004.CrossRefGoogle Scholar
Reynolds, M., & Besner, D. (2004). Neighbourhood density, word frequency, and spelling-sound regularity effects in naming: similarities and differences between skilled readers and the Dual Route Cascaded Computational model. Canadian Journal of Experimental Psychology, 58(1), 13–31. doi: 10.1037/h0087437.CrossRefGoogle ScholarPubMed
Robidoux, S., Stolz, J., & Besner, D. (2010). Visual word recognition: Evidence for global and local control over semantic feedback. Journal of Experimental Psychology-human Perception and Performance, 36(3), 689–703. doi: 10.1037/a0018741.CrossRefGoogle ScholarPubMed
Scaltritti, M., Balota, D. A., & Peressotti, F. (2013). Exploring the additive effects of stimulus quality and word frequency: The influence of local and list-wide prime relatedness. Quarterly Journal of Experimental Psychology, 66(1), 91–107. doi: 10.1080/17470218.2012.698628.CrossRefGoogle ScholarPubMed
Seidenberg, M. S. (2010). Connectionist Models of Word Reading. Current Directions in Psychological Science, 14(5), 238–242. doi: 10.1111/j.0963-7214.2005.00372.x CrossRefGoogle Scholar
Snodgrass, J. G., & Hirshman, E. (1991). Theoretical explorations of the Bruner-Potter 1964 interference effect. Journal of Memory and Language, 30(3), 273–293. doi: 10.1016/0749-596X(91)90037-K.CrossRefGoogle Scholar
Sternberg, S. (1969). The discovery of processing stages: Extensions of Donders’ method. Acta Psychologica, 30(69), 276–315. doi: 10.1016/0001-6918(69)90055-9.CrossRefGoogle Scholar
Stolz, J. A., & Besner, D. (1998). Levels of representation in visual word recognition: A dissociation between morphological and semantic processing. Journal of Experimental Psychology-human Perception and Performance, 24(6), 1642–1655. doi: 10.1037/0096-1523.24.6.1642.CrossRefGoogle ScholarPubMed
Stolz, J. A., & Neely, J. H. (1995). When target degradation does and does not enhance semantic context effects in word recognition. Journal of Experimental Psychology-Learning Memory and Cognition, 21(3), 596–611. doi: 10.1037/0278-7393.21.3.596.CrossRefGoogle Scholar
Thomas, M. A., Neely, J. H., & O’Connor, P. (2012). When word identification gets tough, retrospective semantic processing comes to the rescue. Journal of Memory and Language, 66(4), 623–643. doi: 10.1016/j.jml.2012.02.002.CrossRefGoogle Scholar
van Petten, C., & Kutas, M. (1990). Interactions between sentence context and word frequency in event-related brain potentials. Memory and Cognition, 18(4), 380–393. doi: 10.3758/BF03197127.CrossRefGoogle ScholarPubMed
Wang, F., & Maurer, U. (2017). Top-down modulation of early print-tuned neural activity in reading. Neuropsychologia, 102, 29–38. doi: 10.1016/j.neuropsychologia.2017.05.028.CrossRefGoogle ScholarPubMed
Wang, F., & Maurer, U. (2020). Interaction of top-down category-level expectation and bottom-up sensory input in early stages of visual-orthographic processing. Neuropsychologia, 137, 107299. doi: 10.1016/j.neuropsychologia.2019.107299.CrossRefGoogle ScholarPubMed
Wang, Y., Cui, L., Wang, H., Tian, S., & Zhang, X. (2004). The sequential processing of visual feature conjunction mismatches in the human brain. Psychophysiology, 41(1), 21–29. doi: 10.1111/j.1469-8986.2003.00134.x.CrossRefGoogle ScholarPubMed
Warrington, K. L., Mcgowan, V. A., Paterson, K. B., & White, S. J. (2018). Effects of aging, word frequency, and text stimulus quality on reading across the adult lifespan: Evidence from eye movements. Journal of Experimental Psychology-Learning Memory and Cognition, 44(11), 1714–1729. doi: 10.1037/xlm0000543.CrossRefGoogle ScholarPubMed
Whaley, M. L., Kadipasaoglu, C. M., Cox, S. J., & Tandon, N. (2016). Modulation of Orthographic Decoding by Frontal Cortex. The Journal of Neuroscience, 36(4), 1173–1184. doi: 10.1523/JNEUROSCI.2985-15.2016.CrossRefGoogle ScholarPubMed
Woodhead, Z. V. J., Barnes, G. R., Penny, W., Moran, R., Teki, S., Price, C. J., & Leff, A. P. (2014). Reading Front to Back: MEG Evidence for Early Feedback Effects During Word Recognition. Cerebral Cortex, 24(3), 817–827. doi: 10.1093/cercor/bhs365.CrossRefGoogle ScholarPubMed
Wu, Y., Mo, D., Tsang, Y.-K., & Chen, H.-C. (2012). ERPs reveal sub-lexical processing in Chinese character recognition. Neuroscience Letters, 514(2), 164–168. doi: 10.1016/j.neulet.2012.02.080.CrossRefGoogle ScholarPubMed
Yang, J., Wang, X., Shu, H., & Zevin, J. D. (2012). Task by stimulus interactions in brain responses during Chinese character processing. Neuroimage, 60(2), 979–990. doi: 10.1016/j.neuroimage.2012.01.036.CrossRefGoogle ScholarPubMed
Yap, M. J., & Balota, D. A. (2007). Additive and interactive effects on response time distributions in visual word recognition. Journal of Experimental Psychology-Learning Memory and Cognition, 33(2), 274–296. doi: 10.1037/0278-7393.33.2.274 CrossRefGoogle ScholarPubMed
Yap, M. J., Balota, D., & Tan, S. E. (2013). Additive and interactive effects in semantic priming: Isolating lexical and decision processes in the lexical decision task. Journal of Experimental Psychology-Learning Memory and Cognition, 39(1), 140–158. doi: 10.1037/a0028520.CrossRefGoogle ScholarPubMed
Zhang, Y., Zhou, M., & Wang, Q. (2020). Interactions of stimulus quality and frequency on N400 in Chinese character recognition: Evidence for cascaded processing. Neuroscience Letters, 715. doi: 10.1016/j.neulet.2019.134614.CrossRefGoogle ScholarPubMed
Zhou, X., & Marslen-Wilson, W. (1999). Phonology, orthography, and semantic activation in reading Chinese. Journal of Memory and Language, 41(4), 579–606. doi: 10.1006/jmla.1999.2663.CrossRefGoogle Scholar