Different roles of initial and final character positional probabilities on incidental word learning during Chinese reading

doi:10.3724/SP.J.1041.2024.00281

Abstract

Abstract:

In this study, two parallel experiments were conducted to explore the mode of change in the probability information of the initial and final character positions on word segmentation when learning novel words repeatedly. The incidental word learning paradigm was used during reading, and a two-character pseudo word was used as a novel word. In Experiment 1, the probability of the initial character's position was manipulated to ensure that the final character was the same. In Experiment 2, the probability of the final character's position was manipulated to ensure that the initial character was the same. An eye tracker was used to record the eye movements of college students while reading. The results showed that: (1) The word segmentation effect of the probability information of the initial and final character positions gradually decreased with an increase in the number of times novel words were learned through reading, showing a “familiarity effect.” (2) The "familiarity effect" of the probability information of the initial character position was observed in the two relatively late eye movement indicators of go-past time and total number of fixation, while the “familiarity effect” of the probability information of the final character position began with gaze duration, proceeded to go-past time, and then persisted until total reading time. The results indicated that both the position probability information of the initial and final characters played a role in word segmentation during reading, but the initial character had a longer and more consistent time course, supporting the view that the initial character had an advantage in two-character processing.

Key words: character position probability, word segmentation, incidental word learning, Chinese reading

LIANG Feifei, FENG Linlin, LIU Ying, LI Xin, BAI Xuejun. (2024). Different roles of initial and final character positional probabilities on incidental word learning during Chinese reading. Acta Psychologica Sinica, 56(3), 281-294.

Figures/Tables 10

References 41

[1]	Bai, X. J., Liang, F. F., Blythe, H. I., Zang, C. L., Yan, G. L., & Liversedge, S. P. (2013). Interword spacing effects on the acquisition of new vocabulary for readers of Chinese as a second language. Journal of Research in Reading, 36(S1), S4-S17.
[2]	Bai, X. J., Ma, J., Li, X., Lian, K. Y., Tan, K., Yang, Y., & Liang, F. F. (2019). The efficiency and improvement of novel word learning in Chinese children with developmental dyslexia during natural reading. Acta Psychologica Sinica, 51(4), 471-483. doi: 10.3724/SP.J.1041.2019.00471 URL
[3]	Bai, X. J., Yan, G. L., Liversedge, S. P., Zang, C. L., & Rayner, K. (2008). Reading spaced and unspaced Chinese text: Evidence from eye movements. Journal of Experimental Psychology: Human Perception and Performance, 34(5), 1277-1287. doi: 10.1037/0096-1523.34.5.1277 pmid: 18823210
[4]	Bates, D., Maechler, M., Bolker, B., & Walker, S. (2023). lme4: Linear mixed-effects models using 'Eigen' and S4. Retrieved July 4, 2023, from https://cran.r-project.org/web/packages/lme4/index.html
[5]	Blythe, H. I., Liang, F. F., Zang, C. L., Wang, J. X., Yan, G. L., Bai, X. J., & Liversedge, S. P. (2012). Inserting spaces into Chinese text helps readers to learn new words: An eye movement study. Journal of Memory & Language, 67(2), 241-254.
[6]	Cai, Q., & Brysbaert, M. (2010). SUBTLEX-CH: Chinese word and character frequencies based on film subtitles. PloS One, 5(6), e10729.
[7]	Cao, H. B., Lan, Z. B., Gao, F., Yu, H. T., Li, P., & Wang, J. X. (2023). The role of character positional frequency on word recognition during Chinese reading: Lexical decision and eye movements studies. Acta Psychologica Sinica, 55(2), 159-176. doi: 10.3724/SP.J.1041.2023.00159
[8]	Caramazza, A., Laudanna, A., & Romani, C. (1988). Lexical access and inflectional morphology. Cognition, 28(3), 297-332. pmid: 3359755
[9]	Clifton, C., Ferreira, F., Henderson, J. M., Inhoff, A. W., Liversedge, S. P., Reichle, E. D., & Schotter, E. R. (2016). Eye movements in reading and information processing: Keith Rayner’s 40 year legacy. Journal of Memory and Language, 86, 1-19. doi: 10.1016/j.jml.2015.07.004 URL
[10]	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 pmid: 11212628
[11]	Davis, C. J. (2001). The self-organising lexical acquisition and recognition (SOLAR) model of visual word recognition. Dissertation Abstracts International: Section B: The Sciences and Engineering, 62(1-B), 594.
[12]	Joseph, H., & Nation, K. (2018). Examining incidental word learning during reading in children: The role of context. Journal of Experimental Child Psychology, 166, 190-211. doi: S0022-0965(16)30239-9 pmid: 28942127
[13]	Joseph, H. S., Wonnacott, E., Forbes, P., & Nation, K. (2014). Becoming a written word: Eye movements reveal order of acquisition effects following incidental exposure to new words during silent reading. Cognition, 133(1), 238-248. doi: 10.1016/j.cognition.2014.06.015 pmid: 25058413
[14]	Kasisopa, B., Reilly, R. G., Luksaneeyanawin, S., & Burnham, D. (2013). Eye movements while reading an unspaced writing system: The case of Thai. Vision Research, 86, 71-80. doi: 10.1016/j.visres.2013.04.007 pmid: 23608059
[15]	Kasisopa, B., Reilly, R. G., Luksaneeyanawin, S., & Burnham, D. (2016). Child readers' eye movements in reading Thai. Vision Research, 123, 8-19. doi: 10.1016/j.visres.2015.07.009 pmid: 27137836
[16]	Li, X. S., Huang, L. J. Q., Yao, P. P., & Hyönä, J. (2022). Universal and specific reading mechanisms across different writing systems. Nature Reviews Psychology, 1, 133-144. doi: 10.1038/s44159-022-00022-6
[17]	Li, X. S., & Pollatsek, A. (2020). An integrated model of word processing and eye-movement control during Chinese reading. Psychological Review, 127(6), 1139-1162. doi: 10.1037/rev0000248 URL
[18]	Li, X. S., Rayner, K., & Cave, K. R. (2009). On the segmentation of Chinese words during reading. Cognitive Psychology, 58(4), 525-552. doi: 10.1016/j.cogpsych.2009.02.003 pmid: 19345938
[19]	Lian, K. Y., Ma, J., Wei, L., Zhang, S. W., & Bai, X. J. (2021). The role of character positional frequency on college and primary student in oral reading. Studies of Psychology and Behavior, 19(2), 179-185.
[20]	Liang, F. F., Blythe, H. I., Bai, X. J., Yan, G. L., Li, X., Zang, C. L., & Liversedge, S. P. (2017). The role of character positional frequency on Chinese word learning during natural reading. PloS One, 12(11), e0187656.
[21]	Liang, F. F., Blythe, H. I., Zang, C. L., Bai, X. J., Yan, G. L., & Liversedge, S. P. (2015). Positional character frequency and word spacing facilitate the acquisition of novel words during Chinese children's reading. Journal of Cognitive Psychology, 27(5), 594-608. doi: 10.1080/20445911.2014.1000918 URL
[22]	Liang, F. F., Gao, Q., Li, X., Wang, Y. S., Bai, X. J., & Liversedge, S. P. (2023). The importance of the positional probability of word final (but not word initial) characters for word segmentation and identification in children and adults' natural Chinese reading. Journal of Experimental Psychology: Learning, Memory, and Cognition, 49(1), 98-115.
[23]	Liang, F. F., Ma, J., Bai, X. J., & Liversedge, S. P. (2021). Initial landing position effects on Chinese word learning in children and adults. Journal of Memory and Language, 116(1), 104183.
[24]	Liang, F. F., Ma, J., Li, X., Lian, K. Y., Tan, K., & Bai, X. J. (2019). Saccadic targeting deficits of Chinese children with developmental dyslexia: Evidence from novel word learning in reading. Acta Psychologica Sinica, 51(7), 805-815. doi: 10.3724/SP.J.1041.2019.00805
[25]	Ma, G. J., & Li, X. S. (2015). How character complexity modulates eye movement control in Chinese reading. Reading and Writing, 28(6), 747-761. doi: 10.1007/s11145-015-9548-1 URL
[26]	Milledge, S. V., Liversedge, S. P., & Blythe, H. I. (2022). The importance of the first letter in children’s parafoveal preprocessing in English: Is it phonologically or orthographically driven? Journal of Experimental Psychology: Human Perception and Performance, 48(5), 427-442. doi: 10.1037/xhp0000998 URL
[27]	Nation, K., Angell, P., & Castles, A. (2007). Orthographic learning via self-teaching in children learning to read English: Effects of exposure, durability, and context. Journal of Experimental Child Psychology, 96(1), 71-84. pmid: 16904123
[28]	Pagán, A., & Nation, K. (2019). Learning words via reading: Contextual diversity, spacing, and retrieval effects in adults. Cognitive Science, 43(1), e12705.
[29]	Perea, M., & Acha, J. (2009). Space information is important for reading. Vision Research, 49(15), 1994-2000. doi: 10.1016/j.visres.2009.05.009 pmid: 19463847
[30]	Radach, R., & Kennedy, A. (2004). Theoretical perspectives on eye movements in reading: Past controversies, current issues, and an agenda for future research. European Journal of Cognitive Psychology, 16(1-2), 3-26. doi: 10.1080/09541440340000295 URL
[31]	Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124(3), 372-422. doi: 10.1037/0033-2909.124.3.372 pmid: 9849112
[32]	Rayner, K. (2009). The 35th Sir Frederick Bartlett Lecture: Eye movements and attention in reading, scene perception, and visual search. Quarterly Journal of Experimental Psychology, 62(8), 1457-1506. doi: 10.1080/17470210902816461 URL
[33]	R Development Core Team. (2016). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/
[34]	Tamura, N., Castles, A., & Nation, K. (2017). Orthographic learning, fast and slow: Lexical competition effects reveal the time course of word learning in developing readers. Cognition, 163, 93-102. doi: S0010-0277(17)30060-4 pmid: 28314178
[35]	Tsang, Y.-K., & Zou, Y. (2022). An ERP megastudy of Chinese word recognition. Psychophysiology, 59(11), e14111.
[36]	Wang, W. N., Lu, A. T., He, D. P., Zhang, B., & Zhang, J. X. (2017). ERP evidence for Chinese compound word recognition: Does morpheme work all the time? Neuroquantology, 5(3), 142-152.
[37]	Whitney, C. (2001). How the brain encodes the order of letters in a printed word: The seriol model and selective literature review. Psychonomic Bulletin & Review, 8(2), 221-243. doi: 10.3758/BF03196158 URL
[38]	Yan, G. L., Tian, H. J., Bai, X. J., & Rayner, K. (2006). The effect of word and character frequency on the eye movements of Chinese readers. British Journal of Psychology, 97(2), 259-268. doi: 10.1348/000712605X70066 URL
[39]	Yen, M. -H., Radach, R., Tzeng, J. L., & Tsai, J. L. (2012). Usage of statistical cues for word boundary in reading Chinese sentences. Reading and Writing, 25(5), 1007-1029. doi: 10.1007/s11145-011-9321-z URL
[40]	Yu, L. L., Liu, Y. P., & Reichle, E. D. (2021). A corpus-based versus experimental examination of word- and character-frequency effects in Chinese reading: Theoretical implications for models of reading. Journal of Experimental Psychology: General, 150(8), 1612-1641. doi: 10.1037/xge0001014 URL
[41]	Zang, C. L., Liang, F. F., Bai, X. J., Yan, G. L., & Liversedge, S. P. (2013). Inter-word spacing and landing position effects during Chinese reading in children and adults. Journal of Experimental Psychology: Human Perception & Performance, 39(3), 720-734.

Learning order	Sentence
1 Translation:	从选材到制作勾席/望席需要经过多重工序。 From the selection of materials to the making of the 勾席/望席 multiple processes are required.
2 Translation:	多次地切割使得勾席/望席更加的光彩夺目。 Multiple cuts add to the brilliance of the 勾席/望席.
3 Translation:	很多年轻人认为勾席/望席代表浪漫的爱情。 Many young people believe that 勾席/望席 represents romantic love.
4 Translation:	很多人在购买勾席/望席之前要量手指尺寸。 Before buying a 勾席/望席, many people measure their fingers.
5 Translation:	新郎和新娘交换勾席/望席戴在对方手指上。 The bride and groom exchange 勾席/望席 on each other's fingers.
6 Translation:	为女朋友挑选勾席/望席需要花费很多心思。 Choosing a 勾席/望席 for your girlfriend takes a lot of thought.
Semantic category multiple-choice question	Question: Which of the following categories does the勾席/望席 belong to? (a) headdress (b) animals (c) cup (d) epidemic supplies

Learning order	Sentence
1 Translation:	从选材到制作勾席/望席需要经过多重工序。 From the selection of materials to the making of the 勾席/望席 multiple processes are required.
2 Translation:	多次地切割使得勾席/望席更加的光彩夺目。 Multiple cuts add to the brilliance of the 勾席/望席.
3 Translation:	很多年轻人认为勾席/望席代表浪漫的爱情。 Many young people believe that 勾席/望席 represents romantic love.
4 Translation:	很多人在购买勾席/望席之前要量手指尺寸。 Before buying a 勾席/望席, many people measure their fingers.
5 Translation:	新郎和新娘交换勾席/望席戴在对方手指上。 The bride and groom exchange 勾席/望席 on each other's fingers.
6 Translation:	为女朋友挑选勾席/望席需要花费很多心思。 Choosing a 勾席/望席 for your girlfriend takes a lot of thought.
Semantic category multiple-choice question	Question: Which of the following categories does the勾席/望席 belong to? (a) headdress (b) animals (c) cup (d) epidemic supplies

	b	SE	t/z	95% CI		b	SE	t/z	95% CI
First fixation duration					Go past time
Intercept	5.49	0.01	457.88	[5.46, 5.51]	Intercept	6.02	0.04	136.97	[5.94, 6.11]
Initial Character Positional Probability	-0.01	0.02	-0.62	[-0.04, 0.02]	Initial Character Positional Probability	-0.10	0.03	-3.53	[-0.15, -0.04]
Learning times	-0.03	0.005	-5.21	[-0.04, -0.02]	Learning times	-0.09	0.01	-10.35	[-0.11, -0.07]
Initial Character Positional Probability×Learning times	0.0002	0.004	0.05	[-0.01, 0.01]	Initial Character Positional Probability×Learning times	0.01	0.01	2.04	[0.001, 0.03]
Gaze duration					Regression out probability
Intercept	5.67	0.03	177.65	[5.61, 5.73]	Intercept	-1.13	0.14	-7.79	[-1.41, -0.84]
Initial Character Positional Probability	-0.04	0.02	-2.11	[-0.08, -0.003]	Initial Character Positional Probability	-0.21	0.13	-1.61	[-0.46, 0.05]
Learning times	-0.05	0.01	-8.39	[-0.06, -0.04]	Learning times	-0.17	0.03	-5.99	[-0.22, -0.11]
Initial Character Positional Probability×Learning times	0.01	0.01	1.01	[-0.01, 0.02]	Initial Character Positional Probability×Learning times	0.02	0.03	0.69	[-0.04, 0.09]
Total reading time					Total number of fixations
Intercept	6.20	0.05	137.37	[6.12, 6.29]	Intercept	2.32	0.09	26.55	[2.15, 2.49]
Initial Character Positional Probability	-0.10	0.02	-4.32	[-0.15, -0.06]	Initial Character Positional Probability	-0.33	0.05	-6.63	[-0.42, -0.23]
Learning times	-0.11	0.01	-12.87	[-0.12, -0.09]	Learning times	-0.20	0.02	-12.31	[-0.24, -0.17]
Initial Character Positional Probability×Learning times	0.01	0.01	1.37	[-0.004, 0.02]	Initial Character Positional Probability×Learning times	0.05	0.01	3.97	[0.02, 0.07]

	b	SE	t/z	95% CI		b	SE	t/z	95% CI
First fixation duration					Go past time
Intercept	5.49	0.01	457.88	[5.46, 5.51]	Intercept	6.02	0.04	136.97	[5.94, 6.11]
Initial Character Positional Probability	-0.01	0.02	-0.62	[-0.04, 0.02]	Initial Character Positional Probability	-0.10	0.03	-3.53	[-0.15, -0.04]
Learning times	-0.03	0.005	-5.21	[-0.04, -0.02]	Learning times	-0.09	0.01	-10.35	[-0.11, -0.07]
Initial Character Positional Probability×Learning times	0.0002	0.004	0.05	[-0.01, 0.01]	Initial Character Positional Probability×Learning times	0.01	0.01	2.04	[0.001, 0.03]
Gaze duration					Regression out probability
Intercept	5.67	0.03	177.65	[5.61, 5.73]	Intercept	-1.13	0.14	-7.79	[-1.41, -0.84]
Initial Character Positional Probability	-0.04	0.02	-2.11	[-0.08, -0.003]	Initial Character Positional Probability	-0.21	0.13	-1.61	[-0.46, 0.05]
Learning times	-0.05	0.01	-8.39	[-0.06, -0.04]	Learning times	-0.17	0.03	-5.99	[-0.22, -0.11]
Initial Character Positional Probability×Learning times	0.01	0.01	1.01	[-0.01, 0.02]	Initial Character Positional Probability×Learning times	0.02	0.03	0.69	[-0.04, 0.09]
Total reading time					Total number of fixations
Intercept	6.20	0.05	137.37	[6.12, 6.29]	Intercept	2.32	0.09	26.55	[2.15, 2.49]
Initial Character Positional Probability	-0.10	0.02	-4.32	[-0.15, -0.06]	Initial Character Positional Probability	-0.33	0.05	-6.63	[-0.42, -0.23]
Learning times	-0.11	0.01	-12.87	[-0.12, -0.09]	Learning times	-0.20	0.02	-12.31	[-0.24, -0.17]
Initial Character Positional Probability×Learning times	0.01	0.01	1.37	[-0.004, 0.02]	Initial Character Positional Probability×Learning times	0.05	0.01	3.97	[0.02, 0.07]

Final character type	Examples of target words	Average position probability of the final character	Average position probability of the initial character	Average number of strokes in the final character	Average character frequency of the final character
high probability condition	朴坛	93.5% (86%~100%)	50% (48%~52%)	7.13(2.58)	183(394)
low probability condition	朴吊	8.4% (0~13%)	50% (48%~52%)	7.07(2.32)	254(486)