言语加工过程中预测的形成: 影响因素和神经机制

doi:10.3724/SP.J.1042.2024.01659

摘要/Abstract

摘要： 预测性加工是指在词汇语义加工之前进行有效地预测形成预测性认知。预测性认知形成有两个主要影响因素: 先验知识和语境信息。两个因素对预测性认知形成共同作用, 相互促进。已形成的预测性认知在后续语义加工过程中还会发生相应的改变。本文梳理了能够解释预测性认知形成的理论并探讨了预测性认知形成的神经机制。最后从性别因素、语境信息呈现的时间、语境信息的唤醒度、自变量的控制等方面对未来研究进行了展望。

关键词: 预测性加工, 预测性认知, 先验知识, 语境信息

Abstract: Language is very informative. Using language to communicate allows communicators to communicate efficiently. Communicators could form predictive cognition based on speech information. The predictive cognition is not invariant on the semantic processing. For instance, there are also some occasional misunderstandings during communication. Thus, in order to better understand the meaning of language in the process of sentence comprehension, it is necessary to combine more information beyond semantics, which we called external-linguistic information, such as the speaker's body posture, voice, social status, and scene. Consequently, extra-linguistic information about the speaker's attitudes and emotional states might be expected to play an important role in language comprehension. Which means the notion of word meaning or lexical meaning in a sentence can be understood in different ways. It can be argued that meaning is constructed online each and every time a word is used, by combining extra-linguistic information and contextual cues. The combination of language with different external information produces different meanings, which may lead to deviations in the listener's semantic understanding, that is, ambiguity. In fact, there are many situations in every-day life where pragmatic information about the speaker is relevant for language comprehension, and may specifically influence the interpretation of a speaker's intended utterance meaning. This phenomenon has attracted extensive attention from researchers. Studies found that listeners rely on semantics to understand external information, and at the same time combine external information to understand semantics, and the two links are closely integrated. In linguistics, this combination of individual cognition difference and external information is called context predictive processing. Context predictive processing refers to the effective prediction to form predictive cognition before lexical semantic processing. There are two main factors influencing the formation of predictive cognition: prior knowledge and contextual information. The two factors contribute to predictive cognition and promote each other. It has been widely acknowledged that context predictive processing can affect semantic processing. In the specialization of language research into individual disciplines, and under the umbrella of two-stage theories that suggest pragmatic language processing follows semantic and syntactic processing, pragmatic language processing has often been investigated separately and independently from other aspects of language. In recent years, however, the view of pragmatics as a separable aspect of language processing has begun to change. Multiple studies show that context predictive processing affects the neurophysiological correlates of semantic processing. This paper combs the relevant studies on the formation of cognition by context predictive processing and finds that, in the process of semantic understanding, people's cognition formed by context predictive processing is not the invariant cognition formed by the initial state, but the dynamic processing process that is constantly adjusted with the increase of information received. However, most of the previous studies rarely included the influencing factor of the change of cognition in predictive processing. In order to better investigate the influence of the change of cognition on the semantic processing, this paper reviews the theories that can explain the formation of predictive cognition and explores the neural mechanism of the formation of predictive cognition. Firstly, This paper reviews the influence of predictive cognition on semantic processing and how predictive processing forms predictive cognition. Then, This paper makes clear the influencing factors of predictive cognition formation, that is, the role of prior knowledge and contextual information on predictive cognition formation. Next, this paper focuses on the theoretical explanation of predictive processing and verifies each other with the neural mechanism of predictive cognition formation. It is an attempt to illustrate the dynamic nature of predictive cognition. At last, the possible research directions of dynamic cognition in predictive processing are prospected: from the aspects of gender, the time of presentation of contextual information, the arousal of contextual information and the control of independent variables.

Key words: predictive processing, predictive cognition, prior knowledge, contextual information

隋雪, 李昱霖, 岳泽明, 刘新, 李雨桐, 刘顺华. (2024). 言语加工过程中预测的形成: 影响因素和神经机制. 心理科学进展 , 32(10), 1659-1669.

SUI Xue, LI Yulin, YUE Zeming, LIU Xin, LI Yutong, LIU Shunhua. (2024). Prediction formation during speech perception: Factors and neural mechanisms. Advances in Psychological Science, 32(10), 1659-1669.

参考文献

[1] 刘志方, 仝文, 张智君, 赵亚军. (2020). 语境预测性对阅读中字词加工过程的影响: 眼动证据.心理学报, 52(9), 1031-1047.
[2] 仝文, 余雪, 刘志方, 朱星宇, 齐琦. (2022). 快慢读者利用语境信息的差异: 加工深度的作用.心理与行为研究, 20(4), 450-456.
[3] 王霞, 卢家楣, 陈武英. (2019). 情绪词加工过程及其情绪效应特点: ERP的证据.心理科学进展, 27(11), 1842-1852.
[4] 王祯, 管健. (2021). 积极刻板印象会产生消极影响?心理科学进展, 29(9), 1657-1668.
[5] 杨琪, 蒋晓鸣, 周晓林. (2022). 语言理解中的预设加工.心理科学进展, 30(7), 1511-1523.
[6] Afflerbach, P. (1990). The influence of prior knowledge and text genre on readers' prediction strategies.Journal of Reading Behavior, 22(2), 131-148.
[7] Aristei S., Knoop C. A., Lubrich O., Nehrlich T., Enge A., Stark K.,. Abdel Rahman R. (2022). Affect as Anaesthetic: How emotional contexts modulate the processing of counterintuitive concepts.Language, Cognition and Neuroscience, 38(10), 1514-1530.
[8] Baetens K., der Cruyssen L. V., Achtziger A., Vandekerckhove M., & Van Overwalle F. (2011). N400 and LPP in spontaneous trait inferences. Brain Research, 1418, 83-92.
[9] Bar, M. (2007). The proactive brain: Using analogies and associations to generate predictions.Trends in Cognitive Sciences, 11(7), 280-289.
[10] Bornkessel-Schlesewsky I., Sharrad I., Howlett C. A., Alday P. M., Corcoran A. W., Bellan V., … Schlesewsky M. (2022). Rapid adaptation of predictive models during language comprehension: Aperiodic EEG slope, individual alpha frequency and idea density modulate individual differences in real-time model updating.Frontiers in Psychology, 13, 817516.
[11] Broderick M. P., Di Liberto G. M., Anderson A. J., Rofes A., & Lalor E. C. (2021). Dissociable electrophysiological measures of natural language processing reveal differences in speech comprehension strategy in healthy ageing.Scientific Reports, 11(1), 4963.
[12] Broeker L., Ewolds H., de Oliveira R. F., Künzell S., & Raab M. (2020). Additive effects of prior knowledge and predictive visual information in improving continuous tracking performance.Journal of Cognition, 3(1), 1-12.
[13] Canal, P., Garnham, A., & Oakhill, J. (2015). Beyond gender stereotypes in language comprehension: Self sex-role descriptions affect the brain's potentials associated with agreement processing. Frontiers in Psychology, 6, 1953.
[14] Davis M. H.,& Sohoglu, E. (2020). Three functions of prediction error for Bayesian inference in speech perception. The cognitive neurosciences (6th edn) (Gazzaniga, M. et al., eds), pp. 177-189, MIT Press.
[15] Diekman, A. B., & Eagly, A. H. (2000). Stereotypes as dynamic constructs: Women and men of the past, present, and future.Personality and Social Psychology Bulletin, 26(10), 1171-1188.
[16] Dikker, S., & Pylkkänen, L. (2013). Predicting language: MEG evidence for lexical preactivation.Brain & Language, 127(1), 55-64.
[17] Ding J., Wang L., & Yang Y. (2016). The dynamic influence of emotional words on sentence comprehension: An ERP study.Cognitive, Affective, & Behavioral Neuroscience, 16(3), 433-446.
[18] Ding J., Wang L., & Yang Y. (2019). The influence of emotional words on predictive processing during sentence comprehension.Language, Cognition and Neuroscience, 35(2), 151-162.
[19] Ehrlich, S. F., & Rayner, K. (1981). Contextual effects on word perception and eye movements during reading.Journal of Verbal Learning and Verbal Behavior, 20(6), 641-655.
[20] Elman, J. L. (2009). On the meaning of words and dinosaur bones: Lexical knowledge without a lexicon.Cognitive Science, 33(4), 547-582.
[21] Federmeier K. D., Wlotko E. W., De Ochoa-Dewald E., & Kutas M. (2007). Multiple effects of sentential constraint on word processing.Brain Research, 1146, 75-84.
[22] Frazier, L., & Fodor, J. D. (1978). The sausage machine: A new two-stage parsing model.Cognition, 6(4), 291-325.
[23] Frisson S., Harvey D. R., & Staub A. (2017). No prediction error cost in reading: Evidence from eye movements.Journal of Memory and Language, 95, 200-214.
[24] Friston K. J., Bastos A. M., Pinotsis D., & Litvak V. (2015). LFP and oscillations—What do they tell us?Current Opinion in Neurobiology, 31, 1-6.
[25] Gaschler R., Kemper M., Zhao F., Pumpe I., Ruderisch C. B., Röttger E., & Haider H. (2018). Differential effects of cue-based and sequence knowledge-based predictability on multitasking performance.Acta Psychologica, 191, 76-86.
[26] Goldstein A., Zada Z., Buchnik E., Schain M., Price A., Aubrey B.,. Hasson U. (2022). Shared computational principles for language processing in humans and deep language models.Nature Neuroscience, 25(3), 369-380.
[27] Grant A., Grey S., & van Hell, J. G. (2020). Male fashionistas and female football fans: Gender stereotypes affect neurophysiological correlates of semantic processing during speech comprehension. Journal of Neurolinguistics, 53, 100876.
[28] Gubelmann, R., & Handschuh, S. (2022). Context matters: A pragmatic study of PLMs’ negation understanding. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics(Volume 1: Long Papers), 4602-4621. Dublin, Ireland.
[29] Hegarty, P., & Pratto, F. (2001). The effects of social category norms and stereotypes on explanations for intergroup differences.Journal of Personality and Social Psychology, 80(5), 723-735.
[30] Hinojosa J. A., Moreno E. M., & Ferré P. (2019). Affective neurolinguistics: Towards a framework for reconciling language and emotion.Language, Cognition and Neuroscience, 35(7), 813-839.
[31] Hinojosa J. A., Moreno E. M., & Ferré P. (2020). Affective neurolinguistics: Towards a framework for reconciling language and emotion. Language, Cognition and Neuroscience, 35(7), 813-839.
[32] Hoeks J. C., Stowe L. A., & Doedens G. (2004). Seeing words in context: The interaction of lexical and sentence level information during reading.Cognitive brain research, 19(1), 59-73.
[33] Hopp, H., & Godfroid, A. (2023). Introduction: Second language acquisition and psycholinguistics. In A. Godfroid, & H. Hopp.(Eds.). The Routledge handbook of second language acquisition and psycholinguistics
[34] Huang Z., Feng C., & Qu Q. (2023). Predicting coarse-grained semantic features in language comprehension: Evidence from ERP representational similarity analysis and Chinese classifier.Cerebral Cortex, 33(13), 8312-8320.
[35] Hubbard, R. J., & Federmeier, K. D. (2021). Representational pattern similarity of electrical brain activity reveals rapid and specific prediction during language comprehension.Cerebral Cortex, 31(9), 4300-4313.
[36] Kahalon R., Shnabel N., & Becker J. C. (2020). The effects of exposure to positive gender stereotypes on women’s and men’s performance in counter-stereotypical tasks and pursuit of agentic and communal goals.Social Psychology, 51(1), 50-62.
[37] Kendeou, P., & Van Den Broek, P. (2007). The effects of prior knowledge and text structure on comprehension processes during reading of scientific texts.Memory & Cognition, 35(7), 1567-1577.
[38] Kriegeskorte N., Mur M., & Bandettini P. A. (2008). Representational similarity analysis-connecting the branches of systems neuroscience.Frontiers in Systems Neuroscience, 2, 4.
[39] Kutas, M., & Hillyard, S. A. (1984). Event-Related Brain Potentials (ERPs) Elicited by novel stimuli during sentence processing.Annals of the New York Academy of Sciences, 425, 236-241. DOI: 10.1111/j.1749-6632.1984.tb23540.x.
[40] Kwisthout, J., & Van Rooij, I. (2020). Computational resource demands of a predictive Bayesian brain.Computational Brain & Behavior, 3, 174-188.
[41] Lieder, F., & Griffiths, T. L. (2020). Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources. Behavioral and Brain Sciences 43, e1: 1-60.
[42] Luke, S. G., & Christianson, K. (2016). Limits on lexical prediction during reading.Cognitive Psychology, 88, 22-60.
[43] Lund T. C., Sidhu D. M., & Pexman P. M. (2019). Sensitivity to emotion information in children’s lexical processing. Cognition, 190, 61-71.
[44] Macdonald, S. E. (1994). Gorillas' (gorilla gorilla gorilla) spatial memory in a foraging task.Journal of Comparative Psychology, 108(2), 107-113.
[45] Malmir, A., & Taji, N. (2021). The interplay of action, context, and linguistic vs. non-linguistic resources in L2 pragmatic performance: The case of requests and refusals.Language Related Research, 12(3), 215-253.
[46] McNally, L. (2013). Semantics and pragmatics.WIREs Cognitive Science, 4(3), 285-297.
[47] Pfister R., Heinemann A., Kiesel A., Thomaschke R., & Janczyk M. (2012). Do endogenous and exogenous action control compete for perception?Journal of Experimental Psychology: Human Perception and Performance, 38(2), 279-284.
[48] Piai V., Anderson K. L., Lin J. J., Dewar C., Parvizi J., Dronkers N. F., & Knight R. T. (2016). Direct brain recordings reveal hippocampal rhythm underpinnings of language processing.Proceedings of the National Academy of Sciences, 113(40), 11366-11371.
[49] Regel S., Coulson S., & Gunter T. C. (2010). The communicative style of a speaker can affect language comprehension? ERP evidence from the comprehension of irony. Brain Research, 1311, 121-135.
[50] Reuter T., Emberson L., Romberg A., & Lew-Williams C. (2018). Individual differences in nonverbal prediction and vocabulary size in infancy.Cognition, 176, 215-219.
[51] Ryskin R., Levy R. P., & Fedorenko E. (2020). Do domain- general executive resources play a role in linguistic prediction? Re-evaluation of the evidence and a path forward.Neuropsychologia, 136, 107258.
[52] Ryskin, R., & Nieuwland, M. S. (2023). Prediction during language comprehension: What is next?Trends in Cognitive Sciences, 27(11), 1032-1052.
[53] Shain C., Blank I. A., van Schijndel M., Schuler W., & Fedorenko E. (2020). fMRI reveals language-specific predictive coding during naturalistic sentence comprehension.Neuropsychologia, 138, 107307.
[54] Smith R., Snow P., Serry T., & Hammond L. (2021). The role of background knowledge in reading comprehension: A critical review.Reading Psychology, 42(3), 214-240.
[55] Sohoglu E., Peelle J. E., Carlyon R. P., & Davis M. H. (2012). Predictive top-down integration of prior knowledge during speech perception.Journal of Neuroscience, 32(25), 8443-8453.
[56] Tanenhaus M. K.,& Trueswell, C. (1995). Sentence comprehension. In J. L. Miller, & P. D. Eimas (Eds.). Speech, language, and communication (pp. 217-262). San Diego, CA: Academic Press.
[57] Thomas, S. (1995). Predictive strategies in teaching reading comprehension.Jumal Pendidik dan Pendidikan, 14, 103-112.
[58] Ufer, C., & Blank, H. (2023). Multivariate analysis of brain activity patterns as a tool to understand predictive processes in speech perception.Language, Cognition and Neuroscience, 1-17.
[59] van Berkum, J. J. A. (2018). Language comprehension, emotion, and sociality: Aren’t we missing something? In S.-A. Rueschemeyer, & M. G. Gaskell (Eds.), The Oxford Handbook of psycholinguistics (pp. 644-670). Oxford University Press.
[60] van Berkum, J. J. A. (2019). Language comprehension and emotion: Where are the interfaces, and who cares? In G. I. de Zubicaray, & N. O. Schiller (Eds.), The Oxford handbook of neurolinguistics (pp. 736-766). Oxford University Press.
[61] van Berkum, J. J. A., van den Brink D., Tesink C. M. J. Y., Kos M., & Hagoort P. (2008). The neural integration of speaker and message.Journal of Cognitive Neuroscience, 20(4), 580-591.
[62] processing. Social Cognitive and Affective Neuroscience, 7(2), 173-183.
[63] van den Brink D., van Berkum, J. J. A., Bastiaansen M. C. M., Tesink C. M. J. Y., Kos M., Buitelaar J. K., & Hagoort P. (2012). Empathy matters: ERP evidence for inter-individual differences in social language Vandello, J. A., Hettinger, V. E., Bosson, J. K., & Siddiqi, J. (2013). When equal isn't really equal: The masculine dilemma of seeking work flexibility.Journal of Social Issues, 69(2), 303-321.
[64] Villiger, D. (2023). Stereotypes and self-fulfilling prophecies in the Bayesian brain.Inquiry, 1-25.
[65] Wang L., Bastiaansen M., & Yang Y. (2015). The influence of emotional salience on the integration of person names into context.Brain Research, 1609, 82-92.
[66] Wang L., Kuperberg G., & Jensen O. (2018). Specific lexico-semantic predictions are associated with unique spatial and temporal patterns of neural activity.Elife, 7, e39061.
[67] Wang L., Wlotko E., Alexander E., Schoot L., Kim M., Warnke L., & Kuperberg G. R. (2020). Neural evidence for the prediction of animacy features during language comprehension: Evidence from MEG and EEG representational similarity analysis.Journal of Neuroscience, 40(16), 3278-3291.
[68] Wei W., Huang Z., Feng C., & Qu Q. (2023). Predicting phonological information in language comprehension: Evidence from ERP representational similarity analysis and Chinese idioms.Cerebral Cortex, 33(15), 9367-9375.
[69] White K. R., Crites S. L., Taylor J. H., & Corral G. (2009). Wait, what? Assessing stereotype incongruities using the N400 ERP component.Social Cognitive and Affective Neuroscience, 4(2), 191-198.
[70] Wrobel, M. R. (2020). The impact of lexicon adaptation on the emotion mining from software engineering artifacts.IEEE Access, 8, 48742-48751.
[71] Yao Z., Xuan Y., & Zhu X. (2019). Effect of experience information on emotional word processing in alexithymia.Journal of Affective Disorders, 259, 251-258.
[72] Zhang Q., Ding J., Zhang Z., Yang X., & Yang Y. (2021). The effect of congruent emotional context in emotional word processing during discourse comprehension.Journal of Neurolinguistics, 59, 100989.
[73] Zhang Q., Mou C., Yang X., Yang Y., & Li L. (2022). EXPRESS: The effect of contextual arousal on the integration of emotional words during discourse comprehension.Quarterly Journal of Experimental Psychology, 76(4), 17470218221098838.