无效预期诱发知觉偏差却提升元认知判断准确性

doi:10.3724/SP.J.1041.2026.0336

摘要/Abstract

摘要：

人类生活在一个充满不确定性的世界中, 为了能够及时响应和处理周围信息的变化, 大脑会持续对周围环境产生预期。这些预期能够引导我们的感知和行动, 然而, 不成熟的预测也可能导致决策偏差。本研究通过5个实验探讨无效预期如何影响知觉判断及其后的信心估计。实验1、实验2和实验3分别探究了无效预期在高、中、低三种不同任务难度下对知觉判断和信心估计的影响。实验4引入无预测的基线条件以提供对照, 实验5通过分离预测和知觉判断的按键设置, 排除动作效应的干扰。4个实验均发现(实验3除外), 被试倾向于将知觉刺激判断为与预测一致, 这表明无效预期引起了知觉偏差。在元认知效率(即信心估计的准确性)上, 5个实验一致发现, 被试在匹配试次(预测刺激与知觉判断一致)中的表现显著优于不匹配试次, 表现出更高的元认知水平。综上, 无效预期虽然影响了初级的知觉判断导致了知觉偏差, 但却促进了更高级的元认知心理加工。

关键词: 无效预期, 知觉判断, 自信心, 元认知

Abstract:

Humans live in a world filled with uncertainty. To adapt to and process changes in their surroundings, the brain continuously generates predictions about the environment. Predictions can guide perception and action. However, given the ambiguous and highly unpredictable nature of the external world, individuals often lack sufficient information to form accurate predictions. This study investigates how non-informative prediction, specifically invalid prediction, shapes perceptual judgments and subsequent confidence estimates through five experiments.

In a face/house judgment task, participants were required to determine whether a presented blurry image depicted a face or a house, and then rate their confidence in that perceptual judgment. Before the image was displayed, participants predicted the category of the upcoming image (face or house). Experiment 1 (n = 38, perceptual accuracy ≈ 64%), Experiment 2 (n = 44, perceptual accuracy ≈ 75%) and Experiment 3 (n = 47, perceptual accuracy ≈ 85%) systematically manipulated task difficulty to examine how non-informative predictions modulate perceptual judgments and confidence ratings. Experiment 4 (n = 41) introduced a condition without predictions to provide a baseline for evaluating the predictive effects. In Experiment 5 (n = 40), the response keys for predictions and perceptual judgments were separated to eliminate potential action-related effects.

The results revealed that: (1) non-informative predictions induced perceptual biases under high and moderate task difficulty conditions (except in Experiment 3 with low difficulty), systematically biasing individuals to align their perceptual judgments with prior predictions; (2) non-informative predictions affected subjective confidence, as participants reported higher confidence in trials where their perceptions aligned with their predictions compared to trials where they did not; and (3) non-informative predictions enhanced the accuracy of metacognitive judgments, with individuals exhibiting greater metacognitive efficiency when their perceptual judgments matched predictions compared to mismatched trials.

In summary, while non-informative prediction induces perceptual biases, it enhances metacognitive judgments. These findings highlight the distinct effects of non-informative predictions on perceptual judgments and metacognitive functions. The study contributes to a deeper understanding of the interplay among expectations, perception, and metacognition. Finally, it offers practical implications for optimizing cognitive decision-making and improving metacognitive accuracy.

Key words: non-informative prediction, perceptual judgment, confidence, metacognition

中图分类号:

B842

罗铁勇, 刘翠珍. (2026). 无效预期诱发知觉偏差却提升元认知判断准确性. 心理学报, 58(2), 336-349.

LUO Tieyong, LIU Cuizhen. (2026). Invalid expectations induce perceptual bias yet enhance the accuracy of metacognitive judgments. Acta Psychologica Sinica, 58(2), 336-349.

图/表 9

图1 实验流程图

图2 实验1中被试在匹配试次和不匹配试次中的行为表现对比。(a)匹配试次占比显著高于不匹配试次占比。(b)被试在不匹配试次和匹配试次中的正确率没有显著差异。(c)被试在匹配试次上的信心水平显著高于不匹配试次。(d)被试在匹配试次上的元认知效率显著高于不匹配试次。每对点代表每个被试的值, 箱型中的上横杠线、中线、下横杠线分别代表上四分位数、中位数和下四分位数。彩图见电子版, 下同。

图3 实验2中被试在匹配试次和不匹配试次中的行为表现对比。(a)匹配试次占比显著高于不匹配试次占比。(b)被试在不匹配试次和匹配试次中的正确率没有显著差异。(c)被试在匹配试次上的信心水平显著高于不匹配试次。(d)被试在匹配试次上的元认知效率高于不匹配试次。

图4 实验3中被试在匹配试次和不匹配试次中的行为表现对比。(a)匹配试次占比与不匹配试次占比无显著差异。(b)被试在匹配试次和不匹配试次中的正确率没有显著差异。(c)被试在匹配试次上的信心水平显著高于不匹配试次。(d)被试在匹配试次上的元认知效率显著高于不匹配试次。

图5 实验4中被试在匹配试次、不匹配试次以及无预测试次中的行为表现对比。(a)匹配试次占比显著高于不匹配试次占比。(b)被试在不匹配试次中的正确率显著高于匹配试次和无预测试次, 匹配试次的正确率与无预测试次没有显著差异。(c)被试在匹配试次中的信心水平显著高于不匹配试次, 但两者都与无预测试次的信心水平没有显著差异。(d)被试在匹配试次中的元认知效率显著高于不匹配试次, 但与无预测试次没有显著差异, 不匹配试次的元认知效率显著低于无预测试次。

图6 实验5中被试在匹配试次、不匹配试次以及无预测试次中的行为表现对比。(a)匹配试次占比显著大于不匹配试次占比。(b)被试在不匹配试次中的正确率显著高于匹配试次, 但两者都与无预测试次的正确率没有显著差异。(c)被试在匹配试次中的信心水平显著高于不匹配试次, 但与无预测试次的信心水平没有显著差异, 不匹配试次中的信心水平显著低于无预测试次。(d)被试在匹配试次中的元认知效率显著高于不匹配试次和无预测试次, 不匹配试次中的元认知效率显著低于无预测试次。

表S1 实验1~5中被试在不同block中的平均正确率和标准差

	实验1	实验2	实验3	实验4	实验5
Block1	0.63 ± 0.09	0.74 ± 0.09	0.82 ± 0.10	0.73 ± 0.11	0.78 ± 0.11
Block2	0.63 ±0.09	0.75 ± 0.08	0.85 ± 0.07	0.74 ± 0.08	0.77 ± 0.09
Block3	0.66 ± 0.08	0.76± 0.07	0.87 ± 0.07	0.75 ± 0.10	0.76 ± 0.08
Block4	0.63 ± 0.09	0.73 ± 0.08	0.88 ± 0.05	0.76 ± 0.08	0.77 ± 0.07
Block5				0.76 ± 0.10	0.75 ± 0.08
Block6				0.76 ± 0.08	0.75 ± 0.09
Block7				0.75 ± 0.09	0.78 ± 0.09
Block8				0.76 ± 0.09	0.77 ± 0.07

图S1 五个实验中, 被试在不同block中平均正确率的变化趋势。(a)实验1高任务难度下, 被试在不同block中的平均正确率; (b)实验2中等任务难度下, 被试在不同block中的平均正确率; (c)实验3低任务难度下, 被试在不同block中的平均正确率; (d)实验4中被试在不同block中的平均正确率; (e)实验5中被试在不同block中的平均正确率。每个圆点代表全部被试在该block中的平均正确率, 阴影部分表示95%置信区间。*表示p < 0.05, **表示p < 0.01, ***表示p < 0.001。

表S2 实验1~5中, 被试的正确率、信心水平和元认知效率的相关

	正确率−信心水平	正确率−元认知效率	信心水平−元认知效率
实验1	−0.06	−0.13	−0.07
实验2	−0.02	−0.08	−0.10
实验3	0.42**	− 0.19	0.21
实验4	0.05	−0.50***	0.35*
实验5	0.44**	−0.27	0.10

参考文献 50

[1]	Abaszadeh H., Amani M., & Pordanjani T. R. (2024). The relationship between motivational-cognitive variables, academic self-efficacy of students mediated by parent’s educational expectations, parent-child interaction, and teacher-student interaction. Learning and Motivation, 86, 101983. doi: 10.1016/j.lmot.2024.101983 URL
[2]	Balsdon T., Mamassian P., & Wyart V. (2021). Separable neural signatures of confidence during perceptual decisions. eLife, 10, 68491.
[3]	Botvinick M. M., Braver T. S., Barch D. M., Carter C. S., & Cohen J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108(3), 624-652. doi: 10.1037/0033-295x.108.3.624 pmid: 11488380
[4]	Caziot B., & Mamassian P. (2021). Perceptual confidence judgments reflect self-consistency. Journal of Vision, 21(12), 8.
[5]	Charles L., King J. R., & Dehaene S. (2014). Decoding the dynamics of action, intention, and error detection for conscious and subliminal stimuli. Journal of Neuroscience, 34(4), 1158-1170. doi: 10.1523/JNEUROSCI.2465-13.2014 pmid: 24453309
[6]	Clark A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. The Behavioral and Brain Sciences, 36(3), 181-204. doi: 10.1017/S0140525X12000477 URL
[7]	Constant M., Pereira M., Faivre N., & Filevich E. (2023). Prior information differentially affects discrimination decisions and subjective confidence reports. Nature Communications, 14(1), 5473.
[8]	Croson R., & Sundali J. (2005). The gambler's fallacy and the hot hand: Empirical data from casinos. Journal of Risk and Uncertainty, 30(3), 195-209. doi: 10.1007/s11166-005-1153-2 URL
[9]	Faul F., Erdfelder E., Lang A.-G., & Buchner A. (2007). GPower 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods*, 39(2), 175-191. doi: 10.3758/bf03193146 pmid: 17695343
[10]	Festinger L. (1957). A theory of cognitive dissonance. Stanford University Press.
[11]	Flavell J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34(10), 906-911. doi: 10.1037/0003-066X.34.10.906 URL
[12]	Fleming S. M., & Dolan R. J. (2012). The neural basis of metacognitive ability. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 367( 1594), 1338-1349.
[13]	Fleming S. M., & Lau H. C. (2014). How to measure metacognition. Frontiers in human neuroscience, 8, 443. doi: 10.3389/fnhum.2014.00443 pmid: 25076880
[14]	Fleming S. M., van der Putten E. J., & Daw N. D. (2018). Neural mediators of changes of mind about perceptual decisions. Nature Neuroscience, 21, 617-624. doi: 10.1038/s41593-018-0104-6 pmid: 29531361
[15]	Fleming S. M., Whiteley L., Hulme O. J., Sahani M., & Dolan R. J. (2010). Effects of category-specific costs on neural systems for perceptual decision-making. Journal of Neurophysiology, 103(6), 3238-3247. doi: 10.1152/jn.01084.2009 pmid: 20357071
[16]	Hampton A. N., Adolphs R., Tyszka J. M., & O'Doherty J. P. (2007). Contributions of the amygdala to reward expectancy and choice signals in human prefrontal cortex. Neuron, 55(4), 545-555. doi: 10.1016/j.neuron.2007.07.022 pmid: 17698008
[17]	Hertz U., Blakemore C., & Frith C. D. (2020). I haven’t a clue! Expectations based on repetitions and hints facilitate perceptual experience of ambiguous images. Journal of Experimental Psychology: Human Perception and Performance, 46(8), 831-846. doi: 10.1037/xhp0000749 pmid: 32324029
[18]	Hu X., Zheng J., Su N., Fan T., Yang C., Yin Y., … Luo L. (2021). A Bayesian inference model for metamemory. Psychological Review, 128(5), 824-855. doi: 10.1037/rev0000270 pmid: 34043396
[19]	Jazayeri M., & Movshon J. A. (2007). A new perceptual illusion reveals mechanisms of sensory decoding. Nature, 446, 912. doi: 10.1038/nature05739
[20]	Jiang Y. J., Ma X. X., Jiang Y. T., Ren J. M., & Long Y. T. (2023). The effect of after-encoding rewards on agenda- based learning: The role of reward expectation and reward outcome. Acta Psychologica Sinica, 55(4), 542-555. doi: 10.3724/SP.J.1041.2023.00542 URL
	[姜英杰, 马潇潇, 姜元涛, 任吉梅, 龙翼婷. (2023). 编码后奖赏影响基于议程的学习:奖赏预期和结果的作用. 心理学报, 55(4), 542-555.] doi: 10.3724/SP.J.1041.2023.00542
[21]	Keller G. B., & Mrsic-Flogel T. D. (2018). Predictive processing: A canonical cortical computation. Neuron, 100(2), 424-435. doi: S0896-6273(18)30857-2 pmid: 30359606
[22]	Kepecs A., Uchida N., Zariwala H. A., & Mainen Z. F. (2008). Neural correlates, computation and behavioural impact of decision confidence. Nature, 455(7210), 227-231. doi: 10.1038/nature07200
[23]	Kiani R., & Shadlen M. N. (2009). Representation of confidence associated with a decision by neurons in the parietal cortex. Science, 324(5928), 759-764. doi: 10.1126/science.1169405 pmid: 19423820
[24]	Kok P., Failing M. F., & de Lange F. P. (2014). Prior expectations evoke stimulus templates in the primary visual cortex. Journal of Cognitive Neuroscience, 26(7), 1546-1554. doi: 10.1162/jocn_a_00562 pmid: 24392894
[25]	Leek M. R. (2001). Adaptive procedures in psychophysical research. Perception & Psychophysics, 63(8), 1279-1292. doi: 10.3758/BF03194543 URL
[26]	Liu C., & Yu R. (2024). Neural mechanisms underpinning metacognitive shifts driven by non-informative predictions. NeuroImage, 296, 120670. doi: 10.1016/j.neuroimage.2024.120670 URL
[27]	Lundqvist D., Flykt A., & Öhman A. (1998). Karolinska Directed Emotional Faces (KDEF) [Database record]. APA PsycTests.
[28]	Luu L., & Stocker A. A. (2018). Post-decision biases reveal a self-consistency principle in perceptual inference. eLife, 7, 33334.
[29]	Macmillan N. A., & Creelman C. D. (2005). Detection theory: A user's guide (2nd ed.). Lawrence Erlbaum Associates Publishers.
[30]	Maniscalco B., & Lau H. (2012). A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings. Consciousness and Cognition, 21(1), 422-430. doi: 10.1016/j.concog.2011.09.021 pmid: 22071269
[31]	Maniscalco B., Peters M. A., & Lau H. (2016). Heuristic use of perceptual evidence leads to dissociation between performance and metacognitive sensitivity. Attention, Perception & Psychophysics, 78( 3), 923-937.
[32]	Masson M. E. J. (2011). A tutorial on a practical Bayesian alternative to null-hypothesis significance testing. Behavior Research Methods, 43(3), 679-690. doi: 10.3758/s13428-010-0049-5 pmid: 21302025
[33]	Nelson T. O., & Narens L. (1990). Metamemory: A theoretical framework and new findings. Psychology of Learning and Motivation, 26, 125-173.
[34]	Oskarsson A. T., Van Boven L., McClelland G. H., & Hastie R. (2009). What's next? Judging sequences of binary events. Psychological Bulletin, 135(2), 262-285. doi: 10.1037/a0014821 pmid: 19254080
[35]	Peters M. A. K., Thesen T., Ko Y. D., Maniscalco B., Carlson C., Davidson M., … Lau H. (2017). Perceptual confidence neglects decision-incongruent evidence in the brain. Nature Human Behavior, 1, 139.
[36]	Posner M. I. (1980). Orienting of attention. Quarterly Journal of Experimental Psychology, 32(1), 3-25. doi: 10.1080/00335558008248231 pmid: 7367577
[37]	Rahnev D., Lau H., & de Lange F. P. (2011). Prior expectation modulates the interaction between sensory and prefrontal regions in the human brain. Journal of Neuroscience, 31(29), 10741-10748. doi: 10.1523/JNEUROSCI.1478-11.2011 pmid: 21775617
[38]	Sherman M. T., Seth A. K., Barrett A. B., & Kanai R. (2015). Prior expectations facilitate metacognition for perceptual decision. Consciousness and Cognition, 35, 53-65. doi: 10.1016/j.concog.2015.04.015 pmid: 25973773
[39]	Sherman M. T., Seth A. K., & Kanai R. (2016). Predictions shape confidence in right inferior frontal gyrus. Journal of Neuroscience, 36(40), 10323-10336. pmid: 27707969
[40]	Stein T., & Peelen M. V. (2015). Content-specific expectations enhance stimulus detectability by increasing perceptual sensitivity. Journal of Experimental Psychology: General, 144(6), 1089-1104. doi: 10.1037/xge0000109 URL
[41]	Summerfield C., & de Lange F. P. (2014). Expectation in perceptual decision making: Neural and computational mechanisms. Nature Reviews. Neuroscience, 15(11), 745-756.
[42]	Sun F., Ni Y., Lu W., Su J., Wang S., & Wan X. (2025). Confidence bias prescribes the neurocomputational mechanism of decision-making. Cell Reports, 44(5), 115563. Advance online publication.
[43]	Talluri B. C., Urai A. E., Tsetsos K., Usher M., & Donner T. H. (2018). Confirmation bias through selective overweighting of choice-consistent evidence. Current Biology, 28(19), 3128-3135. doi: S0960-9822(18)30982-5 pmid: 30220502
[44]	Thomas E. R., Rittershofer K., & Press C. (2023). Updating perceptual expectations as certainty diminishes. Cognition, 232, 105356.
[45]	Urai A. E., de Gee J. W., Tsetsos K., & Donner T. H. (2019). Choice history biases subsequent evidence accumulation. eLife, 8, 46331.
[46]	Vlassova A., Donkin C., & Pearson J. (2014). Unconscious information changes decision accuracy but not confidence. Proceedings of the National Academy of Sciences of the United States of America, 111(45), 16214-16218. doi: 10.1073/pnas.1403619111 pmid: 25349435
[47]	von Neumann J., & Morgenstern O. (1944). Theory of games and economic behavior. Princeton University Press.
[48]	Walkey F. H., McClure J., Meyer L. H., & Weir K. F. (2013). Low expectations equal no expectations: Aspirations, motivation, and achievement in secondary school. Contemporary Educational Psychology, 38(4), 306-315. doi: 10.1016/j.cedpsych.2013.06.004 URL
[49]	Yanagisawa H. (2016). A computational model of perceptual expectation effect based on neural coding principles. Journal of Sensory Studies, 31(5), 430-439. doi: 10.1111/joss.2016.31.issue-5 URL
[50]	Yon D., de Lange F. P., & Press C. (2019). The predictive brain as a stubborn scientist. Trends in Cognitive Sciences, 23(1), 6-8.