缺乏專業領域知識是民眾難以判讀錯假訊息的關鍵因素之一，而具備龐大知識庫的大型語言模型（LLMs）正展現出成為人類辨別錯假訊息之認知輔助工具的潛力。然而，依賴外部工具進行認知卸載（cognitive offloading）恐降低個體對資訊本身的記憶表現。據此，本研究認為LLMs可能僅為暫時性的輔具，無助於內在知識的建立；一旦脫離其協助，使用者的辨識能力會下降，且仍可能受到錯假訊息的持續影響效應（CIE；Continued Influence Effect）所干擾。本研究透過一項前導研究與兩項行為實驗，探討三個核心問題：（1）LLMs的錯假訊息辨識能力是否優於人類；（2）使用LLMs是否有助於長遠增進辨別能力，抑或僅具短期成效；（3）LLMs提供的更正訊息能否消除持續影響效應。前導研究建立並篩選出對一般民眾而言「真假難辨」的科普文本作為刺激材料。實驗一採用訊號偵測理論的曲線下面積（AUC）與Cohen’s d作為辨別力指標，比較人類參與者與GPT-4o在辨識前述材料上的能力差異，結果顯示GPT-4o顯著優於人類。實驗二則採三階段設計（閱讀階段、填充階段、測驗），評估LLMs輔助的即時效果、持續效果，以及對CIE的抑制效果。結果顯示，LLMs回應能大幅提升即時辨別力，但此效果在移除輔助後急遽衰退，且其更正訊息無法阻止CIE的出現，顯示無法修正個體的心智模型。本研究結果有助於釐清LLMs作為認知輔具的實際效用與潛在侷限。

Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., & Anadkat, S. (2023). Gpt-4 technical report. arXiv preprint arXiv:2303.08774.
Bai, L., Liu, X., & Su, J. (2023). ChatGPT: The cognitive effects on learning and memory. Brain‐X, 1(3). https://doi.org/10.1002/brx2.30
Bailey, P. E., Leon, T., Ebner, N. C., Moustafa, A. A., & Weidemann, G. (2023). A meta-analysis of the weight of advice in decision-making. Current Psychology, 42(28), 24516–24541.
Bower, G. H., & Morrow, D. G. (1990). Mental models in narrative comprehension. science, 247(4938), 44–48.
Brashier, N. M., Umanath, S., Cabeza, R., & Marsh, E. J. (2017). Competing cues: Older adults rely on knowledge in the face of fluency. Psychol Aging, 32(4), 331–337. https://doi.org/10.1037/pag0000156
Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021). To Trust or to Think. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1–21. https://doi.org/10.1145/3449287
Champion, H. L. O. (2001). The effects of causal knowledge on college students' memory for an observed event. North Carolina State University.
Chan, M.-p. S., Jones, C. R., Hall Jamieson, K., & Albarracín, D. (2017). Debunking: A meta-analysis of the psychological efficacy of messages countering misinformation. Psychological science, 28(11), 1531–1546.
Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., Ye, W., Zhang, Y., Chang, Y., Yu, P. S., Yang, Q., & Xie, X. (2024). A Survey on Evaluation of Large Language Models. ACM Transactions on Intelligent Systems and Technology, 15(3), 1–45. https://doi.org/10.1145/3641289
Choudhury, A., & Shamszare, H. (2023). Investigating the Impact of User Trust on the Adoption and Use of ChatGPT: Survey Analysis. J Med Internet Res, 25, e47184. https://doi.org/10.2196/47184
Cossio, M. (2025). A comprehensive taxonomy of hallucinations in large language models. arXiv preprint arXiv:2508.01781.
Das, J. K., Mondal, S., & Roy, C. K. (2025). Why Do Developers Engage with ChatGPT in Issue-Tracker? Investigating Usage and Reliance on ChatGPT-Generated Code 2025 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER),
Dergaa, I., Chamari, K., Zmijewski, P., & Ben Saad, H. (2023). From human writing to artificial intelligence generated text: examining the prospects and potential threats of ChatGPT in academic writing. Biol Sport, 40(2), 615–622. https://doi.org/10.5114/biolsport.2023.125623
Desender, K., Boldt, A., & Yeung, N. (2018). Subjective confidence predicts information seeking in decision making. Psychological science, 29(5), 761–778.
Duarte, F. (2025). Number of ChatGPT Users. https://explodingtopics.com/blog/chatgpt-users
Dunn, T. L., & Risko, E. F. (2016). Toward a Metacognitive Account of Cognitive Offloading. Cogn Sci, 40(5), 1080–1127. https://doi.org/10.1111/cogs.12273
Ecker, U. K., Lewandowsky, S., Cheung, C. S., & Maybery, M. T. (2015). He did it! She did it! No, she did not! Multiple causal explanations and the continued influence of misinformation. Journal of memory and language, 85, 101–115.
Ecker, U. K., Lewandowsky, S., Swire, B., & Chang, D. (2011). Correcting false information in memory: Manipulating the strength of misinformation encoding and its retraction. Psychonomic bulletin & review, 18(3), 570–578.
Ecker, U. K., Lewandowsky, S., & Tang, D. T. (2010). Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & cognition, 38(8), 1087–1100.
Elischberger, H. B. (2005). The effects of prior knowledge on children's memory and suggestibility. J Exp Child Psychol, 92(3), 247–275. https://doi.org/10.1016/j.jecp.2005.05.002
Elsayed, H. (2024). The impact of hallucinated information in large language models on student learning outcomes: A critical examination of misinformation risks in AI-assisted education. Northern Reviews on Algorithmic Research, Theoretical Computation, and Complexity, 9(8), 11–23.
Eskritt, M., & Ma, S. (2014). Intentional forgetting: Note-taking as a naturalistic example. Memory & cognition, 42(2), 237–246.
Ferguson, A. M., McLean, D., & Risko, E. F. (2015). Answers at your fingertips: Access to the Internet influences willingness to answer questions. Conscious Cogn, 37, 91–102. https://doi.org/10.1016/j.concog.2015.08.008
Firth, J., Torous, J., Stubbs, B., Firth, J. A., Steiner, G. Z., Smith, L., Alvarez-Jimenez, M., Gleeson, J., Vancampfort, D., Armitage, C. J., & Sarris, J. (2019). The "online brain": how the Internet may be changing our cognition. World Psychiatry, 18(2), 119–129. https://doi.org/10.1002/wps.20617
Gilbert, S. J. (2015a). Strategic offloading of delayed intentions into the external environment. Q J Exp Psychol (Hove), 68(5), 971–992. https://doi.org/10.1080/17470218.2014.972963
Gilbert, S. J. (2015b). Strategic use of reminders: Influence of both domain-general and task-specific metacognitive confidence, independent of objective memory ability. Conscious Cogn, 33, 245–260. https://doi.org/10.1016/j.concog.2015.01.006
Granados Samayoa, J. A., & Albarracín, D. (2025). Bypassing versus correcting misinformation: Efficacy and fundamental processes. Journal of Experimental Psychology: General, 154(1), 18.
Grinschgl, S., Meyerhoff, H. S., & Papenmeier, F. (2020). Interface and interaction design: How mobile touch devices foster cognitive offloading. Computers in Human Behavior, 108. https://doi.org/10.1016/j.chb.2020.106317
Grinschgl, S., & Neubauer, A. C. (2022). Supporting Cognition With Modern Technology: Distributed Cognition Today and in an AI-Enhanced Future. Front Artif Intell, 5, 908261. https://doi.org/10.3389/frai.2022.908261
Guo, Z., Lai, A., Thygesen, J. H., Farrington, J., Keen, T., & Li, K. (2024). Large Language Models for Mental Health Applications: Systematic Review. JMIR Ment Health, 11, e57400. https://doi.org/10.2196/57400
Hamilton, K. A., & Yao, M. Z. (2018). Cognitive Offloading and the Extended Digital Self. In Human-Computer Interaction. Theories, Methods, and Human Issues (pp. 257–268). https://doi.org/10.1007/978-3-319-91238-7_22
Heersmink, R., & Sutton, J. (2020). Cognition and the web: extended, transactive, or scaffolded? Erkenntnis, 85(1), 139–164.
Henkel, L. A. (2014). Point-and-shoot memories: the influence of taking photos on memory for a museum tour. Psychol Sci, 25(2), 396–402. https://doi.org/10.1177/0956797613504438
Hernandez-Fernandez, A., & Lewis, M. C. (2019). Brand authenticity leads to perceived value and brand trust. European Journal of Management and Business Economics, 28(3), 222–238.
Hoes, E., Altay, S., & Bermeo, J. (2023). Leveraging ChatGPT for efficient fact-checking. PsyArXiv. April, 3.
Hu, B., Sheng, Q., Cao, J., Shi, Y., Li, Y., Wang, D., & Qi, P. (2024). Bad actor, good advisor: Exploring the role of large language models in fake news detection. Proceedings of the AAAI conference on artificial intelligence,
Huang, T.-R., Cheng, Y.-L., & Rajaram, S. (2024). Unavoidable social contagion of false memory from robots to humans. American Psychologist, 79(2), 285.
Huang, Y., & Sun, L. (2023). FakeGPT: fake news generation, explanation and detection of large language models. arXiv preprint arXiv:2310.05046.
Huanga, T.-R., & Chenga, Y.-L. (2023). Unavoidable Social Contagion of False Memory From Robots to Humans.
Huff, M. J., Davis, S. D., & Meade, M. L. (2013). The effects of initial testing on false recall and false recognition in the social contagion of memory paradigm. Memory & cognition, 41(6), 820–831.
Johnson, H. M., & Seifert, C. M. (1994). Sources of the continued influence effect: When misinformation in memory affects later inferences. Journal of experimental psychology: Learning, memory, and cognition, 20(6), 1420.
Kang, W., & Malvaso, A. (2024). Frequent internet use is associated with better episodic memory performance. Sci Rep, 14(1), 24914. https://doi.org/10.1038/s41598-024-75788-1
Kim, D. Y., & Kim, H.-Y. (2021). Trust me, trust me not: A nuanced view of influencer marketing on social media. Journal of Business Research, 134, 223–232.
Kim, J., Kim, J. H., Kim, C., & Park, J. (2023). Decisions with ChatGPT: Reexamining choice overload in ChatGPT recommendations. Journal of Retailing and Consumer Services, 75. https://doi.org/10.1016/j.jretconser.2023.103494
Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X.-H., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task. arXiv preprint arXiv:2506.08872, 4.
Lewandowsky, S., Ecker, U. K., Seifert, C. M., Schwarz, N., & Cook, J. (2012). Misinformation and its correction: Continued influence and successful debiasing. Psychological science in the public interest, 13(3), 106–131.
Li, Z., Zhang, H., & Zhang, J. (2023). A revisit of fake news dataset with augmented fact-checking by chatgpt. arXiv preprint arXiv:2312.11870.
Meade, M. L., & Roediger, H. L. (2002). Explorations in the social contagion of memory. Memory & cognition, 30(7), 995–1009.
Merckelbach, H., Van Roermund, H., & Candel, I. (2007). Effects of collaborative recall: Denying true information is as powerful as suggesting misinformation. Psychology, Crime & Law, 13(6), 573–581.
Molleman, L., Tump, A. N., Gradassi, A., Herzog, S., Jayles, B., Kurvers, R. H., & van den Bos, W. (2020). Strategies for integrating disparate social information. Proceedings of the Royal Society B: Biological Sciences, 287(1939).
Nam, D., Macvean, A., Hellendoorn, V., Vasilescu, B., & Myers, B. (2024). Using an LLM to Help With Code Understanding Proceedings of the IEEE/ACM 46th International Conference on Software Engineering,
Naveed, H., Khan, A. U., Qiu, S., Saqib, M., Anwar, S., Usman, M., Akhtar, N., Barnes, N., & Mian, A. (2025). A Comprehensive Overview of Large Language Models. ACM Transactions on Intelligent Systems and Technology, 16(5), 1–72. https://doi.org/10.1145/3744746
Pan, Y., Pan, L., Chen, W., Nakov, P., Kan, M.-Y., & Wang, W. (2023). On the risk of misinformation pollution with large language models. Findings of the association for computational linguistics: EMNLP 2023,
Pennycook, G., & Rand, D. G. (2019). Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition, 188, 39–50.
Pescetelli, N., Hauperich, A.-K., & Yeung, N. (2021). Confidence, advice seeking and changes of mind in decision making. Cognition, 215, 104810.
Rader, C. A., Larrick, R. P., & Soll, J. B. (2017). Advice as a form of social influence: Informational motives and the consequences for accuracy. Social and Personality Psychology Compass, 11(8), e12329.
Rich, P. R., Donovan, A. M., & Rapp, D. N. (2023). Cause typicality and the continued influence effect. J Exp Psychol Appl, 29(2), 221–238. https://doi.org/10.1037/xap0000454
Rieh, S. Y., Collins-Thompson, K., Hansen, P., & Lee, H.-J. (2016). Towards searching as a learning process: A review of current perspectives and future directions. Journal of Information Science, 42(1), 19–34. https://doi.org/10.1177/0165551515615841
Risko, E. F., & Gilbert, S. J. (2016). Cognitive Offloading. Trends Cogn Sci, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002
Roediger, H. L., Meade, M. L., & Bergman, E. T. (2001). Social contagion of memory. Psychonomic bulletin & review, 8(2), 365–371.
Schultze, T., Stern, A., & Schulz‐Hardt, S. (2025). Learning Processes in the Judge–Advisor System: A Neglected Advantage of Advice Taking. Journal of Behavioral Decision Making, 38(3), e70029.
Seifert, C. M. (2002). The continued influence of misinformation in memory: What makes a correction effective? In Psychology of learning and motivation (Vol. 41, pp. 265–292). Elsevier.
Shahi, G. K., Dirkson, A., & Majchrzak, T. A. (2021). An exploratory study of COVID-19 misinformation on Twitter. Online Soc Netw Media, 22, 100104. https://doi.org/10.1016/j.osnem.2020.100104
Shao, A. (2025). New sources of inaccuracy? A conceptual framework for studying AI hallucinations. Harvard Kennedy School Misinformation Review.
Sparrow, B., Liu, J., & Wegner, D. M. (2011). Google effects on memory: Cognitive consequences of having information at our fingertips. science, 333(6043), 776–778.
Stojanov, A., Liu, Q., & Koh, J. H. L. (2024). University students’ self-reported reliance on ChatGPT for learning: A latent profile analysis. Computers and Education: Artificial Intelligence, 6. https://doi.org/10.1016/j.caeai.2024.100243
Street, C. N., & Masip, J. (2015). The source of the truth bias: Heuristic processing? Scandinavian Journal of Psychology, 56(3), 254–263.
Sun, Z., Yim, W.-W., Uzuner, O., Xia, F., & Yetisgen, M. (2025). A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination. arXiv preprint arXiv:2505.00008.
Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020). User Experiences of Social Support From Companion Chatbots in Everyday Contexts: Thematic Analysis. J Med Internet Res, 22(3), e16235. https://doi.org/10.2196/16235
Tandoc, E. C. (2021). Fake news. In The Routledge companion to media disinformation and populism (pp. 110–117). Routledge.
Tonmoy, S., Zaman, S., Jain, V., Rani, A., Rawte, V., Chadha, A., & Das, A. (2024). A comprehensive survey of hallucination mitigation techniques in large language models. arXiv preprint arXiv:2401.01313, 6.
Van Der Linden, S. (2022). Misinformation: susceptibility, spread, and interventions to immunize the public. Nature medicine, 28(3), 460–467.
Van Zoonen, W., Luoma-aho, V., & Lievonen, M. (2024). Trust but verify? Examining the role of trust in institutions in the spread of unverified information on social media. Computers in Human Behavior, 150, 107992.
Wang, J., Zhu, Z., Liu, C., Li, R., & Wu, X. (2024). LLM-Enhanced multimodal detection of fake news. PloS one, 19(10), e0312240.
Ward, A. F. (2013). Supernormal: How the Internet Is Changing Our Memories and Our Minds. Psychological Inquiry, 24(4), 341–348. https://doi.org/10.1080/1047840x.2013.850148
Węcel, K., Sawiński, M., Stróżyna, M., Lewoniewski, W., Księżniak, E., Stolarski, P., & Abramowicz, W. (2023). Artificial intelligence—friend or foe in fake news campaigns. Economics and Business Review, 9(2). https://doi.org/10.18559/ebr.2023.2.736
Wierzbicki, A., Shupta, A., & Barmak, O. (2024). Synthesis of model features for fake news detection using large language models. Computational Linguistics Workshop at CoLInS,
Yaniv, I., & Choshen‐Hillel, S. (2012). Exploiting the wisdom of others to make better decisions: Suspending judgment reduces egocentrism and increases accuracy. Journal of Behavioral Decision Making, 25(5), 427–434.
Yaniv, I., & Milyavsky, M. (2007). Using advice from multiple sources to revise and improve judgments. Organizational Behavior and Human Decision Processes, 103(1), 104–120.
Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review. Smart Learning Environments, 11(1). https://doi.org/10.1186/s40561-024-00316-7
Zhang, Y., Li, Y., Cui, L., Cai, D., Liu, L., Fu, T., Huang, X., Zhao, E., Zhang, Y., & Chen, Y. (2025). ? Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models. Computational Linguistics, 51(4), 1373–1418.
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., & Dong, Z. (2023). A survey of large language models. arXiv preprint arXiv:2303.18223, 1(2).
Zhu, G., Shou, Y., Smithson, M., & Platow, M. J. (2025). Conflictive Uncertainty: A Framework for Understanding the Aversion to Conflicting Information in Social Contexts. Personality and Social Psychology Bulletin, 01461672251386102.
Zrnec, A., Poženel, M., & Lavbič, D. (2022). Users’ ability to perceive misinformation: An information quality assessment approach. Information Processing & Management, 59(1), 102739.
江晉諺. (2023). 更正訊息的因果關係強度與工作記憶廣度對假訊息持續影響效果之影響 國立政治大學]. 臺灣博碩士論文知識加值系統. 台北市. https://hdl.handle.net/11296/xhq4va