{"title":"All Illusions Are Wrong, but Some Are Useful.","authors":"Angelika M Stefan, Angelo Pirrone, Nathan J Evans","doi":"10.1007/s42113-026-00323-6","DOIUrl":"10.1007/s42113-026-00323-6","url":null,"abstract":"<p><p>While we agree with the general argument of Shiffrin and colleagues (2026) that scientists fall prey to the illusion of knowledge, we disagree with their argument that illusions present a fundamental problem for science. We argue that theory cannot exist without illusion, and therefore, our goal should not be to avoid illusions, but to create the best illusions possible. Specifically, we argue that many known illusions provide utility for both science and real-world applications, and that attempting to dispel illusions is a fundamental aspect of science. Illusions only become problematic when scientists become complacent and no longer seek to improve understanding, which may be motivated by external incentives, a lack of education, or the perceived high practical utility of the illusion. We conclude that researchers should continue to aim to dispel illusions through progressive research programs, utilising the scientific method to understand what theories can, cannot, and should not predict.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"9 3","pages":"408-410"},"PeriodicalIF":0.0,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13593960/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Combatting Meta-Ignorance in Science Through Dialogs with Diverse Experts.","authors":"Michael Smithson","doi":"10.1007/s42113-026-00316-5","DOIUrl":"10.1007/s42113-026-00316-5","url":null,"abstract":"<p><p>In their provocative paper, Shiffrin, Stigler, and Keil (SSK) declare that scientists have illusions of understanding the phenomena they study \"more deeply\" than they really do. In this comment I grant their claim a degree of truth and focus on the nature of illusions of understanding. These are a form of meta-ignorance (not knowing what you don't know). This comment briefly describes the psychology behind meta-ignorance and how it may be combatted. A crucial property of meta-ignorance is that it is impossible for us to attribute meta-ignorance to our here-and-now selves. It can only be detected by others, but probably not by those who share our views and expertise. Instead, it will most likely be detected by those who think differently from us. Thus, ridding ourselves of meta-ignorance about our scientific understanding requires dialogs with scholars whose backgrounds, expertise, and/or orientations differ from our own.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"9 3","pages":"384-385"},"PeriodicalIF":0.0,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13594014/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kevin O'Neill, Sze-Long Sharon Chung, Adam J L Harris, David A Lagnado, Henrik Singmann
{"title":"Whose Illusion? Causal Inference is Fundamental to Data Analysis and Cognition.","authors":"Kevin O'Neill, Sze-Long Sharon Chung, Adam J L Harris, David A Lagnado, Henrik Singmann","doi":"10.1007/s42113-026-00319-2","DOIUrl":"10.1007/s42113-026-00319-2","url":null,"abstract":"<p><p>The authors (SSK) make wholesale claims that scientists and lay people suffer not only from misunderstandings, but more dangerous illusions of understanding. However, their case is only supported by intuitive examples rather than solid arguments or data. We argue that SSK misrepresent contemporary scientific thinking, ignore recent advances in methods for causal inference, and exaggerate people's biases in everyday causal reasoning. Accordingly, we point out that these advances paint a much more optimistic view of scientific understanding.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"9 3","pages":"405-407"},"PeriodicalIF":0.0,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13594011/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149027071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dominik Bachmann, Leendert van Maanen, Gijs A Holleman, Christian P Janssen
{"title":"Don't forget the world outside the lab - a comment on \"Illusions of Understanding in the Sciences\".","authors":"Dominik Bachmann, Leendert van Maanen, Gijs A Holleman, Christian P Janssen","doi":"10.1007/s42113-026-00327-2","DOIUrl":"10.1007/s42113-026-00327-2","url":null,"abstract":"<p><p>The article \"Illusions of Understanding in the Sciences\" by Shiffrin et al. (2026) describes ways in which scientists can mislead themselves and others about the extent to which they understand the (causal underpinnings of) phenomena they research. In this reply, we highlight how such \"illusions of understanding\" can arise from researchers overestimating the extent to which their results generalize beyond curated lab conditions. Since experimental designs are informed by researchers' theoretical lenses and methodological preferences, we argue that interdisciplinary work (which exposes one to different lenses) on more applied problems (which exposes one to less idealized conditions) can reveal such illusions.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"9 3","pages":"378-380"},"PeriodicalIF":0.0,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13593964/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Response to Commentaries to \"Illusions of Understanding in the Sciences\".","authors":"Richard Shiffrin, Stephen Stigler, Frank Keil","doi":"10.1007/s42113-026-00324-5","DOIUrl":"10.1007/s42113-026-00324-5","url":null,"abstract":"<p><p>The commentaries to the essay \"Illusions of Understanding in the Sciences\" added important perspectives, citations to relevant research, and in some cases alternative views. The number of commentaries (32) make it impossible to respond to them individually. In this response we discuss the major themes that appeared in the commentaries.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"9 3","pages":"454-459"},"PeriodicalIF":0.0,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13594032/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149027063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Tukey's Warning and Pascal's Wager.","authors":"Kenneth J Malmberg, Amy H Criss","doi":"10.1007/s42113-026-00322-7","DOIUrl":"10.1007/s42113-026-00322-7","url":null,"abstract":"<p><p>We comment on an article by Shiffrin, Stigler, and Keil (2026) relating illusory beliefs of understanding to hubris, discussing the dangers associated with accepting a model as a lawful fact and the benefits of questioning one's models.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"9 3","pages":"414-415"},"PeriodicalIF":0.0,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13594022/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149027066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Scientific Misunderstanding: Notes for the Bedeviled.","authors":"David Kellen, Mikhail S Spektor","doi":"10.1007/s42113-026-00337-0","DOIUrl":"10.1007/s42113-026-00337-0","url":null,"abstract":"<p><p>In this paper, we critically examine the essay by Shiffrin, Stigler, and Keil (2026) on scientific understanding. We argue that, despite its uncontroversial moral message, the paper largely fails to articulate a compelling thesis. The central notion of an \"illusion of understanding\" is left insufficiently specified and supported by ill-suited examples. The analysis further rests on a narrow conception of explanation that largely equates understanding with causal claims. When discussing linear regression and related examples (e.g., Simpson's Paradox), the essay also fails to clarify what is actually at stake. Its treatment of regression to the mean, in particular, is misconceived. We conclude by suggesting that discussions of scientific understanding would benefit from a model-hierarchical perspective.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at https://doi.org/10.1007/s42113-026-00337-0.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"9 3","pages":"401-404"},"PeriodicalIF":0.0,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13594071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149027103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Casimir J H Ludwig, Konstantinos Tsetsos, Jeffrey S Bowers
{"title":"Collective Illusions of Understanding in NeuroAI.","authors":"Casimir J H Ludwig, Konstantinos Tsetsos, Jeffrey S Bowers","doi":"10.1007/s42113-026-00321-8","DOIUrl":"10.1007/s42113-026-00321-8","url":null,"abstract":"<p><p>Large language models and other deep neural networks often mimic human behavior well, which has led to strong claims about human and machine intelligence alignment. We argue that such claims reflect an illusory understanding of human mind and brain: Authors routinely equate predictive success with understanding of causal mechanisms-a form of confusing correlation with causation. The illusion is compounded by neglect of the importance of falsification through causal interventions. We speculate on some of the cultural factors that fuel collective illusions of understanding in neuroAI.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"9 3","pages":"391-392"},"PeriodicalIF":0.0,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13594024/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Uncovering Illusions of Understanding: A Falsificationist Perspective.","authors":"Daniel W Heck","doi":"10.1007/s42113-026-00318-3","DOIUrl":"10.1007/s42113-026-00318-3","url":null,"abstract":"<p><p>In a complex world, scientific explanations are always incomplete. Therefore, can we say that scientists generally suffer from \"illusions of understanding?\" I argue that embracing a falsificationist perspective makes scientists aware of the provisional nature of all explanations and highlights the limitations of current scientific knowledge. Tools such as preregistration and formal modeling are well suited for uncovering illusions of understanding.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"9 3","pages":"411-413"},"PeriodicalIF":0.0,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13594063/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149027114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tianye Ma, Jessica Golding, Inik Kim, Shuchen Liu, Sahereh Varastegan, Li Yang, Weiwei Zhang
{"title":"Beyond the Curve: Visual Working Memory and the Illusion of Understanding.","authors":"Tianye Ma, Jessica Golding, Inik Kim, Shuchen Liu, Sahereh Varastegan, Li Yang, Weiwei Zhang","doi":"10.1007/s42113-026-00320-9","DOIUrl":"10.1007/s42113-026-00320-9","url":null,"abstract":"<p><p>Good fits of models can produce an \"illusion of understanding\" when the models lack grounded theoretical interpretations. This is characterized by the recent claims surrounding the good fit of the Target Confusability Competition (TCC) model of visual working memory (VWM). Despite that TCC appears to assume no capacity limits in VWM, we recently found that TCC can outperform capacity-limit models even when capacity limits are the ground truth, reflecting TCC's strong flexibility. This flexibility renders the model unfalsifiable in practice and obscures its mechanistic interpretation. We further argue that the distinction between memory failure and degraded memory precision, captured by the capacity-limit models (e.g., the Mixture model), are supported by converging empirical evidence, highlighting the necessity of empirically testable model predictions for achieving genuine understandings of mechanisms.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"9 3","pages":"381-383"},"PeriodicalIF":0.0,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13593984/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}