{ "version": 1, "updatedAt": "2026-08-18", "reviews": [ { "slug": "attribution-bias-moral-luck", "evidenceStatus": "established moral-judgment phenomenon with multiple contributors", "reviewedAt": "2026-08-18", "qualification": "Resultant moral luck describes cases where judgments of blame, punishment, or moral evaluation differ because otherwise similar actions lead to different outcomes partly outside the agent's control. Outcome information does affect moral judgment in experiments, but the effect should not be reduced to 'people ignore intent.' Mental states, causal responsibility, belief justification, negligence, and the kind of moral judgment being asked about all matter. Some studies find that false or unjustified beliefs explain more of classic moral-luck asymmetries than the bad outcome itself, while still detecting an independent outcome effect.", "mechanism": "Moral judgment integrates information about an agent's intentions, beliefs, causal role, and resulting harm. Harmful outcomes can increase blame or punishment directly and can also change how observers interpret the agent's prior beliefs, recklessness, or justification. This makes moral luck related to outcome bias and hindsight bias without being identical to either: the target judgment is moral responsibility, blame, or punishment rather than generic decision quality or predictability.", "practical": "When reviewing an action after a good or bad outcome, separate at least four questions: what the agent intended, what they reasonably believed at the time, what risks they controlled, and what outcome actually occurred. If the goal is to evaluate the quality or morality of the ex-ante choice, first compare cases that differ only in outcome. Then use the outcome for learning about risk and consequences without silently treating luck as evidence that the original mental state was better or worse than it was.", "sources": [ { "title": "Crime and punishment: distinguishing the roles of causal and intentional analyses in moral judgment", "year": 2008, "type": "behavioral experiments", "doi": "10.1016/j.cognition.2008.03.006", "url": "https://pubmed.ncbi.nlm.nih.gov/18439575/", "sourceId": "src-0539c30082b039b2" }, { "title": "Investigating the Neural and Cognitive Basis of Moral Luck: It's Not What You Do but What You Know", "year": 2010, "type": "behavioral experiment + neuroimaging", "doi": "10.1007/s13164-010-0027-y", "url": "https://pubmed.ncbi.nlm.nih.gov/22558062/", "sourceId": "src-295bb6762d6f7a9f" }, { "title": "Moral luck in investment contexts: We consciously find unprofitable investments less moral", "year": 2023, "type": "two online experiments", "doi": "10.1371/journal.pone.0278677", "url": "https://pubmed.ncbi.nlm.nih.gov/36649364/", "sourceId": "src-0c83b7cb76f4adab" } ], "evidenceClass": "established", "sourceIds": [ "src-0539c30082b039b2", "src-295bb6762d6f7a9f", "src-0c83b7cb76f4adab" ] }, { "slug": "availability-heuristic-anthropomorphism", "evidenceStatus": "established attribution tendency; not inherently an error", "reviewedAt": "2026-08-18", "qualification": "Anthropomorphism is the attribution of humanlike properties, intentions, emotions, or mental states to nonhuman agents. It is a well-established psychological phenomenon, but it is not automatically a cognitive error: humanlike models can sometimes be useful. The risk appears when humanlike cues are treated as evidence for capabilities, understanding, accuracy, consciousness, or motives that have not actually been demonstrated.", "mechanism": "Classic theory links anthropomorphism to accessible human knowledge, motivation to understand and predict an agent, and sociality motives. Experiments show that unpredictability and effectance motivation can increase anthropomorphism. In LLM interfaces, humanlike cues such as voice or first-person framing can also change perceived anthropomorphism and judgments of information accuracy in some contexts, so interface style can become entangled with epistemic trust.", "practical": "Translate a humanlike impression into a capability claim you can test. Fluent language, warmth, first-person phrasing, memory-like behavior, or a voice do not by themselves establish understanding, reliability, intention, or consciousness. When accuracy matters, evaluate outputs against task-specific evidence and documented system capabilities rather than against how human the interaction feels.", "sources": [ { "title": "On seeing human: a three-factor theory of anthropomorphism", "year": 2007, "type": "theory and evidence review", "doi": "10.1037/0033-295X.114.4.864", "url": "https://pubmed.ncbi.nlm.nih.gov/17907867/", "sourceId": "src-24b9394643fb3945" }, { "title": "Making sense by making sentient: effectance motivation increases anthropomorphism", "year": 2010, "type": "experiments", "doi": "10.1037/a0020240", "url": "https://pubmed.ncbi.nlm.nih.gov/20649365/", "sourceId": "src-2c6a7b7f9b72cb27" }, { "title": "Believing Anthropomorphism: Examining the Role of Anthropomorphic Cues on Trust in Large Language Models", "year": 2024, "type": "large online experiment / preprint", "url": "https://arxiv.org/abs/2405.06079", "sourceId": "src-f57ecaab4be6bbd9" }, { "title": "Humanlikeness as design, anthropomorphism as inference: a conceptual framework for human-robot interaction", "year": 2026, "type": "conceptual framework", "doi": "10.3389/fcogn.2026.1786256", "url": "https://doi.org/10.3389/fcogn.2026.1786256", "sourceId": "src-b4d5205770653dfc" } ], "evidenceClass": "established", "sourceIds": [ "src-24b9394643fb3945", "src-2c6a7b7f9b72cb27", "src-f57ecaab4be6bbd9", "src-b4d5205770653dfc" ] }, { "slug": "belief-perseverance-conservatism-bias", "evidenceStatus": "established in belief-updating tasks", "reviewedAt": "2026-08-18", "qualification": "Here, conservatism means under-updating: new evidence changes a probability judgment less than a Bayesian benchmark would predict. It should not be confused with the separate memory phenomenon in this corpus that pulls remembered extreme values toward the middle.", "mechanism": "In probability-updating tasks, people can place too much weight on a prior belief relative to new diagnostic information. Different models explain this pattern in different ways, so the observed under-update is firmer than any single mechanism proposed to explain it.", "practical": "When new evidence matters, write down the prior estimate, the new evidence, and the updated estimate separately. This makes it easier to notice when the answer barely moved simply because the original belief felt familiar. Treat this as a decision procedure, not a guaranteed debiasing cure.", "sources": [ { "title": "The neural basis of belief updating and rational decision making", "year": 2014, "type": "experiment", "doi": "10.1093/scan/nss099", "url": "https://pubmed.ncbi.nlm.nih.gov/22956673/", "sourceId": "src-bec67149c5987009" }, { "title": "Surprisingly rational: probability theory plus noise explains biases in judgment", "year": 2014, "type": "model + experiments", "url": "https://pubmed.ncbi.nlm.nih.gov/25090427/", "sourceId": "src-e5fdd6a6cd55f5a9" } ], "evidenceClass": "established", "sourceIds": [ "src-bec67149c5987009", "src-e5fdd6a6cd55f5a9" ] }, { "slug": "cognitive-bias-anchoring-effect", "evidenceStatus": "well-supported for numerical judgments; strength depends on anchor type and context", "reviewedAt": "2026-08-18", "qualification": "Anchoring is a well-supported effect in which an initial numerical value can pull a later estimate toward it. A 2026 meta-analysis covering 2,601 effect sizes found a large overall effect, but also substantial variation across studies. The effect should not be treated as a rule that every number changes every judgment: incidental anchors, anchors from a different dimension, clearly random values, incentives, and some debiasing conditions were associated with smaller or null effects.", "mechanism": "The original account described people as starting from an anchor and adjusting too little. Later work shows that anchoring is not one simple process. Experiments support insufficient adjustment especially for some self-generated anchors, while externally provided anchors can operate through other judgment processes. The safest summary is therefore behavioral: the starting value can systematically shift the final estimate, while the exact mechanism depends on how the anchor was produced and used.", "practical": "For an important numerical judgment, make an independent estimate before seeing a suggested value when possible. If an anchor is already present, compare against base rates, completed cases, ranges, or several independent estimates rather than adjusting only from that one number. Ask what evidence would justify moving far away from the starting point. Awareness alone is not a guaranteed fix, and a mitigation should be judged by whether it actually improves calibration in the task being used.", "sources": [ { "title": "Judgment under Uncertainty: Heuristics and Biases", "year": 1974, "type": "foundational experimental review", "doi": "10.1126/science.185.4157.1124", "url": "https://doi.org/10.1126/science.185.4157.1124", "sourceId": "src-4e94d948f6b94566" }, { "title": "The anchoring-and-adjustment heuristic: why the adjustments are insufficient", "year": 2006, "type": "experimental studies", "doi": "10.1111/j.1467-9280.2006.01704.x", "url": "https://doi.org/10.1111/j.1467-9280.2006.01704.x", "sourceId": "src-35855b06b246eebe" }, { "title": "Fifty Years of Anchoring Effects: A Theoretical Reintegration and Meta-Analysis", "year": 2026, "type": "meta-analysis", "doi": "10.1287/mnsc.2023.03238", "url": "https://doi.org/10.1287/mnsc.2023.03238", "sourceId": "src-639229081285f101" }, { "title": "How was my performance? Exploring the role of anchoring bias in AI-assisted decision making", "year": 2025, "type": "controlled experiments with managers", "doi": "10.1016/j.ijinfomgt.2025.102875", "url": "https://doi.org/10.1016/j.ijinfomgt.2025.102875", "sourceId": "src-ce811297b2050379" } ], "evidenceClass": "supported", "sourceIds": [ "src-4e94d948f6b94566", "src-35855b06b246eebe", "src-639229081285f101", "src-ce811297b2050379" ] }, { "slug": "cognitive-bias-confirmation-bias", "evidenceStatus": "well established, broad construct", "reviewedAt": "2026-08-18", "qualification": "Confirmation bias is an umbrella label for several ways existing beliefs or hypotheses can influence information search and interpretation. It should not be reduced to one behaviour such as reading only agreeable news, and a preference for confirming tests is not irrational in every task or environment.", "mechanism": "Classic hypothesis-testing work showed that people can seek cases that fit a current hypothesis instead of searching efficiently for cases that could disconfirm it. Broader reviews describe related tendencies in information search, evidence interpretation, memory, and hypothesis testing, with different mechanisms likely contributing across contexts.", "practical": "For an important belief, write down what evidence would count against it before looking for more information. Search deliberately for diagnostic counterexamples and compare competing explanations using the same evidence standard. The goal is not to consume an arbitrary amount of opposing content, but to make falsification possible.", "sources": [ { "title": "On the Failure to Eliminate Hypotheses in a Conceptual Task", "year": 1960, "type": "foundational experiment", "doi": "10.1080/17470216008416717", "url": "https://doi.org/10.1080/17470216008416717", "sourceId": "src-83375467c08e8177" }, { "title": "Confirmation Bias: A Ubiquitous Phenomenon in Many Guises", "year": 1998, "type": "review", "doi": "10.1037/1089-2680.2.2.175", "url": "https://doi.org/10.1037/1089-2680.2.2.175", "sourceId": "src-c4e8734b8f1248ee" } ], "evidenceClass": "established", "sourceIds": [ "src-83375467c08e8177", "src-c4e8734b8f1248ee" ] }, { "slug": "cognitive-bias-curse-of-knowledge", "evidenceStatus": "supported across several perspective-taking tasks", "reviewedAt": "2026-08-18", "qualification": "Knowing more can make it harder to estimate what a less-informed person knows or understands, but the size and mechanism of the effect depend on the task. The useful claim is not that experts are unable to teach beginners; it is that one's own knowledge can contaminate judgments about another person's knowledge unless the perspective gap is made explicit.", "mechanism": "Research has found knowledge-based distortions in economic judgments and interpersonal perspective taking. Proposed mechanisms include anchoring on one's own knowledge, difficulty suppressing known information, missing diagnostic cues about the other person's knowledge, and misattributing the fluency of familiar information. No single mechanism explains every version of the effect.", "practical": "When explaining something to a novice, do not estimate clarity from how easy the idea feels to you. Ask the other person to predict, paraphrase, or perform the next step, and use their errors as feedback. Narrative feedback about the other person's perspective can reduce later egocentric projection more than a simple accuracy score.", "sources": [ { "title": "The Curse of Knowledge in Economic Settings: An Experimental Analysis", "year": 1989, "type": "foundational experiments", "doi": "10.1086/261651", "url": "https://doi.org/10.1086/261651", "sourceId": "src-7055432ee3ef6b7f" }, { "title": "A 'curse of knowledge' in the absence of knowledge? People misattribute fluency when judging how common knowledge is among their peers", "year": 2017, "type": "mechanism experiments", "doi": "10.1016/j.cognition.2017.04.015", "url": "https://pubmed.ncbi.nlm.nih.gov/28641221/", "sourceId": "src-51a2a4c04f206e85" }, { "title": "Lifting the curse of knowing: How feedback improves perspective-taking", "year": 2021, "type": "debiasing experiments", "doi": "10.1177/1747021820987080", "url": "https://pubmed.ncbi.nlm.nih.gov/33427086/", "sourceId": "src-945106dd686dc96f" } ], "evidenceClass": "supported", "sourceIds": [ "src-7055432ee3ef6b7f", "src-51a2a4c04f206e85", "src-945106dd686dc96f" ] }, { "slug": "cognitive-bias-declinism", "evidenceStatus": "umbrella label; direct evidence supports specific decline illusions", "reviewedAt": "2026-08-18", "qualification": "Declinism is best treated here as an umbrella label for judging the present as worse than an idealized past, not as one standardized cognitive-bias construct with a single mechanism. Research directly supports several narrower ingredients: people can remember past experiences more positively than they experienced them, negative affect associated with autobiographical memories often fades faster than positive affect, and large multi-study work finds a pervasive illusion of moral decline. None of this means that every claim of social, technological, institutional, or personal decline is false; real decline must be tested against domain-specific evidence.", "mechanism": "Different decline judgments can arise from different processes. Rosy retrospection can make remembered experiences more positive than they were in the moment. Fading affect can reduce the emotional weight of negative memories faster than positive ones. In perceptions of moral decline, biased exposure to negative information about the present combined with biased memory for the past can create an apparent downward trend. Because these mechanisms differ by domain, 'declinism' should not be used as a universal explanation whenever someone thinks conditions have worsened.", "practical": "Specify the domain and time window before judging decline. Compare the present with contemporaneous records or repeated measurements from the past rather than memory alone. Separate a real trend in the target variable from nostalgia for a broader period, and look for indicators that could show improvement as well as deterioration. If the claim is about morality or social behavior, distinguish perceptions of 'people in general' from direct observations of people you actually know.", "sources": [ { "title": "Temporal Adjustments in the Evaluation of Events: The Rosy View", "year": 1997, "type": "three longitudinal/retrospective studies", "doi": "10.1006/jesp.1997.1333", "url": "https://pubmed.ncbi.nlm.nih.gov/9247371/", "sourceId": "src-c375f114daf625f6" }, { "title": "On misattributing good remembering to a happy past: An investigation into the cognitive roots of nostalgia", "year": 2006, "type": "memory experiments", "doi": "10.1037/1528-3542.6.4.596", "url": "https://pubmed.ncbi.nlm.nih.gov/17144751/", "sourceId": "src-c378be567ea78220" }, { "title": "The illusion of moral decline", "year": 2023, "type": "archival analysis + multi-study experiments", "doi": "10.1038/s41586-023-06137-x", "url": "https://www.nature.com/articles/s41586-023-06137-x", "sourceId": "src-f3b090aa4f2bf959" } ], "evidenceClass": "supported", "sourceIds": [ "src-c375f114daf625f6", "src-c378be567ea78220", "src-f3b090aa4f2bf959" ] }, { "slug": "cognitive-bias-hindsight-bias", "evidenceStatus": "robust", "reviewedAt": "2026-08-18", "qualification": "Knowing an outcome can make the outcome look more predictable in retrospect. The effect has been studied for decades and across many settings, but it does not mean that every confident explanation after an event is biased.", "mechanism": "Outcome knowledge changes how people reconstruct what was knowable before the event. Once the result is known, information that fits it can feel more relevant and the original uncertainty becomes harder to recreate.", "practical": "Keep forecasts, assumptions, and confidence estimates before important outcomes are known. When reviewing a decision later, compare the result with that earlier record instead of reconstructing your prediction from memory. Simply warning yourself about hindsight bias is unlikely to be enough.", "sources": [ { "title": "Hindsight is not equal to foresight: The effect of outcome knowledge on judgment under uncertainty", "year": 1975, "type": "foundational experiments", "doi": "10.1037/0096-1523.1.3.288", "url": "https://doi.org/10.1037/0096-1523.1.3.288", "sourceId": "src-6df53f947e2583fa" }, { "title": "Fifty years of hindsight bias research—Reflection on Fischhoff (1975)", "year": 2025, "type": "review / reflection", "doi": "10.1037/xhp0001232", "url": "https://pubmed.ncbi.nlm.nih.gov/39913490/", "sourceId": "src-75d0eb4cf8b10c07" } ], "evidenceClass": "established", "sourceIds": [ "src-6df53f947e2583fa", "src-75d0eb4cf8b10c07" ] }, { "slug": "cognitive-bias-hungry-judge-effect", "evidenceStatus": "contested observational finding; hunger interpretation not established", "reviewedAt": "2026-08-18", "qualification": "The famous 2011 parole-board study found favorable decisions were more common early in a decision session and immediately after food breaks. It did not directly measure hunger, glucose, fatigue, or mood, and the authors explicitly could not separate eating from rest. A contemporaneous critique argued that non-random case ordering could explain the pattern; the original authors disputed that critique with additional analyses. Treating this as a general law that hungry judges become harsher goes beyond the evidence.", "mechanism": "The original paper interpreted the sequence pattern as consistent with resource depletion and recovery after a break, but its design was observational. Alternative explanations include case ordering and representation differences. Because food, rest, time, sequence position, and other factors were entangled, the study cannot identify hunger itself as the causal mechanism.", "practical": "Do not use this effect as evidence that a decision-maker's hunger caused a specific ruling. The safer operational lesson is broader: high-stakes repeated decisions deserve checks for order effects, fatigue, break structure, workload, and case assignment. If you want to test a hunger hypothesis, measure hunger separately rather than using meal timing as a substitute.", "sources": [ { "title": "Extraneous factors in judicial decisions", "year": 2011, "type": "observational study", "doi": "10.1073/pnas.1018033108", "url": "https://pubmed.ncbi.nlm.nih.gov/21482790/", "sourceId": "src-60924ea4aa8ec622" }, { "title": "Overlooked factors in the analysis of parole decisions", "year": 2011, "type": "data critique / letter", "doi": "10.1073/pnas.1110910108", "url": "https://pubmed.ncbi.nlm.nih.gov/21987788/", "sourceId": "src-90615805724bf091" }, { "title": "Reply to Weinshall-Margel and Shapard: Extraneous factors in judicial decisions persist", "year": 2011, "type": "author reanalysis / reply", "doi": "10.1073/pnas.1112190108", "url": "https://doi.org/10.1073/pnas.1112190108", "sourceId": "src-de25d398141bd9bc" } ], "evidenceClass": "contested", "sourceIds": [ "src-60924ea4aa8ec622", "src-90615805724bf091", "src-de25d398141bd9bc" ] }, { "slug": "cognitive-bias-impact-bias", "evidenceStatus": "well supported, with important forecasting nuances", "reviewedAt": "2026-08-18", "qualification": "People often overestimate how intense or long their emotional reactions to future events will be, especially when the focal event crowds out everything else that will also shape daily experience. The literature is broader than a rule that people always overpredict emotion: forecasting errors vary by event, time horizon, emotion, and what exactly is being predicted.", "mechanism": "Affective-forecasting research identifies several contributors. Focalism makes the target event dominate imagination while ordinary future events receive too little weight. For negative events, people can also underpredict adaptation and the psychological processes that help them make sense of what happened. Available memories can further bias forecasts when memorable examples are atypical.", "practical": "For a major future event, forecast both the focal event and the rest of an ordinary week around it. Estimate intensity and duration separately, then compare later forecasts with actual experience. When possible, use experience-sampling or a reference class of people who have already lived through a similar event instead of relying only on vivid imagination.", "sources": [ { "title": "Immune neglect: a source of durability bias in affective forecasting", "year": 1998, "type": "experiments", "doi": "10.1037/0022-3514.75.3.617", "url": "https://pubmed.ncbi.nlm.nih.gov/9781405/", "sourceId": "src-ebc67fd4db360d62" }, { "title": "Focalism: a source of durability bias in affective forecasting", "year": 2000, "type": "experiments", "doi": "10.1037/0022-3514.78.5.821", "url": "https://pubmed.ncbi.nlm.nih.gov/10821192/", "sourceId": "src-ea6429b27b8d6ccf" }, { "title": "Affective Forecasting: Knowing What to Want", "year": 2005, "type": "review", "doi": "10.1111/j.0963-7214.2005.00355.x", "url": "https://doi.org/10.1111/j.0963-7214.2005.00355.x", "sourceId": "src-bf639003c08f7257" }, { "title": "The impact bias is alive and well", "year": 2013, "type": "reanalysis / response to critique", "doi": "10.1037/a0032662", "url": "https://pubmed.ncbi.nlm.nih.gov/24219785/", "sourceId": "src-902102ef57fb68e5" } ], "evidenceClass": "supported", "sourceIds": [ "src-ebc67fd4db360d62", "src-ea6429b27b8d6ccf", "src-bf639003c08f7257", "src-902102ef57fb68e5" ] }, { "slug": "cognitive-bias-outcome-bias", "evidenceStatus": "replicated", "reviewedAt": "2026-08-18", "qualification": "Outcome bias occurs when knowledge of a result changes how people evaluate the quality of a decision even when the information available at the time of the decision is held constant. Outcomes can still be relevant for learning, so the error is not 'never look at results'; it is using luck or hindsight as if it had been available to the original decision-maker.", "mechanism": "The classic experiments varied outcomes while holding the decision information constant and found more favorable evaluations after good outcomes. A large preregistered replication reproduced the direction of the effect in a medical-decision scenario, including among participants who said outcomes should not influence the evaluation.", "practical": "Review the process twice: first judge the decision using only information that was available when it was made, then use the outcome to update models and future assumptions. Keeping these two questions separate helps distinguish decision quality from luck without throwing away useful feedback.", "sources": [ { "title": "Outcome bias in decision evaluation", "year": 1988, "type": "foundational experiments", "doi": "10.1037/0022-3514.54.4.569", "url": "https://pubmed.ncbi.nlm.nih.gov/3367280/", "sourceId": "src-f1f0400ef362aaa8" }, { "title": "Outcomes Affect Evaluations of Decision Quality: Replication and Extensions of Baron and Hershey's (1988) Outcome Bias Experiment 1", "year": 2023, "type": "preregistered replication", "doi": "10.5334/irsp.751", "url": "https://pubmed.ncbi.nlm.nih.gov/40951810/", "sourceId": "src-1801b1347f89ef91" } ], "evidenceClass": "established", "sourceIds": [ "src-f1f0400ef362aaa8", "src-1801b1347f89ef91" ] }, { "slug": "cognitive-bias-prevention-bias", "evidenceStatus": "single-study / domain-specific", "reviewedAt": "2026-08-18", "qualification": "Prevention Bias is a label introduced in experimental research on information-security investment. In that task, participants favored preventive controls over detection-and-response controls even when the experiment was designed so the two classes had the same return on investment. The evidence does not establish a broad psychological law that people generally overvalue prevention in healthcare, insurance, relationships, or every other domain.", "mechanism": "The original information-security study interprets the pattern through behavioral decision-making mechanisms including reference points, loss aversion, affect, and fast intuitive processing. Those mechanisms are plausible background explanations, but the named Prevention Bias itself is a domain-specific construct from this research program rather than a broadly replicated general-purpose bias.", "practical": "When allocating a security budget, compare prevention, detection, and response using the same expected-loss and marginal-value framework instead of assuming prevention is automatically the superior category. For other domains, do not import the label without evidence that the same allocation pattern and incentives actually apply.", "sources": [ { "title": "Mis-spending on information security measures: Theory and experimental evidence", "year": 2021, "type": "domain-specific behavioral experiment", "doi": "10.1016/j.ijinfomgt.2020.102291", "url": "https://doi.org/10.1016/j.ijinfomgt.2020.102291", "sourceId": "src-eace2b628cde1bce" }, { "title": "Prospect Theory: An Analysis of Decision under Risk", "year": 1979, "type": "mechanism background / foundational theory", "doi": "10.2307/1914185", "url": "https://doi.org/10.2307/1914185", "sourceId": "src-48160eef6b029066" } ], "evidenceClass": "domain-specific", "sourceIds": [ "src-eace2b628cde1bce", "src-48160eef6b029066" ] }, { "slug": "cognitive-bias-projection-bias", "evidenceStatus": "established in intertemporal preference prediction", "reviewedAt": "2026-08-18", "qualification": "Projection bias describes a tendency to overproject current tastes or visceral states onto future preferences. People often understand that tastes will change but underestimate how much they will change. The construct is most directly supported in intertemporal choice and consumer settings; it should not be stretched into a generic explanation for every bad prediction about one's future self.", "mechanism": "Current states provide a salient reference point for forecasting future utility. When people incompletely adjust away from today's tastes, hunger, weather, or other transient conditions, future choices can be distorted. Field and experimental evidence shows that current conditions can influence decisions whose consequences occur later even when those conditions will not persist.", "practical": "For decisions that bind your future self, record the current state that may be contaminating the forecast: hunger, fatigue, mood, weather, novelty, or current preferences. Re-evaluate the decision in a meaningfully different state or use past preference changes as a reference class before making an expensive or difficult-to-reverse commitment.", "sources": [ { "title": "Projection Bias in Predicting Future Utility", "year": 2003, "type": "theory + evidence synthesis", "doi": "10.1162/003355303322552784", "url": "https://doi.org/10.1162/003355303322552784", "sourceId": "src-479735fa3da2d5b3" }, { "title": "Projection Bias in Catalog Orders", "year": 2007, "type": "field evidence", "doi": "10.1257/aer.97.4.1217", "url": "https://www.aeaweb.org/articles?id=10.1257/aer.97.4.1217", "sourceId": "src-3828d4aac220e974" }, { "title": "Assessing Projection Bias in Consumers' Food Preferences", "year": 2016, "type": "incentivized experiment", "doi": "10.1371/journal.pone.0146308", "url": "https://doi.org/10.1371/journal.pone.0146308", "sourceId": "src-b0cc2953c2431e7b" } ], "evidenceClass": "established", "sourceIds": [ "src-479735fa3da2d5b3", "src-3828d4aac220e974", "src-b0cc2953c2431e7b" ] }, { "slug": "cognitive-bias-sunk-cost-effect", "evidenceStatus": "well-supported overall; strength varies by decision type and context", "reviewedAt": "2026-08-18", "qualification": "The sunk cost effect is a documented tendency for irrecoverable prior investments of money, time, or effort to influence later choices. A meta-analytic review found clear evidence for the effect overall, while also showing that its size and moderators differ between utilization decisions and progress decisions. The effect should not be used to label every choice to continue: future value, switching costs, uncertainty, and how close a project is to useful completion can all be relevant to a forward-looking decision.", "mechanism": "Classic experiments linked sunk-cost behavior to reluctance to appear wasteful, while later work has connected it to several decision processes rather than one universal mechanism. In project settings, sunk costs can interact with the perceived need to complete the work. That interaction helps explain why sunk costs are one possible antecedent of escalation of commitment rather than a complete explanation for every escalation case.", "practical": "For a current choice, put irrecoverable past costs in a separate line and evaluate the remaining options using costs and benefits from today forward. Compare continuing with the best realistic alternative use of the remaining money, time, and attention. If completion itself creates future value, include that value explicitly instead of treating 'we are already close' as either automatically rational or automatically biased.", "sources": [ { "title": "The psychology of sunk cost", "year": 1985, "type": "field study and experiments", "doi": "10.1016/0749-5978(85)90049-4", "url": "https://doi.org/10.1016/0749-5978(85)90049-4", "sourceId": "src-25c242b954867ac1" }, { "title": "Looking forward and looking back: integrating completion and sunk-cost effects within an escalation-of-commitment progress decision", "year": 2001, "type": "experimental study", "doi": "10.1037/0021-9010.86.1.104", "url": "https://pubmed.ncbi.nlm.nih.gov/11302222/", "sourceId": "src-812f426a52613922" }, { "title": "On the sunk-cost effect in economic decision-making: a meta-analytic review", "year": 2015, "type": "meta-analysis", "doi": "10.1007/s40685-014-0014-8", "url": "https://doi.org/10.1007/s40685-014-0014-8", "sourceId": "src-ec03691286f7284e" } ], "evidenceClass": "supported", "sourceIds": [ "src-25c242b954867ac1", "src-812f426a52613922", "src-ec03691286f7284e" ] }, { "slug": "cognitive-bias-surrogation", "evidenceStatus": "supported in strategic performance-measure settings", "reviewedAt": "2026-08-18", "qualification": "Surrogation is a domain-specific management-accounting construct: a measure that was designed to represent a strategic objective can start to be treated as though it were the objective itself. It is closely related to metric fixation and proxy problems, but those broader labels should not be treated as exact synonyms without checking the setting and mechanism.", "mechanism": "Strategic performance systems translate hard-to-measure constructs into observable measures. Experiments show that managers can lose sight of the underlying construct and act as if the measure itself were what matters. The literature also provides boundary conditions and interventions: involvement in strategy selection and opportunities to explain decisions narratively can reduce surrogation in studied settings.", "practical": "For every important KPI, write the construct it is meant to represent and at least one important dimension it does not capture. During reviews, discuss the goal before the metric and require a short explanation for major metric-driven decisions. If optimizing the number can make the underlying goal worse, the proxy needs an explicit guardrail or companion measure.", "sources": [ { "title": "Strategy Selection, Surrogation, and Strategic Performance Measurement Systems", "year": 2013, "type": "management-accounting experiments", "doi": "10.1111/j.1475-679X.2012.00465.x", "url": "https://doi.org/10.1111/j.1475-679X.2012.00465.x", "sourceId": "src-bc1fa4e94ab0fa71" }, { "title": "Decreasing Operational Distortion and Surrogation Through Narrative Reporting", "year": 2019, "type": "experiment", "doi": "10.2308/accr-52277", "url": "https://doi.org/10.2308/accr-52277", "sourceId": "src-dd6028af1211deb7" } ], "evidenceClass": "supported", "sourceIds": [ "src-bc1fa4e94ab0fa71", "src-dd6028af1211deb7" ] }, { "slug": "cognitive-bias-systematic-bias", "evidenceStatus": "measurement/statistical concept; not a standalone cognitive-bias construct", "reviewedAt": "2026-08-18", "auditEligible": false, "qualification": "Systematic bias is a general statistical and measurement concept: a non-random tendency for measurements, estimates, or study results to deviate from a reference or target value in a consistent or predictable way. Standards in metrology define systematic measurement error separately from random error and define measurement bias as an estimate of that systematic error. This is not one specific psychological bias with one mechanism, so the page should not present it as a personal cognitive tendency alongside constructs such as confirmation bias or hindsight bias.", "mechanism": "Systematic error can arise from instruments, calibration, sampling, study design, confounding, data collection, analysis, or other structural features of a process. Unlike random variation, it does not reliably disappear just by collecting more observations. In some domains the word 'bias' also describes systematic tendencies in human judgment, but that broader usage does not turn systematic bias itself into a distinct cognitive-bias mechanism.", "practical": "Define the target or reference value, then identify where a process could consistently push estimates in one direction. Use calibration, validation data, representative sampling, design controls, sensitivity analysis, or process changes to estimate and reduce the systematic component. Do not treat repeated error as proof of a psychological cause until measurement, sampling, and procedural explanations have been examined.", "sources": [ { "title": "International vocabulary of metrology — systematic measurement error and measurement bias", "year": 2008, "type": "international metrology vocabulary", "url": "https://www.iso.org/sites/JCGM/VIM/JCGM_200e_FILES/MAIN_JCGM_200e/02_e.html", "sourceId": "src-3b2666bc66255b98" }, { "title": "NIST Technical Note 1297, Appendix D1: Terminology", "year": 1994, "type": "measurement uncertainty guidance", "url": "https://www.nist.gov/pml/nist-technical-note-1297/nist-tn-1297-appendix-d1-terminology", "sourceId": "src-1e7c10d323a7202c" }, { "title": "Bias, Statistical", "year": 2023, "type": "NIST glossary definition", "url": "https://www.nist.gov/glossary-term/19181", "sourceId": "src-c22f45c827d79fb6" } ], "evidenceClass": "concept", "sourceIds": [ "src-3b2666bc66255b98", "src-1e7c10d323a7202c", "src-c22f45c827d79fb6" ] }, { "slug": "cognitive-bias-value-selection-bias", "evidenceStatus": "supported in Bayesian reasoning tasks; limited independent replication", "reviewedAt": "2026-08-18", "qualification": "Value Selection Bias is a narrow reasoning pattern reported in Bayesian diagnostic problems: when participants did not derive the correct solution, incorrect answers were strongly drawn toward numerical values already present in the problem. The evidence supports this behavior across multiple problem variations in the authors' research program, but it should not be generalized to any situation where a person uses an old price, stale KPI, or convenient number.", "mechanism": "The research links value selection to reference dependence and problem representation. When reasoners are uncertain about how to construct the needed calculation, salient values supplied by the problem can look like candidate answers. Numerical ability and the way the reference class is represented affect accuracy, so the pattern is more specific than a generic preference for available numbers.", "practical": "In a probability or diagnostic problem, state the target quantity and reference class before looking for a number to copy into the answer. Write the required relationship or calculation explicitly, then check whether the selected values actually belong to that relationship. If no calculation is needed, be able to explain why rather than assuming that a supplied number must be the answer.", "sources": [ { "title": "Reference Dependence in Bayesian Reasoning", "year": 2019, "type": "doctoral dissertation / multi-experiment research program", "url": "https://digitalcommons.usf.edu/etd/7964/", "sourceId": "src-50e334f70bc2a080" }, { "title": "Reference Dependence in Bayesian Reasoning: Value Selection Bias, Congruence Effects, and Response Prompt Sensitivity", "year": 2022, "type": "peer-reviewed experiments", "doi": "10.3389/fpsyg.2022.729285", "url": "https://pubmed.ncbi.nlm.nih.gov/35369253/", "sourceId": "src-6dfe6272124fdec6" } ], "evidenceClass": "supported", "sourceIds": [ "src-50e334f70bc2a080", "src-6dfe6272124fdec6" ] }, { "slug": "confirmation-bias-backfire-effect", "evidenceStatus": "mixed / conditional", "reviewedAt": "2026-08-18", "qualification": "Corrections usually improve factual accuracy. A true backfire effect, where a correction makes the targeted false belief stronger, appears to be uncommon and sensitive to context and measurement. It should not be treated as the default response to being corrected.", "mechanism": "The label describes a change in belief after corrective information, not ordinary disagreement. Identity, prior beliefs, source trust, and how the correction is measured can affect responses, but the broad claim that facts normally make false beliefs stronger is not supported by the current evidence base.", "practical": "Do not avoid corrections because you fear an automatic backfire. Give the correction clearly, connect it to reliable evidence, and separate belief accuracy from whether the correction changes a person's wider attitudes or behaviour. For important decisions, check the belief again later because correction effects can fade.", "sources": [ { "title": "Searching for the Backfire Effect: Measurement and Design Considerations", "year": 2020, "type": "review", "doi": "10.1016/j.jarmac.2020.06.006", "url": "https://doi.org/10.1016/j.jarmac.2020.06.006", "sourceId": "src-e1813cd3b9123eaf" }, { "title": "Why the backfire effect does not explain the durability of political misperceptions", "year": 2021, "type": "review / perspective", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8053951/", "sourceId": "src-40282584441d78a7" }, { "title": "Factual corrections: Concerns and current evidence", "year": 2024, "type": "review", "doi": "10.1016/j.copsyc.2023.101715", "url": "https://doi.org/10.1016/j.copsyc.2023.101715", "sourceId": "src-4f99e4ca8c1af4fe" } ], "evidenceClass": "mixed", "sourceIds": [ "src-e1813cd3b9123eaf", "src-40282584441d78a7", "src-4f99e4ca8c1af4fe" ] }, { "slug": "confirmation-bias-congruence-bias", "evidenceStatus": "established in hypothesis-testing tasks; narrower than confirmation bias", "reviewedAt": "2026-08-18", "qualification": "Congruence Bias is a narrower hypothesis-testing pattern than the broad umbrella of confirmation bias. In classic work, people overvalued tests that were likely to return a positive result if their leading hypothesis were true, even when other tests were more diagnostic among competing hypotheses. The useful claim is not that every positive test is irrational: a positive test strategy can be efficient in some environments, and the problem depends on whether the chosen test can actually distinguish the focal hypothesis from alternatives.", "mechanism": "Baron, Beattie, and Hershey described congruence bias as overvaluing tests whose expected positive result matches the currently favored hypothesis. Klayman and Ha showed why the closely related positive test strategy should not automatically be treated as a logical error: sampling cases expected under a hypothesis can be informative depending on the structure of the environment. The practical failure occurs when a test is congruent with the focal hypothesis but has low diagnostic value because alternative hypotheses predict the same result.", "practical": "Before choosing a test, list at least one plausible alternative hypothesis and predict the result under both explanations. Prefer observations whose possible outcomes separate the hypotheses rather than merely giving your preferred explanation another chance to say 'yes.' If the same result is expected under several explanations, it is weak evidence even when it matches the preferred hypothesis.", "sources": [ { "title": "Diagnosticity of evidence and congruence bias in hypothesis testing", "year": 1988, "type": "hypothesis-testing experiments", "doi": "10.1016/0749-5978(88)90012-2", "url": "https://doi.org/10.1016/0749-5978(88)90012-2", "sourceId": "src-54ea5126c3035a7e" }, { "title": "Confirmation, disconfirmation, and information in hypothesis testing", "year": 1987, "type": "theory + experimental evidence", "doi": "10.1037/0033-295X.94.2.211", "url": "https://doi.org/10.1037/0033-295X.94.2.211", "sourceId": "src-f3a6c8d07f4870ec" } ], "evidenceClass": "domain-specific", "sourceIds": [ "src-54ea5126c3035a7e", "src-f3a6c8d07f4870ec" ] }, { "slug": "egocentric-bias-planning-fallacy", "evidenceStatus": "well-supported for time estimates; size and causes vary by context", "reviewedAt": "2026-08-18", "qualification": "The planning fallacy is a well-documented tendency for people to predict their own task completion times too optimistically. The effect has been observed across different kinds of tasks, but it is not a rule that every plan will run late. Project overruns can also come from changing scope, dependencies, incentives, poor data, deliberate underestimation, or genuinely unusual events, so a late project should not automatically be diagnosed as a planning fallacy.", "mechanism": "Classic studies found that people often build a detailed scenario for how the current task will unfold while giving too little weight to how long similar tasks actually took. Later reviews describe cognitive, motivational, and social influences rather than one single cause. Recent cross-cultural experiments also suggest that reminders about past experience and the social consequences of delay can change time predictions differently across groups, which is another reason not to treat one mechanism or correction as universal.", "practical": "For an important estimate, start with outcomes from comparable completed work before refining the current plan. Give a range rather than a single precise date, name the main uncertainties, and keep the original estimate so forecast error can be measured later. Reference-class methods can be useful when comparable data exist, but their quality depends on choosing a genuinely relevant class. There is no evidence-based universal rule that every estimate should simply be multiplied by the same fixed buffer.", "sources": [ { "title": "Exploring the Planning Fallacy: Why People Underestimate Their Task Completion Times", "year": 1994, "type": "experimental studies", "doi": "10.1037/0022-3514.67.3.366", "url": "https://doi.org/10.1037/0022-3514.67.3.366", "sourceId": "src-7d92150bd312c109" }, { "title": "The Planning Fallacy: Cognitive, Motivational, and Social Origins", "year": 2010, "type": "research review", "doi": "10.1016/S0065-2601(10)43001-4", "url": "https://doi.org/10.1016/S0065-2601(10)43001-4", "sourceId": "src-cf328825be4a0674" }, { "title": "I Can Do It in No Time! Time Predictions in the Cultural Context", "year": 2026, "type": "cross-cultural experiments", "doi": "10.1177/00220221261418305", "url": "https://doi.org/10.1177/00220221261418305", "sourceId": "src-d6ecdecbd0ffae61" }, { "title": "Reducing risks in megaprojects: The potential of reference class forecasting", "year": 2023, "type": "literature review of reference class forecasting", "doi": "10.1016/j.plas.2023.100103", "url": "https://doi.org/10.1016/j.plas.2023.100103", "sourceId": "src-d98817dbf800d030" } ], "evidenceClass": "supported", "sourceIds": [ "src-7d92150bd312c109", "src-cf328825be4a0674", "src-d6ecdecbd0ffae61", "src-d98817dbf800d030" ] }, { "slug": "false-priors-automation-bias", "evidenceStatus": "established, context-dependent", "reviewedAt": "2026-08-18", "qualification": "Automation bias is a documented pattern of inappropriate reliance on automated cues or recommendations. It can produce commission errors when a user follows incorrect advice and omission errors when a user fails to act because automation did not signal a problem. This does not mean automation is generally harmful: decision support can improve overall performance, and the relevant question is whether reliance remains calibrated when the system is wrong, incomplete, or difficult to verify.", "mechanism": "Research describes automation as a potential heuristic substitute for vigilant information search and processing. Reliance is shaped by trust calibration, attention allocation, workload, task complexity, time pressure, experience, and how difficult it is to verify the automated recommendation. Automation bias overlaps with automation-related complacency but the constructs are not identical.", "practical": "For high-stakes decisions, make a provisional assessment or explicit decision criteria before viewing automated advice when practical. Verify consequential recommendations against independent cues or source data, especially when the system is outside its strongest domain. Design the workflow to reduce verification effort with source links, decomposed checks, calibrated uncertainty, and explicit review responsibility rather than relying on generic warnings to 'be careful'.", "sources": [ { "title": "Automation bias: decision making and performance in high-tech cockpits", "year": 1997, "type": "controlled simulation study", "doi": "10.1207/s15327108ijap0801_3", "url": "https://pubmed.ncbi.nlm.nih.gov/11540946/", "sourceId": "src-0252755d114dfb6c" }, { "title": "Automation bias: a systematic review of frequency, effect mediators, and mitigators", "year": 2011, "type": "systematic review", "doi": "10.1136/amiajnl-2011-000089", "url": "https://pubmed.ncbi.nlm.nih.gov/21685142/", "sourceId": "src-f8b4657a59aad7de" }, { "title": "Automation bias and verification complexity: a systematic review", "year": 2017, "type": "systematic review", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7651899/", "sourceId": "src-965010b0cf7cde95" }, { "title": "What is Wrong With Automation Bias?", "year": 2026, "type": "conceptual analysis", "doi": "10.1007/s13347-026-01090-9", "url": "https://doi.org/10.1007/s13347-026-01090-9", "sourceId": "src-d8b0db0a156ad3a7" } ], "evidenceClass": "established", "sourceIds": [ "src-0252755d114dfb6c", "src-f8b4657a59aad7de", "src-965010b0cf7cde95", "src-d8b0db0a156ad3a7" ] }, { "slug": "framing-effect-core", "evidenceStatus": "risky-choice framing is robust; broader framing types have different evidence", "reviewedAt": "2026-08-18", "qualification": "Framing research covers several distinct paradigms. Risky-choice framing, where equivalent outcomes are presented as gains or losses, is a well-established effect with substantial evidence across many studies. A classic meta-analysis found a reliable small-to-moderate average effect with strong variation across designs, and later reviews and metastudies support broad generalizability. Attribute framing also has supporting evidence, while goal or message framing is less consistently established. These should not be treated as one identical effect caused by any change in wording.", "mechanism": "No single mechanism explains every framing paradigm. In risky choice, reference points, the valence or gist of descriptions, and how outcomes are represented can all affect preference. The 1998 framing typology explicitly separated risky-choice, attribute, and goal framing because they use different tasks and may rely on different processes. The most defensible behavioral claim is that descriptions that preserve much of the underlying decision information can still change judgments or preferences, while the size and direction depend on the task and representation.", "practical": "For an important choice, restate the options using neutral quantities and test equivalent gain and loss descriptions while holding outcomes and probabilities constant. Show complete information when practical, and separate the effect of the frame from missing or mismatched information. If the decision changes, investigate why before assuming one description is the objective truth. Treat this as a consistency check, not a rule that positive or negative wording is always manipulative.", "sources": [ { "title": "The framing of decisions and the psychology of choice", "year": 1981, "type": "foundational experiments", "doi": "10.1126/science.7455683", "url": "https://pubmed.ncbi.nlm.nih.gov/7455683/", "sourceId": "src-bb0fa40a53281f49" }, { "title": "The Influence of Framing on Risky Decisions: A Meta-analysis", "year": 1998, "type": "meta-analysis", "doi": "10.1006/obhd.1998.2781", "url": "https://pubmed.ncbi.nlm.nih.gov/9719656/", "sourceId": "src-0e441147fc835a3d" }, { "title": "All Frames Are Not Created Equal: A Typology and Critical Analysis of Framing Effects", "year": 1998, "type": "critical review and typology", "doi": "10.1006/obhd.1998.2804", "url": "https://doi.org/10.1006/obhd.1998.2804", "sourceId": "src-47b281f980645234" }, { "title": "A systematic review of risky-choice framing effects", "year": 2023, "type": "systematic review", "url": "https://pubmed.ncbi.nlm.nih.gov/37927347/", "sourceId": "src-2e83fc60350e0524" }, { "title": "Risky-choice framing effects persist when option descriptions are matched and complete: A replication and extension of DeKay and Dou (2024)", "year": 2026, "type": "large replication and extension", "doi": "10.3758/s13423-025-02771-w", "url": "https://pubmed.ncbi.nlm.nih.gov/41979840/", "sourceId": "src-dc40b5ceda9cbea1" } ], "evidenceClass": "established", "sourceIds": [ "src-bb0fa40a53281f49", "src-0e441147fc835a3d", "src-47b281f980645234", "src-2e83fc60350e0524", "src-dc40b5ceda9cbea1" ] }, { "slug": "framing-effect-decoy-effect", "evidenceStatus": "well documented, context-dependent", "reviewedAt": "2026-08-18", "qualification": "The decoy effect is a well-documented family of context effects in which adding an inferior or otherwise strategically positioned alternative can change choice shares among the original options. The classic attraction effect uses an asymmetrically dominated decoy that is worse than the target but not necessarily worse than the competitor. The effect is not guaranteed: its size depends on the geometry of the options, prior preferences, task design and other moderators, and preregistered replications have found both successful and weak or null results.", "mechanism": "Research offers several explanations, including contrast between attribute trade-offs, changes in relative value, ease of justification and attention to particular dimensions. A 2024 integrative review concludes that there is still no clear consensus on one underlying mechanism and separates attraction, compromise and phantom-decoy effects rather than treating every extra option as the same phenomenon.", "practical": "When a third option seems to make one plan suddenly look obvious, compare the original alternatives without it and score the options on the attributes that matter to the decision. Check whether the suspected decoy is actually dominated by the target and whether removing it changes the preference between the remaining options. For product or pricing design, test the full choice set rather than assuming that a three-tier menu will reliably create the effect.", "sources": [ { "title": "Choice in Context: Tradeoff Contrast and Extremeness Aversion", "year": 1992, "type": "foundational experiments", "doi": "10.1177/002224379202900301", "url": "https://doi.org/10.1177/002224379202900301", "sourceId": "src-84a20f469659275d" }, { "title": "The asymmetric dominance effect: Reexamination and extension in risky choice – An experimental study", "year": 2019, "type": "experimental replication and extension", "doi": "10.1016/j.joep.2019.05.007", "url": "https://doi.org/10.1016/j.joep.2019.05.007", "sourceId": "src-6f220811f3f7fc69" }, { "title": "Revisiting the decoy effect: replication and extension of Ariely and Wallsten (1995) and Connolly, Reb, and Kausel (2013)", "year": 2021, "type": "preregistered replication", "doi": "10.1080/23743603.2021.1878340", "url": "https://doi.org/10.1080/23743603.2021.1878340", "sourceId": "src-fe432751bebf733b" }, { "title": "An integrative review of the decoy effect on choice behavior", "year": 2024, "type": "systematic integrative review", "doi": "10.1002/mar.22076", "url": "https://doi.org/10.1002/mar.22076", "sourceId": "src-37cf79ece99ea6c9" } ], "evidenceClass": "supported", "sourceIds": [ "src-84a20f469659275d", "src-6f220811f3f7fc69", "src-fe432751bebf733b", "src-37cf79ece99ea6c9" ] }, { "slug": "framing-effect-default-effect", "evidenceStatus": "well supported, highly context-dependent", "reviewedAt": "2026-08-18", "qualification": "Pre-selecting an option often increases the chance that people choose it, but the size of the default effect varies greatly across domains and designs. A 2019 meta-analysis of 58 studies found a substantial average effect together with strong heterogeneity, including some studies with little or even reversed effects. Staying with a default should therefore not be treated as a direct measure of strong preference or as proof that the default improved welfare.", "mechanism": "There is no single mechanism behind every default effect. Research points to several routes: changing the option can require effort or attention; a default can signal what the choice architect recommends; and a pre-selected option can feel like the existing or endowed state. These routes can overlap, and their importance changes with the decision and the interface.", "practical": "Make consequential defaults visible, easy to change and easy to understand. Compare default designs with active choice when practical, and measure whether the resulting choice still serves the user's goals rather than inferring preference from uptake alone. Treat a default as a design decision that needs testing, not as a universal behavioural lever.", "sources": [ { "title": "Recommendations Implicit in Policy Defaults", "year": 2006, "type": "experiments", "doi": "10.1111/j.1467-9280.2006.01721.x", "url": "https://doi.org/10.1111/j.1467-9280.2006.01721.x", "sourceId": "src-7d9d96710fab2c5f" }, { "title": "When and why defaults influence decisions: a meta-analysis of default effects", "year": 2019, "type": "meta-analysis", "doi": "10.1017/bpp.2018.43", "url": "https://doi.org/10.1017/bpp.2018.43", "sourceId": "src-64c4a8072d35fe53" }, { "title": "Generalizability of choice architecture interventions", "year": 2025, "type": "research review", "doi": "10.1038/s44159-025-00471-9", "url": "https://doi.org/10.1038/s44159-025-00471-9", "sourceId": "src-90e516c044f0e914" }, { "title": "How default choice architecture impacts downstream behavior: A taxonomy, theoretical framework, and research agenda", "year": 2026, "type": "research review", "doi": "10.1002/arcp.70007", "url": "https://doi.org/10.1002/arcp.70007", "sourceId": "src-5e28781720bc4f35" } ], "evidenceClass": "supported", "sourceIds": [ "src-7d9d96710fab2c5f", "src-64c4a8072d35fe53", "src-90e516c044f0e914", "src-5e28781720bc4f35" ] }, { "slug": "heuristic-bias-availability-bias", "evidenceStatus": "established heuristic; bias is context-dependent", "reviewedAt": "2026-08-18", "qualification": "Availability is a judgment heuristic: people can use how easily examples or scenarios come to mind when estimating frequency or probability. That shortcut is not automatically an error because memorable or accessible examples can correlate with real frequency. Bias appears when accessibility is driven by factors that are not diagnostic of the quantity being judged.", "mechanism": "The original account focused on the availability of relevant instances and scenarios. Later work examined subjective ease of retrieval as an informational cue, showing that judgments can depend on how easy recall feels as well as on what is recalled. Some later replication attempts in specific contexts have not reproduced every ease-of-retrieval effect, so one narrow mechanism should not be treated as the entire heuristic.", "practical": "When vivid or recent examples are driving a risk estimate, compare recall with base-rate data or a deliberately sampled reference set. Ask whether the examples are easy to remember because they are common or because they are dramatic, recent, repeated, or personally salient.", "sources": [ { "title": "Availability: A heuristic for judging frequency and probability", "year": 1973, "type": "foundational experiments", "doi": "10.1016/0010-0285(73)90033-9", "url": "https://doi.org/10.1016/0010-0285(73)90033-9", "sourceId": "src-723760d16e30b39e" }, { "title": "Ease of retrieval as information: Another look at the availability heuristic", "year": 1991, "type": "experiments", "doi": "10.1037/0022-3514.61.2.195", "url": "https://doi.org/10.1037/0022-3514.61.2.195", "sourceId": "src-f612390f3949191b" }, { "title": "Investigating the ease-of-retrieval effect in an eyewitness context", "year": 2021, "type": "replication / boundary-condition tests", "doi": "10.1080/09658211.2021.1882502", "url": "https://pubmed.ncbi.nlm.nih.gov/33557719/", "sourceId": "src-0ba7057e7c37fb49" } ], "evidenceClass": "established", "sourceIds": [ "src-723760d16e30b39e", "src-f612390f3949191b", "src-0ba7057e7c37fb49" ] }, { "slug": "human-robot-interaction-form", "evidenceStatus": "supported HRI pattern; project label is nonstandard", "reviewedAt": "2026-08-18", "qualification": "Research supports the broader pattern that a robot's appearance, morphology, framing, and human-likeness shape expectations about its competence, social qualities, and likely behavior. However, 'Form-Function Attribution Bias' is not an established standardized name in the literature. On this site it should be treated as a project label for appearance-driven capability expectations, not as a universally recognized cognitive-bias construct.", "mechanism": "Visual and social design cues help users build a mental model before they have much direct performance evidence. Humanlike or technically styled forms can set different expectations, and expectation-setting can change later competence or trust judgments. The direction is not universal: a more anthropomorphic appearance does not always increase perceived competence, empathy, or trust, and effects depend on task and context.", "practical": "Separate what the system looks or sounds like from what it has demonstrated it can do. For an AI or robot, write down the capabilities implied by the interface and then check each against observed performance, documentation, or controlled tests. Designers should communicate limitations and intended functions directly instead of letting appearance carry the whole capability message.", "sources": [ { "title": "Judging a bot by its cover: an experiment on expectation setting for personal robots", "year": 2010, "type": "experiment", "doi": "10.1145/1734454.1734472", "url": "https://doi.org/10.1145/1734454.1734472", "sourceId": "src-e3698f659e09ea7e" }, { "title": "What Does A Robot Look Like?: A Multi-Site Examination of User Expectations About Robot Appearance", "year": 2017, "type": "multi-study exploratory research", "doi": "10.1177/1541931213601786", "url": "https://doi.org/10.1177/1541931213601786", "sourceId": "src-394b04c1e1398c8f" }, { "title": "Feeling with a robot—the role of anthropomorphism by design and the tendency to anthropomorphize in human-robot interaction", "year": 2023, "type": "experiment", "doi": "10.3389/frobt.2023.1149601", "url": "https://doi.org/10.3389/frobt.2023.1149601", "sourceId": "src-09bef5bf4eda8006" }, { "title": "Humanlikeness as design, anthropomorphism as inference: a conceptual framework for human-robot interaction", "year": 2026, "type": "conceptual framework", "doi": "10.3389/fcogn.2026.1786256", "url": "https://doi.org/10.3389/fcogn.2026.1786256", "sourceId": "src-b4d5205770653dfc" } ], "evidenceClass": "supported", "sourceIds": [ "src-e3698f659e09ea7e", "src-394b04c1e1398c8f", "src-09bef5bf4eda8006", "src-b4d5205770653dfc" ] }, { "slug": "logical-fallacy-escalation-of-commitment", "evidenceStatus": "established, but related constructs should be separated", "reviewedAt": "2026-08-18", "qualification": "Sunk-cost effects and escalation of commitment overlap but are not interchangeable. Sunk-cost research asks whether irrecoverable prior investments influence current choices. Escalation of commitment describes persistence or additional resource allocation to a failing course of action and can also be driven by personal responsibility, self-justification, project structure, and other factors.", "mechanism": "Experiments and field studies show that prior investments can increase continuation or utilization, while classic escalation studies found stronger additional investment after negative outcomes when decision-makers were personally responsible for the original choice. Meta-analytic work finds a sunk-cost effect overall but also substantial moderation by decision type and context.", "practical": "Separate already-spent resources from the expected future costs and benefits of the next decision. For projects, define exit criteria before bad news arrives and ask an independent reviewer to assess the next increment of investment. Do not assume that every continued project is irrational: new information about future value can justify continuation even when past costs are sunk.", "sources": [ { "title": "Knee-deep in the big muddy: a study of escalating commitment to a chosen course of action", "year": 1976, "type": "foundational experiment", "doi": "10.1016/0030-5073(76)90005-2", "url": "https://doi.org/10.1016/0030-5073(76)90005-2", "sourceId": "src-644614d8919a2ee4" }, { "title": "The psychology of sunk cost", "year": 1985, "type": "field study + experiments", "doi": "10.1016/0749-5978(85)90049-4", "url": "https://doi.org/10.1016/0749-5978(85)90049-4", "sourceId": "src-25c242b954867ac1" }, { "title": "On the sunk-cost effect in economic decision-making: a meta-analytic review", "year": 2015, "type": "meta-analysis", "doi": "10.1007/s40685-014-0014-8", "url": "https://doi.org/10.1007/s40685-014-0014-8", "sourceId": "src-ec03691286f7284e" } ], "evidenceClass": "established", "sourceIds": [ "src-644614d8919a2ee4", "src-25c242b954867ac1", "src-ec03691286f7284e" ] }, { "slug": "memory-bias-continued-influence-effect", "evidenceStatus": "well-established persistence after correction; corrections usually still help", "reviewedAt": "2026-08-18", "qualification": "The continued influence effect describes a specific pattern: false or outdated information can keep affecting later reasoning even after a clear correction. It does not mean the correction had no effect, that the person rejected the correction, or that belief in the false claim became stronger. Corrections often reduce misinformation reliance without removing it completely. That distinction matters because continued influence is much broader than the rarer backfire outcome.", "mechanism": "Research does not support one single explanation for every continued-influence result. Accounts include difficulty updating a mental model once misinformation has filled an explanatory role, retrieval competition between the original information and the correction, familiarity, source memory, and changes in how the original source is judged. Recent reviews therefore treat continued influence as a family of updating and retrieval problems rather than one simple memory failure.", "practical": "Correct important misinformation clearly rather than leaving it unchallenged. When the false claim explained an event, provide a credible alternative explanation when one is available. Detailed corrections and repetition can help in many settings, and current evidence gives little reason to avoid a correction simply because it repeats the false claim. Keep the corrected information easy to find and measure the same belief or inference again when the distinction matters. Do not label disagreement, distrust, or incomplete updating as a backfire effect unless the targeted false belief actually becomes stronger.", "sources": [ { "title": "Misinformation and Its Correction: Continued Influence and Successful Debiasing", "year": 2012, "type": "research review", "doi": "10.1177/1529100612451018", "url": "https://doi.org/10.1177/1529100612451018", "sourceId": "src-9811dba9926395b6" }, { "title": "Debunking: A Meta-Analysis of the Psychological Efficacy of Messages Countering Misinformation", "year": 2017, "type": "meta-analysis", "doi": "10.1177/0956797617714579", "url": "https://doi.org/10.1177/0956797617714579", "sourceId": "src-6891b8bf627a6ec7" }, { "title": "The psychological drivers of misinformation belief and its resistance to correction", "year": 2022, "type": "research review", "doi": "10.1038/s44159-021-00006-y", "url": "https://doi.org/10.1038/s44159-021-00006-y", "sourceId": "src-422189df9f5faa26" }, { "title": "Effective correction of misinformation", "year": 2023, "type": "research review", "doi": "10.1016/j.copsyc.2023.101712", "url": "https://doi.org/10.1016/j.copsyc.2023.101712", "sourceId": "src-80cc3fc9e30c3c69" }, { "title": "Factual corrections: Concerns and current evidence", "year": 2024, "type": "research review", "doi": "10.1016/j.copsyc.2023.101715", "url": "https://doi.org/10.1016/j.copsyc.2023.101715", "sourceId": "src-4f99e4ca8c1af4fe" } ], "evidenceClass": "established", "sourceIds": [ "src-9811dba9926395b6", "src-6891b8bf627a6ec7", "src-422189df9f5faa26", "src-80cc3fc9e30c3c69", "src-4f99e4ca8c1af4fe" ] }, { "slug": "memory-bias-fading-affect-bias", "evidenceStatus": "supported across autobiographical-memory research, with moderators", "reviewedAt": "2026-08-18", "qualification": "Fading Affect Bias describes an average asymmetry in autobiographical memory: affect linked to negative events tends to weaken faster over time than affect linked to positive events. It is a group-level pattern with important moderators, not a rule that every painful memory becomes mild or positive. Recent large-sample work also finds the pattern when people directly report how the affect of their memories changed, rather than relying only on calculated differences between retrospective ratings.", "mechanism": "The literature describes the pattern rather than one single settled mechanism. Social rehearsal, emotion regulation, meaning making, memory reconstruction, event characteristics, and individual differences can all matter. The useful distinction is that Fading Affect Bias concerns change in emotional intensity associated with autobiographical memories, not a general claim that factual details of the past become more positive.", "practical": "When comparing how a past event felt with how a similar event feels now, separate remembered facts from remembered emotional intensity. If the comparison matters, use contemporaneous records or repeated ratings instead of assuming today's emotional reaction is an unchanged copy of the original experience. Do not use Fading Affect Bias to dismiss persistent negative memories or individual experiences that do not follow the average pattern.", "sources": [ { "title": "The Fading Affect Bias: Its History, Its Implications, and Its Future", "year": 2014, "type": "research review", "doi": "10.1016/B978-0-12-800052-6.00003-2", "url": "https://doi.org/10.1016/B978-0-12-800052-6.00003-2", "sourceId": "src-52af9360ef62152d" }, { "title": "Evidence for a fading affect bias in subjectively assessed affect changes in autobiographical memory", "year": 2025, "type": "large-sample autobiographical-memory study", "doi": "10.3389/fpsyg.2025.1608751", "url": "https://pubmed.ncbi.nlm.nih.gov/40792093/", "sourceId": "src-816c78f1bdb1f366" } ], "evidenceClass": "supported", "sourceIds": [ "src-52af9360ef62152d", "src-816c78f1bdb1f366" ] }, { "slug": "memory-bias-rosy-retrospection", "evidenceStatus": "supported in event-recollection studies; narrower than general nostalgia", "reviewedAt": "2026-08-18", "qualification": "Rosy Retrospection describes a pattern in which later evaluations of a past experience can become more positive than evaluations recorded during the experience itself. The classic work studied specific meaningful events and found more positive anticipation and later recollection than in-the-moment experience. This does not mean that every fond memory is false, that all past periods were worse than remembered, or that nostalgia is itself a bias.", "mechanism": "In the foundational studies, negative thoughts increased during the event because of distractions, disappointment, and less positive self-evaluation, but these influences were short-lived. Later positive recollection can also interact with memory accessibility and interpretation. The evidence is firmer for the observed shift in evaluations than for one universal mechanism that explains every positive memory of the past.", "practical": "When a remembered event is being used as evidence for a current decision, compare the later memory with records made during the event when those records exist: notes, ratings, messages, photos with context, costs, incidents, or repeated measurements. Treat a positive memory as meaningful experience, but do not automatically treat it as an accurate historical average of how the event felt at the time.", "sources": [ { "title": "Temporal Adjustments in the Evaluation of Events: The Rosy View", "year": 1997, "type": "three event-evaluation studies", "doi": "10.1006/jesp.1997.1333", "url": "https://pubmed.ncbi.nlm.nih.gov/9247371/", "sourceId": "src-c375f114daf625f6" }, { "title": "On misattributing good remembering to a happy past: An investigation into the cognitive roots of nostalgia", "year": 2006, "type": "memory experiments", "doi": "10.1037/1528-3542.6.4.596", "url": "https://pubmed.ncbi.nlm.nih.gov/17144751/", "sourceId": "src-c378be567ea78220" } ], "evidenceClass": "domain-specific", "sourceIds": [ "src-c375f114daf625f6", "src-c378be567ea78220" ] }, { "slug": "probability-bias-subadditivity-effect", "evidenceStatus": "established with boundary conditions", "reviewedAt": "2026-08-18", "qualification": "People often give a larger total probability when an event is unpacked into separate possibilities than when the same event is judged as one packed category. The effect is not universal: how the possibilities are described and how typical they are can change or even reverse an unpacking effect.", "mechanism": "Support theory models probability judgments as depending partly on the support that comes to mind for the focal event and its alternatives. Unpacking can make component possibilities more available or easier to evaluate, increasing their combined judged probability.", "practical": "For consequential forecasts, compare the packed event with a mutually exclusive, collectively exhaustive breakdown. If the estimates change greatly when the wording changes, investigate the representation before acting on the numbers. The goal is consistency, not forcing every judgment to match one decomposition.", "sources": [ { "title": "Support theory: A nonextensional representation of subjective probability", "year": 1994, "type": "theory + experiments", "doi": "10.1037/0033-295X.101.4.547", "url": "https://doi.org/10.1037/0033-295X.101.4.547", "sourceId": "src-e5eecafb672bd8ed" }, { "title": "Unpacking, repacking, and anchoring: advances in support theory", "year": 1997, "type": "theory + experiments", "doi": "10.1037/0033-295X.104.2.406", "url": "https://pubmed.ncbi.nlm.nih.gov/9127585/", "sourceId": "src-9f9930db613528b2" }, { "title": "Typical versus atypical unpacking and superadditive probability judgment", "year": 2004, "type": "boundary-condition experiments", "doi": "10.1037/0278-7393.30.3.573", "url": "https://pubmed.ncbi.nlm.nih.gov/15099126/", "sourceId": "src-ffae461551a5d8e7" } ], "evidenceClass": "mixed", "sourceIds": [ "src-e5eecafb672bd8ed", "src-9f9930db613528b2", "src-ffae461551a5d8e7" ] }, { "slug": "prospect-theory-loss-aversion", "evidenceStatus": "influential and widely estimated; magnitude and robustness debated", "reviewedAt": "2026-08-18", "qualification": "Loss aversion is a central component of prospect theory and many studies estimate losses as receiving greater subjective weight than gains around a reference point. However, the effect should not be summarized with one universal coefficient. A 2024 interdisciplinary meta-analysis of 607 estimates reported a mean coefficient near 1.96, while another 2024 meta-analysis of individual risky-choice datasets estimated about 1.31. A 2025 re-analysis of the larger dataset found little evidence of loss aversion in some symmetric, unordered designs, showing that task structure and analysis can materially change the result.", "mechanism": "Prospect theory represents outcomes as gains and losses relative to a reference point and allows the value function to be steeper for losses than for gains. That model is useful descriptively, but observed loss-sensitive behaviour can also be affected by probability weighting, payoff ordering, attention, reference-point construction and other features of the task. The current debate is therefore about both magnitude and what experimental patterns should count as clean evidence for loss aversion.", "practical": "For a consequential choice, state the reference point and compare matched gains and losses using the same probabilities and time horizon. Re-express options as absolute outcomes when possible and check whether the preference survives. Do not import a fixed 'losses count twice' coefficient into a new setting without evidence from that setting, and keep loss aversion separate from sunk-cost reasoning about resources that are already irrecoverable.", "sources": [ { "title": "Prospect Theory: An Analysis of Decision under Risk", "year": 1979, "type": "foundational theory and experiments", "doi": "10.2307/1914185", "url": "https://doi.org/10.2307/1914185", "sourceId": "src-48160eef6b029066" }, { "title": "Meta-analysis of Empirical Estimates of Loss Aversion", "year": 2024, "type": "interdisciplinary meta-analysis", "doi": "10.1257/jel.20221698", "url": "https://doi.org/10.1257/jel.20221698", "sourceId": "src-e8a194eeb0017a8a" }, { "title": "A meta-analysis of loss aversion in risky contexts", "year": 2024, "type": "meta-analysis of risky-choice datasets", "doi": "10.1016/j.joep.2024.102740", "url": "https://doi.org/10.1016/j.joep.2024.102740", "sourceId": "src-368a6a9d36d10612" }, { "title": "Loss aversion is not robust: A re-meta-analysis", "year": 2025, "type": "re-meta-analysis", "doi": "10.1016/j.joep.2025.102801", "url": "https://doi.org/10.1016/j.joep.2025.102801", "sourceId": "src-72b6f021c45204d9" } ], "evidenceClass": "mixed", "sourceIds": [ "src-48160eef6b029066", "src-e8a194eeb0017a8a", "src-368a6a9d36d10612", "src-72b6f021c45204d9" ] }, { "slug": "prospect-theory-status-quo-bias", "evidenceStatus": "well established, broader than interface defaults", "reviewedAt": "2026-08-18", "qualification": "Status quo bias describes extra preference for an option because it is the current or existing state. Classic experiments and field observations found disproportionate persistence with status quo options, but keeping the current option is not automatically a bias. Switching can have real financial, practical, learning or uncertainty costs that make staying reasonable.", "mechanism": "Several explanations can contribute to status quo persistence, including reference dependence and loss aversion, attention, uncertainty, cognitive effort and psychological commitment to what is already in place. Experimental work also shows that a status quo can shape which alternatives receive attention. This is broader than a designed default: a current provider, workflow or possession can be the status quo even when no interface pre-selects it.", "practical": "For an important review, compare the current option and realistic alternatives as if the choice were being made today. List genuine switching costs separately from familiarity or decision effort. If staying still has the best expected value after that comparison, keeping the status quo may be rational rather than biased.", "sources": [ { "title": "Status quo bias in decision making", "year": 1988, "type": "foundational experiments and field evidence", "doi": "10.1007/BF00055564", "url": "https://doi.org/10.1007/BF00055564", "sourceId": "src-e857ab07ab417658" }, { "title": "Limited attention and status quo bias", "year": 2017, "type": "model and laboratory experiments", "doi": "10.1016/j.jet.2017.01.009", "url": "https://doi.org/10.1016/j.jet.2017.01.009", "sourceId": "src-638a04b999a9cc4b" }, { "title": "When and why defaults influence decisions: a meta-analysis of default effects", "year": 2019, "type": "meta-analysis with status-quo channel analysis", "doi": "10.1017/bpp.2018.43", "url": "https://doi.org/10.1017/bpp.2018.43", "sourceId": "src-64c4a8072d35fe53" } ], "evidenceClass": "established", "sourceIds": [ "src-e857ab07ab417658", "src-638a04b999a9cc4b", "src-64c4a8072d35fe53" ] }, { "slug": "self-assessment-dunning", "evidenceStatus": "supported but often overstated", "reviewedAt": "2026-08-18", "qualification": "The original studies found substantial overestimation among low performers in specific tasks, but the popular story that incompetent people are simply very confident while experts systematically underestimate themselves is too broad. Later work showed that common quartile plots can exaggerate the pattern through regression and better-than-average effects, while a 2023 reanalysis using more appropriate methods still found a small significant effect in intelligence data.", "mechanism": "The original account proposed that limited skill can also limit the metacognitive ability needed to recognize errors. However, observed calibration curves also reflect statistical and measurement properties. The safest interpretation is about miscalibration between objective performance and self-assessment, not a universal personality rule about confidence and competence.", "practical": "Do not use Dunning–Kruger as a label for people you disagree with. For your own decisions, pair confidence with objective feedback, calibration data, or tests of performance. Track whether confidence changes appropriately after feedback rather than assuming that confidence itself reveals competence.", "sources": [ { "title": "Unskilled and unaware of it: how difficulties in recognizing one's own incompetence lead to inflated self-assessments", "year": 1999, "type": "foundational experiments", "doi": "10.1037/0022-3514.77.6.1121", "url": "https://pubmed.ncbi.nlm.nih.gov/10626367/", "sourceId": "src-c707dd3996062dff" }, { "title": "The Dunning-Kruger effect is (mostly) a statistical artefact: Valid approaches to testing the hypothesis with individual differences data", "year": 2020, "type": "methodological critique + empirical test", "doi": "10.1016/j.intell.2020.101449", "url": "https://doi.org/10.1016/j.intell.2020.101449", "sourceId": "src-8333008aad319119" }, { "title": "Reevaluating the Dunning-Kruger effect: A response to and replication of Gignac and Zajenkowski (2020)", "year": 2023, "type": "reanalysis + replication", "doi": "10.1016/j.intell.2022.101717", "url": "https://doi.org/10.1016/j.intell.2022.101717", "sourceId": "src-7a75dea73a03745a" } ], "evidenceClass": "supported", "sourceIds": [ "src-c707dd3996062dff", "src-8333008aad319119", "src-7a75dea73a03745a" ] }, { "slug": "self-assessment-hot", "evidenceStatus": "well established across state-dependent judgment research", "reviewedAt": "2026-08-18", "qualification": "The hot–cold empathy gap describes difficulty predicting preferences, behavior, or experience across different visceral or affective states. In a relatively cold state, people can underappreciate how pain, hunger, sexual arousal, craving, fear, anger, and other hot states will change motivation and choice; in a hot state, they can also overproject the current state into the future. The pattern can be intrapersonal or interpersonal and should not be reduced to a generic claim that emotion always causes bad decisions.", "mechanism": "Visceral and affective states change the relative desirability and salience of actions. Forecasts made outside the target state rely on an incomplete simulation of those motivational changes. The empathy-gap framework overlaps with projection bias and affective forecasting, but focuses specifically on failures to appreciate state-dependent changes rather than on every error in predicting future feelings or preferences.", "practical": "For a consequential decision that will be made or lived through in a different state, identify the relevant state explicitly and gather evidence from people or past episodes in that state. Use precommitments for predictable hot-state choices, but keep an escape route when the future state contains information the cold-state planner could not know. In interpersonal decisions, ask what the other person's current pain, craving, fear, or arousal changes rather than assuming your present state is a neutral reference point.", "sources": [ { "title": "Out of Control: Visceral Influences on Behavior", "year": 1996, "type": "foundational theory / evidence synthesis", "doi": "10.1006/obhd.1996.0028", "url": "https://doi.org/10.1006/obhd.1996.0028", "sourceId": "src-30911b2f36e96c9d" }, { "title": "Hot-cold empathy gaps and medical decision making", "year": 2005, "type": "review", "doi": "10.1037/0278-6133.24.4.S49", "url": "https://pubmed.ncbi.nlm.nih.gov/16045419/", "sourceId": "src-adcadd8b7935bf35" }, { "title": "fMRI evidence of a hot-cold empathy gap in hypothetical and real aversive choices", "year": 2013, "type": "behavioral experiment + neuroimaging", "doi": "10.3389/fnins.2013.00104", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC3677130/", "sourceId": "src-26f3b86cc6d92ecf" } ], "evidenceClass": "established", "sourceIds": [ "src-30911b2f36e96c9d", "src-adcadd8b7935bf35", "src-26f3b86cc6d92ecf" ] }, { "slug": "truth-judgment-illusory-truth-effect", "evidenceStatus": "robust", "reviewedAt": "2026-08-18", "qualification": "Repeated information is, on average, judged as more truthful than comparable new information. The size of the effect varies with the material and procedure, and repetition does not make every claim believable or erase all prior knowledge.", "mechanism": "Repetition can make information easier or more coherent to process and retrieve. People can then use that subjective ease or familiarity as one cue when judging truth, even though repetition itself is not evidence that the statement is correct.", "practical": "When a claim feels true mainly because it sounds familiar, switch from familiarity to source checking. Ask where the claim came from and what evidence would support it. Do not treat familiarity as proof, but do not treat repetition as proof of falsehood either.", "sources": [ { "title": "Frequency and the conference of referential validity", "year": 1977, "type": "foundational experiment", "doi": "10.1016/S0022-5371(77)80012-1", "url": "https://doi.org/10.1016/S0022-5371(77)80012-1", "sourceId": "src-c393b2ca6b3172da" }, { "title": "The effects of repetition frequency on the illusory truth effect", "year": 2021, "type": "experiments", "doi": "10.1186/s41235-021-00301-5", "url": "https://pubmed.ncbi.nlm.nih.gov/33983553/", "sourceId": "src-e17b930d0863e88a" }, { "title": "The illusory truth effect: A review of how repetition increases belief in misinformation", "year": 2024, "type": "review", "doi": "10.1016/j.copsyc.2023.101736", "url": "https://pubmed.ncbi.nlm.nih.gov/38113667/", "sourceId": "src-7faeefb6c3a24d1d" } ], "evidenceClass": "established", "sourceIds": [ "src-c393b2ca6b3172da", "src-e17b930d0863e88a", "src-7faeefb6c3a24d1d" ] } ], "schemaVersion": "1.0.0", "releaseVersion": "2026.08.18" }