{ "schemaVersion": "1.0.0", "releaseVersion": "2026.08.18", "updatedAt": "2026-08-18", "skills": [ { "slug": "evidence-evaluation", "title": "Evidence evaluation", "summary": "Judge whether a claim deserves confidence before repetition, fluency or preference turns into evidence.", "outcome": "You can separate a persuasive claim from the evidence that should change your mind.", "whenToUse": [ "A team already prefers one explanation and is gathering support for it.", "A claim appears in many places but may trace back to one source.", "A vivid example is dominating broader evidence.", "An AI answer sounds convincing but important claims are still unverified." ], "actions": [ "Write what evidence would weaken the preferred conclusion before searching for more support.", "Trace repeated claims back to genuinely independent sources.", "Compare vivid examples with a base rate, reference class or broader sample.", "Keep verified facts, plausible inferences and open questions separate." ], "contexts": [ { "slug": "checking-claims-misinformation", "title": "Checking claims & misinformation", "summary": "Use these evidence-reviewed lenses when deciding whether a repeated claim is trustworthy, checking a correction, or testing an explanation against plausible alternatives.", "url": "https://cognitive-biases.github.io/contexts/checking-claims-misinformation/", "practiceUrl": "https://cognitive-biases.github.io/practice/checking-claims-misinformation/" }, { "slug": "ai-assisted-decisions", "title": "AI-assisted decisions", "summary": "Use these evidence-reviewed lenses when a chatbot, model, recommendation system, or automated decision aid is influencing what you believe or do.", "url": "https://cognitive-biases.github.io/contexts/ai-assisted-decisions/", "practiceUrl": "https://cognitive-biases.github.io/practice/ai-assisted-decisions/" }, { "slug": "work-project-decisions", "title": "Work & project decisions", "summary": "Use these evidence-reviewed lenses when a team is choosing a direction, interpreting results, deciding whether to continue, or turning metrics into action.", "url": "https://cognitive-biases.github.io/contexts/work-project-decisions/", "practiceUrl": "https://cognitive-biases.github.io/practice/work-project-decisions/" } ], "biases": [ "cognitive-bias-confirmation-bias", "truth-judgment-illusory-truth-effect", "heuristic-bias-availability-bias", "memory-bias-continued-influence-effect" ], "lenses": [ { "slug": "cognitive-bias-confirmation-bias", "title": "Confirmation Bias", "url": "https://cognitive-biases.github.io/biases/cognitive-bias-confirmation-bias/", "evidenceUrl": "https://cognitive-biases.github.io/biases/cognitive-bias-confirmation-bias/#evidence", "evidenceStatus": "well established, broad construct", "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." }, { "slug": "truth-judgment-illusory-truth-effect", "title": "Illusory Truth Effect", "url": "https://cognitive-biases.github.io/biases/truth-judgment-illusory-truth-effect/", "evidenceUrl": "https://cognitive-biases.github.io/biases/truth-judgment-illusory-truth-effect/#evidence", "evidenceStatus": "robust", "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." }, { "slug": "heuristic-bias-availability-bias", "title": "Availability Heuristic", "url": "https://cognitive-biases.github.io/biases/heuristic-bias-availability-bias/", "evidenceUrl": "https://cognitive-biases.github.io/biases/heuristic-bias-availability-bias/#evidence", "evidenceStatus": "established heuristic; bias is context-dependent", "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." }, { "slug": "memory-bias-continued-influence-effect", "title": "Continued Influence Effect", "url": "https://cognitive-biases.github.io/biases/memory-bias-continued-influence-effect/", "evidenceUrl": "https://cognitive-biases.github.io/biases/memory-bias-continued-influence-effect/#evidence", "evidenceStatus": "well-established persistence after correction; corrections usually still help", "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." } ], "canonicalUrl": "https://cognitive-biases.github.io/skills/evidence-evaluation/" }, { "slug": "decision-making-under-uncertainty", "title": "Decision making under uncertainty", "summary": "Make useful choices when outcomes are not known and one number, story or recent event can pull judgment too strongly.", "outcome": "You can make a decision without pretending uncertainty has disappeared.", "whenToUse": [ "A deadline, probability or budget is being discussed as one precise number.", "An early estimate is shaping every later estimate.", "The latest success or failure is dominating the next decision.", "The team is continuing mainly because it has already invested a lot." ], "actions": [ "Start with a range or reference class before negotiating a precise number.", "Create an independent estimate before looking at a strong anchor when possible.", "Separate already-spent resources from the future cost and value of the next step.", "Record assumptions that would make you reopen the decision." ], "contexts": [ { "slug": "forecasting-future-choices", "title": "Forecasting & future choices", "summary": "Use these evidence-reviewed lenses when estimating uncertainty, imagining future feelings or preferences, or reviewing forecasts after the outcome is known.", "url": "https://cognitive-biases.github.io/contexts/forecasting-future-choices/", "practiceUrl": "https://cognitive-biases.github.io/practice/forecasting-future-choices/" }, { "slug": "project-estimation-delivery", "title": "Project estimation & delivery", "summary": "Use these evidence-reviewed lenses when setting a deadline, estimating effort, discussing delivery risk, or learning from a project that finished later than expected.", "url": "https://cognitive-biases.github.io/contexts/project-estimation-delivery/", "practiceUrl": "https://cognitive-biases.github.io/practice/project-estimation-delivery/" }, { "slug": "work-project-decisions", "title": "Work & project decisions", "summary": "Use these evidence-reviewed lenses when a team is choosing a direction, interpreting results, deciding whether to continue, or turning metrics into action.", "url": "https://cognitive-biases.github.io/contexts/work-project-decisions/", "practiceUrl": "https://cognitive-biases.github.io/practice/work-project-decisions/" } ], "biases": [ "cognitive-bias-anchoring-effect", "probability-bias-subadditivity-effect", "heuristic-bias-availability-bias", "logical-fallacy-escalation-of-commitment" ], "lenses": [ { "slug": "cognitive-bias-anchoring-effect", "title": "Anchoring Effect", "url": "https://cognitive-biases.github.io/biases/cognitive-bias-anchoring-effect/", "evidenceUrl": "https://cognitive-biases.github.io/biases/cognitive-bias-anchoring-effect/#evidence", "evidenceStatus": "well-supported for numerical judgments; strength depends on anchor type and context", "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." }, { "slug": "probability-bias-subadditivity-effect", "title": "Subadditivity Effect", "url": "https://cognitive-biases.github.io/biases/probability-bias-subadditivity-effect/", "evidenceUrl": "https://cognitive-biases.github.io/biases/probability-bias-subadditivity-effect/#evidence", "evidenceStatus": "established with boundary conditions", "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." }, { "slug": "heuristic-bias-availability-bias", "title": "Availability Heuristic", "url": "https://cognitive-biases.github.io/biases/heuristic-bias-availability-bias/", "evidenceUrl": "https://cognitive-biases.github.io/biases/heuristic-bias-availability-bias/#evidence", "evidenceStatus": "established heuristic; bias is context-dependent", "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." }, { "slug": "logical-fallacy-escalation-of-commitment", "title": "Escalation of Commitment", "url": "https://cognitive-biases.github.io/biases/logical-fallacy-escalation-of-commitment/", "evidenceUrl": "https://cognitive-biases.github.io/biases/logical-fallacy-escalation-of-commitment/#evidence", "evidenceStatus": "established, but related constructs should be separated", "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." } ], "canonicalUrl": "https://cognitive-biases.github.io/skills/decision-making-under-uncertainty/" }, { "slug": "forecasting", "title": "Forecasting", "summary": "Estimate what may happen next by using comparable evidence, explicit uncertainty and records made before the outcome is known.", "outcome": "You can produce forecasts that are easier to challenge, compare and learn from later.", "whenToUse": [ "A project deadline is being built only from an inside plan.", "A current feeling or preference is being projected far into the future.", "A vivid recent example is being used as the main probability estimate.", "A finished outcome now feels as if it had always been obvious." ], "actions": [ "Check comparable completed cases before refining the current plan.", "State a range and the factors that would push the result toward either end.", "Store the forecast and assumptions before the outcome.", "Review forecast quality against the original record, not reconstructed memory." ], "contexts": [ { "slug": "forecasting-future-choices", "title": "Forecasting & future choices", "summary": "Use these evidence-reviewed lenses when estimating uncertainty, imagining future feelings or preferences, or reviewing forecasts after the outcome is known.", "url": "https://cognitive-biases.github.io/contexts/forecasting-future-choices/", "practiceUrl": "https://cognitive-biases.github.io/practice/forecasting-future-choices/" }, { "slug": "project-estimation-delivery", "title": "Project estimation & delivery", "summary": "Use these evidence-reviewed lenses when setting a deadline, estimating effort, discussing delivery risk, or learning from a project that finished later than expected.", "url": "https://cognitive-biases.github.io/contexts/project-estimation-delivery/", "practiceUrl": "https://cognitive-biases.github.io/practice/project-estimation-delivery/" } ], "biases": [ "egocentric-bias-planning-fallacy", "cognitive-bias-anchoring-effect", "heuristic-bias-availability-bias", "cognitive-bias-hindsight-bias" ], "lenses": [ { "slug": "egocentric-bias-planning-fallacy", "title": "Planning Fallacy", "url": "https://cognitive-biases.github.io/biases/egocentric-bias-planning-fallacy/", "evidenceUrl": "https://cognitive-biases.github.io/biases/egocentric-bias-planning-fallacy/#evidence", "evidenceStatus": "well-supported for time estimates; size and causes vary by context", "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." }, { "slug": "cognitive-bias-anchoring-effect", "title": "Anchoring Effect", "url": "https://cognitive-biases.github.io/biases/cognitive-bias-anchoring-effect/", "evidenceUrl": "https://cognitive-biases.github.io/biases/cognitive-bias-anchoring-effect/#evidence", "evidenceStatus": "well-supported for numerical judgments; strength depends on anchor type and context", "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." }, { "slug": "heuristic-bias-availability-bias", "title": "Availability Heuristic", "url": "https://cognitive-biases.github.io/biases/heuristic-bias-availability-bias/", "evidenceUrl": "https://cognitive-biases.github.io/biases/heuristic-bias-availability-bias/#evidence", "evidenceStatus": "established heuristic; bias is context-dependent", "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." }, { "slug": "cognitive-bias-hindsight-bias", "title": "Hindsight Bias", "url": "https://cognitive-biases.github.io/biases/cognitive-bias-hindsight-bias/", "evidenceUrl": "https://cognitive-biases.github.io/biases/cognitive-bias-hindsight-bias/#evidence", "evidenceStatus": "robust", "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." } ], "canonicalUrl": "https://cognitive-biases.github.io/skills/forecasting/" }, { "slug": "metacognition", "title": "Metacognition", "summary": "Inspect how a judgment was formed, what influenced it and what would make you change it.", "outcome": "You can review your reasoning process without confusing confidence, outcome and hindsight with decision quality.", "whenToUse": [ "A good result is being treated as proof that the original decision process was good.", "A bad result makes the warning signs seem obvious in retrospect.", "You are mainly testing arguments that support a preferred answer.", "You need to learn from a past decision without rewriting what was knowable at the time." ], "actions": [ "Freeze what was actually known before the outcome.", "Judge decision-process quality separately from the eventual result.", "Write what would change your mind before collecting more evidence.", "Compare the review with the original forecast, notes or assumptions." ], "contexts": [ { "slug": "work-project-decisions", "title": "Work & project decisions", "summary": "Use these evidence-reviewed lenses when a team is choosing a direction, interpreting results, deciding whether to continue, or turning metrics into action.", "url": "https://cognitive-biases.github.io/contexts/work-project-decisions/", "practiceUrl": "https://cognitive-biases.github.io/practice/work-project-decisions/" }, { "slug": "forecasting-future-choices", "title": "Forecasting & future choices", "summary": "Use these evidence-reviewed lenses when estimating uncertainty, imagining future feelings or preferences, or reviewing forecasts after the outcome is known.", "url": "https://cognitive-biases.github.io/contexts/forecasting-future-choices/", "practiceUrl": "https://cognitive-biases.github.io/practice/forecasting-future-choices/" }, { "slug": "ai-assisted-decisions", "title": "AI-assisted decisions", "summary": "Use these evidence-reviewed lenses when a chatbot, model, recommendation system, or automated decision aid is influencing what you believe or do.", "url": "https://cognitive-biases.github.io/contexts/ai-assisted-decisions/", "practiceUrl": "https://cognitive-biases.github.io/practice/ai-assisted-decisions/" } ], "biases": [ "cognitive-bias-outcome-bias", "cognitive-bias-hindsight-bias", "cognitive-bias-confirmation-bias", "attribution-bias-moral-luck" ], "lenses": [ { "slug": "cognitive-bias-outcome-bias", "title": "Outcome Bias", "url": "https://cognitive-biases.github.io/biases/cognitive-bias-outcome-bias/", "evidenceUrl": "https://cognitive-biases.github.io/biases/cognitive-bias-outcome-bias/#evidence", "evidenceStatus": "replicated", "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." }, { "slug": "cognitive-bias-hindsight-bias", "title": "Hindsight Bias", "url": "https://cognitive-biases.github.io/biases/cognitive-bias-hindsight-bias/", "evidenceUrl": "https://cognitive-biases.github.io/biases/cognitive-bias-hindsight-bias/#evidence", "evidenceStatus": "robust", "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." }, { "slug": "cognitive-bias-confirmation-bias", "title": "Confirmation Bias", "url": "https://cognitive-biases.github.io/biases/cognitive-bias-confirmation-bias/", "evidenceUrl": "https://cognitive-biases.github.io/biases/cognitive-bias-confirmation-bias/#evidence", "evidenceStatus": "well established, broad construct", "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." }, { "slug": "attribution-bias-moral-luck", "title": "Moral Luck", "url": "https://cognitive-biases.github.io/biases/attribution-bias-moral-luck/", "evidenceUrl": "https://cognitive-biases.github.io/biases/attribution-bias-moral-luck/#evidence", "evidenceStatus": "established moral-judgment phenomenon with multiple contributors", "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." } ], "canonicalUrl": "https://cognitive-biases.github.io/skills/metacognition/" }, { "slug": "information-verification", "title": "Information verification", "summary": "Check where information came from, whether sources are independent and whether a correction has really replaced the old explanation.", "outcome": "You can verify important claims without treating repetition as independent confirmation.", "whenToUse": [ "A claim feels familiar because it has appeared repeatedly.", "Several summaries may all depend on the same original source.", "A correction was issued but the old story still shapes later reasoning.", "Generated answers repeat a claim without showing where it came from." ], "actions": [ "Trace the claim to the earliest reliable or primary source you can inspect.", "Count independent sources, not repetitions.", "After a correction, restate the supported replacement explanation explicitly.", "Mark claims as verified, unverified or disputed instead of flattening them into one confidence level." ], "contexts": [ { "slug": "checking-claims-misinformation", "title": "Checking claims & misinformation", "summary": "Use these evidence-reviewed lenses when deciding whether a repeated claim is trustworthy, checking a correction, or testing an explanation against plausible alternatives.", "url": "https://cognitive-biases.github.io/contexts/checking-claims-misinformation/", "practiceUrl": "https://cognitive-biases.github.io/practice/checking-claims-misinformation/" }, { "slug": "ai-assisted-decisions", "title": "AI-assisted decisions", "summary": "Use these evidence-reviewed lenses when a chatbot, model, recommendation system, or automated decision aid is influencing what you believe or do.", "url": "https://cognitive-biases.github.io/contexts/ai-assisted-decisions/", "practiceUrl": "https://cognitive-biases.github.io/practice/ai-assisted-decisions/" } ], "biases": [ "truth-judgment-illusory-truth-effect", "memory-bias-continued-influence-effect", "cognitive-bias-confirmation-bias", "heuristic-bias-availability-bias" ], "lenses": [ { "slug": "truth-judgment-illusory-truth-effect", "title": "Illusory Truth Effect", "url": "https://cognitive-biases.github.io/biases/truth-judgment-illusory-truth-effect/", "evidenceUrl": "https://cognitive-biases.github.io/biases/truth-judgment-illusory-truth-effect/#evidence", "evidenceStatus": "robust", "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." }, { "slug": "memory-bias-continued-influence-effect", "title": "Continued Influence Effect", "url": "https://cognitive-biases.github.io/biases/memory-bias-continued-influence-effect/", "evidenceUrl": "https://cognitive-biases.github.io/biases/memory-bias-continued-influence-effect/#evidence", "evidenceStatus": "well-established persistence after correction; corrections usually still help", "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." }, { "slug": "cognitive-bias-confirmation-bias", "title": "Confirmation Bias", "url": "https://cognitive-biases.github.io/biases/cognitive-bias-confirmation-bias/", "evidenceUrl": "https://cognitive-biases.github.io/biases/cognitive-bias-confirmation-bias/#evidence", "evidenceStatus": "well established, broad construct", "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." }, { "slug": "heuristic-bias-availability-bias", "title": "Availability Heuristic", "url": "https://cognitive-biases.github.io/biases/heuristic-bias-availability-bias/", "evidenceUrl": "https://cognitive-biases.github.io/biases/heuristic-bias-availability-bias/#evidence", "evidenceStatus": "established heuristic; bias is context-dependent", "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." } ], "canonicalUrl": "https://cognitive-biases.github.io/skills/information-verification/" }, { "slug": "ai-assisted-reasoning", "title": "AI-assisted reasoning", "summary": "Use AI as a decision aid without confusing fluent output, humanlike presentation or repeated generated claims with verified capability and evidence.", "outcome": "You can use an AI system to expand reasoning while keeping important judgments independently checkable.", "whenToUse": [ "A chatbot recommendation is influencing an important decision.", "The model gives the first number, score or estimate in the discussion.", "A polished conversational style makes the system feel more capable than you have tested.", "You are using AI mainly to strengthen a conclusion you already prefer." ], "actions": [ "Write a provisional judgment or success criterion before asking AI for advice when the decision matters.", "Separate interface quality from the capability needed for the task.", "Verify consequential claims against an independent source rather than another generated answer.", "Ask for counterevidence and alternative explanations before finalizing the decision." ], "contexts": [ { "slug": "ai-assisted-decisions", "title": "AI-assisted decisions", "summary": "Use these evidence-reviewed lenses when a chatbot, model, recommendation system, or automated decision aid is influencing what you believe or do.", "url": "https://cognitive-biases.github.io/contexts/ai-assisted-decisions/", "practiceUrl": "https://cognitive-biases.github.io/practice/ai-assisted-decisions/" }, { "slug": "checking-claims-misinformation", "title": "Checking claims & misinformation", "summary": "Use these evidence-reviewed lenses when deciding whether a repeated claim is trustworthy, checking a correction, or testing an explanation against plausible alternatives.", "url": "https://cognitive-biases.github.io/contexts/checking-claims-misinformation/", "practiceUrl": "https://cognitive-biases.github.io/practice/checking-claims-misinformation/" }, { "slug": "work-project-decisions", "title": "Work & project decisions", "summary": "Use these evidence-reviewed lenses when a team is choosing a direction, interpreting results, deciding whether to continue, or turning metrics into action.", "url": "https://cognitive-biases.github.io/contexts/work-project-decisions/", "practiceUrl": "https://cognitive-biases.github.io/practice/work-project-decisions/" } ], "biases": [ "false-priors-automation-bias", "cognitive-bias-anchoring-effect", "availability-heuristic-anthropomorphism", "human-robot-interaction-form", "cognitive-bias-confirmation-bias", "truth-judgment-illusory-truth-effect" ], "lenses": [ { "slug": "false-priors-automation-bias", "title": "Automation Bias", "url": "https://cognitive-biases.github.io/biases/false-priors-automation-bias/", "evidenceUrl": "https://cognitive-biases.github.io/biases/false-priors-automation-bias/#evidence", "evidenceStatus": "established, context-dependent", "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." }, { "slug": "cognitive-bias-anchoring-effect", "title": "Anchoring Effect", "url": "https://cognitive-biases.github.io/biases/cognitive-bias-anchoring-effect/", "evidenceUrl": "https://cognitive-biases.github.io/biases/cognitive-bias-anchoring-effect/#evidence", "evidenceStatus": "well-supported for numerical judgments; strength depends on anchor type and context", "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." }, { "slug": "availability-heuristic-anthropomorphism", "title": "Anthropomorphism", "url": "https://cognitive-biases.github.io/biases/availability-heuristic-anthropomorphism/", "evidenceUrl": "https://cognitive-biases.github.io/biases/availability-heuristic-anthropomorphism/#evidence", "evidenceStatus": "established attribution tendency; not inherently an error", "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." }, { "slug": "human-robot-interaction-form", "title": "Appearance–Capability Expectation", "url": "https://cognitive-biases.github.io/biases/human-robot-interaction-form/", "evidenceUrl": "https://cognitive-biases.github.io/biases/human-robot-interaction-form/#evidence", "evidenceStatus": "supported HRI pattern; project label is nonstandard", "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." }, { "slug": "cognitive-bias-confirmation-bias", "title": "Confirmation Bias", "url": "https://cognitive-biases.github.io/biases/cognitive-bias-confirmation-bias/", "evidenceUrl": "https://cognitive-biases.github.io/biases/cognitive-bias-confirmation-bias/#evidence", "evidenceStatus": "well established, broad construct", "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." }, { "slug": "truth-judgment-illusory-truth-effect", "title": "Illusory Truth Effect", "url": "https://cognitive-biases.github.io/biases/truth-judgment-illusory-truth-effect/", "evidenceUrl": "https://cognitive-biases.github.io/biases/truth-judgment-illusory-truth-effect/#evidence", "evidenceStatus": "robust", "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." } ], "canonicalUrl": "https://cognitive-biases.github.io/skills/ai-assisted-reasoning/" } ] }