The decision question
How can I separate what I know, what another person knows and what I infer about their competence or responsibility?
Reviewed lenses
- Anthropomorphism – when nonhuman systems are read as humanlike — 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.
- Curse of Knowledge – when what you know distorts what you expect others to know — 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.
- Appearance–Capability Expectation – when a robot’s design changes what you expect it can do — 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.
- Declinism – when the present is judged against an idealized past — 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.
- Hot–Cold Empathy Gap – when one state is a poor guide to choices in another — 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.
- Illusory Truth Effect – when repeated lies start to feel like truth — 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.
- Moral Luck – when outcomes change blame for otherwise similar choices — 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.
- Anchoring Effect – when a starting number pulls later estimates toward it — 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.
- Automation Bias – when automated advice replaces independent checking — 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.
- Availability Heuristic – when easy-to-recall examples shape frequency judgments — 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.
Decision guides
- Work & project decisions
- Forecasting & future choices
- AI-assisted decisions
- Project estimation & delivery
- Checking claims & misinformation
- Comparing plans & pricing
- Defaults, settings & choice architecture
- Presenting risk & options
- Reviewing KPIs & proxy metrics
- Was the past really better?
- Continue, change, or stop a project
How to use this page
Start with the decision, not with a label for a person. Open the evidence behind the lenses that fit, compare nearby concepts when needed, and keep uncertainty visible.

