Social judgment and perspective

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.
• Zero-Sum Bias – when we see competition where gains can coexist — Meegan introduced zero-sum bias in three university-grading experiments where grades were based on an absolute standard and high grades were not rationed. Participants were nevertheless more likely to predict a low next grade after seeing that many high grades had already been awarded. This is direct evidence for zero-sum judgment in that paradigm, not proof that every competitive interpretation has the same cause. Later reviews describe a much broader literature on zero-sum beliefs across interpersonal, intergroup, organizational and international settings. That wider literature often studies beliefs or mindsets rather than the exact 2010 task, so the concepts should be connected without pretending they are identical.
• Illusory Truth Effect – when repetition makes a claim feel more true — Repeated claims receive higher truth ratings than comparable new claims on average, but the effect is heterogeneous and does not generalize equally to every kind of statement. A 2026 systematic review and meta-analysis covering 182 studies, 366 effect sizes and more than 31,000 participants found a reliable corrected average effect. A later 2026 paper with two preregistered experiments found little or no meaningful repetition effect for social-political opinion statements. The useful claim is therefore narrower than 'repeat anything and people will believe it.'
• 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.
• Unit Bias – when one unit quietly becomes the ‘right’ amount — The original unit-bias paper reported experiments in which changing the size of a food unit or serving tool changed how much people selected or consumed even when they could take as many units as they wanted. Later work by Geier and Rozin also found default-standard-unit effects in estimates of body weight and meal calories. These studies support sensitivity to salient units, but they do not prove that every default package, batch, or interface unit causes the same effect in every domain.
• 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.

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.