Appraisal tendency framework
Each emotion biases judgment in its own predictable direction.
What it means
The appraisal-tendency framework explains how specific emotions, rather than just overall good or bad mood, exert distinct and predictable influences on judgment and choice by carrying over the appraisal dimensions that define them. Each emotion is characterized by a core appraisal theme, such as certainty, control, or agency, and that theme acts as a cognitive lens applied to unrelated decisions. This is why two same-valence emotions diverge: fear (appraisals of uncertainty and low control) makes people see more risk, while anger (certainty and individual control) makes them more optimistic and risk-tolerant. The framework moved the field beyond simple valence-based models toward emotion-specific predictions and explains carryover from incidental emotions. It is a foundational tool in the affective science of decision making.
How the carryover works
Carryover depends on the appraisal outliving the event that caused it. The trigger is specific, but the appraisal it installs is not tagged to that source: it becomes a general perceptual set applied to whatever gets judged next. People rarely notice the borrowing, so the effect survives into unrelated tasks minutes later. The framework predicts matching rather than blanket influence. An emotion biases judgments turning on the same appraisal dimension it carries, so anger's certainty moves estimates of how knowable an outcome is more than it moves unrelated evaluations. In mood-as-information studies, telling people where the feeling came from can weaken the carryover, the signature of a misattributed cue rather than a changed belief.
What the evidence shows
The founding studies crossed dispositional and manipulated emotion across Experiments 1-3, measuring fear and anger as traits and then inducing them. A nationally representative field study after the September 2001 attacks reproduced the split on real risks: fear raised risk estimates and support for precautionary policy, anger lowered both. The sharpest demonstration used money. Lerner, Small and Loewenstein found incidental disgust cut selling and choice prices alike and erased the endowment effect, while sadness cut selling prices but raised choice prices, reversing it. A preregistered replication (N=403) later recovered the sadness effect but not the disgust pattern. A later meta-analysis found same-valence emotions diverge in their effects on judgment, with effect sizes varying by emotion and outcome.
Limits and caveats
Support is uneven across outcomes. A registered-report replication by Lu, Efendic and Feldman recovered the predicted pattern for optimistic risk estimates overall, and for positive and ambiguous events, but not for risk preference itself and not for negative events. Their extension added hope, positive in valence but low in certainty and control, and it failed to behave as the appraisal logic requires. That matters, because the framework should generalize beyond the fear-anger pair it was built on. Anger also resists a single explanation: its effects can come from shallow, heuristic processing as readily as from certainty appraisals. The dimension list is not settled either, making post hoc predictions easy to construct.
Using it in practice
The value is diagnostic before it is prescriptive. Any measurement taken while people feel something unrelated is contaminated: a risk survey during a distressing news cycle, a pricing study after a frustrating checkout, a review panel after a hostile meeting. The fix is to record the emotion and test whether it tracks the outcome, not to pretend it is absent. For communication, match the appraisal to the response wanted rather than the valence. Provoking anger about a hazard tends to lower perceived risk while raising appetite for confrontation; provoking fear moves perceived risk the other way, raising it. Source misattribution can weaken carryover in mood-as-information studies, though Lerner et al. (2015) judge such effortful strategies unreliable and favour changing the choice context.
Related but distinct
The affect heuristic and risk-as-feelings also treat feeling as an input to judgment, but they run on valence and intensity: bad feeling, high risk. The appraisal-tendency framework exists because that is too coarse, and its evidence is the cases where valence makes the wrong call, such as fear against anger or sadness against disgust. It differs likewise from mood-congruent judgment, which predicts negative states darken evaluations across the board. Appraisal theory of emotion is the parent, explaining how appraisals generate emotions. This framework runs the arrow the other way, asking what an already-active emotion does to the next, unrelated judgment.
Examples
After a frightening experience, people judge unrelated risks as higher, whereas after an angering one they judge the same risks as lower, just as their differing appraisals predict.
A sad film clip before an unrelated shopping task leaves people willing to pay more for a plain water bottle; a disgusting clip leaves them wanting to be rid of it.
Angry jurors, carrying appraisals of certainty and blame, reach for a person to punish; sad ones, carrying loss and low control, reach instead for the circumstances.
A negotiator who arrives angry from a delayed flight reads the counterpart's position as knowable and controllable, so concedes less and pushes harder. The flight is forgotten; the certainty it installed is not.
An interviewer still anxious from a tense budget meeting appraises through uncertainty, and judges the unconventional outsider's chance of failing in the role as higher than the evidence in the file supports.
First described in Lerner & Keltner (2000, 2001).
Key references
- Lu, S., Efendic, E., & Feldman, G. (2025). Associations of fear, anger, happiness, and hope with risk judgments: Revisiting appraisal-tendency framework with a replication and extensions Registered Report of Lerner and Keltner (2001). Journal of Personality and Social Psychology. Advance online publication. doi.org/10.1037/pspp0000586
- Lerner, J. S., Li, Y., Valdesolo, P., & Kassam, K. S. (2015). Emotion and decision making. Annual Review of Psychology, 66, 799-823. doi.org/10.1146/annurev-psych-010213-115043
- Angie, A. D., Connelly, S., Waples, E. P., & Kligyte, V. (2011). The influence of discrete emotions on judgement and decision-making: A meta-analytic review. Cognition and Emotion, 25(8), 1393-1422. doi.org/10.1080/02699931.2010.550751
- Lerner, J. S., & Tiedens, L. Z. (2006). Portrait of the angry decision maker: How appraisal tendencies shape anger's influence on cognition. Journal of Behavioral Decision Making, 19(2), 115-137. doi.org/10.1002/bdm.515
- Lerner, J. S., Small, D. A., & Loewenstein, G. (2004). Heart strings and purse strings: Carryover effects of emotions on economic decisions. Psychological Science, 15(5), 337-341. doi.org/10.1111/j.0956-7976.2004.00679.x
- Chaudhury, S. H., & Garg, N. (2023). "Heart strings and purse strings" revisited: A preregistered replication and extension. Journal of Experimental Psychology: General, 152(7), 1873-1886. doi.org/10.1037/xge0001372
- Lerner, J. S., Gonzalez, R. M., Small, D. A., & Fischhoff, B. (2003). Effects of fear and anger on perceived risks of terrorism: A national field experiment. Psychological Science, 14(2), 144-150. doi.org/10.1111/1467-9280.01433
- Lerner, J. S., & Keltner, D. (2001). Fear, anger, and risk. Journal of Personality and Social Psychology, 81(1), 146-159. doi.org/10.1037/0022-3514.81.1.146