Overgeneralization
Draw a sweeping conclusion from one or a few negative events.
Why it works
The brain generalizes from experience as a learning mechanism — one bad dog means "be careful around dogs." Overgeneralization applies this to self-evaluation: one failure becomes "I always fail," one rejection becomes "I’m always rejected." The overgeneralization works by converting a specific, bounded event into a universal, dispositional conclusion about character or fate. The negative generalization is maintained because confirming evidence (future failures) is noticed while disconfirming evidence (past successes) is discounted.
How to do it
- Spot the generalizing words: "always," "never," "everyone," "no one," "everything."
- Convert the generalization to the specific: "I failed this presentation" not "I always fail."
- Count: how many times has this actually happened vs. not happened?
- Ask: "What does this one data point actually tell me, and what doesn’t it tell me?"
- Develop a balanced statement that acknowledges the specific event without extending to permanent, universal conclusions.
Evidence
Overgeneralization as a negative-attribution style is related to Abramson’s learned helplessness model (stable, global attribution for negative events as a depression risk factor). CBT targeting this attribution style has a solid evidence base for depression prevention and treatment. (clinical)
Abramson’s model uses "global/specific" and "stable/unstable" rather than "overgeneralization" specifically; Burns’ term captures a similar but not identical construct.
Sources
- Abramson, Seligman & Teasdale (1978), learned helplessness in humans, Journal of Abnormal Psychology
Common mistake
Replacing the overgeneralization with an equally global positive statement ("I always do fine") rather than a specific and bounded accurate one — the goal is accuracy, not reversal.
Practice this with IX Coach
IX Coach reflects back when you use universal language about yourself — "I always," "I never" — and asks for the specific instances, building a more calibrated picture over time.
7 days free, then $40/month (~$1.30/day).