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Negativity bias

Curated Category: affect-weighting

Negative information receives disproportionate weight in interpretation.

The narrative overweights threats and failures while underweighting neutral or positive evidence, amplifying pessimistic conclusions.

Detection signals

  • Threat-heavy framing dominates summary.
  • Positive counterevidence is minimized.
  • Adverse scenarios presented as default.

Caution notes

  • High-risk contexts may legitimately prioritize downside.
  • Mark only when weighting is clearly asymmetric.

AI-generated domain examples

Generate illustrative examples for a selected domain/topic. Generated output is separated from canonical ontology content.

Related biases