📌 What Problem This Paper Tackles:

Political experiments that aim to test how specific emotions shape behavior often produce collateral shifts in other emotions. Those collateral effects make it hard to attribute outcomes to a single emotion. This letter proposes using causal mediation analysis to separate the direct and indirect pathways linking emotion manipulations to political behavior.

🔬 How the Study Was Designed:

• An experiment manipulated emotions and measured dyadic trust using the standard trust game.
• Causal mediation analysis was applied to partition the total effect of emotion manipulations into effects transmitted through specific emotional states (mediators) versus other channels.

📈 Key Findings:

• Negative emotions can reduce trusting behavior, but this decline occurs only when the negative emotion lowers people’s certainty about their situation.
• Anxiety, characterized by low certainty, produced a negative impact on trust.
• Anger and guilt, which differ in control appraisals but both involve high certainty, showed no effect on trusting behavior.
• Importantly, analyses that do not use causal mediation would have mistakenly ascribed a positive effect of anxiety on trust, illustrating how standard approaches can mischaracterize emotional effects.

💡 Why This Matters:

• Demonstrates that emotional valence alone is insufficient to predict political behavior; the certainty dimension of emotions can be decisive.
• Shows causal mediation analysis as a practical tool for disentangling targeted emotional effects from collateral emotional changes in experiments, improving causal inference about how emotions shape trust and other political behaviors.