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Table · dataset · 2026

Table 1_Affective integration in climate change communication: recency effects and negativity bias in responses to climate picture sequences.docx

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<p>This research explored climate pictures as affect-eliciting stimuli, and how the affective experiences can have an impact on pro-environmental intentions.

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In an online within-subject design experiment (N = 464), participants engaged in twelve experimental trials, each containing one picture of a wildfire, one of a forest, and one depicting a pro-environmental action, presented in various orders. Following each trial, participants rated their affective experiences and pro-environmental intentions.

The results indicated that affective valence (positive-negative experience) was sensitive to the presentation order of the pictures. Using a quasi-Bayesian approach to mediation analysis, we found that the presentation order indirectly influenced pro-environmental intentions through changes in affective valence. Furthermore, hierarchical Bayesian modeling revealed that normative valence ratings of pictures, picture type, and the position of the pictures in the presentation order (first, second, or third) predicted the affective valence reported by participants.

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Specifically, the model highlighted a significant recency effect, indicating that the final picture in each sequence exerted a greater impact on the reported affective valence. Additionally, while all picture types predicted affective valence, wildfire pictures exerted a stronger influence on affective experience than forest pictures, consistent with negativity bias, proposing that negative information carries greater weight than positive information.

These findings suggest that affective responses to climate change communication are shaped by both recency effects and the stronger weighting of negative relative to positive climate-related stimuli, highlighting the importance of understanding how multiple affective inputs jointly influence behavioral motivation. These results suggest that our computational model of affect can inform the design of climate change communication strategies to more effectively leverage emotional responses to motivate pro-environmental action.</p>

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