Facilitating Visualizations of Future Emotions: Leveraging the Narrative Immersion Model to Explore the Potential of Narratives to Reduce Affective Forecasting Errors

Kerstin Hundal, Courtney L. Scherr and Brian J. Zikmund-Fisher
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Kerstin Hundal: Department of Communication Studies, Northwestern University, Evanston, IL, USA
Courtney L. Scherr: Department of Communication Studies, Northwestern University, Evanston, IL, USA
Brian J. Zikmund-Fisher: Department of Health Behavior and Health Equity, University of Michigan School of Public Health, Ann Arbor, MI, USA

Medical Decision Making, 2026, vol. 46, issue 1, 3-14

Abstract: Background Affective forecasting errors (i.e., errors in people’s predictions about future emotions) are common in health decision making and can negatively affect health outcomes. Although narrative interventions have been used to mitigate these errors, many studies did not clearly identify the specific errors targeted or examine the impact of different narrative types on affective forecasting. We applied the narrative immersion model (NIM) to capture the nuances of narratives on mitigating specific affective forecasting errors in health decision making. Methods Using a narrative review of existing narrative affective forecasting interventions, we investigated the potential of experience, process, and outcome narratives to reduce specific affective forecasting errors (e.g., focalism, immune neglect). Results Different narrative types—experience, process, and outcome—may play distinct roles in mitigating affective forecasting errors. Experience narratives may reduce affective forecasting errors by describing what people most likely (targeted) or might (representative) experience, process narratives by modeling optimal decision making, and outcome narratives by broadening people’s understanding of possible emotional outcomes. We further discuss how narrative characteristics related to content and structure (e.g., perspective taking, transportation, etc.) may advance narrative effects on affective forecasting. Conclusions Our findings have implications for intervention design as they facilitate the selection of narrative types tailored to specific affective forecasting errors (e.g., framing, misconstruals, or impact bias). Highlights Specific affective forecasting errors may be reduced through different types of narratives, but greater understanding is needed regarding the exact mechanisms. The narrative immersion model is a useful framework to investigate the potential of experience, process, and outcome narratives to reduce specific types of affective forecasting errors. We describe the pathways through which narrative types most likely influence affective forecasting and facilitate the choice of narrative message type for a specific affective forecasting error. Narratives designed for affective forecasting interventions should include detailed and realistic descriptions of people’s emotional health care experiences. Other narrative characteristics (e.g., realism, perspective taking, transportation) might affect a person’s ability to imagine future emotional health states, and future research should consider their effects on affective forecasting.

Keywords: affective forecasting; narratives; health decision making; emotions; communication (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:46:y:2026:i:1:p:3-14

DOI: 10.1177/0272989X251377745

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