Thomas Yibai Li
Last Name
Li
First name
Thomas Yibai
Email
thomas.li@unisg.ch
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Item type:Publication, No Feeling for the Future: Re-examining the Role of Emotions and Cognition in Forecasting(2026-04-28)Our study set out to build on Pham et al.'s (2012) "emotional oracle effect," which claimed that one's trust-in-feelings (TIF) enhances forecasting accuracy by unlocking tacit knowledge. Inspired by this interesting finding, we aimed to explore how one's TIF interacts with crystallized intelligence (CI) and fluid intelligence (FI) to strengthen our understanding of the interplay between emotions and cognition when making predictions. However, a surprising discovery reshaped our investigation: the foundational TIF mechanism itself failed to replicate. Across two robust studies (N = 226; confirmation study N = 432), TIF showed negligible and inconsistent direct effect on prediction accuracy, challenging the theory's core premise. While marginally significant, results suggest TIF might improve accuracy for individuals with high CI; these effects were limited and contingent on specific contexts. Our findings undermine the generalizability of the emotional oracle effect and highlight the fragility of its theoretical foundation. The results present a cautionary tale about building on and extending theories without first thoroughly validating core mechanisms.Type:journal article - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Sad But Accurate? Examining the Role of Discrete Emotions on Prediction Performance(2026-04-28)How do discrete emotions influence prediction accuracy? Drawing on appraisal-tendency framework and approach-avoidance models, we hypothesized that approach-oriented emotions with high certainty (anger, happiness) would enhance prediction accuracy through heuristic processing, while avoidance-oriented emotions with low certainty appraisals (fear, sadness) would trigger over-deliberation and impair performance. We further tested whether trust-in-feelings (TIF) moderates these relationships. Participants (N = 740) underwent emotion induction and TIF manipulation (high vs. none) before making predictions about weather, movie box office performance and stock market movements. Results contradicted predictions: fear and sadness improved weather prediction accuracy (ORs = 1.67-2.28), performing comparably to anger and happiness. Effects were domainspecific, emerging only for weather. TIF showed minimal effects (one of seven predictions reached significance), representing a failure to replicate Pham et al.'s (2012) "emotional oracle effect". These findings challenge assumptions about negative emotions in forecasting and suggest emotional engagement - rather than discrete emotions or TIF - drives accuracy.Type:journal article