The expectation-updating mechanism in gratitude: A predictive coding perspective
Ke Ding1, Haiqi Lin1, Guanmin Liu2
1School of Psychology, Shenzhen University.
Abstract:
The fluctuations in emotions during constant help are unexplained by traditional emotion theories but may align with the predictive coding theory. This theory suggests that individuals tend to form expectations of others' help during social interactions. When outcomes exceed expectations, positive prediction errors are generated, potentially increasing gratitude. Conversely, constant help may build up expectations that surpass outcomes, resulting in negative prediction errors and reduced gratitude. Nevertheless, there is a lack of studies to examine the relationship between prediction errors and gratitude and its underlying mechanism. Here, we conducted two studies. Study 1 consistently found that higher expectations were associated with lower gratitude, when benefactors refused to help, in both reward-gaining and punishment-avoiding tasks. Moreover, prediction errors were positively and reliably linked to gratitude. Study 2 further identified that gratitude dynamically changed through an expectation-updating mechanism. A computational model incorporating predictive coding outperformed traditional theories in predicting the dynamics of gratitude. The findings support predictive coding theory, providing a temporal perspective and a mechanistic understanding of the fluctuations in gratitude, thus having implications for new interventions to improve mental health and well-being. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
Related Concept Videos
Graded Potential
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...
Expected Value
Predicting Reaction Outcomes
Hindsight Biases
End Point Prediction: Gran Plot
For potentiometric titration, the Gran plot is created by plotting...
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.


