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Published on: March 1, 2019
Prediction-based neural mechanisms for shielding the self from existential threat
Y Dor-Ziderman1, A Lutz2, A Goldstein3
1Gonda Brain Research Center, Bar Ilan University, Ramat-Gan, Israel.
The brain may downregulate self-perception processes to avoid awareness of mortality, a mechanism linked to death-denial. This neurobiological finding sheds light on how the mind protects itself from existential threats.
Area of Science:
- Neuroscience
- Cognitive Psychology
- Existential Psychology
Background:
- Humans possess an innate tendency to avoid confronting their mortality.
- The neuronal underpinnings of this death-denial mechanism remain largely unexplored.
Purpose of the Study:
- To investigate the hypothesis that prediction-based neural mechanisms mediate death-denial.
- To explore how self-specific predictive processes are modulated by death-related stimuli.
- To establish a link between these neural mechanisms and the fear of death.
Main Methods:
- Utilized magnetoencephalography (MEG) with a visual mismatch paradigm.
- Coupled death-related words with self-face and other-face representations.
- Employed a behavioral active inference experiment using morphed facial stimuli.
Main Results:
- Self-specific predictive processes were downregulated when perceiving death-related stimuli.
- The brain's automatic prediction response to deviancy was eliminated when death words were coupled with self-faces, but not other-faces or negative words.
- A functional link was found between the impact of death on self-image versus other-image, predicting fear of death.
Conclusions:
- The study proposes a plausible neural basis for death-denial, involving the downregulation of self-specific predictive processes.
- This mechanism appears to shield the self from existential threat.
- Findings suggest that modulating self-perception is key to understanding and potentially mitigating existential anxiety.
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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.

