Neural basis of guilt: a quantitative and connectivity meta-analysis of functional imaging studies
Bibliographic record
Abstract
Background. Guilt is the feeling of having committed some wrong against oneself or another person. While guilt is a part of normal human experience, there is also a relationship between excessive guilt and several mental disorders, including depression, post-traumatic stress disorder, obsessive-compulsive disorder, psychosis, antisocial personality disorder and suicide. Understanding the normal and pathological physiology of guilt is therefore important. In recent years, a number of neuroimaging studies have investigated the neural correlates of guilt. Here, we aimed at quantitatively summarizing the published neuroimaging studies of guilt-processing using meta-analytic methods. Methods. A systematic review of literature conducted until January 2014 found eleven studies meeting inclusion criteria, including ten using whole-brain functional Magnetic Resonance Imaging (fMRI) and one using Positron Emission Tomography (PET) for a total of 196 participants. As few studies were conducted on patients, we restricted our analyses to healthy individuals to reduce heterogeneity. A meta-analysis was then conducted using the activation likelihood estimation program GingerALE. An additional Meta-Analytic Connectivity Modelling (MACM) analysis was conducted to investigate functional connectivity of significant clusters. Results. The analysis revealed ten significant brain clusters of activation encompassing the left medial frontal gyrus, bilateral anterior cingulate cortex, superior temporal gyrus, precuneus, lingual gyrus, cuneus and insula. Conclusions. Our analysis identified a network of connected brain regions playing a central role in guilt processing, areas that are thought to be involved in abstract moral value processing, self-representation and theory of mind, and encompassing the default-mode network. We believe that these results contribute to a broader understanding of guilt and could ultimately enable the development of targeted forms of treatment in mental health conditions when guilt is a significant issue.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.029 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".