The conflict negativity: A neural correlate of value conflict and indecision during financial decision making
Bibliographic record
Abstract
Abstract Individuals struggle when making financial decisions, sometimes preferring to avoid difficult decisions and receive lower future rewards over actively deliberating between options of similar value. Here, we examine how conflict deriving from objective and subjective value characteristics of stocks, as well as the behavioural and phenomenological correlates of decision conflict, are accompanied by variation in a thus far understudied ERP component, the conflict negativity (CN). In a novel EEG paradigm (N = 53), we simulated a financial decision situation in which participants made incentivized choices between different, sometimes conflicting, stock options. Our results indicate that participants become slower, more undecided, and less pleased, when choosing between similar options compared to choices in which one option clearly outweighs the other. This effect even held when participants chose between two objectively good alternatives. We further provide preliminary evidence that the CN, a negative-going ERP recorded over the medial prefrontal cortex, is not only sensitive to decision conflict, but also predicts behavioural indecision. What is more, subjective value characteristics of stocks, impressions based on brand perception of the stock options, modestly influenced affective and behavioural reactions over and above objective stock characteristics. While our results are at odds with assumptions made by classic economic theory, they may serve as one out of several indicators as to why private investors seem to avoid financial decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".