The conflict negativity: Neural correlate of value conflict and indecision during financial decision making
Bibliographic record
Abstract
Individuals struggle when making financial decisions, sometimes preferring lower future rewards over actively making decisions at all. 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 take longer, are more undecided and less pleased, when choosing between conflicting options compared to choices where one option is obviously better than the alternative—even when choosing between two objectively good alternatives. We further provide preliminary evidence that the CN, a negative-going ERP recorded over the medial prefrontal cortex, not only reacts to conflict decisions but also predicts participants’ behavioural indecision during choice. What is more, subjective value characteristics of stocks, impressions based on brand perception of the stock options, influenced affective and behavioral reactions over and above objective stock characteristics. While our results are at odds with assumptions made by classic economic theory, they can be applied to real world observations on private investor behaviour.
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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 | 0.000 | 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".