Abnormal Causal Attribution Leads to Advantageous Economic Decision-making: A Neuropsychological Approach
Bibliographic record
Abstract
People tend to assume that outcomes are caused by dispositional factors, for example, a person's constitution or personality, even when the actual cause is due to situational factors, for example, luck or coincidence. This is known as the "correspondence bias." This tendency can lead normal, intelligent persons to make suboptimal decisions. Here, we used a neuropsychological approach to investigate the neural basis of the correspondence bias, by studying economic decision-making in patients with damage to the ventromedial pFC (vmPFC). Given the role of the vmPFC in social cognition, we predicted that vmPFC is necessary for the normal correspondence bias. In our experiment, consistent with expectations, healthy (n = 46) and brain-damaged (n = 30) comparison participants displayed the correspondence bias during economic decision-making and invested no differently when given dispositional or situational information. By contrast, vmPFC patients (n = 17) displayed a lack of correspondence bias and invested more when given dispositional than situational information. The results support the conclusion that vmPFC is critical for normal social inference and the correspondence bias. The findings help clarify the important (and sometimes disadvantageous) role of social inference in economic decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".