That’s not fair: Children’s neural computations of fairness and their impact on resource allocation behaviors and judgments.
Bibliographic record
Abstract
The ability to distinguish between mere equality in resource distributions and fairness based on a broader range of contextual factors is of paramount importance in social decision making and is a critical component of morality. Children's developmental shift from viewing inequality as a dichotomous moral issue toward a more nuanced understanding of partial inequality has been well documented across middle childhood and is attributed to a host of potential theoretical underpinnings, including developing number concept, increased regard for one's social status, and a maturing concept of fairness. The current study examined the electrophysiological markers associated with children's (N = 83; 4 to 8 years of age) third-party evaluations of equal, slightly unequal, and extremely unequal resource distributions, documenting the timing of fairness considerations. It further explored the link between individual differences in these neural computations and children's allocation behaviors and judgments. Event-related potentials demonstrated an early differentiation between equality and any type of inequality reflected by a medial frontal negativity. Later (after 500 ms), extreme inequality was discriminated from equality and slight inequality. Differences in later waveforms predicted sharing and third-party contextual resource distributions, accounting for wealth and merit. These results illuminate the multifaceted nature of developing neural computations of fairness and illustrate the value of a multiple levels of analysis approach in contributing theoretical clarity toward the developmental science of moral cognition and behavior. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.000 |
| 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) | 0.000 | 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".