Biased or Motivated? Starting Point Biases May Erroneously Capture Motivated Attentional Dynamics
Bibliographic record
Abstract
Computational models of choice have shown success at formalizing and testing specific mechanisms underlying choice processes. In the domain of altruistic choice, models such as the drift diffusion model (DDM) have been used to study whether prosociality is a dual process, with automatic, rapid generosity biases and an effortful deliberative process. However, current debates on generosity biases have not reached consensus on the mechanism(s) involved-- from automatic response tendencies to motivated changes in attention-- or whether such biases are generous or selfish. I hypothesized that prosocial choice involves a directed, early attentional bias, and that models which did not account for this prioritization mechanism may erroneously attribute these effects to other parameters, particularly starting point biases. This thesis work identifies how underlying mechanisms of generosity biases could be obscured or mimicked by ill-suited parameters. I discuss the implications and limitations of using a relatively simple DDM to study attentional influences.
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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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".