Editorial: Exploration of decision neuroscience research in the digital era
Bibliographic record
Abstract
1 Introduction The digital era has revolutionized the way we study human decision-making. Advances in neuroimaging, computational modeling, and machine learning have provided insights into the complex processes of decision making. This Research Topic, Exploration of Decision Neuroscience Research in the Digital Era, brings together cutting-edge studies that leverage modern technologies—such as eye-tracking, neuroimaging, digital dynamic assessment, and generative narrative survey—to examine the neural and behavioral underpinnings of decision-making. 2 Contributions to the Research Topic The articles featured in this collection illustrate the breadth of current approaches: Huang et al (2025) introduce a maze-based digital assessment paradigm to detect early cognitive decline in Parkinson's disease; Wong et al. (2024) apply generative narrative surveys to capture real-world decision-making in varied social contexts; Zhou et al. (2024) employ eye-tracking to study intertemporal loss decisions; and Horr et al. (2023) demonstrate how machine learning applied to EEG signals can accurately predict online purchasing behavior.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.017 | 0.011 |
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".