Editorial: Exploration of decision neuroscience research in the digital era
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 it