FRIENDs: Brain-monitoring agents for adaptive socio-technical systems
Bibliographic record
Abstract
Brain-monitoring is quickly becoming an important field of research, with potentially significant impacts on how people will interact with technology. As understandings of the inner-workings of the brain become more accurate technologies are becoming more advanced, smaller, cheaper, and ubiquitous. It is expected that new forms of computing that take advantage of brain states will be developed. This will enable systems to be highly aware of user mental contexts (emotions, intentions, and moods). These systems would display higher autonomic behavior and would streamline user-interaction while managing the use of brain context data for applications and services. There are few studies of how to develop and make use of agent architectures in this new domain. Current approaches target a single user and application situation. To be ubiquitous it is unrealistic for applications to have specialized overhead for individual users. Personalizable, but distributed approaches are needed. To realize a general purpose agent for brain-monitoring and management of brain context is the goal of this work. This involves the selection of a brain-monitoring paradigm, the selection of an agent architecture paradigm, an inferencing mechanism, and the combination of the three towards a unified framework. Core motivations are discussed, and an early agent framework design (FRIEND) is presented, along with proposed proof-of-concept applications for using brain context.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".