Value directed learning of gestures and facial displays
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
This paper presents a method for learning decision theoretic models of facial expressions and gestures from video data. We consider that the meaning of a facial display or gesture to an observer is contained in its relationship to context, actions and outcomes. An agent wishing to capitalize on these relationships must distinguish facial displays and gestures according to how they help the agent to maximize utility. This paper demonstrates how an agent can learn relationships between unlabeled observations of a person's face and gestures, the context, and its own actions and utility function. The agent needs no prior knowledge about the number or the structure of the gestures and facial displays that are valuable to distinguish. The agent discovers classes of human non-verbal behaviors, as well as which are important for choosing actions that optimize over the utility of possible outcomes. This value-directed model learning allows an agent to focus resources on recognizing only those behaviors which are useful to distinguish. We show results in a simple gestural robotic control problem and in a simple card game played by two human players.
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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 it