Bibliographic record
Abstract
With the advancement of AI and Robotics, computer systems have been put to many practical uses in a variety of domains like healthcare, retail, households, and more. As AI agents become a part of our day-to-day life, successful human-machine interaction becomes an essential part of the experience. Understanding the nuances of human social interaction remains a challenging area of research, but there is growing consensus that emotional identity, or what social face a person presents in a given context, is a critical aspect. Therefore, understanding the identities displayed by humans, and the identity of agents and the social context, is a crucial skill for a socially interactive agent. In this paper, we provide an overview of a sociological theory of interaction called Affect Control Theory (ACT), and its recent extension, BayesACT. We discuss how this theory can track fine grained dynamics of an interaction, and explore how the associated computational model of emotion can be used by socially interactive agents. ACT considers the cultural sentiments (emotional feelings) about concepts for the context, the identities at play, and the emotions felt, and aims towards a successful interaction with the aim of maximizing emotional coherence. We argue that an AI agent's understanding of itself, and of the culture and context it is in, can change human perception of an agent from something that is machine-like, to something that can establish and maintain a meaningful emotional connection.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".