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Sprint projectAug 16, 2026Birmingham, UK

Familiar Self, Unfamiliar Other: Accuracy and Confidence in Predicting Affective Self-Reports

Teri-Louise Grassow

Submitted to Digital Minds Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Familiar Self, Unfamiliar Other: Accuracy and Confidence in Predicting Affective Self-Reports

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This study tests whether Claude Sonnet 5 predicts an unfamiliar AI model's self-reported feelings as well as it predicts its own — and whether it's equally confident either way. Prediction accuracy was strong and nearly identical in both cases, but confidence was not: Claude was moderately confident predicting itself, and notably underconfident predicting the unfamiliar model, despite that prediction being just as accurate. This suggests AI systems may under-trust their own good judgments about unfamiliar systems — a pattern worth understanding as AI systems increasingly evaluate one another.

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How much would this matter for the field if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. - There is very little data: only six scenarios with five runs each. This makes it hard to know how robust the result is.

    - The prediction task also seems quite easy. The low cross-model error may therefore mostly show that the scenarios are easy to guess from their content, rather than that Claude is good at modelling GPT-5.6 Sol specifically.

    - Claude is explicitly told which model it is predicting. So the simplest explanation for the confidence gap is just a prior like: “I know Claude better than this unfamiliar model, so I should be less confident.”

    -It is unfortunate that there is no simple control separating the model label from the actual target, for example by hiding or swapping the model identity. That could have tested whether the confidence gap is just caused by the label “unfamiliar model,” or whether something more interesting is going on.

Cite this project

@misc{grassow2026familiar,
  title = {{Familiar Self, Unfamiliar Other: Accuracy and Confidence in Predicting Affective Self-Reports}},
  author = {Teri-Louise Grassow},
  year = {2026},
  month = aug,
  note = {Submitted to Digital Minds Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/familiar-self-unfamiliar-other-accuracy-and-confidence-in-predicting-affective-selfreports-0b4g}},
  url = {https://apartresearch.com/sprints/projects/familiar-self-unfamiliar-other-accuracy-and-confidence-in-predicting-affective-selfreports-0b4g}
}

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026