Yoshizawa et al. (2026)

Feeling of Knowing Metacognition

• Measured in AI

  Likewise, on Japanese general-knowledge questions rated after each answer, 87 people discriminated right from wrong answers at an area of 0.76 (SD 0.14) and GPT-4, run 87 times, at 0.63 (SD 0.20); again a retrospective confidence result, not FOK

Confidence Calibration Metacognition

• Measured in AI

  A peer-reviewed 2026 study gave 87 Japanese-speaking adults (recruited on X, answering an online form in September–October 2023) and 87 separate runs of GPT-4 the same 60 general-knowledge questions about Japan as open-ended recall, four-choice and two-choice items, each followed by a 0–100% confidence rating; GPT-4 was queried in December 2025 through OpenAI's programming interface under the setting "gpt-4", which points to a June 2023 version, one question per fresh request, so it answered only from what it learned in training

• Measured in AI

  GPT-4 answered more questions correctly than people (78–88% vs 52–73% across formats) but reported 93–97% confidence throughout, overconfident by 8–15 percentage points, while people's average confidence was within 4 points of their accuracy; individual people varied widely (SD of over- or underconfidence 15–16 points, computed from the authors' public data), so GPT-4's bias sits within the human range, about 0.6–0.75 SD above the human mean

• Measured in AI

  For resolution, scored as type-2 AUROC (the chance that a correct answer drew higher confidence than a wrong one, .5 = chance), GPT-4 matched people on two-choice (.73 vs .70, SD .14) and open-ended recall (.78 vs .81, SD .12) but fell below chance on four-choice items (.40 vs .76, SD .13), where it was more confident in its wrong answers (99%) than its right ones (96%); across questions, people's confidence tracked item difficulty far more closely than GPT-4's (r = .83 vs .33)

• Measured in AI

  The study's efficiency measures are information-based (meta-I and meta-I2r), not meta-d′ or M-ratio, and they do not line up with its AUROCs: on open-ended recall AUROC shows no reliable difference while both information measures favour people, and a mutual-information score counts GPT-4's inverted four-choice confidence as positive information; the text also reverses the direction of a format effect shown in the authors' own statistical output, and its "All" AUROC averages the three format scores rather than pooling trials

• Measured in AI

  Both are direct comparisons and both put AI at or above people, so the rating is Comparable: in the forecasting, trivia and records studies the best systems matched or beat people's calibration, and on general-knowledge recall GPT-4 was more accurate than people and within the human range on calibration and on two of three resolution formats. The important caveat is overconfidence: GPT-4 was 8–15 points more confident than it was accurate where people were within 4, and on four-choice items it was more confident in its wrong answers than its right ones. The rating is provisional because the recall study tests one 2023 system whose 87 runs were near-duplicates, and the other study's items are forecasts and estimates rather than answers from memory

Metacognitive Control Metacognition

• Measured in AI

  Same-task human–AI studies so far measure monitoring, not control: with 87 adults and GPT-4 on the same 60 quiz questions, confidence separated right from wrong answers less well for GPT-4 (type-2 AUROC 0.63) than for people on average (0.76, SD 0.14), though within the human spread, and no decision to answer or withhold was recorded