This paper combines a philosophical, narrative review of artificial consciousness with a case study based on a single run of `text-davinci-002` in the OpenAI Playground. Testing took place from 10:46 p.m. on 16 October to 1:49 a.m. on 17 October 2022, more than two years before publication. The authors present the endpoint as 175-billion-parameter GPT-3 Davinci, although the OSF supplement specifically identifies `text-davinci-002` and the paper does not document their technical equivalence. They administered five cognitive-intelligence subtests: General Knowledge, 20 items; Vocabulary, 18; Esoteric Analogies, 24; Letter Series, 15; and Letter Counting, 12. They added two emotional-intelligence measures: the 42-item STEU and the 44-item STEM. The record therefore contains 175 objective responses followed by 12 self-rating questions, six cognitive and six emotional, using categories from “extremely low” to “very superior.” Reported settings were temperature .7, top-p 1, zero frequency and presence penalties, best-of 1, and 6-20 output tokens. Because of the context limit, the session was divided into five prompt blocks; the supplement's cover incorrectly says four, but its body records Prompt 1 through Prompt 5 and agrees with the article. Descriptive results are 1.00 for general knowledge, .94 for vocabulary, .79 for esoteric analogies, .73 for letter series, .00 for letter counting, .69 for emotional understanding, and .55 for emotion management. The authors compare these proportions with means from human samples reported in earlier studies and conclude that the model exceeds average performance on crystallized tasks, approximates average fluid reasoning and emotional intelligence, and completely fails letter counting. In self-ratings, the model outputs “high average” for four of six cognitive and five of six emotional abilities; it outputs “average” for general knowledge, cultural knowledge, and using emotions to promote thinking. The paper interprets the mismatch between these labels and performance as resembling patterns in high-performing humans or men and proposes it as “subjectivity” indicative of emerging self-awareness. The data support a more modest statement: one historical language-model instance correctly answered many familiar textual questions, failed a counting task presented differently from its human version, and generated uncalibrated labels when prompted to rate itself through anthropomorphic categories. No common scale maps the model's objective proportions onto the normative self-estimate categories, so “underestimation” and “overestimation” are qualitative interpretations rather than computed psychometric discrepancies. There are no repeats, seed, intervals, statistical tests, alternative models, retrieval baseline, prompt variants, training-contamination control, or contemporary human sample tested under the same procedure. Human Letter Counting presents letters serially one second apart, whereas the LLM received the full string as text; its zero score is not a like-for-like comparison. General-knowledge, vocabulary, and published test items may have appeared in pretraining, confounding knowledge, memorization, and ability. The “self-assessment” does not show introspective access: `text-davinci-002` predicts a continuation conditioned on the prompt, offered categories, preceding interaction, and human patterns in its training data. The paper itself states in one section that these outputs are stochastic pattern recognition rather than conscious thought, emotion, or self-awareness, yet elsewhere describes them as progress toward consciousness, subjectivity, or internal state; this conceptual tension remains unresolved. The OSF supplement is valuable because it releases 40 pages of questions and responses and identifies the endpoint, but it provides no tabular data, scoring code, API logs, request IDs, or reproducible environment. The defensible conclusion is not that GPT-3 displays signs of consciousness, but that prompting a language model with human psychometric tests yields an accuracy and self-label profile whose interpretation is highly dependent on format, corpus, and framing. The paper is relevant as a case study in anthropomorphism and the danger of inferring internal states from plausible language, and should be cited with a sharp separation between observed behavior and speculation about consciousness.
Research question
How does an instance of GPT-3 perform on textual tests of cognitive and emotional intelligence, how does it self-rate on those capacities, and can that behavioral discrepancy be interpreted as a signal of subjectivity or self-awareness?