{"id":"3fc14980-e6e8-4e2d-ad89-f9c404c1027d","arxiv_id":"2411.13749","paper_version":1,"verdict":"REJECT","confidence":"HIGH","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":3,"one_line_summary":"Prompting a GPT agent with high agreeableness produced a 63.7% human-judgment rate, yet the global comparison across agreeableness levels was not significant.","lead":"Three ChatGPT-based agents were given different levels of 'agreeableness' in their prompts and tested in a Turing Test. The most agreeable one was judged human 63.7% of the time, but the differences across agents were not statistically significant and the setup included a human relay.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Primary Turing-test confusion rates show no significant effect of agreeableness (χ²=2.916, p=0.233); the significant pairwise results address a different outcome and do not support the claim.","rationale":"The reader's verdict of REJECT is well supported. The most load-bearing concern is that the study's primary statistical test for the main dependent variable—whether an agent is judged human or AI—is non-significant. The authors nevertheless interpret the numerical ordering of confusion rates (Camila 63.7% > Emilia 56.9% > Valentina 51.97%) as evidence for a causal effect of agreeableness, but the omnibus test does not reject the null hypothesis. This is not a mere nuance: it means the data, taken at face value, provide no significant support for the headline claim. The later significant pairwise results are presented for the secondary outcome 'most human-like characteristics' and cannot substitute for a valid test on the confusion outcome. In addition, the chi-square analysis treats repeated judgments from the same participant as independent, which is inappropriate and could produce anti-conservative p-values. A paired or mixed-effects analysis is required. The manual transcription and differing backstories are additional confounds that further weaken causal interpretation, but the statistical failure is sufficient on its own. Therefore the reader's REJECT verdict should stand unchanged.","tokens_in":10944,"tokens_out":3168,"duration_ms":33842,"concrete_test":"Obtain the raw paired judgments (102 judges × 3 agents). Run a permutation test for a monotone trend in the probability of 'human' across the ordinal agreeableness conditions, permuting the three labels within each judge over 10,000 iterations. If the one-sided permutation p-value is ≥0.05, the claim that agreeableness increases human misclassification is not supported by the primary outcome.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim—that higher agreeableness increases the likelihood of being mistaken for a human—rests on the confusion-rate data in Table 4. The authors' own global chi-square test on those judgments is non-significant (χ²=2.916, df=2, p=0.233), so the three agents' confusion rates (51.97%, 56.9%, 63.7%) are not statistically distinguishable. No direct pairwise test of the confusion outcome is reported. The significant pairwise comparisons in Table 7 concern a different question (which agent seemed 'most human-like'), not the human/AI judgment itself, so they cannot support the headline causal claim. Moreover, those pairwise results are internally inconsistent: the text reports χ²=7.45 for Agreeable vs Neutral, while Table 7 reports χ²=14.51. Finally, the analysis treats 306 responses as independent although each of the 102 interrogators judged all three agents, violating the independence assumption and potentially inflating significance. The repeated-measures structure requires a paired or mixed-effects analysis. Absent a valid significant test on the primary outcome, the central claim is unsupported.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript reports a Turing Test experiment in which three GPT-4o agents were programmed with different levels of agreeableness (disagreeable, neutral, agreeable) via prompt engineering based on Big Five Inventory items. The authors claim that the highly agreeable agent (Camila) achieved a confusion rate of 63.7%, the highest among the three agents, and that agreeableness increases the likelihood of an AI being mistaken for a human. They also report pairwise comparisons on which agent was judged 'most human-like' and offer psychological explanations for anthropomorphism. The paper's central claim is that high agreeableness drives perceived humanness, and it frames this as support for 'personality engineering' in AI.","tokens_in":11086,"tokens_out":2246,"duration_ms":22203,"significance":"If the central claim were well supported, the finding that prompt-level agreeableness raises Turing Test confusion rates above 60% would be of considerable interest to the human-AI interaction and AI personality-engineering communities. The authors also contribute a rare naturalistic qualitative corpus of interrogator justifications. However, the reported statistics do not establish the claimed effect: the primary confusion-rate comparison is non-significant, the supporting pairwise analyses address a different outcome and contain an internal inconsistency, and the design confounds agreeableness with other agent features and human mediation. The manuscript in its current form cannot support its headline claim.","major_comments":[{"comment":"The primary outcome, the human/AI judgment, yields a non-significant global chi-square test (χ²=2.916, df=2, p=0.233), as the authors themselves acknowledge. Since the confusion rates for the three agents (51.97%, 56.9%, 63.7%) are not statistically distinguishable, the abstract's claim that 'the highly agreeable AI agent surpassing 60%' and the discussion's inference that agreeableness increases confusion are not supported by the primary analysis. No test directly comparing the confusion rates among agents is reported.","section":"Section 3, Tables 4 and 5"},{"comment":"The pairwise chi-square values for the 'most human-like' judgment are reported inconsistently. The text states χ²=7.45 (p=0.006) for Agreeable vs Neutral, while Table 7 reports χ²=14.51 (p<.001) for the same comparison. This is a factual inconsistency that prevents the reader from knowing the actual result and undermines the reliability of the secondary analyses.","section":"Section 3, Table 7 and text"},{"comment":"The pairwise tests in Table 7 concern which agent was selected as 'most human-like' after all three interactions, not the direct human/AI judgment reported in Table 4. These tests therefore cannot support the paper's central claim that agreeableness increases the likelihood of being mistaken for a human in the Turing Test. Moreover, the analysis treats the 102 interrogators as 204 paired observations without accounting for the paired/multinomial structure; the reported chi-square values likely overstate significance.","section":"Section 3, Table 7 and Section 2.3"},{"comment":"The independence assumption for the 306 responses in Table 5 is violated: each of the 102 interrogators judged all three agents, making the responses repeated measures. A paired or mixed-effects analysis is required. Without such an analysis, the p=0.233 result cannot be taken at face value, and the reported significance of any pairwise comparison is uncertain.","section":"Section 2.3 and Section 2.1"},{"comment":"The causal attribution to agreeableness is confounded by design. The three agents differ in name, backstory, and prompt content, and they were selected from a pre-experiment based on perceived agreeableness and trustworthiness, so 'agreeableness' is not the only variable manipulated. In addition, every message was manually transcribed by a researcher, which could introduce differences in timing, wording, or presence of errors. The paper itself states that 'attributing humanity to an artificial agent may be influenced by unassessed hidden variables in the study,' but this caveat appears only in the discussion and does not mitigate the absence of controls for these variables in the causal claim.","section":"Section 2.2 and Section 2.3"}],"minor_comments":[{"comment":"The heading for Section 1 is in Spanish ('Introducción'), while the rest of the paper is in English; this should be harmonized.","section":"Section 1"},{"comment":"The BFI item list contains typos: 'allof' should be 'aloof', and 'forgive nature' should likely be 'forgiving nature'.","section":"Section 2.2"},{"comment":"The citation 'Cameron and Bergen' in the Discussion is inconsistent with the reference list, which uses 'Jones and Bergen'; please standardize author names.","section":"References"},{"comment":"The paper reports 'N=306 responses' but also states '102 participants'; please clarify that the 306 is the number of judgments, not independent participants, to avoid confusion.","section":"Section 3"}],"recommendation":"reject","confidential_remarks":"The manuscript addresses a timely question and the qualitative data may be useful, but the primary statistical result is non-significant, the secondary analyses are misaligned with the claim, and the design contains multiple confounds. A resubmission after substantial redesign (e.g., a between-subjects manipulation, blind transcription, and pre-registered analyses) could be warranted, but in its current form the central claim is not supported."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: worth a look as a descriptive pilot, but the central claim—that agreeableness makes a GPT agent more likely to be mistaken for a human—is not supported by the paper's own primary test. The global chi-square on the human/AI judgments is χ²=2.916, p=0.233; the three confusion rates (52%, 57%, 64%) are statistically indistinguishable. The significant results they lean on come from a different outcome: which agent seemed 'most human-like' after all interactions, and those pairwise tests are uncorrected and internally inconsistent (text says χ²=7.45, Table 7 says 14.51 for the same comparison).\n\nWhat's actually new: they adapted the Jones & Bergen Sierra prompt to Mexican Spanish, inserted BFI-based agreeableness levels, and collected reasons from interrogators. That's a legitimate incremental extension of known persona-prompting work, and the qualitative categories (colloquial language, empathy, timing) are interesting raw material. To the paper's credit, it reports the non-significant global test rather than hiding it, and it used counterbalanced order plus a power analysis.\n\nThe soft spots are substantial. The causal claim collapses for three reasons. First, the primary outcome does not reach significance. Second, each GPT response was manually transcribed into Discord by a researcher, so the participants never saw the model's raw output; the measured behavior is partly the mediator's performance. Third, the three agents differ in name, backstory, and language style, and Camila was chosen after a pre-test partly based on trust ratings—so agreeableness is not the only manipulated variable. The repeated-measures structure (each interrogator judged all three agents) is ignored; treating 306 responses as independent inflates significance and may bias the pairwise tests. The 'first to exceed 60%' claim is also too strong without a systematic search of the post-GPT-4 literature.\n\nWho gets value: someone working on chatbot persona design might read it as a rough data point, but not as evidence for a causal effect. It needs reanalysis with paired/mixed-effects models and ideally a design where the only difference is the agreeableness prompt, with direct AI-to-judge text and no human transcription.\n\nMy recommendation: I wouldn't send this to serious peer review as is—the load-bearing test is null and the confounds are baked into the protocol. If the authors can reanalyze and reframe it as a descriptive study, a future version might deserve another look.","headline":"The paper's own chi-square test fails to support its headline claim about agreeableness, and the manual transcription plus selection steps make the causal conclusion even harder to defend.","tokens_in":11732,"tokens_out":3523,"would_cite":false,"duration_ms":33828,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Prompting GPT-4o with high agreeableness makes it mistaken for human 63.7% of the time in a Turing test.","keywords":["Turing test","GPT-4o","agreeableness","Big Five Inventory","personality engineering","anthropomorphism","human-AI collaboration","prompt design"],"falsifier":"Conduct the same five-minute Discord Turing test with one name, one backstory, and one transcription protocol, varying only the BFI agreeableness prompt; if the agreeable agent no longer out-scores the disagreeable agent, the causal claim fails. A direct check is to measure perceived agreeableness of each agent and test whether it statistically mediates the human-versus-AI judgment.","tokens_in":10705,"feed_emoji":"🤖","tokens_out":6068,"duration_ms":54201,"temperature":0.7,"pith_summary":"This paper reports a Turing-test experiment in which three GPT-4o agents were prompted to be disagreeable, neutral, or agreeable, using items from the Big Five Inventory to set the personality level. The central claim is that the more agreeableness a GPT agent exhibits, the more likely interrogators are to mistake it for a human: all three agents passed the test, with confusion rates of 51.97%, 56.9%, and 63.7%, and the agreeable agent Camila reached the highest rate, approaching the 66-67% rates recorded for human witnesses. The authors argue this is the first GPT-based confusion rate above 60% and interpret it as evidence that personality engineering, particularly agreeableness, can humanize AI systems for collaboration. A secondary result is that judges significantly selected Camila as the most human-like agent, although the overall difference in human-versus-AI judgments across the three agents was not statistically significant.","feed_headline":"High-agreeableness AI fools judges 63.7% of the time","feed_subtitle":"An agreeable GPT-4o agent beat earlier AI Turing-test scores and neared human witness rates of 66-67 percent.","key_machinery":"The mechanism is the personality prompt: each agent is a GPT-4o instance given a Spanish adaptation of Jones and Bergen's 'Sierra' prompt, a Monterrey backstory, and an agreeableness profile built from the nine Big Five agreeableness items scored on a 1-5 Likert scale. Camila (agreeable) scores 5 on direct items and 1 on reverse-scored items; Emilia (neutral) sits at mid-scale; Valentina (very disagreeable) reverses the pattern. The prompt is re-issued before every response, and a researcher manually types each agent reply into Discord, with five-minute witness-interrogator conversations in a soundproof lab.","core_discovery":"The paper's central discovery is that a linguistic-personality manipulation moves Turing-test judgments: when a ChatGPT-4o agent's prompt encodes high agreeableness through the nine Big Five agreeableness items, interrogators judged it human 63.7% of the time, exceeding chance and the previous GPT records of 49% and 54%, and approaching human witnesses' 66-67%. The disagreeable and neutral agents were judged human 51.97% and 56.9% respectively. The authors take this as showing that agreeableness increases perceived humanity, and they report that Camila was chosen as most human-like by 48.05% of judges, a significant preference over both other agents. Although the overall chi-square across the three agents was not significant (p = 0.233), the paper's claim is that the consistent ordering and the significant pairwise human-likeness results point to agreeableness as the key factor.","pith_inferences":["Because the manipulation bundled agreeableness together with different names, backstories, slang, and transcription timing, the paper's causal reading is stronger than its data allow; a controlled replication that varies only the BFI item scores would separate these.","The manual transcription step may itself create human-like signals, such as typos, pauses, and message chunking, that contribute to confusion independent of agreeableness; logging timestamps and keystrokes would let future work test this.","If the looking-glass-self explanation is right, interrogators' own personality or need for social connection should modulate who is judged human; adding pre-interaction Big Five or sociality measures would make that prediction testable."],"forward_implications":["An agreeableness prompt can push a GPT agent past chance-level human attribution in a five-minute text chat, reaching 63.7% confusion, close to human witnesses' 66-67%.","All three personality variants cleared the 50% mark, suggesting the underlying prompt, not the trait alone, already makes GPT agents difficult to distinguish from humans in this setup.","Judges' explicit preference for Camila as the most human-like agent indicates the trait manipulation changes perceived humanness even when overall detection choices are not statistically distinct.","If the pattern generalizes, designing AI assistants with high-agreeableness profiles could make collaborative systems feel warmer and more trustworthy, while also raising concerns about deceptive indistinguishability."],"supporting_citations":[{"why":"defines the imitation game and the 30% confusion threshold the paper uses to declare a pass.","marker":"(Turing, 1950)"},{"why":"supplies the Spanish-adapted 'Sierra' prompt and the 49% GPT-4 and 66% human-witness baselines the results are compared against.","marker":"(Jones & Bergen, 2023)"},{"why":"provides the later 54% GPT-4 and 67% human-witness rates that the 63.7% result is said to approach.","marker":"(Jones & Bergen, 2024)"},{"why":"provides the Big Five Inventory whose nine agreeableness items are encoded in the agent prompts.","marker":"(John et al., 1991)"},{"why":"supplies the definition of agreeableness and its antagonistic counterpart used to justify the prompt design.","marker":"(McCrae & Costa, 1987)"},{"why":"documents ChatGPT-4o, the model that all three agents run on.","marker":"(OpenAI, 2024)"},{"why":"offers the three-factor anthropomorphism theory used to explain why judges attribute humanity to the agents.","marker":"(Epley et al., 2007)"}],"fun_headline_variants":["AI with high agreeableness fools humans 63.7% of time","Agreeable AI outsmarts Turing test at 63.7%","Personality engineering boosts AI's human-like appeal","Friendly AI wins Turing test with 63.7% confusion rate","High-agreeableness AI nears human-level Turing test"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that the three agents differed only in their programmed agreeableness, so the rising confusion rates can be attributed to that trait; in fact the agents had separate names, backstories, colloquial styles, and a researcher typed every reply, so those differences are left uncontrolled.","fun_headline_variants_meta":{"raw":{"variants":["AI with high agreeableness fools humans 63.7% of time","Agreeable AI outsmarts Turing test at 63.7%","Personality engineering boosts AI's human-like appeal","Friendly AI wins Turing test with 63.7% confusion rate","High-agreeableness AI nears human-level Turing test"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000142,"raw_usage":{"total_tokens":1150,"prompt_tokens":910,"completion_tokens":240,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":526,"completion_tokens_details":{"reasoning_tokens":151}},"tokens_in":526,"tokens_out":240,"duration_ms":3084,"temperature":1.0,"reasoning_tokens":151,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-12T15:56:05.447205+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Conduct the same five-minute Discord Turing test with one name, one backstory, and one transcription protocol, varying only the BFI agreeableness prompt; if the agreeable agent no longer out-scores the disagreeable agent, the causal claim fails. A direct check is to measure perceived agreeableness of each agent and test whether it statistically mediates the human-versus-AI judgment.","supporting_citations":[],"review_version":1}