{
  "schema":"ULTRACON_CON_AI_EXPERIMENTS_V1",
  "version":"4.0",
  "updated":"2026-09-13",
  "principle":"Experiments study human attribution to machines and hybrid teams; they do not infer machine psychology from folk labels.",
  "experiments":[
    {"id":"CAI01","name":"SOURCE-SWAP","question":"Does the same error look more or less stupid when attributed to a human, LLM, institution or group?","design":"Between-subject source label with identical response content and outcome.","measures":["stupidity attribution","competence","trust","blame","willingness to reuse"],"controls":["identical content","identical stated confidence"],"sources":["S65","S66","S67"],"non_inference":"source effects do not reveal objective intelligence"},
    {"id":"CAI02","name":"ERROR-TYPE MATRIX","question":"Which machine failures trigger the strongest folk judgment of stupidity?","design":"Factorial manipulation of factual ignorance, confabulation, pragmatic mismatch, tool failure, temporal inconsistency and over-literalism.","measures":["stupidity attribution","perceived cause","repairability","trust change"],"sources":["S79"],"non_inference":"folk clusters are not technical ontologies"},
    {"id":"CAI03","name":"CONFIDENCE × CORRECTNESS","question":"Is a confidently wrong AI judged differently from an uncertainly wrong one?","design":"Cross correctness with verbal confidence while holding explanation length constant.","measures":["stupidity","arrogance/overconfidence attribution","trust","blame"],"sources":["S05","S67"],"non_inference":"verbal confidence is not direct access to internal model confidence"},
    {"id":"CAI04","name":"UPDATE TEST","question":"How much does failure to revise after correction drive the judgment 'AI is stupid'?","design":"Provide identical correction; manipulate acceptance, partial update, rational rejection with evidence, or repeated error.","measures":["stupidity","rigidity","trust","perceived agency"],"sources":["S69","S79"],"non_inference":"rejecting a correction can be rational if the correction is weak"},
    {"id":"CAI05","name":"EX-ANTE / EX-POST","question":"Does outcome knowledge alter judgments of human versus AI decision quality?","design":"Present identical uncertain decision before or after revealing favorable/unfavorable outcome.","measures":["decision quality","stupidity","competence","blame"],"sources":["S58","S60","S62"],"non_inference":"outcome information is not always illegitimate for learning"},
    {"id":"CAI06","name":"ANTHROPOMORPHISM LAYER","question":"Do human-like cues change how an identical machine error is interpreted?","design":"Manipulate neutral interface versus human-like name, voice/persona or mental-state language.","measures":["stupidity","intentionality","experience attribution","trust","forgiveness"],"sources":["S67","S68"],"non_inference":"anthropomorphic judgments do not establish consciousness"},
    {"id":"CAI07","name":"ERROR RECOVERY / ALGORITHM AVERSION","question":"Does one visible algorithm error cause larger trust loss than an equivalent human error, and can transparent correction recover it?","design":"Human versus algorithm advisor, one controlled error, then optional correction and uncertainty disclosure.","measures":["reliance","trust calibration","stupidity attribution"],"sources":["S64","S65","S69"],"non_inference":"algorithm aversion is not a universal human trait"},
    {"id":"CAI08","name":"APPRECIATION BASELINE","question":"When do users give algorithmic advice more weight than human advice?","design":"Equivalent numeric advice, source varied, user expertise measured/manipulated.","measures":["weight of advice","accuracy","stupidity attribution after success/failure"],"sources":["S66"],"non_inference":"algorithm appreciation does not establish warranted trust"},
    {"id":"CAI09","name":"SOURCE DISCLOSURE","question":"Does knowing advice is AI-generated change usefulness and stupidity judgments independent of quality?","design":"Blind evaluation followed by source revelation, with expert-quality matched content.","measures":["usefulness","trust","stupidity","credibility"],"sources":["S70"],"non_inference":"source disclosure effects do not establish bias in every domain"},
    {"id":"CAI10","name":"HYBRID CASCADE","question":"Can a locally reasonable human and locally reasonable AI form a collectively bad information cascade?","design":"Private evidence plus sequential AI/human advice with controlled accuracy cues and correlated evidence.","measures":["final accuracy","advice weighting","collective stupidity attribution","private-public divergence"],"sources":["S52","S77"],"non_inference":"bad team output does not imply an individual stupid component"}
  ]
}