{"id":"https://openalex.org/W7135034708","doi":"https://doi.org/10.1145/3776734.3794531","title":"Bad Idea or Good Prediction? Comparing VLM and Human Anticipatory Judgment","display_name":"Bad Idea or Good Prediction? Comparing VLM and Human Anticipatory Judgment","publication_year":2026,"publication_date":"2026-03-12","ids":{"openalex":"https://openalex.org/W7135034708","doi":"https://doi.org/10.1145/3776734.3794531"},"language":null,"primary_location":{"id":"doi:10.1145/3776734.3794531","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3776734.3794531","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030027463","display_name":"Maria Teresa Parreira","orcid":"https://orcid.org/0000-0001-6191-3127"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Maria Teresa Parreira","raw_affiliation_strings":["Cornell University, New York, USA"],"raw_orcid":"https://orcid.org/0000-0001-6191-3127","affiliations":[{"raw_affiliation_string":"Cornell University, New York, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128828369","display_name":"Hongjin Quan","orcid":null},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongjin Quan","raw_affiliation_strings":["Cornell Tech, New York, USA"],"raw_orcid":"https://orcid.org/0009-0006-5793-9408","affiliations":[{"raw_affiliation_string":"Cornell Tech, New York, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052588546","display_name":"Adolfo G. Ramirez-Aristizabal","orcid":"https://orcid.org/0000-0002-2178-7389"},"institutions":[{"id":"https://openalex.org/I4210099672","display_name":"Accenture (United States)","ror":"https://ror.org/013g16z83","country_code":"US","type":"company","lineage":["https://openalex.org/I4210093804","https://openalex.org/I4210099672"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Adolfo G. Ramirez-Aristizabal","raw_affiliation_strings":["Accenture, San Francisco, USA"],"raw_orcid":"https://orcid.org/0000-0002-2178-7389","affiliations":[{"raw_affiliation_string":"Accenture, San Francisco, USA","institution_ids":["https://openalex.org/I4210099672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5128864190","display_name":"Wendy Ju","orcid":null},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wendy Ju","raw_affiliation_strings":["Cornell Tech, New York, USA"],"raw_orcid":"https://orcid.org/0000-0002-3119-611X","affiliations":[{"raw_affiliation_string":"Cornell Tech, New York, USA","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5030027463"],"corresponding_institution_ids":["https://openalex.org/I205783295"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.48705179,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"933","last_page":"937"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10709","display_name":"Social Robot Interaction and HRI","score":0.3862000107765198,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10709","display_name":"Social Robot Interaction and HRI","score":0.3862000107765198,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.3321000039577484,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11094","display_name":"Face Recognition and Perception","score":0.08990000188350677,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.555400013923645},{"id":"https://openalex.org/keywords/test","display_name":"Test (biology)","score":0.48989999294281006},{"id":"https://openalex.org/keywords/human\u2013robot-interaction","display_name":"Human\u2013robot interaction","score":0.3630000054836273},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.3280999958515167},{"id":"https://openalex.org/keywords/exploratory-research","display_name":"Exploratory research","score":0.32109999656677246},{"id":"https://openalex.org/keywords/anticipation","display_name":"Anticipation (artificial intelligence)","score":0.3206000030040741},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.30709999799728394}],"concepts":[{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.6514000296592712},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.555400013923645},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.48989999294281006},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.48899999260902405},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4300000071525574},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.4226999878883362},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40610000491142273},{"id":"https://openalex.org/C145460709","wikidata":"https://www.wikidata.org/wiki/Q859951","display_name":"Human\u2013robot interaction","level":3,"score":0.3630000054836273},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.3280999958515167},{"id":"https://openalex.org/C85973986","wikidata":"https://www.wikidata.org/wiki/Q1091731","display_name":"Exploratory research","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C176777502","wikidata":"https://www.wikidata.org/wiki/Q4774623","display_name":"Anticipation (artificial intelligence)","level":2,"score":0.3206000030040741},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2939999997615814},{"id":"https://openalex.org/C3018260909","wikidata":"https://www.wikidata.org/wiki/Q1322871","display_name":"Exploratory analysis","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C86658582","wikidata":"https://www.wikidata.org/wiki/Q1432778","display_name":"Social cognition","level":3,"score":0.25619998574256897},{"id":"https://openalex.org/C3017944768","wikidata":"https://www.wikidata.org/wiki/Q1450463","display_name":"Poison control","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C2779778163","wikidata":"https://www.wikidata.org/wiki/Q774081","display_name":"Implicit-association test","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3776734.3794531","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3776734.3794531","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W4391351849","https://openalex.org/W4403677113","https://openalex.org/W4403918936","https://openalex.org/W4406711794"],"related_works":[],"abstract_inverted_index":{"Anticipatory":[0],"reasoning":[1],"\u2013":[2,15],"predicting":[3,120],"whether":[4,28,61],"situations":[5,62],"will":[6,63],"resolve":[7],"positively":[8],"or":[9,66],"negatively":[10],"by":[11,45,82,147],"interpreting":[12],"contextual":[13],"cues":[14],"is":[16,172],"crucial":[17],"for":[18,167,182],"robots":[19],"operating":[20],"in":[21,119,154,184],"human":[22,49,84,108,127,134,149],"environments.":[23],"This":[24],"exploratory":[25],"study":[26],"evaluates":[27],"Vision":[29],"Language":[30],"Models":[31],"(VLMs)":[32],"possess":[33],"such":[34],"predictive":[35],"capabilities.":[36],"First,":[37],"we":[38,69],"test":[39,93],"VLMs":[40,79,95,142,164],"on":[41],"direct":[42],"outcome":[43],"prediction":[44],"inputting":[46],"videos":[47],"of":[48,74,87],"and":[50,96,104,176],"robot":[51],"scenarios":[52],"with":[53,132],"outcomes":[54,81,103,146],"removed,":[55],"asking":[56],"the":[57,124],"models":[58],"to":[59,139,144,174],"predict":[60,80,145],"end":[64],"well":[65],"poorly.":[67],"Second,":[68],"introduce":[70],"a":[71],"novel":[72],"evaluation":[73],"anticipatory":[75,168],"social":[76,156],"intelligence:":[77],"can":[78],"analyzing":[83,148],"facial":[85,150],"reactions":[86],"people":[88],"watching":[89],"these":[90],"scenarios?":[91],"We":[92],"multiple":[94],"compare":[97],"their":[98,170],"predictions":[99],"against":[100],"both":[101],"true":[102,121],"judgments":[105,135],"from":[106,137],"29":[107],"participants.":[109],"The":[110],"best-performing":[111],"VLM":[112],"(Gemini":[113],"2.0":[114],"Flash)":[115],"achieved":[116],"70.0%":[117],"accuracy":[118],"outcomes,":[122],"outperforming":[123],"average":[125],"individual":[126,133],"(62.1%":[128],"\u00b1":[129],"6.2%).":[130],"Agreement":[131],"ranged":[136],"44.4%":[138],"69.7%.":[140],"Critically,":[141],"struggled":[143],"reactions,":[151],"suggesting":[152],"limitations":[153],"leveraging":[155],"cues.":[157],"These":[158],"preliminary":[159],"findings":[160],"indicate":[161],"that":[162],"while":[163],"show":[165],"promise":[166],"reasoning,":[169],"performance":[171],"sensitive":[173],"model":[175],"prompt":[177],"selection,":[178],"warranting":[179],"further":[180],"investigation":[181],"applications":[183],"HRI.":[185]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2026-03-13T00:00:00"}
