{"id":"https://openalex.org/W2587333025","doi":"https://doi.org/10.1109/smc.2016.7844440","title":"Active Bayesian observer correcting overconfidence effects due to E-type confirmatory bias","display_name":"Active Bayesian observer correcting overconfidence effects due to E-type confirmatory bias","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W2587333025","doi":"https://doi.org/10.1109/smc.2016.7844440","mag":"2587333025"},"language":"en","primary_location":{"id":"doi:10.1109/smc.2016.7844440","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2016.7844440","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5047571162","display_name":"Kazunori Fujimoto","orcid":"https://orcid.org/0000-0001-9486-4077"},"institutions":[{"id":"https://openalex.org/I916559398","display_name":"Kindai University","ror":"https://ror.org/05kt9ap64","country_code":"JP","type":"education","lineage":["https://openalex.org/I916559398"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazunori Fujimoto","raw_affiliation_strings":["Faculty of Business Administration, Kindai University, Osaka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Business Administration, Kindai University, Osaka, Japan","institution_ids":["https://openalex.org/I916559398"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007060371","display_name":"Jun Muramatsu","orcid":"https://orcid.org/0000-0001-5016-5717"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jun Muramatsu","raw_affiliation_strings":["Communication Science Laboratories, NTT Corporation, Kyoto, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Communication Science Laboratories, NTT Corporation, Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2553,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.5844034,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"2016","issue":null,"first_page":"001443","last_page":"001448"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9794999957084656,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/overconfidence-effect","display_name":"Overconfidence effect","score":0.8004474639892578},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6894581317901611},{"id":"https://openalex.org/keywords/confirmatory-factor-analysis","display_name":"Confirmatory factor analysis","score":0.5611560940742493},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5543941259384155},{"id":"https://openalex.org/keywords/observer","display_name":"Observer (physics)","score":0.5122024416923523},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45094987750053406},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.4464230537414551},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4103417992591858},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3984535336494446},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3519226908683777},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.34131425619125366},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2766548693180084},{"id":"https://openalex.org/keywords/structural-equation-modeling","display_name":"Structural equation modeling","score":0.15916913747787476},{"id":"https://openalex.org/keywords/social-psychology","display_name":"Social psychology","score":0.13530859351158142}],"concepts":[{"id":"https://openalex.org/C51110983","wikidata":"https://www.wikidata.org/wiki/Q16503490","display_name":"Overconfidence effect","level":2,"score":0.8004474639892578},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6894581317901611},{"id":"https://openalex.org/C40722632","wikidata":"https://www.wikidata.org/wiki/Q5160137","display_name":"Confirmatory factor analysis","level":3,"score":0.5611560940742493},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5543941259384155},{"id":"https://openalex.org/C2780704645","wikidata":"https://www.wikidata.org/wiki/Q9251458","display_name":"Observer (physics)","level":2,"score":0.5122024416923523},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45094987750053406},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.4464230537414551},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4103417992591858},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3984535336494446},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3519226908683777},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.34131425619125366},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2766548693180084},{"id":"https://openalex.org/C71104824","wikidata":"https://www.wikidata.org/wiki/Q1476639","display_name":"Structural equation modeling","level":2,"score":0.15916913747787476},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.13530859351158142},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/smc.2016.7844440","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2016.7844440","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},{"id":"mag:2750698060","is_oa":false,"landing_page_url":"http://jglobal.jst.go.jp/en/public/201702264475683645","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320313220","display_name":"Kindai University","ror":"https://ror.org/05kt9ap64"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1538066839","https://openalex.org/W1568586930","https://openalex.org/W1589928278","https://openalex.org/W1938670616","https://openalex.org/W1990584714","https://openalex.org/W1993884581","https://openalex.org/W2027163931","https://openalex.org/W2028211630","https://openalex.org/W2073018996","https://openalex.org/W2075585362","https://openalex.org/W2081280292","https://openalex.org/W2088480602","https://openalex.org/W2159322587","https://openalex.org/W2303207362","https://openalex.org/W4232199300","https://openalex.org/W4239041053","https://openalex.org/W4245271589","https://openalex.org/W4249934999","https://openalex.org/W6697851924"],"related_works":["https://openalex.org/W4253467046","https://openalex.org/W4251085376","https://openalex.org/W2066240519","https://openalex.org/W2775388773","https://openalex.org/W2991634017","https://openalex.org/W2110063637","https://openalex.org/W2354626691","https://openalex.org/W1980773669","https://openalex.org/W2001454647","https://openalex.org/W3121409907"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,39,55,62,66,79,82,101,111],"preliminary":[4],"analysis":[5],"of":[6,27,41,96,118],"an":[7],"active":[8,120],"Bayesian":[9,87,121],"observer":[10],"that":[11],"communicates":[12],"with":[13,65,81],"humans":[14],"to":[15,21,77,92,109],"correct":[16],"the":[17,94,119],"overconfidence":[18],"effects":[19,95],"due":[20],"their":[22],"confirmatory":[23,28,52,74],"biases.":[24],"Two":[25],"types":[26],"biases,":[29],"called":[30],"Z-":[31,97],"and":[32,36,46,57,98],"E-types,":[33],"are":[34],"introduced":[35],"formalized":[37],"using":[38],"model":[40,89,106],"noisy":[42],"channels":[43],"between":[44],"signals":[45],"perceptions.":[47],"Persons":[48],"suffering":[49,71],"from":[50,72],"Z-type":[51],"bias":[53,75],"receive":[54,78],"signal":[56,64,80],"perceive":[58],"it":[59],"incorrectly":[60],"as":[61],"different":[63],"certain":[67,83],"probability,":[68],"while":[69],"those":[70],"E-type":[73],"fail":[76],"probability.":[84],"A":[85],"dynamic":[86],"network":[88],"is":[90],"developed":[91],"analyze":[93],"E-types":[99],"in":[100],"unified":[102],"way.":[103],"The":[104],"analytical":[105],"enables":[107],"us":[108],"give":[110],"theoretical":[112],"insight":[113],"into":[114],"some":[115],"basic":[116],"properties":[117],"observers.":[122]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
