{"id":"https://openalex.org/W7148782391","doi":"https://doi.org/10.48550/arxiv.2604.02220","title":"Visual Decoding Operators: Towards a Compositional Theory of Visualization Perception","display_name":"Visual Decoding Operators: Towards a Compositional Theory of Visualization Perception","publication_year":2026,"publication_date":"2026-04-02","ids":{"openalex":"https://openalex.org/W7148782391","doi":"https://doi.org/10.48550/arxiv.2604.02220"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.02220","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02220","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.02220","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132857377","display_name":"Sheng Long","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Long, Sheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132841085","display_name":"Remco Chang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chang, Remco","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132911377","display_name":"Eugene Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Eugene","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035544999","display_name":"Alex Kale","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kale, Alex","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132872974","display_name":"Matthew Kay","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kay, Matthew","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9933000206947327,"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"}},"topics":[{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9933000206947327,"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/T11904","display_name":"Spatial Cognition and Navigation","score":0.001500000013038516,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.00039999998989515007,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/visualization","display_name":"Visualization","score":0.6822999715805054},{"id":"https://openalex.org/keywords/data-visualization","display_name":"Data visualization","score":0.49059998989105225},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.4765999913215637},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4438000023365021},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.428600013256073},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.427700012922287},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4251999855041504},{"id":"https://openalex.org/keywords/chart","display_name":"Chart","score":0.41269999742507935},{"id":"https://openalex.org/keywords/operationalization","display_name":"Operationalization","score":0.40560001134872437},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.36880001425743103}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.73580002784729},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.6822999715805054},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.538100004196167},{"id":"https://openalex.org/C172367668","wikidata":"https://www.wikidata.org/wiki/Q6504956","display_name":"Data visualization","level":3,"score":0.49059998989105225},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.4765999913215637},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4496999979019165},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4438000023365021},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.428600013256073},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.427700012922287},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4251999855041504},{"id":"https://openalex.org/C190812933","wikidata":"https://www.wikidata.org/wiki/Q28923","display_name":"Chart","level":2,"score":0.41269999742507935},{"id":"https://openalex.org/C9354725","wikidata":"https://www.wikidata.org/wiki/Q286017","display_name":"Operationalization","level":2,"score":0.40560001134872437},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.36880001425743103},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.36550000309944153},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.3610000014305115},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.35580000281333923},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.33739998936653137},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.3352999985218048},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33230000734329224},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.32019999623298645},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.31220000982284546},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.28929999470710754},{"id":"https://openalex.org/C28883458","wikidata":"https://www.wikidata.org/wiki/Q3427422","display_name":"Statistical graphics","level":3,"score":0.27649998664855957},{"id":"https://openalex.org/C14669888","wikidata":"https://www.wikidata.org/wiki/Q4014850","display_name":"Creative visualization","level":3,"score":0.2759000062942505},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.27149999141693115},{"id":"https://openalex.org/C185578843","wikidata":"https://www.wikidata.org/wiki/Q10609775","display_name":"Information visualization","level":3,"score":0.26980000734329224},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.25780001282691956},{"id":"https://openalex.org/C98385598","wikidata":"https://www.wikidata.org/wiki/Q1339385","display_name":"Empirical distribution function","level":2,"score":0.2538999915122986}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.02220","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02220","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.02220","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02220","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Prior":[0],"work":[1],"on":[2,112],"perceptual":[3,87],"effectiveness":[4],"has":[5],"decomposed":[6,82],"visualizations":[7],"into":[8,83],"smaller":[9],"common":[10],"units":[11],"(e.g.,":[12],"channels":[13],"such":[14],"as":[15,56],"angle,":[16],"position,":[17],"and":[18,68,89,130,151,170,207,223],"length)":[19],"to":[20,31,109,161,187],"establish":[21],"rankings.":[22],"While":[23],"useful,":[24],"these":[25],"decompositions":[26],"lack":[27],"the":[28,125,136,162,192,211],"computational":[29],"structure":[30],"predict":[32,110],"performance":[33,111],"for":[34,43],"new":[35,41],"visualization":[36,54,189,201],"x":[37],"task":[38],"combinations,":[39],"requiring":[40],"experiments":[42],"each.":[44],"We":[45,98,181],"propose":[46],"an":[47],"alternative":[48],"unit":[49],"of":[50,58,106,172,194,198,220],"analysis:":[51],"operationalizing":[52],"quantitative":[53],"interpretation":[55],"sequences":[57],"composable":[59],"visual":[60],"decoding":[61],"operators.":[62],"Using":[63],"probability":[64],"density":[65],"function":[66,71],"(PDF)":[67],"cumulative":[69],"distribution":[70],"(CDF)":[72],"charts,":[73],"we":[74,144],"examine":[75],"how":[76],"four":[77],"chart-specific":[78],"tasks":[79],"can":[80],"be":[81],"five":[84,175],"reusable,":[85],"chart-agnostic":[86],"operations":[88],"characterize":[90],"their":[91],"error":[92],"profiles":[93],"through":[94],"hierarchical":[95],"Bayesian":[96],"modeling.":[97],"then":[99],"test":[100],"generalizability":[101],"by":[102],"composing":[103],"one":[104,203],"kind":[105],"learned":[107],"operators":[108,146],"a":[113,140,195],"structurally":[114],"different":[115,196],"task:":[116],"Moritz":[117],"et":[118],"al.'s":[119],"[37]":[120],"scatterplot":[121],"mean-estimation":[122],"experiment,":[123],"where":[124,204],"chart":[126,128],"type,":[127],"dimensions,":[129],"analytic":[131],"goal":[132],"all":[133],"differ":[134],"from":[135],"learning":[137],"conditions.":[138],"With":[139],"pre-registered":[141],"analysis":[142],"plan,":[143],"compose":[145],"under":[147],"six":[148],"candidate":[149],"strategies":[150],"evaluate":[152],"each":[153],"against":[154],"empirical":[155,188,200],"data":[156],"with":[157],"no":[158],"parameters":[159],"fit":[160],"response":[163],"data.":[164],"One":[165],"strategy":[166],"captures":[167],"both":[168],"bias":[169],"variance":[171],"observed":[173],"responses;":[174],"alternatives":[176],"fail":[177],"in":[178,213],"distinguishable":[179],"ways.":[180],"argue":[182],"that":[183],"this":[184,221],"decoding-operator-oriented":[185],"approach":[186],"research":[190],"demonstrates":[191],"feasibility":[193],"way":[197],"doing":[199],"research,":[202],"findings":[205],"compose,":[206],"predictions":[208],"extend":[209],"beyond":[210],"conditions":[212],"which":[214],"they":[215],"were":[216],"measured.":[217],"Free":[218],"copy":[219],"paper":[222],"supplemental":[224],"materials:":[225],"https://osf.io/prtfq.":[226]},"counts_by_year":[],"updated_date":"2026-07-04T06:09:54.619538","created_date":"2026-04-04T00:00:00"}