{"id":"https://openalex.org/W4385488952","doi":"https://doi.org/10.1109/ijcnn54540.2023.10192038","title":"Modelling Grid Navigation Using Reinforcement Learning Linear Ballistic Accumulators","display_name":"Modelling Grid Navigation Using Reinforcement Learning Linear Ballistic Accumulators","publication_year":2023,"publication_date":"2023-06-18","ids":{"openalex":"https://openalex.org/W4385488952","doi":"https://doi.org/10.1109/ijcnn54540.2023.10192038"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn54540.2023.10192038","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn54540.2023.10192038","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","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/A5100923848","display_name":"Gautham Venugopal","orcid":null},"institutions":[{"id":"https://openalex.org/I64189192","display_name":"International Institute of Information Technology, Hyderabad","ror":"https://ror.org/05f11g639","country_code":"IN","type":"education","lineage":["https://openalex.org/I64189192"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Gautham Venugopal","raw_affiliation_strings":["International Institute of Information Technology,Cognitive Science Lab,Hyderabad,India","Cognitive Science Lab, International Institute of Information Technology, Hyderabad, India"],"affiliations":[{"raw_affiliation_string":"International Institute of Information Technology,Cognitive Science Lab,Hyderabad,India","institution_ids":["https://openalex.org/I64189192"]},{"raw_affiliation_string":"Cognitive Science Lab, International Institute of Information Technology, Hyderabad, India","institution_ids":["https://openalex.org/I64189192"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049423985","display_name":"Raju S. Bapi","orcid":"https://orcid.org/0000-0003-2204-0890"},"institutions":[{"id":"https://openalex.org/I64189192","display_name":"International Institute of Information Technology, Hyderabad","ror":"https://ror.org/05f11g639","country_code":"IN","type":"education","lineage":["https://openalex.org/I64189192"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Bapi Surampudi Raju","raw_affiliation_strings":["International Institute of Information Technology,Cognitive Science Lab,Hyderabad,India","Cognitive Science Lab, International Institute of Information Technology, Hyderabad, India"],"affiliations":[{"raw_affiliation_string":"International Institute of Information Technology,Cognitive Science Lab,Hyderabad,India","institution_ids":["https://openalex.org/I64189192"]},{"raw_affiliation_string":"Cognitive Science Lab, International Institute of Information Technology, Hyderabad, India","institution_ids":["https://openalex.org/I64189192"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100923848"],"corresponding_institution_ids":["https://openalex.org/I64189192"],"apc_list":null,"apc_paid":null,"fwci":0.1813,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.48198839,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"6","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10042","display_name":"Neural and Behavioral Psychology Studies","score":0.9940000176429749,"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"}},"topics":[{"id":"https://openalex.org/T10042","display_name":"Neural and Behavioral Psychology Studies","score":0.9940000176429749,"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"}},{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.9900000095367432,"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"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9896000027656555,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7573927640914917},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7415202856063843},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6571560502052307},{"id":"https://openalex.org/keywords/action-selection","display_name":"Action selection","score":0.6320135593414307},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.607528805732727},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.6022137403488159},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5192434787750244},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5065723657608032},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.4890318810939789},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.443642795085907},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.42126187682151794},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.41154569387435913},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.19104862213134766},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15036439895629883}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7573927640914917},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7415202856063843},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6571560502052307},{"id":"https://openalex.org/C166109690","wikidata":"https://www.wikidata.org/wiki/Q4677422","display_name":"Action selection","level":3,"score":0.6320135593414307},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.607528805732727},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.6022137403488159},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5192434787750244},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5065723657608032},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.4890318810939789},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.443642795085907},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.42126187682151794},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.41154569387435913},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.19104862213134766},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15036439895629883},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn54540.2023.10192038","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn54540.2023.10192038","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/14","score":0.4300000071525574,"display_name":"Life below water"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W41554520","https://openalex.org/W1907005258","https://openalex.org/W1967627760","https://openalex.org/W1983297775","https://openalex.org/W1985454926","https://openalex.org/W1991530889","https://openalex.org/W1992373090","https://openalex.org/W1995491246","https://openalex.org/W1999696971","https://openalex.org/W2003178747","https://openalex.org/W2005121348","https://openalex.org/W2006114123","https://openalex.org/W2040774694","https://openalex.org/W2044915344","https://openalex.org/W2045968318","https://openalex.org/W2050618783","https://openalex.org/W2059511681","https://openalex.org/W2060715457","https://openalex.org/W2073158860","https://openalex.org/W2086295830","https://openalex.org/W2096228907","https://openalex.org/W2098205603","https://openalex.org/W2117402031","https://openalex.org/W2117726420","https://openalex.org/W2120261153","https://openalex.org/W2122066429","https://openalex.org/W2136582516","https://openalex.org/W2149565728","https://openalex.org/W2160668068","https://openalex.org/W2168175751","https://openalex.org/W2169454018","https://openalex.org/W2263529863","https://openalex.org/W2561424158","https://openalex.org/W2888883957","https://openalex.org/W2923045238","https://openalex.org/W2930108284","https://openalex.org/W2956120613","https://openalex.org/W3034707373","https://openalex.org/W3134856020","https://openalex.org/W3156174535","https://openalex.org/W4289494292","https://openalex.org/W6771912780"],"related_works":["https://openalex.org/W2015051472","https://openalex.org/W2168501056","https://openalex.org/W2120009678","https://openalex.org/W2037601570","https://openalex.org/W2912947802","https://openalex.org/W2123856982","https://openalex.org/W1950622696","https://openalex.org/W2109007220","https://openalex.org/W4221149483","https://openalex.org/W2611757366"],"abstract_inverted_index":{"Reinforcement":[0],"Learning":[1],"(RL)":[2],"models":[3,9,28,131,146,207,217,228,246],"constitute":[4],"an":[5,97],"important":[6],"subset":[7],"of":[8,15,59,64,84,166,179,201,243,264,282],"used":[10,161,171],"in":[11,26,52,62,104,162,172,191,279],"studying":[12],"many":[13,83],"facets":[14],"human":[16,102,180,236],"learning":[17],"including":[18],"Motor":[19,283],"Sequence":[20],"Learning.":[21,284],"However,":[22],"conventional":[23,244],"action":[24,202],"selection":[25],"RL":[27,93,135,245],"such":[29,74,227],"as":[30,67,75],"softmax-based":[31],"choice":[32],"rules":[33],"lack":[34],"biological":[35],"plausibility":[36],"and":[37,144,152,164,193,247,267],"do":[38,46],"not":[39,47],"offer":[40],"mechanistic":[41],"explanations.":[42],"Furthermore,":[43],"they":[44],"also":[45,248],"use":[48,92,276],"response":[49,251],"time":[50],"data":[51,211],"model":[53,99,101,138],"fitting,":[54],"which":[55],"can":[56,122],"be":[57,273],"indicative":[58],"the":[60,85,105,120,173,177,189,205,219,250,255,262,280],"difference":[61],"value":[63],"alternate":[65],"choices":[66],"perceived":[68],"by":[69,235,254],"subjects.":[70],"Evidence":[71],"Accumulation":[72],"Models(EAM)":[73],"Linear":[76],"Ballistic":[77],"Accumulators(LBA)":[78],"provide":[79,218],"a":[80,112,116,137,184,197],"solution":[81],"to":[82,100,114,210,222,231,241,275],"above":[86],"problems.":[87],"In":[88],"this":[89],"study,":[90],"we":[91,182,212],"algorithms":[94],"integrated":[95],"with":[96,238],"LBA":[98],"behaviour":[103],"Grid-Sailing":[106],"Task.":[107],"The":[108],"task":[109],"involves":[110],"navigating":[111],"grid":[113],"reach":[115],"goal":[117],"position":[118],"where":[119,156,270],"participant":[121],"choose":[123],"from":[124],"three":[125,133,206],"possible":[126],"actions.":[127],"We":[128,224,260],"fit":[129,209,221],"RLLBA":[130],"using":[132,139],"different":[134],"algorithms:":[136],"only":[140],"Model":[141,150,153],"Based":[142,151,158],"updates,":[143],"two":[145],"that":[147,214,226,242],"arbitrates":[148],"between":[149,188],"Free":[154],"learning,":[155],"Weight":[157],"Arbitration":[159,169],"is":[160,170],"one":[163],"Value":[165],"Information(Vol)":[167],"based":[168,199,216],"other.":[174],"When":[175,204],"following":[176],"actions":[178,233],"subjects,":[181],"find":[183,213,225],"significant":[185],"negative":[186],"correlation":[187],"Variance":[190],"Q-values":[192],"Response":[194],"Time,":[195],"motivating":[196],"competition":[198],"mechanism":[200],"selection.":[203],"are":[208,229],"VoI":[215],"best":[220],"data.":[223],"able":[230],"predict":[232,249],"made":[234],"participants":[237],"accuracy":[239],"comparable":[240],"times":[252],"taken":[253],"subject":[256],"within":[257],"reasonable":[258],"margins.":[259],"discuss":[261],"implications":[263],"these":[265],"results":[266],"outline":[268],"scenarios":[269],"it":[271],"would":[272],"advantageous":[274],"RLEAM":[277],"especially":[278],"domain":[281]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-24T23:09:58.560324","created_date":"2025-10-10T00:00:00"}