Contents of the JASIS&T special issue on Neuro-Information Science.
To see the special issue on Wiley’s website click here.
Cover image
List of articles:
216150
YU5MAHHA
items
1
0
default
asc
1
1
355
https://jacekg.ischool.utexas.edu/wp-content/plugins/zotpress/
%7B%22status%22%3A%22success%22%2C%22updateneeded%22%3Afalse%2C%22instance%22%3A%22zotpress-8034e7666a05fbd93b5f2c8bad502e7a%22%2C%22meta%22%3A%7B%22request_last%22%3A0%2C%22request_next%22%3A0%2C%22used_cache%22%3Atrue%7D%2C%22data%22%3A%5B%7B%22key%22%3A%22L8856X6P%22%2C%22library%22%3A%7B%22id%22%3A216150%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Jones%20et%20al.%22%2C%22parsedDate%22%3A%222019%22%2C%22numChildren%22%3A2%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EJones%2C%20L.%20M.%2C%20Wright%2C%20K.%20D.%2C%20Jack%2C%20A.%20I.%2C%20Friedman%2C%20J.%20P.%2C%20Fresco%2C%20D.%20M.%2C%20Veinot%2C%20T.%2C%20Lu%2C%20W.%2C%20%26amp%3B%20Moore%2C%20S.%20M.%20%282019%29.%20The%20relationships%20between%20health%20information%20behavior%20and%20neural%20processing%20in%20african%20americans%20with%20prehypertension.%20%3Ci%3EJournal%20of%20the%20Association%20for%20Information%20Science%20and%20Technology%3C%5C%2Fi%3E%2C%20%3Ci%3E0%3C%5C%2Fi%3E%280%29.%20%3Ca%20target%3D%27_blank%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1002%5C%2Fasi.24098%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1002%5C%2Fasi.24098%3C%5C%2Fa%3E%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fjacekg.ischool.utexas.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D216150%26amp%3Bitem_key%3DL8856X6P%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22The%20relationships%20between%20health%20information%20behavior%20and%20neural%20processing%20in%20african%20americans%20with%20prehypertension%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Lenette%20M.%22%2C%22lastName%22%3A%22Jones%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Kathy%20D.%22%2C%22lastName%22%3A%22Wright%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Anthony%20I.%22%2C%22lastName%22%3A%22Jack%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jared%20P.%22%2C%22lastName%22%3A%22Friedman%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22David%20M.%22%2C%22lastName%22%3A%22Fresco%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Tiffany%22%2C%22lastName%22%3A%22Veinot%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Wei%22%2C%22lastName%22%3A%22Lu%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Shirley%20M.%22%2C%22lastName%22%3A%22Moore%22%7D%5D%2C%22abstractNote%22%3A%22Information%20behavior%20may%20enhance%20hypertension%20self-management%20in%20African%20Americans.%20The%20goal%20of%20this%20substudy%20was%20to%20examine%20the%20relationships%20between%20measures%20of%20self-reported%20health%20information%20behavior%20and%20neural%20measures%20of%20health%20information%20processing%20in%20a%20sample%20of%2019%20prehypertensive%20African%20Americans%20%28mean%20age%20%3D%2052.5%2C%2052.6%25%20women%29.%20We%20measured%20%28a%29%20health%20information%20seeking%2C%20sharing%2C%20and%20use%20%28surveys%29%20and%20%28b%29%20neural%20activity%20using%20functional%20magnetic%20resonance%20imaging%20%28fMRI%29%20to%20assess%20response%20to%20health%20information%20videos.%20We%20hypothesized%20that%20differential%20activation%20%28comparison%20of%20analytic%20vs.%20empathic%20brain%20activity%20when%20watching%20a%20specific%20type%20of%20video%29%20would%20indicate%20better%20function%20in%20three%2C%20distinct%20cognitive%20domains%3A%20%28a%29%20Analytic%20Network%2C%20%28b%29%20Default%20Mode%20Network%20%28DMN%29%2C%20and%20%28c%29%20ventromedial%20prefrontal%20cortex%20%28vmPFC%29.%20Scores%20on%20the%20information%20sharing%20measure%20%28but%20not%20seeking%20or%20use%29%20were%20positively%20associated%20with%20differential%20activation%20in%20the%20vmPFC%20%28rs%20%3D%20.53%2C%20p%20%3D%20.02%29%20and%20the%20DMN%20%28rs%20%3D%20.43%2C%20p%20%3D%20.06%29.%20Our%20findings%20correspond%20with%20previous%20work%20indicating%20that%20activation%20of%20the%20DMN%20and%20vmPFC%20is%20associated%20with%20sharing%20information%20to%20persuade%20others%20and%20with%20behavior%20change.%20Although%20health%20information%20is%20commonly%20conveyed%20as%20detached%20and%20analytic%20in%20nature%2C%20our%20findings%20suggest%20that%20neural%20processing%20of%20socially%20and%20emotionally%20salient%20health%20information%20is%20more%20closely%20associated%20with%20health%20information%20sharing.%22%2C%22date%22%3A%222019%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1002%5C%2Fasi.24098%22%2C%22ISSN%22%3A%222330-1643%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Fonlinelibrary.wiley.com%5C%2Fdoi%5C%2Fabs%5C%2F10.1002%5C%2Fasi.24098%22%2C%22collections%22%3A%5B%22FVXB2PVS%22%2C%22BGDAKHGJ%22%2C%22YU5MAHHA%22%5D%2C%22dateModified%22%3A%222023-02-07T21%3A50%3A44Z%22%7D%7D%2C%7B%22key%22%3A%22QU85F6KE%22%2C%22library%22%3A%7B%22id%22%3A216150%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Kim%20and%20Kim%22%2C%22parsedDate%22%3A%222019%22%2C%22numChildren%22%3A2%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EKim%2C%20H.%20H.%2C%20%26amp%3B%20Kim%2C%20Y.%20H.%20%282019%29.%20ERP%5C%2FMMR%20Algorithm%20for%20Classifying%20Topic-Relevant%20and%20Topic-Irrelevant%20Visual%20Shots%20of%20Documentary%20Videos.%20%3Ci%3EJournal%20of%20the%20Association%20for%20Information%20Science%20and%20Technology%3C%5C%2Fi%3E%2C%20%3Ci%3E0%3C%5C%2Fi%3E%280%29.%20%3Ca%20target%3D%27_blank%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1002%5C%2Fasi.24179%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1002%5C%2Fasi.24179%3C%5C%2Fa%3E%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fjacekg.ischool.utexas.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D216150%26amp%3Bitem_key%3DQU85F6KE%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22ERP%5C%2FMMR%20Algorithm%20for%20Classifying%20Topic-Relevant%20and%20Topic-Irrelevant%20Visual%20Shots%20of%20Documentary%20Videos%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Hyun%20Hee%22%2C%22lastName%22%3A%22Kim%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Yong%20Ho%22%2C%22lastName%22%3A%22Kim%22%7D%5D%2C%22abstractNote%22%3A%22We%20propose%20and%20evaluate%20a%20video%20summarization%20method%20based%20on%20a%20topic%20relevance%20model%2C%20a%20maximal%20marginal%20relevance%20%28MMR%29%2C%20and%20discriminant%20analysis%20to%20generate%20a%20semantically%20meaningful%20video%20skim.%20The%20topic%20relevance%20model%20uses%20event-related%20potential%20%28ERP%29%20components%20to%20describe%20the%20process%20of%20topic%20relevance%20judgment.%20More%20specifically%2C%20the%20topic%20relevance%20model%20indicates%20that%20N400%20and%20P600%2C%20which%20have%20been%20successfully%20applied%20to%20the%20mismatch%20process%20of%20a%20stimulus%20and%20the%20discourse-internal%20reorganization%20and%20integration%20process%20of%20a%20stimulus%2C%20respectively%2C%20are%20used%20for%20the%20topic%20mismatch%20process%20of%20a%20topic-irrelevant%20video%20shot%20and%20the%20topic%20formation%20process%20of%20a%20topic-relevant%20video%20shot.%20To%20evaluate%20our%20proposed%20ERP%5C%2FMMR-based%20method%2C%20we%20compared%20the%20video%20skims%20generated%20by%20the%20ERP%5C%2FMMR-based%2C%20ERP-based%2C%20and%20shot%20boundary%20detection%20%28SBD%29%20methods%20with%20ground%20truth%20skims.%20The%20results%20showed%20that%20at%20a%20significance%20level%20of%200.05%2C%20the%20ROUGE-1%20scores%20of%20the%20ERP%5C%2FMMR%20method%20are%20statistically%20higher%20than%20those%20of%20the%20SBD%20method%2C%20and%20the%20diversity%20scores%20of%20the%20ERP%5C%2FMMR%20method%20are%20statistically%20higher%20than%20those%20of%20the%20ERP%20method.%20This%20study%20suggested%20that%20the%20proposed%20method%20may%20be%20applied%20to%20the%20construction%20of%20a%20video%20skim%20without%20operational%20intervention%2C%20such%20as%20the%20insertion%20of%20a%20black%20screen%20between%20video%20shots.%22%2C%22date%22%3A%222019%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1002%5C%2Fasi.24179%22%2C%22ISSN%22%3A%222330-1643%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fonlinelibrary.wiley.com%5C%2Fdoi%5C%2Fabs%5C%2F10.1002%5C%2Fasi.24179%22%2C%22collections%22%3A%5B%22FVXB2PVS%22%2C%22BGDAKHGJ%22%2C%22YU5MAHHA%22%5D%2C%22dateModified%22%3A%222023-02-07T21%3A50%3A25Z%22%7D%7D%2C%7B%22key%22%3A%22RJ4DVP6Y%22%2C%22library%22%3A%7B%22id%22%3A216150%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Jacucci%20et%20al.%22%2C%22parsedDate%22%3A%222019%22%2C%22numChildren%22%3A2%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EJacucci%2C%20G.%2C%20Barral%2C%20O.%2C%20Daee%2C%20P.%2C%20Wenzel%2C%20M.%2C%20Serim%2C%20B.%2C%20Ruotsalo%2C%20T.%2C%20Pluchino%2C%20P.%2C%20Freeman%2C%20J.%2C%20Gamberini%2C%20L.%2C%20Kaski%2C%20S.%2C%20%26amp%3B%20Blankertz%2C%20B.%20%282019%29.%20Integrating%20neurophysiologic%20relevance%20feedback%20in%20intent%20modeling%20for%20information%20retrieval.%20%3Ci%3EJournal%20of%20the%20Association%20for%20Information%20Science%20and%20Technology%3C%5C%2Fi%3E%2C%20%3Ci%3E70%3C%5C%2Fi%3E%289%29%2C%20917%26%23x2013%3B930.%20%3Ca%20target%3D%27_blank%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1002%5C%2Fasi.24161%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1002%5C%2Fasi.24161%3C%5C%2Fa%3E%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fjacekg.ischool.utexas.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D216150%26amp%3Bitem_key%3DRJ4DVP6Y%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Integrating%20neurophysiologic%20relevance%20feedback%20in%20intent%20modeling%20for%20information%20retrieval%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Giulio%22%2C%22lastName%22%3A%22Jacucci%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Oswald%22%2C%22lastName%22%3A%22Barral%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Pedram%22%2C%22lastName%22%3A%22Daee%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Markus%22%2C%22lastName%22%3A%22Wenzel%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Baris%22%2C%22lastName%22%3A%22Serim%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Tuukka%22%2C%22lastName%22%3A%22Ruotsalo%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Patrik%22%2C%22lastName%22%3A%22Pluchino%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jonathan%22%2C%22lastName%22%3A%22Freeman%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Luciano%22%2C%22lastName%22%3A%22Gamberini%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Samuel%22%2C%22lastName%22%3A%22Kaski%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Benjamin%22%2C%22lastName%22%3A%22Blankertz%22%7D%5D%2C%22abstractNote%22%3A%22The%20use%20of%20implicit%20relevance%20feedback%20from%20neurophysiology%20could%20deliver%20effortless%20information%20retrieval.%20However%2C%20both%20computing%20neurophysiologic%20responses%20and%20retrieving%20documents%20are%20characterized%20by%20uncertainty%20because%20of%20noisy%20signals%20and%20incomplete%20or%20inconsistent%20representations%20of%20the%20data.%20We%20present%20the%20first-of-its-kind%2C%20fully%20integrated%20information%20retrieval%20system%20that%20makes%20use%20of%20online%20implicit%20relevance%20feedback%20generated%20from%20brain%20activity%20as%20measured%20through%20electroencephalography%20%28EEG%29%2C%20and%20eye%20movements.%20The%20findings%20of%20the%20evaluation%20experiment%20%28N%20%3D%2016%29%20show%20that%20we%20are%20able%20to%20compute%20online%20neurophysiology-based%20relevance%20feedback%20with%20performance%20significantly%20better%20than%20chance%20in%20complex%20data%20domains%20and%20realistic%20search%20tasks.%20We%20contribute%20by%20demonstrating%20how%20to%20integrate%20in%20interactive%20intent%20modeling%20this%20inherently%20noisy%20implicit%20relevance%20feedback%20combined%20with%20scarce%20explicit%20feedback.%20Although%20experimental%20measures%20of%20task%20performance%20did%20not%20allow%20us%20to%20demonstrate%20how%20the%20classification%20outcomes%20translated%20into%20search%20task%20performance%2C%20the%20experiment%20proved%20that%20our%20approach%20is%20able%20to%20generate%20relevance%20feedback%20from%20brain%20signals%20and%20eye%20movements%20in%20a%20realistic%20scenario%2C%20thus%20providing%20promising%20implications%20for%20future%20work%20in%20neuroadaptive%20information%20retrieval%20%28IR%29.%22%2C%22date%22%3A%222019%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1002%5C%2Fasi.24161%22%2C%22ISSN%22%3A%222330-1643%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Fasistdl.onlinelibrary.wiley.com%5C%2Fdoi%5C%2Fabs%5C%2F10.1002%5C%2Fasi.24161%22%2C%22collections%22%3A%5B%22FVXB2PVS%22%2C%22YU5MAHHA%22%5D%2C%22dateModified%22%3A%222023-02-07T21%3A48%3A48Z%22%7D%7D%2C%7B%22key%22%3A%22YX53V2WR%22%2C%22library%22%3A%7B%22id%22%3A216150%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Moshfeghi%20and%20Pollick%22%2C%22parsedDate%22%3A%222019%22%2C%22numChildren%22%3A2%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EMoshfeghi%2C%20Y.%2C%20%26amp%3B%20Pollick%2C%20F.%20E.%20%282019%29.%20Neuropsychological%20model%20of%20the%20realization%20of%20information%20need.%20%3Ci%3EJournal%20of%20the%20Association%20for%20Information%20Science%20and%20Technology%3C%5C%2Fi%3E%2C%20%3Ci%3E70%3C%5C%2Fi%3E%289%29%2C%20954%26%23x2013%3B967.%20%3Ca%20target%3D%27_blank%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1002%5C%2Fasi.24242%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1002%5C%2Fasi.24242%3C%5C%2Fa%3E%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fjacekg.ischool.utexas.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D216150%26amp%3Bitem_key%3DYX53V2WR%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Neuropsychological%20model%20of%20the%20realization%20of%20information%20need%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Yashar%22%2C%22lastName%22%3A%22Moshfeghi%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Frank%20E.%22%2C%22lastName%22%3A%22Pollick%22%7D%5D%2C%22abstractNote%22%3A%22The%20main%20goal%20of%20information%20retrieval%20%28IR%29%20is%20to%20satisfy%20information%20need%20%28IN%29.%20IN%20refers%20to%20a%20complex%20concept%3A%20at%20the%20very%20initial%20state%20of%20the%20phenomenon%20%28that%20is%2C%20at%20a%20visceral%20level%29%2C%20even%20the%20searcher%20may%20not%20be%20aware%20of%20its%20existence.%20Thus%2C%20despite%20advances%20in%20the%20past%20few%20decades%20in%20both%20the%20IR%20and%20relevant%20scientific%20communities%2C%20we%20do%20not%20fully%20understand%20how%20an%20IN%20emerges%20and%20how%20it%20is%20physically%20manifested.%20In%20this%20article%20we%20aim%20to%20inform%20a%20holistic%20view%20of%20the%20realization%20of%20IN%20using%20functional%20magnetic%20resonance%20imaging.%20We%20collected%20new%20data%20of%20brain%20activity%20of%2024%20participants%20while%20they%20formulated%20and%20stated%20a%20realization%20of%20IN%20in%20a%20Question%20Answering%20task%2C%20focusing%20on%20a%20distributed%20set%20of%20brain%20regions%20associated%20with%20activities%20related%20to%20IN%2C%20found%20in%20our%20previous%20study.%20Results%20of%20a%20functional%20connectivity%20analysis%20led%20us%20to%20propose%20a%20neuropsychological%20model%20of%20the%20realization%20of%20IN.%20Our%20model%20consists%20of%20three%20components%3A%20%28a%29%20a%20successful%20memory%20retrieval%20component%2C%20%28b%29%20an%20information%20flow%20regulation%20component%2C%20and%20%28c%29%20a%20high-level%20perception%20component.%20We%20believe%20this%20study%20constitutes%20an%20important%20step%20in%20unraveling%20the%20nature%20of%20IN%20and%20how%20to%20better%20satisfy%20IN.%22%2C%22date%22%3A%222019%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1002%5C%2Fasi.24242%22%2C%22ISSN%22%3A%222330-1643%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Fasistdl.onlinelibrary.wiley.com%5C%2Fdoi%5C%2Fabs%5C%2F10.1002%5C%2Fasi.24242%22%2C%22collections%22%3A%5B%22FVXB2PVS%22%2C%22YU5MAHHA%22%5D%2C%22dateModified%22%3A%222023-02-07T21%3A48%3A44Z%22%7D%7D%2C%7B%22key%22%3A%22HQ5HME6F%22%2C%22library%22%3A%7B%22id%22%3A216150%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Wu%20et%20al.%22%2C%22parsedDate%22%3A%222019%22%2C%22numChildren%22%3A2%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EWu%2C%20Y.%2C%20Liu%2C%20Y.%2C%20Tsai%2C%20Y.-H.%20R.%2C%20%26amp%3B%20Yau%2C%20S.-T.%20%282019%29.%20Investigating%20the%20role%20of%20eye%20movements%20and%20physiological%20signals%20in%20search%20satisfaction%20prediction%20using%20geometric%20analysis.%20%3Ci%3EJournal%20of%20the%20Association%20for%20Information%20Science%20and%20Technology%3C%5C%2Fi%3E%2C%20%3Ci%3E70%3C%5C%2Fi%3E%289%29%2C%20981%26%23x2013%3B999.%20%3Ca%20target%3D%27_blank%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1002%5C%2Fasi.24240%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1002%5C%2Fasi.24240%3C%5C%2Fa%3E%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fjacekg.ischool.utexas.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D216150%26amp%3Bitem_key%3DHQ5HME6F%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Investigating%20the%20role%20of%20eye%20movements%20and%20physiological%20signals%20in%20search%20satisfaction%20prediction%20using%20geometric%20analysis%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Yingying%22%2C%22lastName%22%3A%22Wu%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Yiqun%22%2C%22lastName%22%3A%22Liu%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Yen-Hsi%20Richard%22%2C%22lastName%22%3A%22Tsai%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Shing-Tung%22%2C%22lastName%22%3A%22Yau%22%7D%5D%2C%22abstractNote%22%3A%22Two%20general%20challenges%20faced%20by%20data%20analysis%20are%20the%20existence%20of%20noise%20and%20the%20extraction%20of%20meaningful%20information%20from%20collected%20data.%20In%20this%20study%2C%20we%20used%20a%20multiscale%20framework%20to%20reduce%20the%20effects%20caused%20by%20noise%20and%20to%20extract%20explainable%20geometric%20properties%20to%20characterize%20finite%20metric%20spaces.%20We%20conducted%20lab%20experiments%20that%20integrated%20the%20use%20of%20eye-tracking%2C%20electrodermal%20activity%20%28EDA%29%2C%20and%20user%20logs%20to%20explore%20users%27%20information-seeking%20behaviors%20on%20search%20engine%20result%20pages%20%28SERPs%29.%20Experimental%20results%20of%201%2C590%20search%20queries%20showed%20that%20the%20proposed%20strategies%20effectively%20predicted%20query-level%20user%20satisfaction%20using%20EDA%20and%20eye-tracking%20data.%20The%20bootstrap%20analysis%20showed%20that%20combining%20EDA%20and%20eye-tracking%20data%20with%20user%20behavior%20data%20extracted%20from%20user%20logs%20led%20to%20a%20significantly%20better%20linear%20model%20fit%20than%20using%20user%20behavior%20data%20alone.%20Furthermore%2C%20cross-user%20and%20cross-task%20validations%20showed%20that%20our%20methods%20can%20be%20generalized%20to%20different%20search%20engine%20users%20performing%20different%20preassigned%20tasks.%22%2C%22date%22%3A%222019%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1002%5C%2Fasi.24240%22%2C%22ISSN%22%3A%222330-1643%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fasistdl.onlinelibrary.wiley.com%5C%2Fdoi%5C%2Fabs%5C%2F10.1002%5C%2Fasi.24240%22%2C%22collections%22%3A%5B%22FVXB2PVS%22%2C%22YU5MAHHA%22%5D%2C%22dateModified%22%3A%222023-02-07T21%3A48%3A41Z%22%7D%7D%2C%7B%22key%22%3A%22KXXPZI4Y%22%2C%22library%22%3A%7B%22id%22%3A216150%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Gwizdka%20et%20al.%22%2C%22parsedDate%22%3A%222019%22%2C%22numChildren%22%3A2%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EGwizdka%2C%20J.%2C%20Moshfeghi%2C%20Y.%2C%20%26amp%3B%20Wilson%2C%20M.%20L.%20%282019%29.%20Introduction%20to%20the%20special%20issue%20on%20neuro-information%20science.%20%3Ci%3EJournal%20of%20the%20Association%20for%20Information%20Science%20and%20Technology%3C%5C%2Fi%3E%2C%20%3Ci%3E70%3C%5C%2Fi%3E%289%29%2C%20911%26%23x2013%3B916.%20%3Ca%20target%3D%27_blank%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1002%5C%2Fasi.24263%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1002%5C%2Fasi.24263%3C%5C%2Fa%3E%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fjacekg.ischool.utexas.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D216150%26amp%3Bitem_key%3DKXXPZI4Y%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Introduction%20to%20the%20special%20issue%20on%20neuro-information%20science%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jacek%22%2C%22lastName%22%3A%22Gwizdka%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Yashar%22%2C%22lastName%22%3A%22Moshfeghi%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Max%20L.%22%2C%22lastName%22%3A%22Wilson%22%7D%5D%2C%22abstractNote%22%3A%22%22%2C%22date%22%3A%222019%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1002%5C%2Fasi.24263%22%2C%22ISSN%22%3A%222330-1643%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fasistdl.onlinelibrary.wiley.com%5C%2Fdoi%5C%2Fabs%5C%2F10.1002%5C%2Fasi.24263%22%2C%22collections%22%3A%5B%22FVXB2PVS%22%2C%226JWU3SHV%22%2C%22YU5MAHHA%22%5D%2C%22dateModified%22%3A%222023-02-07T21%3A48%3A39Z%22%7D%7D%2C%7B%22key%22%3A%22Z6FKVEFB%22%2C%22library%22%3A%7B%22id%22%3A216150%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Xu%20and%20Zhang%22%2C%22parsedDate%22%3A%222019%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EXu%2C%20C.%2C%20%26amp%3B%20Zhang%2C%20Q.%20%282019%29.%20The%20dominant%20factor%20of%20social%20tags%20for%20users%26%23x2019%3B%20decision%20behavior%20on%20e%26%23x2010%3Bcommerce%20websites%3A%20Color%20or%20text.%20%3Ci%3EJournal%20of%20the%20Association%20for%20Information%20Science%20and%20Technology%3C%5C%2Fi%3E.%20%3Ca%20target%3D%27_blank%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1002%5C%2Fasi.24118%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1002%5C%2Fasi.24118%3C%5C%2Fa%3E%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fjacekg.ischool.utexas.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D216150%26amp%3Bitem_key%3DZ6FKVEFB%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22The%20dominant%20factor%20of%20social%20tags%20for%20users%5Cu2019%20decision%20behavior%20on%20e%5Cu2010commerce%20websites%3A%20Color%20or%20text%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Chen%22%2C%22lastName%22%3A%22Xu%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Qin%22%2C%22lastName%22%3A%22Zhang%22%7D%5D%2C%22abstractNote%22%3A%22%22%2C%22date%22%3A%222019%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1002%5C%2Fasi.24118%22%2C%22ISSN%22%3A%222330-1643%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Fonlinelibrary.wiley.com%5C%2Fdoi%5C%2Fabs%5C%2F10.1002%5C%2Fasi.24118%22%2C%22collections%22%3A%5B%22FVXB2PVS%22%2C%22BGDAKHGJ%22%2C%22YU5MAHHA%22%5D%2C%22dateModified%22%3A%222019-06-29T12%3A36%3A39Z%22%7D%7D%5D%7D
Jones, L. M., Wright, K. D., Jack, A. I., Friedman, J. P., Fresco, D. M., Veinot, T., Lu, W., & Moore, S. M. (2019). The relationships between health information behavior and neural processing in african americans with prehypertension. Journal of the Association for Information Science and Technology, 0(0). https://doi.org/10.1002/asi.24098 Cite
Kim, H. H., & Kim, Y. H. (2019). ERP/MMR Algorithm for Classifying Topic-Relevant and Topic-Irrelevant Visual Shots of Documentary Videos. Journal of the Association for Information Science and Technology, 0(0). https://doi.org/10.1002/asi.24179 Cite
Jacucci, G., Barral, O., Daee, P., Wenzel, M., Serim, B., Ruotsalo, T., Pluchino, P., Freeman, J., Gamberini, L., Kaski, S., & Blankertz, B. (2019). Integrating neurophysiologic relevance feedback in intent modeling for information retrieval. Journal of the Association for Information Science and Technology, 70(9), 917–930. https://doi.org/10.1002/asi.24161 Cite
Moshfeghi, Y., & Pollick, F. E. (2019). Neuropsychological model of the realization of information need. Journal of the Association for Information Science and Technology, 70(9), 954–967. https://doi.org/10.1002/asi.24242 Cite
Wu, Y., Liu, Y., Tsai, Y.-H. R., & Yau, S.-T. (2019). Investigating the role of eye movements and physiological signals in search satisfaction prediction using geometric analysis. Journal of the Association for Information Science and Technology, 70(9), 981–999. https://doi.org/10.1002/asi.24240 Cite
Gwizdka, J., Moshfeghi, Y., & Wilson, M. L. (2019). Introduction to the special issue on neuro-information science. Journal of the Association for Information Science and Technology, 70(9), 911–916. https://doi.org/10.1002/asi.24263 Cite
Xu, C., & Zhang, Q. (2019). The dominant factor of social tags for users’ decision behavior on e‐commerce websites: Color or text. Journal of the Association for Information Science and Technology. https://doi.org/10.1002/asi.24118 Cite