03867nam 22007935 450 99621364950331620200702061959.03-319-12054-910.1007/978-3-319-12054-6(CKB)3710000000269682(SSID)ssj0001372702(PQKBManifestationID)11888494(PQKBTitleCode)TC0001372702(PQKBWorkID)11305040(PQKB)10191555(DE-He213)978-3-319-12054-6(MiAaPQ)EBC6282950(MiAaPQ)EBC5590646(Au-PeEL)EBL5590646(OCoLC)895035420(PPN)182097714(EXLCZ)99371000000026968220141023d2014 u| 0engurnn#008mamaatxtccrModeling Decisions for Artificial Intelligence[electronic resource] 11th International Conference, MDAI 2014, Tokyo, Japan, October 29-31, 2014, Proceedings /edited by Vicenç Torra, Yasuo Narukawa, Yasunori Endo1st ed. 2014.Cham :Springer International Publishing :Imprint: Springer,2014.1 online resource (XVIII, 241 p. 47 illus.)Lecture Notes in Artificial Intelligence ;8825Includes index.3-319-12053-0 Aggregation Operators and Decision Making -- Inference Systems Optimization -- Clustering and Similarity -- Data Mining and Data Privacy.This book constitutes the proceedings of the 11th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2014, held in Tokyo, Japan, in October 2014. The 19 revised full papers presented together with an invited paper were carefully reviewed and selected from 38 submissions. They deal with the theory and tools for modeling decisions, as well as applications that encompass decision making processes and information fusion techniques and are organized in topical sections on aggregation operators and decision making, optimization, clustering and similarity, and data mining and data privacy.Lecture Notes in Artificial Intelligence ;8825Artificial intelligencePattern recognitionData miningInformation storage and retrievalNumerical analysisArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XData Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Information Storage and Retrievalhttps://scigraph.springernature.com/ontologies/product-market-codes/I18032Numeric Computinghttps://scigraph.springernature.com/ontologies/product-market-codes/I1701XArtificial intelligence.Pattern recognition.Data mining.Information storage and retrieval.Numerical analysis.Artificial Intelligence.Pattern Recognition.Data Mining and Knowledge Discovery.Information Storage and Retrieval.Numeric Computing.006.3Torra Vicençedthttp://id.loc.gov/vocabulary/relators/edtNarukawa Yasuoedthttp://id.loc.gov/vocabulary/relators/edtEndo Yasunoriedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK996213649503316Modeling Decisions for Artificial Intelligence772296UNISA02487nam 2200433z- 450 991034673810332120210211(CKB)4920000000094333(oapen)https://directory.doabooks.org/handle/20.500.12854/54088(oapen)doab54088(EXLCZ)99492000000009433320202102d2018 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierMultitasking: Executive Functioning in Dual-Task and Task Switching SituationsFrontiers Media SA20181 online resource (196 p.)Frontiers Research Topics2-88945-453-3 Multitasking refers to performance of multiple tasks. The most prominent types of multitasking are situations including either temporal overlap of the execution of multiple tasks (i.e., dual tasking) or executing multiple tasks in varying sequences (i.e., task switching). In the literature, numerous attempts have aimed at theorizing about the specific characteristics of executive functions that control interference between simultaneously and/or sequentially active component of task-sets in these situations. However, these approaches have been rather vague regarding explanatory concepts (e.g., task-set inhibition, preparation, shielding, capacity limitation), widely lacking theories on detailed mechanisms and/ or empirical evidence for specific subcomponents. The present research topic aims at providing a selection of contributions on the details of executive functioning in dual-task and task switching situations. The contributions specify these executive functions by focusing on (1) fractionating assumed mechanisms into constituent subcomponents, (2) their variations by age or in clinical subpopulations, and/ or (3) their plasticity as a response to practice and training.MultitaskingPsychologybicssccognitive flexibilitycognitive plasticitydual taskingmultitaskingPRPtask switchingPsychologyMike Wendtauth1292350Tilo StrobachauthMarkus JanczykauthBOOK9910346738103321Multitasking: Executive Functioning in Dual-Task and Task Switching Situations3022202UNINA