text_combined: tkzmdoapcscmuedu apologies receive multiple copies various mailing lists new directions decoding mental states fmri data httpwwwcscmuedufmriworkshop held nips 06 whistler canada december 8 9 important dates 2 page abstract submission deadline november 2 notification acceptance early november workshop date december 8 9 program committee johndylan haynes mpi human cognitive brain sciencesleipzig francisco pereira carnegie mellon university tom mitchell carnegie mellon university overview past five years machine learning classifiers met great interest field cognitive neuroscience purpose decoding mental states given observed fmri data work received considerable attention seen way overcome limitations conventional fmri analysis methods whereas conventional fmri research focused spatially localizing cognitive modules decodingbased research allows first time study neural encoding specific mental contents human brain recent progress also raised number fundamental questions practice using classifiers decoding interpretation results implications theories cognitive neuroscience workshop following goals 1 give overview decoding mental states fmri 2 present cutting edge research address fundamental practical challenges 3 provide venue discussion broader questions may result agenda field scope aim several cognitive neuroscience researchers give overview talks introduce members nips community field challenges perspective also leave ample space submitted presentations discussion high level interested decoding help modelbuilding cognitive neuroscience ultimately help develop theories neural representation explain decodingidentified structure fmri data technical level considering specific issues classifiers used confirmatory scientific tool existing theories hypotheses characteristics fmri datasets affect current machine learning wisdom feasible use nonlinear classifiers linear ones suffice regularization useful form appropriate feature selection strategies work general success depend entirely activation structure study hypothesis structure activation influence choices prior information used decoding done dynamic conditions feasible decode multiple superimposed mental states space time one perform inference using data acquired different contextual conditions multiple subjects multiple studies lowdimensional representations fmri data better decoding activation structures classifiers learn location activation one attach statistical significance decoding results activation structure identified especially believe fmri decoding high interest also machine learning experts given number type specific open questions another application domain thus requirement machine learning researchers come new methods creative applications workshop also designed facilitate entry field put contact cognitive neuroscientists receptive computational expertise creativity submissions invite proposals presentations addressing questions related issues welcome presentations completed work workinprogress well papers discussing potential research directions surveys recent developments would like present workshop please send abstract 2 pages long nips format httpleonbottoucomnips excluding citations pdf preferred tkzmdoapcscmuedu soon possible later november 2 2006 select presentations final program posted early november thu 07 aug 2008 063644 0400 uai call abstracts nips workshop new directions decoding mental states fmri data