▁which ▁will ▁allow ▁the ▁network ▁to ▁incorpor ate ▁new ▁data ▁with ▁little ▁inter ference ▁with ▁existing ▁data . ▁Specifically , ▁an ▁input ▁vector ▁which ▁follows ▁the ▁same ▁pattern ▁of ▁regular ities ▁as ▁the ▁previously ▁trained ▁data ▁should ▁not ▁cause ▁a ▁dr ast ically ▁different ▁pattern ▁of ▁activation ▁at ▁the ▁hidden ▁layer ▁or ▁dr ast ically ▁alter ▁weights . ▁ ▁To ▁test ▁their ▁hypothesis , ▁Mc R ae ▁and ▁H ether ington ▁( 1 9 9 3 ) ▁compared ▁the ▁performance ▁of ▁a ▁na ï ve ▁and ▁pre - tra ined ▁auto - enc oder ▁back prop ag ation ▁network ▁on ▁three ▁simulations ▁of ▁ver bal ▁learning ▁tasks . ▁The ▁pre - tra ined ▁network ▁was ▁trained ▁using ▁letter ▁based ▁representations ▁of ▁English ▁mon os yll ab ic ▁words ▁or ▁English ▁word ▁pairs . ▁ ▁All ▁three ▁tasks ▁involved ▁the ▁learning ▁of ▁some ▁con son ant - v owel - con son ant ▁( C VC ) ▁strings ▁or ▁C VC ▁pairs ▁( list ▁A ), ▁followed ▁by ▁training ▁on ▁a ▁second ▁list ▁of ▁these ▁items ▁( list ▁B ). ▁After wards , ▁the ▁distributions ▁of ▁the ▁hidden ▁node ▁activ ations ▁were ▁compared ▁between ▁the ▁na ï ve ▁and ▁pre - tra ined ▁network . ▁In ▁all ▁three ▁tasks , ▁the ▁representations ▁of ▁a ▁C VC ▁in ▁the ▁na ï ve ▁network ▁t ended ▁to ▁be ▁spread ▁fairly ▁even ly ▁across ▁all ▁hidden ▁nodes , ▁whereas ▁most ▁hidden ▁nodes ▁were ▁in active ▁in ▁the ▁pre - tra ined ▁network . ▁Furthermore , ▁in ▁the