, ▁R ous set ▁and ▁Ans ▁( 2 0 0 9 ) ▁have ▁also ▁shown ▁that ▁pseud op at tern s ▁origin ating ▁from ▁an ▁artificial ▁rever ber ating ▁neural ▁network ▁could ▁indu ce ▁familiar ity ▁in ▁humans ▁with ▁never ▁seen ▁items ▁in ▁the ▁way ▁predicted ▁by ▁simulations ▁conducted ▁with ▁a ▁two - network ▁artificial ▁neural ▁architecture . ▁Furthermore , ▁Ans ▁( 2 0 0 4 ) ▁has ▁implemented ▁a ▁version ▁of ▁the ▁self - ref res hing ▁mechanism ▁using ▁only ▁one ▁network ▁trained ▁by ▁the ▁Con tr ast ive ▁Heb bian ▁Learning ▁rule , ▁a ▁training ▁rule ▁considered ▁as ▁more ▁real istic ▁than ▁the ▁largely ▁used ▁back prop ag ation ▁algorithm , ▁but ▁fort un ately ▁equivalent ▁to ▁the ▁latter . ▁ ▁So ▁far , ▁the ▁different ▁solutions ▁to ▁cat ast roph ic ▁inter ference ▁that ▁have ▁been ▁presented ▁concern ▁tasks ▁of ▁sequ ential ▁learning ▁involving ▁only ▁non - tem por ally ▁ordered ▁lists ▁of ▁items . ▁But , ▁to ▁be ▁cred ible , ▁the ▁self - ref res hing ▁mechanism ▁for ▁' static ' ▁learning ▁has ▁to ▁en comp ass ▁our ▁human ▁ability ▁to ▁learn ▁serial ly ▁many ▁temporal ▁sequences ▁of ▁patterns ▁without ▁cat ast roph ic ▁inter ference ▁( e . g . ▁learning ▁one ▁song ▁followed ▁by ▁learning ▁a ▁second ▁song ▁without ▁forget ting ▁the ▁first ▁one ). ▁This ▁was ▁done ▁by ▁Ans , ▁R ous set , ▁French ▁and ▁Mus ca ▁( 2 0 0 4 ) ▁who ▁have ▁presented , ▁in ▁addition ▁to ▁simulation ▁work , ▁an