▁old ▁information , ▁par alle ling ▁the ▁find ings ▁of ▁McC los key ▁and ▁C ohen ▁( 1 9 8 9 ). ▁Rat cl iff ▁also ▁found ▁that ▁the ▁resulting ▁outputs ▁were ▁often ▁a ▁bl end ▁of ▁the ▁previous ▁input ▁and ▁the ▁new ▁input . ▁In ▁larger ▁networks , ▁items ▁learned ▁in ▁groups ▁( e . g . ▁AB ▁then ▁CD ) ▁were ▁more ▁resist ant ▁to ▁forget ting ▁than ▁were ▁items ▁learned ▁sing ly ▁( e . g . ▁A ▁then ▁B ▁then ▁C … ). ▁However , ▁the ▁forget ting ▁for ▁items ▁learned ▁in ▁groups ▁was ▁still ▁large . ▁Adding ▁new ▁hidden ▁units ▁to ▁the ▁network ▁did ▁not ▁reduce ▁inter ference . ▁▁ ▁Disc rim ination ▁between ▁the ▁studied ▁items ▁and ▁previously ▁un seen ▁items ▁decre ased ▁as ▁the ▁network ▁learned ▁more . ▁ ▁This ▁finding ▁contradict s ▁with ▁studies ▁on ▁human ▁memory , ▁which ▁indicated ▁that ▁disc rim ination ▁increases ▁with ▁learning . ▁Rat cl iff ▁attempted ▁to ▁alle vi ate ▁this ▁problem ▁by ▁adding ▁' response ▁nodes ' ▁that ▁would ▁select ively ▁respond ▁to ▁old ▁and ▁new ▁inputs . ▁However , ▁this ▁method ▁did ▁not ▁work ▁as ▁these ▁response ▁nodes ▁would ▁become ▁active ▁for ▁all ▁inputs . ▁A ▁model ▁which ▁used ▁a ▁context ▁pattern ▁also ▁failed ▁to ▁increase ▁disc rim ination ▁between ▁new ▁and ▁old ▁items . ▁ ▁Pro posed ▁solutions ▁ ▁Many ▁research ers ▁have ▁suggested ▁that ▁the ▁main ▁cause ▁of ▁cat ast roph ic ▁inter ference ▁is ▁overlap ▁in ▁the ▁representations ▁at ▁the ▁hidden ▁layer ▁of ▁distributed