▁pseudo - rec urrent ▁model ▁show ▁reduced ▁inter ference ▁but ▁also ▁it ▁models ▁list - length ▁and ▁list - str ength ▁effects ▁seen ▁in ▁humans . ▁The ▁list - length ▁effect ▁means ▁that ▁adding ▁new ▁items ▁to ▁a ▁list ▁har ms ▁the ▁memory ▁of ▁earlier ▁items . ▁Like ▁humans , ▁the ▁pseudo ▁rec urrent ▁network ▁showed ▁a ▁more ▁grad ual ▁forget ting ▁when ▁to ▁be ▁trained ▁list ▁is ▁length ened . ▁The ▁list - str ength ▁effect ▁means ▁that ▁when ▁the ▁strength ▁of ▁recognition ▁for ▁one ▁item ▁is ▁increased , ▁there ▁is ▁no ▁effect ▁on ▁the ▁recognition ▁of ▁the ▁other ▁list ▁items . ▁This ▁is ▁an ▁important ▁finding ▁as ▁other ▁models ▁often ▁exhib it ▁a ▁decrease ▁in ▁the ▁recognition ▁of ▁other ▁list ▁items ▁when ▁one ▁list ▁item ▁is ▁strength ened . ▁Since ▁the ▁direct ▁copying ▁of ▁weights ▁from ▁the ▁early ▁processing ▁area ▁to ▁the ▁final ▁storage ▁area ▁does ▁not ▁seem ▁highly ▁bi olog ically ▁pla us ible , ▁the ▁transfer ▁of ▁information ▁to ▁the ▁final ▁storage ▁area ▁can ▁be ▁done ▁through ▁training ▁the ▁final ▁storage ▁area ▁with ▁pseud op at tern s ▁created ▁by ▁the ▁early ▁processing ▁area . ▁However , ▁a ▁dis adv antage ▁of ▁the ▁pseudo - rec urrent ▁model ▁is ▁that ▁the ▁number ▁of ▁hidden ▁units ▁in ▁the ▁early ▁processing ▁and ▁final ▁storage ▁sub - network s ▁must ▁be ▁identical . ▁ ▁Ne ural ▁networks ▁with ▁self - ref res hing ▁memory ▁ ▁Following ▁the ▁same ▁basic ▁idea ▁contributed ▁by ▁Rob ins , ▁Ans ▁and ▁R ous set ▁( 1