, ▁also ▁known ▁as ▁cat ast roph ic ▁forget ting , ▁is ▁the ▁t endency ▁of ▁an ▁artificial ▁neural ▁network ▁to ▁completely ▁and ▁ab rupt ly ▁forget ▁previously ▁learned ▁information ▁upon ▁learning ▁new ▁information . ▁Ne ural ▁networks ▁are ▁an ▁important ▁part ▁of ▁the ▁network ▁approach ▁and ▁connection ist ▁approach ▁to ▁cogn itive ▁science . ▁These ▁networks ▁use ▁computer ▁simulations ▁to ▁try ▁to ▁model ▁human ▁behav i ours , ▁such ▁as ▁memory ▁and ▁learning . ▁Cat ast roph ic ▁inter ference ▁is ▁an ▁important ▁issue ▁to ▁consider ▁when ▁creating ▁connection ist ▁models ▁of ▁memory . ▁It ▁was ▁originally ▁brought ▁to ▁the ▁attention ▁of ▁the ▁scientific ▁community ▁by ▁research ▁from ▁McC los key ▁and ▁C ohen ▁( 1 9 8 9 ), ▁and ▁Rat cl iff ▁( 1 9 9 0 ). ▁It ▁is ▁a ▁radical ▁manifest ation ▁of ▁the ▁' sens itivity - st ability ' ▁d ile m ma ▁or ▁the ▁' st ability - pl astic ity ' ▁d ile m ma . ▁Specifically , ▁these ▁problems ▁refer ▁to ▁the ▁issue ▁of ▁being ▁able ▁to ▁make ▁an ▁artificial ▁neural ▁network ▁that ▁is ▁sensitive ▁to , ▁but ▁not ▁dis rupted ▁by , ▁new ▁information . ▁Look up ▁tables ▁and ▁connection ist ▁networks ▁lie ▁on ▁the ▁opposite ▁sides ▁of ▁the ▁stability ▁pl astic ity ▁spectrum . ▁The ▁former ▁remains ▁completely ▁stable ▁in ▁the ▁presence ▁of ▁new ▁information ▁but ▁la cks ▁the ▁ability ▁to ▁general ize , ▁i . e . ▁infer ▁general ▁principles , ▁from ▁new ▁inputs . ▁On ▁the ▁other ▁hand ,