▁experiment ▁that ▁ev id ences ▁a ▁close ▁similarity ▁between ▁the ▁behaviour ▁of ▁humans ▁and ▁the ▁behaviour ▁of ▁the ▁proposed ▁neur om im etic ▁architecture . ▁ ▁Lat ent ▁learning ▁ ▁Lat ent ▁Learning ▁is ▁a ▁technique ▁used ▁by ▁Gut stein ▁& ▁St ump ▁( 2 0 1 5 ) ▁both ▁to ▁mit ig ate ▁cat ast roph ic ▁inter ference ▁and ▁to ▁take ▁advantage ▁of ▁transfer ▁learning . ▁R ather ▁than ▁manip ulating ▁the ▁representations ▁for ▁new ▁classes ▁used ▁by ▁the ▁hidden ▁nodes , ▁this ▁approach ▁tries ▁to ▁train ▁optimal ▁representations ▁for ▁new ▁classes ▁into ▁the ▁output ▁nodes . ▁It ▁cho oses ▁output ▁enc od ings ▁that ▁are ▁least ▁likely ▁to ▁cat ast roph ically ▁inter f ere ▁with ▁existing ▁responses . ▁ ▁Given ▁a ▁net ▁that ▁has ▁learned ▁to ▁disc rim inate ▁among ▁one ▁set ▁of ▁classes ▁using ▁Error ▁Correct ing ▁Output ▁C odes ▁( E CO C ) ▁( as ▁opposed ▁to ▁ 1 ▁hot ▁codes ), ▁optimal ▁enc od ings ▁for ▁new ▁classes ▁are ▁chosen ▁by ▁obser ving ▁the ▁net ' s ▁average ▁responses ▁to ▁them . ▁Since ▁these ▁average ▁responses ▁arose ▁while ▁learning ▁the ▁original ▁set ▁of ▁classes ▁without ▁any ▁expos ure ▁to ▁the ▁new ▁classes , ▁they ▁are ▁referred ▁to ▁as ▁' Lat ently ▁Lear ned ▁Enc od ings '. ▁This ▁termin ology ▁bor rows ▁from ▁the ▁concept ▁of ▁Lat ent ▁Learning , ▁as ▁introduced ▁by ▁Tol man ▁in ▁ 1 9 3 0 . ▁In ▁effect , ▁this ▁technique ▁uses ▁transfer ▁learning ▁to ▁avoid ▁cat ast roph