Magnuson, James S.

English

eScholarship - University of California

UC MercedProceedings of the Annual Meeting of the Cognitive Science SocietyTitleDoes Probability Matching Require Complex Representations?Permalinkhttps://escholarship.org/uc/item/6b63v9wbJournalProceedings of the Annual Meeting of the Cognitive Science Society, 18(0)AuthorMagnuson, James S.Publication Date1996 Peer reviewedeScholarship.org Powered by the California Digital LibraryUniversity of CaliforniaD o e s Probabilit y  M a t c h i n g Requ i r e C o m p l e x Representations ? James S .  Magnuso n Brai n an d Cognitiv e Science s Universit y o f  Rochester .  Melior a Hal l Rochester ,  N Y 1462 7 m a g n u s o n S b c s . r o c h e s t e r . e d u In t roduc t io n Representationa l  ̂ preache s t o anima l  behavio r  (e.g. ,  Gal -listel ,  1990 ;  Myerso n an d Miezin ,  1980 )  posi t  tha t  comple x grou p behavio r  result s fro m comple x representation s o f  event s withi n th e centra l  nervou s system s o f  individua l  animals .  Fo r example ,  duck s feedin g fro m tw o foo d source s distribut e themselve s proportionatel y t o th e densit y o f  foo d availabl e at  eac h source .  Thi s phenomenon ,  probabilit y  matching ,  i s typicall y explaine d b y attributin g representation s o f  th e den -sit y o f  foo d availabl e a t  eac h sourc e withi n th e C N S o f  eac h duck . Ar e suc h comple x representation s necessar y t o explai n probabilit y  matching ? Drawin g o n th e ecologica l  approac h t o perceptio n (e.g. ,  Gibson ,  1986 )  an d o n method s use d i n arti -ficial  lif e researc h (e.g. ,  Reynolds ,  1987) ,  1  propos e a  simple r model ,  i n whic h probabilit y  matchin g emerge s whe n eac h anima l  follow s a  simpl e behaviora l  rul e (g o t o th e neares t morse l  o f  food) ,  an d a  simpl e constrain t  o f  th e environmen t i s assume d (large r  morsel s tak e longe r  t o ea t  tha n smalle r morsels) . Simulations I n orde r  t o compar e non-representationa l  an d representationa l models ,  1  implemente d bot h i n compute r  simulations .  Th e rate s o f  dispersa l  an d th e magnitud e o f  foo d "morsels "  a t  tw o foo d source s o n opposit e end s o f  a  "pond "  coul d b e specified . Durin g a  simulation ,  whe n a  duc k mad e contac t  wit h a  morse l of  food ,  th e duc k woul d remai n stationar y unti l  i t  wa s finished "eating. "  Eatin g time ,  i n updat e cycles ,  wa s define d a s th e magnitud e o f  a  morse l  o f  food ;  give n a  morse l  o f  magnitud e 5,  a  duc k eatin g tha t  morse l  woul d remai n stationar y fo r  5 updat e cycles .  A t  th e beginnin g o f  a  simulation ,  a  specifie d number  o f  duck s wa s randoml y distribute d throughou t  th e pond .  A t  eac h tim e step ,  o r  cycle ,  foo d wa s distribute d nea r  a sourc e accordin g t o tha t  source' s dispersa l  rate .  Subsequently , th e location s o f  th e duck s wer e update d i n rando m order ,  eithe r i n accordanc e wit h th e constraint s o f  th e non-representationa l model ,  o r  base d o n represenution s o f  foo d densit y a t  eac h foo d source .  Fift y simulation s wer e ru n wit h bot h models . Results and Discussion Most  importantly ,  th e non-representationa l  simulatio n demonsuate d tha t  comple x representation s ar e no t  necessar y t o explai n probabilit y  matching ,  a s i t  ca n accoun t  fo r  dat a fro m probabilit y  matchin g experiments .  Fo r  example .  Harpe r (1982 )  reporte d tha t  rea l  duck s matche d 1: 2 dispersa l  rat e ratio s withi n approximatel y 9 0 seconds ,  an d 1: 2 morse l  mag -nitud e ratio s withi n ̂ proximatel y 30 0 seconds .  I n th e non -representationa l  simulation ,  difference s i n rat e wer e matche d withi n ̂ proximatel y 1(X )  updat e cycles ,  an d difference s i n magnitud e wer e matche d withi n q)proximatel y 30 0 updat e cycles . Althoug h th e representationa l  mode l  (wit h eac h individua l representin g foo d densit y a t  eac h source )  als o ̂ proximate s th e result s reporte d wit h rea l  ducks ,  i t  i s les s stabl e an d les s realisti c tha n th e non-representationa l  model .  I t  i s les s stabl e i n tha t  ther e wa s muc h greate r  variabilit y  i n th e proportion s of  duck s a t  eac h sourc e afte r  th e poin t  a t  whid i  th e non -representationa l  simulatio n ha d reache d a  stead y state .  Vari -ous non-realisti c behavior s wer e observe d i n th e simulation s of  th e representationa l  model .  Fo r  example ,  sinc e duck s wer e drive n b y globa l  rathe r  tha n loca l  representation s o f  th e envi -ronment ,  onc e a  duc k ha d decide d t o switc h foo d sources ,  i t woul d ignor e ne w foo d appearin g nearb y i n orde r  t o mov e l o th e othe r  source . Th e ke y poin t  i s  tha t  i n implementin g a  representationa l model ,  behavior s fo r  movin g abou t  i n th e worl d (an d fo r avoidin g unrealisti c behavior s lik e th e on e jus t  described ) must  b e devised .  However ,  th e non-representationa l  mode l demonstrate s tha t  extremel y simpl e rule s ca n bot h provid e th e behavior s necessar y fo r  movin g abou t  i n th e worl d an d accoun t  fo r  th e comple x phenomen a o f  probabilit y  matching . References Gallistel ,  C .  R .  (1990) .  Th e Organizatio n o f  U a m i n g . Boston :  MIT . Gibson ,  J .  J .  (1986) .  Th e Ecologica l  Approac h t o Visua l Perception .  Hillsdale ,  NJ :  Erlbaum . HarpCT,  D .  G .  C .  (1982) .  Competitiv e foragin g i n mallards : Idea l  fre e ducks .  Anima l  Behaviour ,  30 ,  575-584 . Myerson ,  J .  an d Miezin .  F .  M .  (1980) .  Th e kinetic s o f  choice : An operan t  system s analysis .  Psychologica l  Review ,  87 , 160-174 . Reynolds ,  C .  W .  (1987) .  Hocks ,  herds ,  an d schools .  A distribute d behaviora l  model .  Compute r  Graphics ,  21(4) , ( S I G G R A PH '8 7 Conferenc e Proceedings) ,  A C M ,  N e w York ,  25-34 . 799 

Does Probability Matching Require Complex Representations?

https://escholarship.org/content/qt6b63v9wb/qt6b63v9wb.pdf?t=sh6leo

Does Probability Matching Require Complex Representations?

Abstract

Similar works

Full text

Available Versions

eScholarship - University of California