Causal Categorization with Bayes Nets
Authors: Bob Rehder
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: A  theory  of  categorization  is  presented  in  which  knowledge  of  causal  relationships  between  category  features  is  represented  as  a  Bayesian  network.  Referred  to  as  causal-model  theory,  this  theory  predicts  that  objects  are  classified  as  category  members  to  the  extent they  are  likely  to  have  been  produced  by  a categorys  causal  model.  On  this  view,  people  have  models  of  the  world  that  lead  them  to  expect  a  certain  distribution  of  features  in  category  members  (e.g.,  correlations  between  feature  pairs  that  are  directly  connected  by  causal  relationships),  and  consider  exemplars  good  category  members  when  they  manifest  those  expectations.  These  expectations  include  sensitivity  to  higher-order  feature  interactions  that emerge from the  asymmetries inherent in causal relationships. 
Research  on  the  topic  of categorization  has  traditionally  focused  on  the  problem  of  learning  new  categories  given  observations  of  category  members.  In  contrast,  the  theory-based  view  of  categories  emphasizes  the  influence  of  the  prior  theoretical  knowledge  that  learners  often  contribute  to  their  representations  of  categories  [1].  However, in  contrast to  models  accounting  for  the  effects  of empirical  observations,  there  have  been  few  models  developed  to  account for  the  effects of prior knowledge.  The  purpose  of  this  article  is  to  present  a  model  of  categorization  referred  to  as  causal-model  theory  or  CMT  [2,  3].  According  to  CMT,  people 's  know ledge  of  many  categories  includes  not only  features,  but  also  an  explicit representation  of the  causal mechanisms that people believe link the features  of many categories. 
In  this  article  I  apply  CMT  to  the  problem  of  establishing  objects  category  membership.  In  the  psychological  literature  one  standard  view  of  categorization  is  that  objects  are  placed  in  a  category  to  the  extent they  have  features  that  have  often  been  observed  in  members  of that category.  For example, an  object that has  most of  the  features  of  birds  (e.g.,  wings,  fly,  build  nests  in  trees,  etc.)  and  few  features  of  other  categories  is  thought to  be  a  bird. This  view  of categorization  is  formalized  by  prototype  models  in  which  classification  is  a  function  of the  similarity  (i.e. , number  of  shared  features)  between  a  mental  representation  of  a  category  prototype  and  a  to-be-classified  object.  However ,  a  well-known  difficulty  with  prototype  models  is  that  a  features  contribution  to  category  membership  is  independent  of the  presence  or  absence  of  other  features.  In  contrast ,  consideration  of  a  categorys  theoretical  influence  which  combinations  of  features  make  for  knowledge  acceptable  category  members.  For  example ,  people  believe  that  birds  have  nests  in  trees  because  they  can  fly , and  in  light  of this  knowledge  an  animal  that  doesnt  fly