LEADER 04459nam 22006732 450 001 9910814447803321 005 20160219152805.0 010 $a0-511-86164-8 010 $a1-107-21366-5 010 $a1-283-00622-7 010 $a9786613006226 010 $a0-511-92132-2 010 $a0-511-85997-X 010 $a0-511-86084-6 010 $a0-511-85823-X 010 $a0-511-85736-5 010 $a0-511-85910-4 035 $a(CKB)2560000000059717 035 $a(EBL)615765 035 $a(OCoLC)703137554 035 $a(SSID)ssj0000469293 035 $a(PQKBManifestationID)11303693 035 $a(PQKBTitleCode)TC0000469293 035 $a(PQKBWorkID)10510213 035 $a(PQKB)11202333 035 $a(UkCbUP)CR9780511921322 035 $a(Au-PeEL)EBL615765 035 $a(CaPaEBR)ebr10449507 035 $a(CaONFJC)MIL300622 035 $a(MiAaPQ)EBC615765 035 $a(PPN)26133994X 035 $a(EXLCZ)992560000000059717 100 $a20100927d2011|||| uy| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aFormal approaches in categorization /$fEmmanuel M. Pothos, Andy J. Wills, Editors$b[electronic resource] 210 1$aCambridge :$cCambridge University Press,$d2011. 215 $a1 online resource (xii, 336 pages) $cdigital, PDF file(s) 300 $aTitle from publisher's bibliographic system (viewed on 05 Oct 2015). 311 $a0-521-14072-2 311 $a0-521-19048-7 320 $aIncludes bibliographical references and index. 327 $aMachine generated contents note: 1. Introduction Emmanuel M. Pothos and Andy J. Wills; 2. The generalized context model: an exemplar model of classification Robert M. Nosofsky; 3. Prototype models of categorization: basic formulation, predictions, and limitations John Paul Minda and J. David Smith; 4. COVIS F. Gregory Ashby, Erick J. Paul and W. Todd Maddox; 5. Semantics without categorization Timothy T. Rogers and James L. McClelland; 6. Models of attentional learning John K. Kruschke; 7. An elemental model of associative learning and memory Evan Livesey and Ian McLaren; 8. Nonparametric Bayesian models of categorization Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini, Daniel J. Navarro and Joshua B. Tenenbaum; 9. The simplicity model of unsupervised categorization Emmanuel M. Pothos, Nick Chater and Peter Hines; 10. Adaptive clustering models of categorization John V. McDonnell and Todd M. Gureckis; 11. COBWEB models of categorization and probabilistic concept formation Wayne Iba and Pat Langley; 12. The knowledge and resonance (KRES) model of category learning Harlan D. Harris and Bob Rehder; 13. The contribution (and drawbacks) of models to the study of concepts Gregory L. Murphy; 14. Formal models of categorization: insights from cognitive neuroscience Lukas Strnad, Stefano Anzellotti and Alfonso Caramazza; 15. Comments on models and categorization theories: the razor's edge Douglas Medin. 330 $aThe process of constructing concepts underpins our capacity to encode information in an efficient and competent manner and also, ultimately, our ability to think in terms of abstract ideas such as justice, love and happiness. But what are the mechanisms which correspond to psychological categorization processes? This book unites many prominent approaches in modelling categorization. Each chapter focuses on a particular formal approach to categorization, presented by the proponent(s) or advocate(s) of that approach, and the authors consider the relation of this approach to other models and the ultimate objectives in their research programmes. The volume evaluates progress that has been made in the field and where it goes from here. This is an essential companion to any scientist interested in the formal description of categorization and, more generally, in formal approaches to cognition. It will be the definitive guide to formal approaches in categorization research for years to come. 606 $aCategorization (Psychology) 615 0$aCategorization (Psychology) 676 $a153.2 686 $aPSY008000$2bisacsh 702 $aPothos$b Emmanuel M.$f1973- 702 $aWills$b Andy J.$f1972- 801 0$bUkCbUP 801 1$bUkCbUP 906 $aBOOK 912 $a9910814447803321 996 $aFormal approaches in categorization$94073369 997 $aUNINA