<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rumelhart, D.</style></author><author><style face="normal" font="default" size="100%">Abrahamson, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A model for analogical reasoning</style></title><secondary-title><style face="normal" font="default" size="100%">Cognitive Psychology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1973</style></year></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">1-28</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A theory of analogical reasoning is proposed in which the elements of a set of concepts, e.g., animals, are represented as points in a multidimensional Euclidean space. Four elements A,B,C,D, are in an analogical relationship A:B::C:D if the vector distance from A to B is the same as that from C to D. Given three elements A,B,C, an ideal solution point I for A:B::C:? exists. In a problem A:B::C:D1, …, Di, …, Dn, the probability of choosing Di as the best solution is a monotonic decreasing function of the absolute distance of Di from I. A stronger decision rule incorporating a negative exponential function in Luce&#039;s choice rule is also proposed. Both the strong and weak versions of the theory were supported in two experiments where Ss rank-ordered the alternatives in problems A:B::C:D1,D2, D3D4. In a third experiment the theory was applied and further tested in teaching new concepts by analogy.&lt;/p&gt;
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