Analyzing Raven’s Intelligence Test: Cognitive Model, Demand, and Complexity

Part of the Studies in Computational Intelligence book series (SCI, volume 548)


Identifying hidden functions and patterns in data and reasoning about them is a relevant part of any inference system. Differential psychologists typically measure humans’ ability to reason by intelligence tests such as Raven’s Standard or Advanced Progressive Matrices test. Human reasoning difficulty, however, for IQ-test items is almost always determined empirically: An item’s difficulty is measured by the number of reasoners who are able to solve it. So far this method has proven successful: Nearly all IQ-Tests are designed this way. Still, it is most desirable to have a computational model that captures the inherent reasoning complexity and allows for a predictive classification of problems. In this article, we first analyze and classify fluid intelligence test items as they are common in tests like Raven’s Standard and Progressive Matrices and Catell’s Culture Fair Test. This functional classification allows us to develop a model in the cognitive architecture ACT-R that solves 66 of 72 problems from both Raven tests. We argue that in addition to visual and formal reasoning difficulty the way information is processed in the modular structure of the human mind should be taken into account. A simpler (but functionally equivalent) version of the APM is developed and tested on humans. Implications for developing a cognitive system that can classify human reasoning systems are discussed.


Analogical reasoning Pattern recognition IQ-test problems 



MR’s research was supported by the German Research Foundation (DFG) in the Transregional Collaborative Research Center, SFB/TR 8 within projects R8-[CSPACE] and by a grant from the DFG within the priority program “New Frameworks of Rationality” (SPP 1516). The authors are grateful to Stephanie Schwenke for proof reading and for various discussions above such topics with Philip Stahl.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.University of FreiburgCenter for Cognitive ScienceFreiburgGermany
  2. 2.Ludwig-Maximilians-Universität MünchenMünchenGermany

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