An anthropomorphic method for progressive matrix problems

Progressive matrix problems are frequently used in modern IQ tests. In a progressive matrix problem, the task is to identify the missing element that completes the pattern of a pictorial matrix. We present a method for solving progressive matrix problems. The method is not limited to problems that are on the multiple choice format, which makes it potentially useful for solving real-world pattern discovery problems that do not come with predefined answer alternatives. The method is anthropomorphic in the sense that it uses certain problem solving strategies that were reported by high-achieving human solvers. We also describe a computer program implementing this method. The computer program was tested on the sets C, D, and E of Raven's Standard Progressive Matrices test and it produced correct solutions for 28 of the 36 problems considered. This score corresponds roughly to an IQ of 100. Finally, we conclude that it is possible to solve progressive matrix problems without analyzing potential answer alternatives and discuss some implications of this finding.

[1]  Lance J. Rips,et al.  Reasoning : studies of human inference and its foundations , 2008 .

[2]  Elaine Rich,et al.  Automata, Computability and Complexity: Theory and Applications , 2007 .

[3]  N. Chater,et al.  Simplicity: a unifying principle in cognitive science? , 2003, Trends in Cognitive Sciences.

[4]  Kenneth D. Forbus,et al.  Analogy with Qualitative Spatial Representations Can Simulate Solving Raven's Progressive Matrices , 2007 .

[5]  Maithilee Kunda,et al.  Taking a Look (Literally!) at the Raven’s Intelligence Test: Two Visual Solution Strategies , 2010 .

[6]  Ming Li,et al.  An Introduction to Kolmogorov Complexity and Its Applications , 2019, Texts in Computer Science.

[7]  G. Groth-Marnat.  Handbook of Psychological Assessment , 2016 .

[8]  E. Leeuwenberg.  A perceptual coding language for visual and auditory patterns. , 1971, The American journal of psychology.

[9]  M. Meo,et al.  Element salience as a predictor of item difficulty for Raven's Progressive Matrices , 2007 .

[10]  Selmer Bringsjord,et al.  What is Artificial Intelligence? Psychometric AI as an Answer , 2003, IJCAI.

[11]  N. Chater.  The Search for Simplicity: A Fundamental Cognitive Principle? , 1999 .

[12]  D. Bors,et al.  Eye-movement analysis demonstrates strategic influences on intelligence , 2006 .

[13]  J. Raven,et al.  Manual for Raven's progressive matrices and vocabulary scales , 1962 .

[14]  Kenneth D. Forbus,et al.  CogSketch: open-domain sketch understanding for cognitive science research and for education , 2008, SBM'08.

[15]  M. Wertheimer.  Laws of organization in perceptual forms. , 1938 .

[16]  R. Sternberg.  Advances in the psychology of human intelligence , 1982 .

[17]  M A Just,et al.  From the SelectedWorks of Marcel Adam Just 1990 What one intelligence test measures : A theoretical account of the processing in the Raven Progressive Matrices Test , 2016 .

[18]  Emanuel Leeuwenberg,et al.  Structural information of visual patterns: an efficient coding system in perception , 1967 .

[19]  Claes Strannegård.  Anthropomorphic Artificial Intelligence , 2005 .

[20]  Kenneth D. Forbus,et al.  A Structure-Mapping Model of Raven's Progressive Matrices , 2010 .

[21]  Achille C. Varzi.  Patterns, Rules, and Inferences , 2008 .

[22]  Kenneth D. Forbus,et al.  Open-Domain Sketch Understanding: The nuSketch Approach , 2004, AAAI Technical Report.

[23]  John McCarthy,et al.  WHAT IS ARTIFICIAL INTELLIGENCE , 1998 .

[24]  C. E. Bethell-Fox,et al.  Adaptive reasoning: Componential and eye movement analysis of geometric analogy performance ☆ , 1984 .

[25]  G. Whipple,et al.  The Psychological Methods of Testing Intelligence , 1915, The Psychological Clinic.

[26]  Shane Legg,et al.  Tests of Machine Intelligence , 2006, 50 Years of Artificial Intelligence.

[27]  Chris Eliasmith,et al.  A Neural Model of Rule Generation in Inductive Reasoning , 2011, Top. Cogn. Sci..