David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
Learn more about PhilPapers
Topics in Cognitive Science 3 (1):140-153 (2011)
Inductive reasoning is a fundamental and complex aspect of human intelligence. In particular, how do subjects, given a set of particular examples, generate general descriptions of the rules governing that set? We present a biologically plausible method for accomplishing this task and implement it in a spiking neuron model. We demonstrate the success of this model by applying it to the problem domain of Raven's Progressive Matrices, a widely used tool in the field of intelligence testing. The model is able to generate the rules necessary to correctly solve Raven's items, as well as recreate many of the experimental effects observed in human subjects
|Keywords||Raven's Progressive Matrices Rule generation Vector Symbolic Architectures Neural Engineering Framework Inductive reasoning Realistic neural modeling Cognitive modeling Fluid intelligence|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
Tony A. Plate (2003). Holographic Reduced Representation: Distributed Representation for Cognitive Structures. Center for the Study of Language and Information.
Citations of this work BETA
Chris Eliasmith (2012). The Complex Systems Approach: Rhetoric or Revolution. Topics in Cognitive Science 4 (1):72-77.
Timothy T. Rogers & James L. McClelland (2014). Parallel Distributed Processing at 25: Further Explorations in the Microstructure of Cognition. Cognitive Science 38 (6):1024-1077.
Peter Blouw, Eugene Solodkin, Paul Thagard & Chris Eliasmith (2015). Concepts as Semantic Pointers: A Framework and Computational Model. Cognitive Science 40 (1):n/a-n/a.
Jean-Frédéric de Pasquale & Pierre Poirier (2016). Convolution and Modal Representations in Thagard and Stewart’s Neural Theory of Creativity: A Critical Analysis. Synthese 193 (5):1535-1560.
Similar books and articles
Trevor J. M. Bench-Capon & Giovanni Sartor (2003). A Model of Legal Reasoning with Cases Incorporating Theories and Values. Artificial Intelligence 150 (1-2):97-143.
Manfred Jaeger (2005). A Logic for Inductive Probabilistic Reasoning. Synthese 144 (2):181 - 248.
Kathleen Freeman & Arthur M. Farley (1996). A Model of Argumentation and its Application to Legal Reasoning. Artificial Intelligence and Law 4 (3-4):163-197.
Jaap Hage (1996). A Theory of Legal Reasoning and a Logic to Match. Artificial Intelligence and Law 4 (3-4):199-273.
Karsten R. Stueber (2005). How to Think About Rules and Rule Following. Philosophy of the Social Sciences 35 (3):307-323.
Jean-Baptiste Van der Henst (2002). Mental Model Theory Versus the Inference Rule Approach in Relational Reasoning. Thinking and Reasoning 8 (3):193 – 203.
Roger White (2005). Explanation as a Guide to Induction. Philosophers' Imprint 5 (2):1-29.
Maxwell J. Roberts, Heather Welfare, Doreen P. Livermore & Alice M. Theadom (2000). Context, Visual Salience, and Inductive Reasoning. Thinking and Reasoning 6 (4):349 – 374.
Jürgen Hollatz (1999). Analogy Making in Legal Reasoning with Neural Networks and Fuzzy Logic. Artificial Intelligence and Law 7 (2-3):289-301.
F. Bergadano (1993). Machine Learning and the Foundations of Inductive Inference. Minds and Machines 3 (1):31-51.
Pierre Barrouillet & Henry Markovits (2002). Is the Self-Organizing Consciousness Framework Compatible with Human Deductive Reasoning? Behavioral and Brain Sciences 25 (3):330-331.
Added to index2011-01-11
Total downloads72 ( #57,296 of 1,792,839 )
Recent downloads (6 months)26 ( #31,212 of 1,792,839 )
How can I increase my downloads?