Transitive choices by a simple, fully connected, backpropagation neural network: implications for the comparative study of transitive inference. |
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Authors: | C De Lillo D Floreano F Antinucci |
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Institution: | (1) Department of Psychology, University of Leicester, University Road, Leicester LE1 7RH, UK,;(2) Evolutionary and Adaptive Systems, Institute of Robotics, Swiss Federal Institute of Technology (EPFL), 1015 Lausanne, Switzerland,;(3) Istituto di Psicologia C.N.R., Via Aldrovandi 16b, 00197 Roma, Italy, |
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Abstract: | In search of the minimal requirements for transitive reasoning, a simple neural network was trained and tested on the non-verbal
version of the conventional "five-term-series task" – a paradigm used with human adults, children and a variety of non-human
species. The transitive performance of the network was analogous in several aspects to that reported for children and animals.
The three effects usually associated with transitive choices i.e. "symbolic distance", "lexical marking" and "end-anchor",
were also clearly shown by the neural network. In a second experiment, where the training conditions were manipulated, the
network failed to match the behavioural pattern reported for human adults in the test following an ordered presentation of
the premises. However, it mimicked young children's performance when tested with a novel comparison term. Although we do not
intend to suggest a new model of transitive inference, we conclude, in line with other authors, that a simple error-correcting
rule can generate transitive behaviour similar to the choice pattern of children and animals in the binary form of the five-term-series
task without requiring high-order logical or paralogical abilities. The analysis of the training history and of the final
internal structure of the network reveals the associative strategy employed. However, our results indicate that the scope
of the associative strategy used by the network might be limited. The extent to which the conventional five-term-series task,
in absence of appropriate manipulations of training and testing conditions, is suitable to detect cognitive differences across
species is also discussed on the basis of our results.
Accepted after revision: 29 May 2001
Electronic Publication |
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Keywords: | Transitive inference Neural networks Five-term-series task Cognition |
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