DocumentCode :
1928731
Title :
Cognitive modeling of symbolic-like relationships with the adaptive neural network associator (ANNA)
Author :
Spiegel, Rainer
Author_Institution :
Goldsmiths Coll., Univ. of London, UK
Volume :
4
fYear :
2003
fDate :
20-24 July 2003
Firstpage :
2746
Abstract :
In their influential articles (Science, 283, 1999), Marcus, Vijayan, Bandi Rao and Vishton (pp. 77-80) and Pinker (pp. 40-41) argue that a prominent model of associative learning, the simple recurrent network, SRN, would fail to simulate rule-learning by seven-month-old infants. Furthermore, the authors argue for the consideration of rule based, symbolic explanations. Subsequently, several authors proposed variations of the simple recurrent network that were better able to model the infant data, but Marcus argued that these models would themselves implement (hidden) rule-mechanisms. Moreover, he was able to show in a further experimental test that one of these models predicted exactly the opposite of what was found in an infant learning experiment. The paper proposes ANNA, a new recurrent neural network architecture that is fully based on associative mechanisms, i.e. ANNA does not implement rules. ANNA succeeds in simulating the infant results. This includes Marcus´ recent experimental tests. The author therefore argues that the results of Marcus do not necessarily prove that infants make use of rules (though they might apply rules). Moreover, ANNA shows that rule/symbolic-like relationships can at least sometimes arise out of associations.
Keywords :
adaptive systems; associative processing; learning (artificial intelligence); recurrent neural nets; ANNA; adaptive neural network associator; associative learning; associative mechanisms; cognitive modeling; human learning; infant learning experiment; recurrent neural network; rule based symbolic explanations; seven-month-old infants; simple recurrent network; symbolic-like relationships; Adaptive systems; Biological neural networks; Cognitive science; Educational institutions; Humans; Neural networks; Pediatrics; Psychology; Recurrent neural networks; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN :
1098-7576
Print_ISBN :
0-7803-7898-9
Type :
conf
DOI :
10.1109/IJCNN.2003.1224002
Filename :
1224002
Link To Document :
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