Semantic integration by pattern priming: experiment and cortical network model

Abstract : Neural network models describe semantic priming effects by way of mechanisms of activation of neuron coding for the words that rely strongly on synaptic efficacies between pairs of neurons. Biologically inspired Hebbian learning defines efficacy values as a function of the activity of pre- and post-synaptic neurons only. It generates only pair associations between words in the semantic network. However, the statistical analysis of large text databases points to the frequent occurrence not only of pairs of words (e.g., “the way”) but also of patterns of more than two words (e.g., “by the way”). The learning of these frequent patterns of words is not reducible to associations between pairs of words but must take into account the higher level of coding of three-word patterns. The processing and learning of pattern of words challenges classical Hebbian learning algorithms used in biologically inspired models of priming. The aim of the present study was to test the effects of patterns on the semantic processing of words and investigates how an inter-synaptic learning algorithm succeeds at reproducing the experimental data. The experiment manipulates the frequency of occurrence of patterns of three words in a multiple-paradigm protocol. Results show for the first time that target words benefit more priming when embedded in a pattern with the two primes than when only associated with each prime in a pair. A biologically inspired, inter-synaptic learning algorithm is tested that potentiates synapses as a function of the activation of more than two pre- and post-synaptic neurons. Simulations show that the network can learn patterns of three words to reproduce the experimental results
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Contributor : Damon Mayaffre <>
Submitted on : Tuesday, September 13, 2016 - 11:23:14 AM
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Frédéric Lavigne, Dominique Longrée, Damon Mayaffre, Sylvie Mellet. Semantic integration by pattern priming: experiment and cortical network model. Cognitive Neurodynamics, Springer Verlag, 2016, ⟨10.1007/s11571-016-9410-4⟩. ⟨hal-01365139⟩



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