Deceased July 13, 2012.
Spreading Activation in an Attractor Network With Latching Dynamics: Automatic Semantic Priming Revisited
Article first published online: 24 OCT 2012
Copyright © 2012 Cognitive Science Society, Inc.
Volume 36, Issue 8, pages 1339–1382, November/December 2012
How to Cite
Lerner, I., Bentin, S. and Shriki, O. (2012), Spreading Activation in an Attractor Network With Latching Dynamics: Automatic Semantic Priming Revisited. Cognitive Science, 36: 1339–1382. doi: 10.1111/cogs.12007
- Issue published online: 2 NOV 2012
- Article first published online: 24 OCT 2012
- Received 12 December 2011; received in revised form 23 April 2012; accepted 2 May 2012
- Word recognition;
- Semantic priming;
- Neural networks;
- Distributed representations;
- Latching dynamics
Localist models of spreading activation (SA) and models assuming distributed representations offer very different takes on semantic priming, a widely investigated paradigm in word recognition and semantic memory research. In this study, we implemented SA in an attractor neural network model with distributed representations and created a unified framework for the two approaches. Our models assume a synaptic depression mechanism leading to autonomous transitions between encoded memory patterns (latching dynamics), which account for the major characteristics of automatic semantic priming in humans. Using computer simulations, we demonstrated how findings that challenged attractor-based networks in the past, such as mediated and asymmetric priming, are a natural consequence of our present model’s dynamics. Puzzling results regarding backward priming were also given a straightforward explanation. In addition, the current model addresses some of the differences between semantic and associative relatedness and explains how these differences interact with stimulus onset asynchrony in priming experiments.