Automatic and Controlled Processes in Semantic Priming: an Attractor Neural Network Model with Latching Dynamics

Automatic and Controlled Processes in Semantic Priming: an Attractor Neural Network Model with Latching Dynamics Itamar Lerner (itamar.lerner@gmail.com) Interdisciplinary Center for Neural Computation, The Hebrew University of Jerusalem Giv'at Ram, Jerusalem 91904 Israel Shlomo Bentin (shlomo.bentin@huji.ac.il) Department of Psychology, The Hebrew University of Jerusalem Mount Scopus; Jerusalem 91905 Israel Oren Shriki (oren70@gmail.com) Department of Physiology and Neurobiology, Faculty of Medicine, Ben-Gurion University of the Negev Be'er-Sheva 84105 Israel Abstract Semantic priming involves a combination of automatic proc- esses like spreading activation (SA) and controlled processes like expectancy and semantic matching. An alternative ac- count for automatic priming has been suggested using attrac- tor neural networks. Such networks offer a more biologically plausible model of real neuronal dynamics but fall short in explaining several important effects such as mediated and asymmetrical priming, as well as controlled effects. We de- scribe a new attractor network which incorporates synaptic adaptation mechanisms and performs latching dynamics. We show that this model can implement spreading activation in a statistical manner and therefore exhibit all priming effects previously attributed to automatic priming. In addition, we show how controlled processes are implemented in the same network, explaining many other semantic priming results. Keywords: Semantic priming; Attractor networks; Latching dynamics Introduction Semantic priming is one of the most important phenomena in the study of word perception and semantic memory. In a typical priming experiment (Neely, 1991), subjects are visu- ally exposed to two words in succession, the prime and the target, and are required to silently read the prime and either name the target (pronunciation task), or decide whether it is a real word or not (lexical decision task). The target could either be semantically related or unrelated to the prime (or a nonword, in case of the lexical decision task). The priming effect is expressed as shorter average reaction times (RT) and reduced error rates in the related relative to unrelated condition. Sometimes, a neutral prime is used (e.g. a row of X’s) to allow the differentiation between response facilita- tion (in the related condition) and inhibition (in the unre- lated condition). Computational accounts for semantic priming are divided between models based on automatic processes and those based on controlled processes. The most famous among the automatic accounts for priming is the spreading activation (SA) theory of Collins & Loftus (1975). This model sug- gests that concepts in semantic memory are represented by nodes that are connected to each other according to their semantic relatedness. When a concept is activated (by exter- nal or internal input) the activity spreads to related concepts (see figure 1). In priming experiments, activation of the prime concept (e.g. table) leads to activation of its related concepts (e.g. chair). This pre-activation facilitates the rec- ognition of subsequent related targets. If an unrelated or a neutral target appears, no such head-start is available. Hence spreading activation may account for the facilitation com- ponent of semantic priming. Automatic priming can also be conceived in attractor networks with distributed representa- tions of concepts (e.g. Mason, 1995). In such models con- cepts are represented by activity patterns of neurons’ as- semblies and semantic relationship is implemented as corre- lation between these representations. When the prime ap- pears, the network converges on its corresponding activity pattern. When the target is then presented, the network changes its activity pattern from that of the prime to the one corresponding to the target. If the target is related to the prime, fewer changes need to take place due to the correla- tions; therefore, the convergence takes less time and a prim- ing effect emerges. Attractor networks are probably more true to the biologi- cal nature of real neuronal dynamics which include content- addressable memories, distributed representations and at- tractor states. However, they fall short in explaining several important priming results. Mediated priming is one example (e.g. McNamara, 1992): It was found that word pairs which are indirectly related to each other (i.e., related only through a mediating word, like lion and stripes, related through ti- ger) can nevertheless prime each other. Allowing activation to spread to more than one step, SA theories could easily account for such effects. Attractor networks, on the other hand, cannot explain mediated priming since the activation patterns of indirectly related pairs are not correlated. Simi- larly, whereas SA models allow asymmetric connections between nodes and therefore allow asymmetric priming (in which the magnitude of priming varies according to which word in a given pair is designated prime and which is the target; e.g. pay-check vs. check-pay), such an effect cannot