Abstract
For the last four decades, semantic priming—the facilitation in recognition of a target word when it follows the presentation of a semantically related prime word—has been a central topic in research of human cognitive processing. Studies have drawn a complex picture of findings which demonstrated the sensitivity of this priming effect to a unique combination of variables, including, but not limited to, the type of relatedness between primes and targets, the prime-target Stimulus Onset Asynchrony (SOA), the relatedness proportion (RP) in the stimuli list and the specific task subjects are required to perform. Automatic processes depending on the activation patterns of semantic representations in memory and controlled strategies adapted by individuals when attempting to maximize their recognition performance have both been implicated in contributing to the results. Lately, we have published a new model of semantic priming that addresses the majority of these findings within one conceptual framework. In our model, semantic memory is depicted as an attractor neural network in which stochastic transitions from one stored pattern to another are continually taking place due to synaptic depression mechanisms. We have shown how such transitions, in combination with a reinforcement-learning rule that adjusts their pace, resemble the classic automatic and controlled processes involved in semantic priming and account for a great number of the findings in the literature. Here, we review the core findings of our model and present new simulations that show how similar principles of parameter-adjustments could account for additional data not addressed in our previous studies, such as the relation between expectancy and inhibition in priming, target frequency and target degradation effects. Finally, we describe two human experiments that validate several key predictions of the model.
Highlights
One of the most widely investigated phenomena in cognitive psychology is semantic priming
Due to the wide range of cognitive processes involved, it is hardly surprising that very different models have been suggested over the years to account for semantic priming, models which have typically referred to only subsets of the whole spectrum of findings in the literature and quite often could not be reconciled with one another
CONCLUDING REMARKS In this review we attempted to give a general and concise description of our recent neural network model of semantic memory, which was designed to account for various findings in the semantic priming literature within a unified framework
Summary
One of the most widely investigated phenomena in cognitive psychology is semantic priming. Certain models ascertain that this effect stems from the temporal propagation of semantic activation in memory These models predict a quick increase in the priming effect, followed by a decrease (Collins and Loftus, 1975; den Heyer and Briand, 1986). Other models attribute the SOA effect to neuronal properties of the brain structures involved in storing semantic memory, predicting either an increase or decrease, depending on the type of relation between prime and target (Plaut, 1995). As it turns out, all models are right—and www.frontiersin.org
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