Abstract
Semantic priming is traditionally viewed as an effect that rapidly decays. A new view of long-term word priming in attractor neural networks is proposed. The model predicts long-term semantic priming under certain conditions. That is, the task must engage semantic-level processing to a sufficient degree. The predictions were confirmed in computer simulations and in 3 experiments. Experiment 1 showed that when target words are each preceded by multiple semantically related primes, there is long-lag priming on a semantic-decision task but not on a lexical-decision task. Experiment 2 replicated the long-term semantic priming effect for semantic decisions with only one prime per target. Experiment 3 demonstrated semantic priming with much longer word lists at lags of 0, 4, and 8 items. These are the first experiments to demonstrate a semantic priming effect spanning many intervening items and lasting much longer than a few seconds.
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