Abstract: Lexical decision is a widely used paradigm in research on word recognition.A computation model based on distributed semantic representation was presented to explain the major phenomena which were found in lexical decision task.The model consisted of a forward network of five sets of orthographic,hidden,semantic,lexicon and decision units.Weights on connections between orthographic, hidden and semantic units were modified during a training phase using the back propagation learning algorithm.Weights on connections between semantic,lexicon and decision units were fixed before the learning process.The model simulated some aspects of human performance in lexical decision task,including a)frequency effect;b)semantic priming effect;c)the interaction between frequency and context;d)word repetition effect;e)degradation effect.The performance of the model came from the architecture of the model and the learning rule.

Key words: semantics, lexical decision, connectionism