Dynamics of activation in semantic and episodic memory.

Spreading-activation models for the structure of semantic and episodic memory postulate a network of interconnected nodes in which activation spreads from a source node to recipient nodes. These models account for a broad range of memory-related processes, including word recognition, sentence verification, prose comprehension, and sentence production. A fundamental question regarding this account concerns the nature of activation growth at each node in the network. Two mutually exclusive possibilities are (a) that activation grows in a discrete fashion, making abrupt transitions between two or more distinct states and (b) that activation grows continuously from a resting level to an asymptotic level. In the present article, we characterize this dichotomy with examples from the literature, and we apply an adaptive priming procedure for testing discrete versus continuous activation models. Our procedure involves the presentation of prime stimuli at various moments before a test stimulus; subjects are required to make a lexical (word/nonword) decision about the test stimulus. The duration of the interval between the prime and test stimuli is varied adaptively on the basis of subjects' performance. Reaction times are recorded as a function of this duration. According to discrete activation models, there is a unique reaction-time distribution associated with each possible state of node activation. The distribution of reaction times observed when the test stimulus appears near the moment of transition between discrete states should therefore constitute a finite mixture of the underlying basis distributions associated with the individual discrete activation states. The mixture proportion will depend on the relation between the priming interval and the distribution of state-transition times. Continuous activation models assert instead that activation grows continuously over time and that there is a unique reaction-time distribution associated with any given degree of intermediate priming. Such models predict that no finite mixture distribution will emerge when the priming interval has a fixed intermediate duration. Two experiments with the adaptive priming procedure are reported to test these alternative predictions. In Experiment 1, the prime and test stimuli were semantically associated words (e.g., bread-butter). In Experiment 2, episodic associations between the prime and test stimuli were established through paired associate learning. For both cases, the mixture prediction failed, and two-state discrete activation models were rejected.(ABSTRACT TRUNCATED AT 400 WORDS)