Yantis S, Meyer D E
Department of Psychology, Johns Hopkins University, Baltimore, Maryland 21218.
J Exp Psychol Gen. 1988 Jun;117(2):130-47. doi: 10.1037//0096-3445.117.2.130.
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)
语义记忆和情景记忆结构的扩散激活模型假定存在一个相互连接的节点网络，激活从源节点传播到接收节点。这些模型解释了广泛的与记忆相关的过程，包括单词识别、句子验证、散文理解和句子生成。关于这一解释的一个基本问题涉及网络中每个节点激活增长的性质。两种相互排斥的可能性是：（a）激活以离散方式增长，在两个或多个不同状态之间进行突然转变；（b）激活从静止水平持续增长到渐近水平。在本文中，我们用文献中的例子描述了这种二分法，并应用一种自适应启动程序来测试离散激活模型与连续激活模型。我们的程序包括在测试刺激之前的不同时刻呈现启动刺激；要求受试者对测试刺激做出词汇（单词/非单词）判断。启动刺激和测试刺激之间的间隔持续时间根据受试者的表现进行自适应变化。反应时间作为该持续时间的函数被记录下来。根据离散激活模型，与节点激活的每个可能状态相关联都有一个独特的反应时间分布。因此，当测试刺激出现在离散状态之间的转变时刻附近时观察到的反应时间分布应该构成与各个离散激活状态相关联 的潜在基础分布的有限混合。混合比例将取决于启动间隔与状态转变时间分布之间的关系。相反，连续激活模型断言激活随时间连续增长，并且与任何给定程度的中间启动相关联都有一个独特的反应时间分布。此类模型预测，当启动间隔具有固定的中间持续时间时，不会出现有限混合分布。报告了两个采用自适应启动程序的实验来测试这些替代预测。在实验1中，启动刺激和测试刺激是语义相关的单词（例如，面包 -黄油）。在实验2中，通过配对联想学习在启动刺激和测试刺激之间建立情景联想。对于这两种情况，混合预测都失败了，并且两种状态的离散激活模型被否定。
