Jacob Len P L, Huber David E
University of Massachusetts, Amherst, 135 Hicks Way, Tobin Hall, Amherst MA 01003.
Comput Brain Behav. 2020 Jun;3(2):208-227. doi: 10.1007/s42113-019-00071-w. Epub 2019 Dec 18.
Huber and O'Reilly (2003) proposed that neural habituation aids perceptual processing, separating neural responses to currently viewed objects from recently viewed objects. However, synaptic depression has costs, producing repetition deficits. Prior work confirmed the transition from repetition benefits to deficits with increasing duration of a prime object, but the prediction of enhanced novelty detection was not tested. The current study examined this prediction with a same/different word priming task, using support vector machine (SVM) classification of EEG data, ERP analyses focused on the N400, and dynamic neural network simulations fit to behavioral data to provide a priori predictions of the ERP effects. Subjects made same/different judgements to a response word in relation to an immediately preceding brief target word; prime durations were short (50ms) or long (400ms), and long durations decreased P100/N170 responses to the target word, suggesting that this manipulation increased habituation. Following long duration primes, correct "different" judgments of primed response words increased, evidencing enhanced novelty detection. An SVM classifier predicted trial-by-trial behavior with 66.34% accuracy on held-out data, with greatest predictive power at a time pattern consistent with the N400. The habituation model was augmented with a maintained semantics layer (i.e., working memory) to generate behavior and N400 predictions. A second experiment used response-locked ERPs, confirming the model's assumption that residual activation in working memory is the basis of novelty decisions. These results support the theory that neural habituation enhances novelty detection, and the model assumption that the N400 reflects updating of semantic information in working memory.
休伯和奥赖利(2003年)提出,神经习惯化有助于感知处理,将对当前所视物体的神经反应与最近所视物体的神经反应区分开来。然而,突触抑制是有代价的,会产生重复缺陷。先前的研究证实,随着启动刺激物体持续时间的增加,会从重复获益转变为重复缺陷,但增强新奇性检测的预测并未得到检验。本研究使用脑电图数据的支持向量机(SVM)分类、聚焦于N400的事件相关电位(ERP)分析以及拟合行为数据的动态神经网络模拟,通过相同/不同单词启动任务来检验这一预测,以提供ERP效应的先验预测。受试者针对紧接在前的简短目标单词对一个反应单词做出相同/不同的判断;启动刺激的持续时间较短(50毫秒)或较长(400毫秒),较长的持续时间会降低对目标单词的P100/N170反应,这表明这种操作增加了习惯化。在长时间启动刺激之后,对启动反应单词的正确“不同”判断增加,证明新奇性检测得到了增强。一个SVM分类器在留出的数据上以66.34%的准确率预测逐次试验的行为,在与N400一致的时间模式下具有最大的预测能力。通过添加一个维持语义层(即工作记忆)来增强习惯化模型,以生成行为和N400预测。第二个实验使用反应锁定的ERP,证实了该模型的假设,即工作记忆中的残余激活是新奇性决策的基础。这些结果支持了神经习惯化增强新奇性检测的理论,以及N400反映工作记忆中语义信息更新的模型假设。