Neural habituation enhances novelty detection: an EEG study of rapidly presented words.

Computational brain & behavior Pub Date : 2020-06-01 Epub Date: 2019-12-18 DOI:10.1007/s42113-019-00071-w
Len P L Jacob, David E Huber
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Abstract

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.

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神经习惯化可增强新奇感检测:快速呈现单词的脑电图研究。
Huber 和 O'Reilly(2003 年)提出,神经习惯化有助于感知处理,将神经对当前观看物体和最近观看物体的反应分离开来。然而,突触抑制是有代价的,它会产生重复障碍。先前的研究证实,随着主要对象持续时间的增加,重复的益处会转变为缺陷,但对新奇事物检测增强的预测却没有进行测试。本研究利用支持向量机(SVM)对脑电图数据进行分类,对 N400 进行ERP分析,并对行为数据进行动态神经网络模拟,从而对ERP效应进行先验预测。受试者根据紧随其后的简短目标词对反应词做出相同/不同的判断;prime 持续时间有短(50 毫秒)和长(400 毫秒)之分,长持续时间会降低目标词的 P100/N170 反应,表明这种操作会增加习惯性。在长持续时间引物之后,对引物反应词的正确 "不同 "判断增加了,这证明新颖性检测增强了。SVM 分类器对保留数据的逐次试验行为预测准确率为 66.34%,在与 N400 一致的时间模式下预测能力最强。习惯化模型通过一个保持语义层(即工作记忆)来生成行为和 N400 预测结果。第二个实验使用了反应锁定的 ERPs,证实了该模型的假设,即工作记忆中的残余激活是新奇决定的基础。这些结果支持了神经习惯性增强新奇事物检测的理论,以及 N400 反映了工作记忆中语义信息更新的模型假设。
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