Computational brain & behavior最新文献_第10页

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

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

{"title":"Neural habituation enhances novelty detection: an EEG study of rapidly presented words.","authors":"Len P L Jacob, David E Huber","doi":"10.1007/s42113-019-00071-w","DOIUrl":"10.1007/s42113-019-00071-w","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"3 2","pages":"208-227"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7447193/pdf/nihms-1546975.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38414587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explanation or Modeling: a Reply to Kellen and Klauer 解释还是建模:对Kellen和Klauer的回复

Computational brain & behavior Pub Date : 2020-04-15 DOI: 10.1007/s42113-020-00077-9

Marco Ragni, P. Johnson-Laird

引用次数: 2

Beyond Rescorla–Wagner: the Ups and Downs of Learning 超越Rescorla-Wagner:学习的起起落落

Computational brain & behavior Pub Date : 2020-04-10 DOI: 10.1007/s42113-021-00103-4

G. Calcagni, Justin A. Harris, R. Pellón

引用次数: 1

Real-time Adaptive Design Optimization Within Functional MRI Experiments 功能MRI实验中的实时自适应设计优化

Computational brain & behavior Pub Date : 2020-04-02 DOI: 10.1007/s42113-020-00079-7

Giwon Bahg, P. Sederberg, Jay I. Myung, Xiangrui Li, M. Pitt, Zhong-Lin Lu, Brandon M. Turner

引用次数: 3

Modeling the Wason Selection Task: a Response to Ragni and Johnson-Laird (2020) 建模沃森选择任务:对Ragni和Johnson-Laird(2020)的回应

Computational brain & behavior Pub Date : 2020-04-01 DOI: 10.1007/s42113-020-00086-8

David Kellen, K. C. Klauer

引用次数: 1

A Cautionary Note on Evidence-Accumulation Models of Response Inhibition in the Stop-Signal Paradigm 关于停止-信号范式中反应抑制的证据积累模型的警告

Computational brain & behavior Pub Date : 2020-03-30 DOI: 10.1007/s42113-020-00075-x

D. Matzke, G. Logan, A. Heathcote

引用次数: 12

Modeling Preference Reversals in Context Effects over Time 随着时间的推移，环境影响下偏好逆转的建模

Computational brain & behavior Pub Date : 2020-03-27 DOI: 10.1007/s42113-020-00078-8

Andrea M. Cataldo, A. Cohen

引用次数: 9

Hierarchical Hidden Markov Models for Response Time Data 响应时间数据的层次隐马尔可夫模型

Computational brain & behavior Pub Date : 2020-03-26 DOI: 10.1007/s42113-020-00076-w

D. Kunkel, Zhifei Yan, P. Craigmile, M. Peruggia, T. Van Zandt

引用次数: 3

Generalization at Retrieval Using Associative Networks with Transient Weight Changes 基于瞬态权值变化的关联网络的检索泛化

Computational brain & behavior Pub Date : 2020-03-21 DOI: 10.31234/osf.io/3nzgh

Kevin D. Shabahang, H. Yim, S. Dennis

{"title":"Generalization at Retrieval Using Associative Networks with Transient Weight Changes","authors":"Kevin D. Shabahang, H. Yim, S. Dennis","doi":"10.31234/osf.io/3nzgh","DOIUrl":"https://doi.org/10.31234/osf.io/3nzgh","url":null,"abstract":"Without having seen a bigram like “her buffalo”, you can easily tell that it is congruent because “buffalo” can be aligned with more common nouns like “cat” or “dog” that have been seen in contexts like “her cat” or “her dog”—the novel bigram structurally aligns with representations in memory. We present a new class of associative nets we call Dynamic-Eigen-Nets , and provide simulations that show how they generalize to patterns that are structurally aligned with the training domain. Linear-Associative-Nets respond with the same pattern regardless of input, motivating the introduction of saturation to facilitate other response states. However, models using saturation cannot readily generalize to novel, but structurally aligned patterns. Dynamic-Eigen-Nets address this problem by dynamically biasing the eigenspectrum towards external input using temporary weight changes. We demonstrate how a two-slot Dynamic-Eigen-Net trained on a text corpus provides an account of bigram judgment-of-grammaticality and lexical decision tasks, showing it can better capture syntactic regularities from the corpus compared to the Brain-State-in-a-Box and the Linear-Associative-Net. We end with a simulation showing how a Dynamic-Eigen-Net is sensitive to syntactic violations introduced in bigrams, even after the associations that encode those bigrams are deleted from memory. Over all simulations, the Dynamic-Eigen-Net reliably outperforms the Brain-State-in-a-Box and the Linear-Associative-Net. We propose Dynamic-Eigen-Nets as associative nets that generalize at retrieval, instead of encoding, through recurrent feedback.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"6 1","pages":"124-155"},"PeriodicalIF":0.0,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88739560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Hierarchical Latent Space Network Model for Population Studies of Functional Connectivity 功能连通性人口研究的层次潜空间网络模型

Computational brain & behavior Pub Date : 2020-03-19 DOI: 10.1007/s42113-020-00080-0

James D. Wilson, S. Cranmer, Zhonglin Lu

引用次数: 3