Computational brain & behavior最新文献_第7页

Scale-Dependent Relationships in Natural Language. 自然语言中的尺度依赖关系。

Computational brain & behavior Pub Date : 2021-06-01 Epub Date: 2021-01-04 DOI: 10.1007/s42113-020-00094-8

Aakash Sarkar, Marc W Howard

{"title":"Scale-Dependent Relationships in Natural Language.","authors":"Aakash Sarkar, Marc W Howard","doi":"10.1007/s42113-020-00094-8","DOIUrl":"10.1007/s42113-020-00094-8","url":null,"abstract":"Language, like other natural sequences, exhibits statistical dependencies at a wide range of scales (Lin & Tegmark, 2016). However, many statistical learning models applied to language impose a sampling scale while extracting statistical structure. For instance, Word2Vec creates vector embeddings by sampling context in a window around each word, the size of which defines a strong scale; relationships over much larger temporal scales would be invisible to the algorithm. This paper examines the family of Word2Vec embeddings generated while systematically manipulating the size of the context window. The primary result is that different linguistic relationships are preferentially encoded at different scales. Different scales emphasize different syntactic and semantic relations between words, as assessed both by analogical reasoning tasks in the Google Analogies test set and human similarity rating datasets WordSim-353 and SimLex-999. Moreover, the neighborhoods of a given word in the embeddings change considerably depending on the scale. These results suggest that sampling at any individual scale can only identify a subset of the meaningful relationships a word might have, and point toward the importance of developing scale-free models of semantic meaning.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"4 ","pages":"164-177"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42113-020-00094-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39268103","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: 1

Bayes Factor Model Comparisons Across Parameter Values for Mixed Models 混合模型跨参数值的贝叶斯因子模型比较

Computational brain & behavior Pub Date : 2021-05-28 DOI: 10.1007/s42113-021-00117-y

M. Linde, D. van Ravenzwaaij

引用次数: 3

Embracing New Techniques in Deep Learning for Estimating Image Memorability 采用深度学习的新技术来估计图像记忆性

Computational brain & behavior Pub Date : 2021-05-21 DOI: 10.1007/s42113-022-00126-5

Coen D. Needell, Wilma A. Bainbridge

引用次数: 24

Exploring the Effects of Perceptual Separability on Human-Automation Team Efficiency 探索感知可分离性对人-自动化团队效率的影响

Computational brain & behavior Pub Date : 2021-05-08 DOI: 10.1007/s42113-021-00108-z

Sidney T. Scott-Sharoni, Yusuke Yamani, Cara M. Kneeland, Shelby K. Long, Jing Chen, Joseph W. Houpt

引用次数: 2

Dynamic Perception of Well-Learned Perceptual Objects 学习良好的感知对象的动态感知

Computational brain & behavior Pub Date : 2021-05-03 DOI: 10.1007/s42113-021-00107-0

Samuel M. Harding, D. Cousineau, R. Shiffrin

引用次数: 0

The Discovery and Interpretation of Evidence Accumulation Stages 证据积累阶段的发现与解释

Computational brain & behavior Pub Date : 2021-04-27 DOI: 10.1007/s42113-021-00105-2

Leendert van Maanen, Oscar Portoles, J. Borst

引用次数: 4

Exploring the Performance Consequences of Target Prevalence and Ecological Display Designs When Using an Automated Aid 探索目标流行率和生态显示设计在使用自动化辅助时的性能后果

Computational brain & behavior Pub Date : 2021-04-12 DOI: 10.1007/s42113-021-00104-3

Cara M. Kneeland, Joseph W. Houpt, K. Bennett

引用次数: 2

Neural Networks Trained on Natural Scenes Exhibit Gestalt Closure 在自然场景上训练的神经网络表现出完形闭合

Computational brain & behavior Pub Date : 2021-04-09 DOI: 10.1007/s42113-021-00100-7

Been Kim, Emily Reif, M. Wattenberg, Samy Bengio, M. Mozer

引用次数: 35

Alleviating the Cold Start Problem in Adaptive Learning using Data-Driven Difficulty Estimates 用数据驱动的难度估计缓解自适应学习中的冷启动问题

Computational brain & behavior Pub Date : 2021-03-15 DOI: 10.1007/s42113-021-00101-6

Maarten van der Velde, Florian Sense, J. Borst, H. van Rijn

引用次数: 7

Modulation of Dopamine for Adaptive Learning: A Neurocomputational Model. 多巴胺对适应性学习的调节:一个神经计算模型。

Computational brain & behavior Pub Date : 2021-03-01 Epub Date: 2020-06-12 DOI: 10.1007/s42113-020-00083-x

Jeffrey B Inglis, Vivian V Valentin, F Gregory Ashby

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引用次数: 1