2019 Conference on Cognitive Computational Neuroscience最新文献

Narratives as Networks: Predicting Memory from the Structure of Naturalistic Events 作为网络的叙事:从自然事件的结构预测记忆

2019 Conference on Cognitive Computational Neuroscience Pub Date : 2021-04-24 DOI: 10.32470/ccn.2019.1170-0

Hongmi Lee, Janice Chen

引用次数: 8

Do LSTMs know about Principle C? lstm知道原理C吗?

2019 Conference on Cognitive Computational Neuroscience Pub Date : 2019-09-14 DOI: 10.32470/ccn.2019.1241-0

Jeff Mitchell, N. Kazanina, Conor J. Houghton, J. Bowers

引用次数: 3

Subtractive gating improves generalization in working memory tasks 减法门控提高工作记忆任务的泛化能力

2019 Conference on Cognitive Computational Neuroscience Pub Date : 2019-09-14 DOI: 10.32470/ccn.2019.1352-0

M. L. Montero, Gaurav Malhotra, J. Bowers, R. P. Costa

{"title":"Subtractive gating improves generalization in working memory tasks","authors":"M. L. Montero, Gaurav Malhotra, J. Bowers, R. P. Costa","doi":"10.32470/ccn.2019.1352-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1352-0","url":null,"abstract":"It is largely unclear how the brain learns to generalize to new situations. Although deep learning models offer great promise as potential models of the brain, they break down when tested on novel conditions not present in their training datasets. One of the most successful models in machine learning are gated-recurrent neural networks. Because of its working memory properties here we refer to these networks as working memory networks (WMN). We compare WMNs with a biologically motivated variant of these networks. In contrast to the multiplicative gating used by WMNs, this new variant operates via subtracting gating (subWMN). We tested these two models in a range of working memory tasks: orientation recall with distractors, orientation recall with update/addition and distractors, and a more challenging task: sequence recognition based on the machine learning handwritten digits dataset. We evaluated the generalization properties of these two networks in working memory tasks by measuring how well they copped with three working memory loads: memory maintenance over time, making memories distractor-resistant and memory updating. Across these tests subWMNs perform better and more robustly than WMNs. These results suggests that the brain may rely on subtractive gating for improved generalization in working memory tasks.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114365889","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: 0

Adversarial Training of Neural Encoding Models on Population Spike Trains 种群尖峰列车神经编码模型的对抗性训练

2019 Conference on Cognitive Computational Neuroscience Pub Date : 2019-09-13 DOI: 10.32470/ccn.2019.1263-0

Poornima Ramesh, Mohamad Atayi, J. Macke

引用次数: 8

Unfolding of multisensory inference in the brain and behavior 多感官推理在大脑和行为中的展开

2019 Conference on Cognitive Computational Neuroscience Pub Date : 2019-09-13 DOI: 10.32470/ccn.2019.1219-0

Yinan Cao, Hame Park, Bruno L. Giordano, C. Kayser, C. Spence, C. Summerfield

引用次数: 0

Evolving the Olfactory System 进化嗅觉系统

2019 Conference on Cognitive Computational Neuroscience Pub Date : 2019-09-11 DOI: 10.32470/ccn.2019.1355-0

G. R. Yang, Peter Y. Wang, Yi Sun, Ashok Litwin-Kumar, R. Axel, L. Abbott

引用次数: 2

How do people learn how to plan? 人们是如何学会计划的?

2019 Conference on Cognitive Computational Neuroscience Pub Date : 2019-09-01 DOI: 10.32470/ccn.2019.1313-0

Y. Jain, Sanit Gupta, V. Rakesh, P. Dayan, Frederick Callaway, Falk Lieder

引用次数: 8

Modeling the development of decision making in volatile environments using strategies, reinforcement learning, and Bayesian inference 使用策略、强化学习和贝叶斯推理对易变环境中决策制定的发展进行建模

2019 Conference on Cognitive Computational Neuroscience Pub Date : 2019-09-01 DOI: 10.32470/ccn.2019.1409-0

Maria K. Eckstein, Sarah L. Master, R. Dahl, L. Wilbrecht, A. Collins

{"title":"Modeling the development of decision making in volatile environments using strategies, reinforcement learning, and Bayesian inference","authors":"Maria K. Eckstein, Sarah L. Master, R. Dahl, L. Wilbrecht, A. Collins","doi":"10.32470/ccn.2019.1409-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1409-0","url":null,"abstract":"Continuously adjusting behavior in changing environments is a crucial skill for intelligent creatures, but we know little about how this ability develops in humans. Here, we investigate this question in a large sample using behavioral analyses and computational modeling. We assessed over 200 participants (ages 8-30) on a probabilistic, volatile reinforcement learning task, and measured pubertal development status and salivary testosterone. We used three classes of models to analyze behavior on the task: fixed strategies, incremental reinforcement learning, and Bayesian inference. All model classes provided converging evidence for a decrease in decision noise or exploration with age. Individual models also provided insight into unique aspects of decision making, such as changes in estimated reward probabilities, and sed-specific changes in the sensitivity to positive versus negative outcomes. Our results show that the combination of models can provide detailed insight into the development of decision making, and into complex cognition more generally.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132299871","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

Reading times and temporo-parietal BOLD activity encode the semantic hierarchy of language prediction 阅读时间和颞顶叶BOLD活动编码语言预测的语义层次

2019 Conference on Cognitive Computational Neuroscience Pub Date : 2019-09-01 DOI: 10.32470/ccn.2019.1333-0

L. Schmitt, J. Erb, Sarah Tune, A. Rysop, G. Hartwigsen, J. Obleser

{"title":"Reading times and temporo-parietal BOLD activity encode the semantic hierarchy of language prediction","authors":"L. Schmitt, J. Erb, Sarah Tune, A. Rysop, G. Hartwigsen, J. Obleser","doi":"10.32470/ccn.2019.1333-0","DOIUrl":"https://doi.org/10.32470/ccn.2019.1333-0","url":null,"abstract":"When poor acoustics challenge speech comprehension, listeners are thought to increasingly draw on semantic context to predict upcoming speech. However, previous research focused mostly on speech material with short timescales of context (e.g., isolated sentences). In an fMRI experiment, 30 participants listened to a one-hour narrative incorporating a multitude of timescales while confronted with competing resynthesized natural sounds. We modeled semantic predictability at five timescales of increasing context length by computing the similarity between word embeddings. An encoding model revealed that short informative timescales are coupled to increased activity in the posterior portion of superior temporal gyrus, whereas long informative timescales are coupled to increased activity in parietal regions like the angular gyrus. In a second experiment, we probed the behavioral relevance of semantic timescales in language prediction: 11 participants performed a self-paced reading task on a text version of the narrative. Reading times sped up for the shortest informative timescale, but also tended to speed up for the longest informative timescales. Our results suggest that short-term dependencies as well as the gist of a story drive behavioral processing fluency and engage a temporo-parietal processing hierarchy.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131728655","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: 0

Tracking Naturalistic Linguistic Predictions with Deep Neural Language Models 用深度神经语言模型跟踪自然语言预测

2019 Conference on Cognitive Computational Neuroscience Pub Date : 2019-09-01 DOI: 10.32470/CCN.2019.1096-0

Micha Heilbron, Benedikt V. Ehinger, P. Hagoort, F. P. Lange

{"title":"Tracking Naturalistic Linguistic Predictions with Deep Neural Language Models","authors":"Micha Heilbron, Benedikt V. Ehinger, P. Hagoort, F. P. Lange","doi":"10.32470/CCN.2019.1096-0","DOIUrl":"https://doi.org/10.32470/CCN.2019.1096-0","url":null,"abstract":"Prediction in language has traditionally been studied using simple designs in which neural responses to expected and unexpected words are compared in a categorical fashion. However, these designs have been contested as being `prediction encouraging', potentially exaggerating the importance of prediction in language understanding. A few recent studies have begun to address these worries by using model-based approaches to probe the effects of linguistic predictability in naturalistic stimuli (e.g. continuous narrative). However, these studies so far only looked at very local forms of prediction, using models that take no more than the prior two words into account when computing a word's predictability. Here, we extend this approach using a state-of-the-art neural language model that can take roughly 500 times longer linguistic contexts into account. Predictability estimates from the neural network offer a much better fit to EEG data from subjects listening to naturalistic narrative than simpler models, and reveal strong surprise responses akin to the P200 and N400. These results show that predictability effects in language are not a side-effect of simple designs, and demonstrate the practical use of recent advances in AI for the cognitive neuroscience of language.","PeriodicalId":281121,"journal":{"name":"2019 Conference on Cognitive Computational Neuroscience","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124481344","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: 14