Dataset Information

Enhancing long-term memory with stimulation tunes visual attention in one trial.

ABSTRACT: Scientists have long proposed that memory representations control the mechanisms of attention that focus processing on the task-relevant objects in our visual field. Modern theories specifically propose that we rely on working memory to store the object representations that provide top-down control over attentional selection. Here, we show that the tuning of perceptual attention can be sharply accelerated after 20 min of noninvasive brain stimulation over medial-frontal cortex. Contrary to prevailing theories of attention, these improvements did not appear to be caused by changes in the nature of the working memory representations of the search targets. Instead, improvements in attentional tuning were accompanied by changes in an electrophysiological signal hypothesized to index long-term memory. We found that this pattern of effects was reliably observed when we stimulated medial-frontal cortex, but when we stimulated posterior parietal cortex, we found that stimulation directly affected the perceptual processing of the search array elements, not the memory representations providing top-down control. Our findings appear to challenge dominant theories of attention by demonstrating that changes in the storage of target representations in long-term memory may underlie rapid changes in the efficiency with which humans can find targets in arrays of objects.

SUBMITTER: Reinhart RM

PROVIDER: S-EPMC4299199 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Publications

Enhancing long-term memory with stimulation tunes visual attention in one trial.

Reinhart Robert M G RM Woodman Geoffrey F GF

Proceedings of the National Academy of Sciences of the United States of America 20141229 2

Scientists have long proposed that memory representations control the mechanisms of attention that focus processing on the task-relevant objects in our visual field. Modern theories specifically propose that we rely on working memory to store the object representations that provide top-down control over attentional selection. Here, we show that the tuning of perceptual attention can be sharply accelerated after 20 min of noninvasive brain stimulation over medial-frontal cortex. Contrary to preva ...[more]

PMID: 25548192

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Project description:BackgroundThe prediction and prevention of influenza is a public health issue of great concern, and the study of timely acquisition of influenza transmission trend has become an important research topic. For achieving more quicker and accurate detection and prediction, the data recorded on the Internet, especially on the search engine from Google or Baidu are widely introduced into this field. Moreover, with the development of intelligent technology and machine learning algorithm, many updated and advanced trend tracking and forecasting methods are also being used in this research problem.MethodsIn this paper, a new recurrent neural network architecture, attention-based long short-term memory model is proposed for influenza surveillance. This is a kind of deep learning model which is trained by processing from Baidu Index series so as to fit the real influenza survey time series. Previous studies on influenza surveillance by Baidu Index mostly used traditional autoregressive moving average model or classical machine learning models such as logarithmic linear regression, support vector regression or multi-layer perception model to fit influenza like illness data, which less considered the deep learning structure. Meanwhile, some new model that considered the deep learning structure did not take into account the application of Baidu index data. This study considers introducing the recurrent neural network with long short-term memory combined with attention mechanism into the influenza surveillance research model, which not only fits the research problems well in model structure, but also provides research methods based on Baidu index.ResultsThe actual survey data and Baidu Index data are used to train and test the proposed attention-based long short-term memory model and the other comparison models, so as to iterate the value of the model parameters, and to describe and predict the influenza epidemic situation. The experimental results show that our proposed model has better performance in the mean absolute error, mean absolute percentage error, index of agreement and other indicators than the other comparison models.ConclusionOur proposed attention-based long short-term memory model vividly verifies the ability of this attention-based long short-term memory structure for better surveillance and prediction the trend of influenza. In comparison with some of the latest models and methods in this research field, the model we proposed is also excellent in effect, even more lightweight and robust. Future research direction can consider fusing multimodal data based on this model and developing more application scenarios.

Dataset Information

Enhancing long-term memory with stimulation tunes visual attention in one trial.

Publications

Enhancing long-term memory with stimulation tunes visual attention in one trial.

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