scholarly journals The Hippocampus Generalizes across Memories that Share Item and Context Information

2019 ◽  
Vol 31 (1) ◽  
pp. 24-35 ◽  
Author(s):  
Laura A. Libby ◽  
Zachariah M. Reagh ◽  
Nichole R. Bouffard ◽  
J. Daniel Ragland ◽  
Charan Ranganath
Keyword(s):  
Episodic Memory ◽  
Multivariate Analyses ◽  
Contextual Information ◽  
Activity Patterns ◽  
Context Information ◽  
Major Area ◽  
Hippocampal Activity ◽  
Similar Item ◽  
Single Attribute ◽  
The Way

Episodic memory is known to rely on the hippocampus, but how the hippocampus organizes different episodes to permit their subsequent retrieval remains controversial. One major area of debate hinges on a discrepancy between two hypothesized roles of the hippocampus: differentiating between similar events to reduce interference and assigning similar representations to events that share overlapping items and contextual information. Here, we used multivariate analyses of activity patterns measured with fMRI to characterize how the hippocampus distinguishes between memories based on similarity at the level of items and/or context. Hippocampal activity patterns discriminated between events that shared either item or context information but generalized across events that shared similar item–context associations. The current findings provide evidence that, whereas the hippocampus can reduce mnemonic interference by separating events that generalize along a single attribute dimension, overlapping hippocampal codes may support memory for events with overlapping item–context relations. This lends new insights into the way the hippocampus may balance multiple mnemonic operations in adaptively guiding behavior.

scholarly journals The hippocampus generalizes across memories that share item and context information

2016 ◽  
Author(s):  
Laura A. Libby ◽  
J. Daniel Ragland ◽  
Charan Ranganath
Keyword(s):  
Magnetic Resonance Imaging ◽  
Multivariate Analyses ◽  
Activity Patterns ◽  
Alternative View ◽  
Context Information ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Hippocampal Activity ◽  
Similar Item ◽  
Single Attribute

ABSTRACTEpisodic memory is known to rely on the hippocampus, but how the hippocampus organizes different episodes to permit their subsequent retrieval remains controversial. According to one view, hippocampal coding differentiates between similar events to reduce interference, whereas an alternative view is that the hippocampus assigns similar representations to events that share item and context information. Here, we used multivariate analyses of activity patterns measured with functional magnetic resonance imaging (fMRI) to characterize how the hippocampus distinguishes between memories based on similarity of their item and/or context information. Hippocampal activity patterns discriminated between events that shared either item or context information, but generalized across events that shared similar item-context associations. The current findings provide novel evidence that, whereas the hippocampus can resist mnemonic interference by separating events that generalize along a single attribute dimension, overlapping hippocampal codes may support memory for events with overlapping item-context relations.

scholarly journals Hippocampal and cortical mechanisms at retrieval explain variability in episodic remembering in older adults

2020 ◽  
Author(s):  
Alexandra N. Trelle ◽  
Valerie A. Carr ◽  
Scott A. Guerin ◽  
Monica K. Thieu ◽  
Manasi Jayakumar ◽  
...  
Keyword(s):  
Older Adults ◽  
Episodic Memory ◽  
Multivariate Analyses ◽  
Memory Decline ◽  
Cognitively Normal ◽  
Pattern Completion ◽  
Age Related ◽  
Hippocampal Activity ◽  
Fundamental Process ◽  
Individual Memory

Age-related episodic memory decline is characterized by striking heterogeneity across individuals. Hippocampal pattern completion is a fundamental process supporting episodic memory. Yet, the degree to which this mechanism is impaired with age, and contributes to variability in episodic memory, remains unclear. We combine univariate and multivariate analyses of fMRI data from a large cohort of cognitively normal older adults (N=100; 60-82 yrs) to measure hippocampal activity and cortical reinstatement during retrieval of trial-unique associations. Trial-wise analyses revealed that hippocampal activity predicted cortical reinstatement strength, and these two metrics of pattern completion independently predicted retrieval success. However, increased age weakened cortical reinstatement and its relationship to memory behaviour. Critically, individual differences in the strength of hippocampal activity and cortical reinstatement explained unique variance in performance across multiple assays of episodic memory. These results indicate that fMRI indices of hippocampal pattern completion explain within- and across-individual memory variability in older adults.

scholarly journals CA1 and CA3 differentially support spontaneous retrieval of episodic contexts within human hippocampal subfields

2017 ◽  
Author(s):  
Halle R. Dimsdale-Zucker ◽  
Maureen Ritchey ◽  
Arne D. Ekstrom ◽  
Andrew P. Yonelinas ◽  
Charan Ranganath
Keyword(s):  
Episodic Memory ◽  
Multivariate Analyses ◽  
Contextual Information ◽  
Critical Role ◽  
Mechanistic Models ◽  
Opposite Pattern ◽  
Hippocampal Subfields ◽  
Neural Representations ◽  
Pattern Similarity ◽  
Spontaneous Retrieval

AbstractThe hippocampus plays a critical role in spatial and episodic memory. Mechanistic models predict that hippocampal subfields have computational specializations that differentially support memory. However, there is little empirical evidence suggesting differences between the subfields, particularly in humans. To clarify how hippocampal subfields support human spatial and episodic memory, we developed a virtual reality paradigm where participants passively navigated through houses (spatial contexts) across a series of videos (episodic contexts). We then used multivariate analyses of high-resolution fMRI data to identify neural representations of contextual information during recollection. Multi-voxel pattern similarity analyses revealed that CA1 represented objects that shared an episodic context as more similar than those from different episodic contexts. CA23DG showed the opposite pattern, differentiating between objects encountered in the same episodic context. The complementary characteristics of these subfields explain how we can parse our experiences into cohesive episodes while retaining the specific details that support vivid recollection.

scholarly journals Encoding and Recall of Spatio-Temporal Episodic Memory in Real Time

2017 ◽  
Author(s):  
Poo-Hee Chang ◽  
Ah-Hwee Tan
Keyword(s):  
Episodic Memory ◽  
Real Time ◽  
State Of The Art ◽  
Contextual Information ◽  
Activity Patterns ◽  
Memory Model ◽  
Data Set ◽  
Episodic Events ◽  
Spatio Temporal ◽  
High Level

Episodic memory enables a cognitive system to improve its performance by reflecting upon past events. In this paper, we propose a computational model called STEM for encoding and recall of episodic events together with the associated contextual information in real time. Based on a class of self-organizing neural networks, STEM is designed to learn memory chunks or cognitive nodes, each encoding a set of co-occurring multi-modal activity patterns across multiple pattern channels. We present algorithms for recall of events based on partial and inexact input patterns. Our empirical results based on a public domain data set show that STEM displays a high level of efficiency and robustness in encoding and retrieval with both partial and noisy search cues when compared with a state-of-the-art associative memory model.

Minimizing Bias in Forensic Handwriting Examinations

2015 ◽  
Vol 25 ◽  
pp. 17-26 ◽  
Author(s):  
L. C. Alewijnse ◽  
E.J.A.T. Mattijssen ◽  
R.D. Stoel
Keyword(s):  
Decision Making ◽  
Information Management ◽  
Contextual Information ◽  
Context Information ◽  
Decision Making Process ◽  
Potential Bias ◽  
Potential Influence ◽  
Management Procedures

The purpose of this paper is to contribute to the increasing awareness about the potential bias on the interpretation and conclusions of forensic handwriting examiners (FHEs) by contextual information. We briefly provide the reader with an overview of relevant types of bias, the difficulties associated with studying bias, the sources of bias and their potential influence on the decision making process in casework, and solutions to minimize bias in casework. We propose that the limitations of published studies on bias need to be recognized and that their conclusions must be interpreted with care. Instead of discussing whether bias is an issue in casework, the forensic handwriting community should actually focus on how bias can be minimized in practice. As some authors have already shown (e.g., Found & Ganas, 2014), it is relatively easy to implement context information management procedures in practice. By introducing appropriate procedures to minimize bias, not only forensic handwriting examination will be improved, it will also increase the acceptability of the provided evidence during court hearings. Purchase Article - $10

Chinese Image Captioning via Fuzzy Attention-based DenseNet-BiLSTM

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-18 ◽  
Author(s):  
Huimin Lu ◽  
Rui Yang ◽  
Zhenrong Deng ◽  
Yonglin Zhang ◽  
Guangwei Gao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Contextual Information ◽  
Image Features ◽  
Context Information ◽  
Global Information ◽  
Image Description ◽  
Image Captioning ◽  
Image Content ◽  
Single Feature ◽  
Proposed Model

Chinese image description generation tasks usually have some challenges, such as single-feature extraction, lack of global information, and lack of detailed description of the image content. To address these limitations, we propose a fuzzy attention-based DenseNet-BiLSTM Chinese image captioning method in this article. In the proposed method, we first improve the densely connected network to extract features of the image at different scales and to enhance the model’s ability to capture the weak features. At the same time, a bidirectional LSTM is used as the decoder to enhance the use of context information. The introduction of an improved fuzzy attention mechanism effectively improves the problem of correspondence between image features and contextual information. We conduct experiments on the AI Challenger dataset to evaluate the performance of the model. The results show that compared with other models, our proposed model achieves higher scores in objective quantitative evaluation indicators, including BLEU , BLEU , METEOR, ROUGEl, and CIDEr. The generated description sentence can accurately express the image content.

A service-oriented context-awareness reasoning framework and its implementation

2018 ◽  
Vol 36 (6) ◽  
pp. 1114-1134 ◽  
Author(s):  
Xiufeng Cheng ◽  
Jinqing Yang ◽  
Lixin Xia
Keyword(s):  
Context Awareness ◽  
Service Providers ◽  
User Behavior ◽  
Contextual Information ◽  
Sensor Data ◽  
Context Information ◽  
Content Type ◽  
Low Level ◽  
Service Oriented ◽  
High Level

PurposeThis paper aims to propose an extensible, service-oriented framework for context-aware data acquisition, description, interpretation and reasoning, which facilitates the development of mobile applications that provide a context-awareness service.Design/methodology/approachFirst, the authors propose the context data reasoning framework (CDRFM) for generating service-oriented contextual information. Then they used this framework to composite mobile sensor data into low-level contextual information. Finally, the authors exploited some high-level contextual information that can be inferred from the formatted low-level contextual information using particular inference rules.FindingsThe authors take “user behavior patterns” as an exemplary context information generation schema in their experimental study. The results reveal that the optimization of service can be guided by the implicit, high-level context information inside user behavior logs. They also prove the validity of the authors’ framework.Research limitations/implicationsFurther research will add more variety of sensor data. Furthermore, to validate the effectiveness of our framework, more reasoning rules need to be performed. Therefore, the authors may implement more algorithms in the framework to acquire more comprehensive context information.Practical implicationsCDRFM expands the context-awareness framework of previous research and unifies the procedures of acquiring, describing, modeling, reasoning and discovering implicit context information for mobile service providers.Social implicationsSupport the service-oriented context-awareness function in application design and related development in commercial mobile software industry.Originality/valueExtant researches on context awareness rarely considered the generation contextual information for service providers. The CDRFM can be used to generate valuable contextual information by implementing more reasoning rules.

Going Their Separate Ways: Dissociation of Hippocampal and Dorsolateral Prefrontal Activation during Episodic Retrieval and Post-retrieval Processing

2010 ◽  
Vol 22 (3) ◽  
pp. 513-525 ◽  
Author(s):  
Sarah L. Israel ◽  
Tyler M. Seibert ◽  
Michelle L. Black ◽  
James B. Brewer
Keyword(s):  
Episodic Memory ◽  
Memory Retrieval ◽  
Relative Increase ◽  
Network Activity ◽  
Default Network ◽  
Episodic Retrieval ◽  
Retrieval Task ◽  
Paired Associates ◽  
Hippocampal Activity ◽  
Dorsolateral Prefrontal

Hippocampal activity is modulated during episodic memory retrieval. Most consistently, a relative increase in activity during confident retrieval is observed. Dorsolateral prefrontal cortex (DLPFC) is also activated during retrieval, but may be more generally activated during cognitive-control processes. The “default network,” regions activated during rest or internally focused tasks, includes the hippocampus, but not DLPFC. Therefore, DLPFC and the hippocampus should diverge during difficult tasks suppressing the default network. It is unclear, however, whether a difficult episodic memory retrieval task would suppress the default network due to difficulty or activate it due to internally directed attention. We hypothesized that a task requiring episodic retrieval followed by rumination on the retrieved item would increase DLPFC activity, but paradoxically reduce hippocampal activity due to concomitant suppression of the default network. In the present study, blocked and event-related fMRI were used to examine hippocampal activity during episodic memory recollection and postretrieval processing of paired associates. Subjects were asked to make living/nonliving judgments about items visually presented (classify) or items retrieved from memory (recall–classify). Active and passive baselines were used to differentiate task-related activity from default-network activity. During the “recall–classify” task, anterior hippocampal activity was selectively reduced relative to “classify” and baseline tasks, and this activity was inversely correlated with DLPFC. Reaction time was positively correlated with DLPFC activation and default-network/hippocampal suppression. The findings demonstrate that frontal and hippocampal activity are dissociated during difficult episodic retrieval tasks and reveal important considerations for interpreting hippocampal activity associated with successful episodic retrieval.

Chinese POS Disambiguation and Unknown Word Guessing with Lexicalized HMMs

2009 ◽  
pp. 1595-1607
Author(s):  
Guohong Fu ◽  
Kang-Kwong Luke
Keyword(s):  
Contextual Information ◽  
Word Formation ◽  
Morphological Features ◽  
Single Task ◽  
Unknown Word ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Peking University ◽  
The Way

This article presents a lexicalized HMM-based approach to Chinese part-of-speech (POS) disambiguation and unknown word guessing (UWG). In order to explore word-internal morphological features for Chinese POS tagging, four types of pattern tags are defined to indicate the way lexicon words are used in a segmented sentence. Such patterns are combined further with POS tags. Thus, Chinese POS disambiguation and UWG can be unified as a single task of assigning each known word to input a proper hybrid tag. Furthermore, a uniformly lexicalized HMM-based tagger also is developed to perform this task, which can incorporate both internal word-formation patterns and surrounding contextual information for Chinese POS tagging under the framework of HMMs. Experiments on the Peking University Corpus indicate that the tagging precision can be improved with efficiency by the proposed approach.

scholarly journals Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hui Ning ◽  
Qian Li
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Contextual Information ◽  
Rapid Development ◽  
Theoretical Research ◽  
Context Information ◽  
Music Recommendation ◽  
Recommendation Algorithm ◽  
Points Of Interest ◽  
Popularity Prediction

Collaborative filtering technology is currently the most successful and widely used technology in the recommendation system. It has achieved rapid development in theoretical research and practice. It selects information and similarity relationships based on the user’s history and collects others that are the same as the user’s hobbies. User’s evaluation information is to generate recommendations. The main research is the inadequate combination of context information and the mining of new points of interest in the context-aware recommendation process. On the basis of traditional recommendation technology, in view of the characteristics of the context information in music recommendation, a personalized and personalized music based on popularity prediction is proposed. Recommended algorithm is MRAPP (Media Recommendation Algorithm based on Popularity Prediction). The algorithm first analyzes the user’s contextual information under music recommendation and classifies and models the contextual information. The traditional content-based recommendation technology CB calculates the recommendation results and then, for the problem that content-based recommendation technology cannot recommend new points of interest for users, introduces the concept of popularity. First, we use the memory and forget function to reduce the score and then consider user attributes and product attributes to calculate similarity; secondly, we use logistic regression to train feature weights; finally, appropriate weights are used to combine user-based and item-based collaborative filtering recommendation results. Based on the above improvements, the improved collaborative filtering recommendation algorithm in this paper has greatly improved the prediction accuracy. Through theoretical proof and simulation experiments, the effectiveness of the MRAPP algorithm is demonstrated.

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