scholarly journals Video data for the cognitive mapping process of NeuroBayesSLAM system

Data in Brief ◽  
2020 ◽  
Vol 30 ◽  
pp. 105637
Author(s):  
Taiping Zeng ◽  
Bailu Si
Keyword(s):  
Cognitive Mapping ◽  
Video Data ◽  
Mapping Process

scholarly journals Mice identify subgoal locations through an action-driven mapping process

2021 ◽  
Author(s):  
Philip Shamash ◽  
Tiago Branco
Keyword(s):  
Closed Loop ◽  
Cognitive Mapping ◽  
Planning Process ◽  
Spatial Analyses ◽  
Mental Maps ◽  
Starting Position ◽  
Mapping Process ◽  
Biased Diffusion ◽  
Learning As A Process ◽  
The Relationship

Mammals instinctively explore and form mental maps of their spatial environments. Models of cognitive mapping in neuroscience mostly depict map-learning as a process of random or biased diffusion. In practice, however, animals explore spaces using structured, purposeful, sensory-guided actions. Here we test the hypothesis that executing specific exploratory actions is a key strategy for building a cognitive map. Previous work has shown that in arenas with obstacles and a shelter, mice spontaneously learn efficient multi-step escape routes by memorizing allocentric subgoal locations. We thus used threat-evoked escape to probe the relationship between ethological exploratory behavior and allocentric spatial memory. Using closed-loop neural manipulations to interrupt running movements during exploration, we found that blocking runs targeting an obstacle edge abolished subgoal learning. In contrast, blocking other movements while sparing edge-directed runs had no effect on memorizing subgoals. Finally, spatial analyses suggest that the decision to use a subgoal during escape takes into account the mouse's starting position relative to the layout of the environment. We conclude that mice use an action-driven learning process to identify subgoals and that these subgoals are then integrated into a map-based planning process. We suggest a conceptual framework for spatial learning that is compatible with the successor representation from reinforcement learning and sensorimotor enactivism from cognitive science.

School travel modes and children’s spatial cognition

Urban Studies ◽  
2016 ◽  
Vol 54 (7) ◽  
pp. 1578-1600 ◽  
Author(s):  
Jo-Ting Fang ◽  
Jen-Jia Lin
Keyword(s):  
Spatial Cognition ◽  
Spatial Representation ◽  
Cognitive Mapping ◽  
Home School ◽  
Transportation Modes ◽  
New Knowledge ◽  
Mapping Process ◽  
Children's Travel ◽  
Linear Regressions ◽  
Northern Taiwan

This study broadens understanding of how children’s travel modes influence the development of their spatial cognition, specifically the development of their spatial representation of home–school routes. Data were collected using a questionnaire survey and a cognitive mapping process at an elementary school in northern Taiwan. The sample, which comprised 521 Grades 1–6 children aged 7–12 years, was analysed through linear regressions. Empirical results indicate that the use of independent, active or non-motorised transportation modes improved the children’s spatial cognition regarding their home–school routes. This study not only provides new knowledge about the relationships between travel modes and the spatial cognition of children, but also identifies policy directions in relation to school transportation and the development of spatial cognition in children.

Bayesian removal of noise for increased sensitivity in vector pattern recognition lattice imaging of interfaces

1993 ◽  
Vol 51 ◽  
pp. 994-995
Author(s):  
L. Fei ◽  
P. Fraundorf
Keyword(s):  
Pattern Recognition ◽  
Perfect Crystal ◽  
Unit Cell ◽  
Ion Milling ◽  
Beam Radiation ◽  
Major Interest ◽  
Unit Cells ◽  
Mapping Process ◽  
Increased Sensitivity ◽  
Electron Microscopes

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.

Strukturen von Argumentationssequenzen in Gruppen

2002 ◽  
Vol 33 (2) ◽  
pp. 65-81 ◽  
Author(s):  
Elisabeth Brauner ◽  
Bernhard Orth
Keyword(s):  
Cognitive Mapping ◽  
Soziale Repräsentationen

Zusammenfassung: Die sozialpsychologische Gruppenforschung hat in den vergangenen Jahren eine Reihe von Veränderungen erfahren. Hierzu gehören einerseits Verlagerungen inhaltlicher Schwerpunkte der Forschung hin zur Analyse von Informationsverarbeitungsprozessen und andererseits Weiterentwicklungen methodischer Ansätze. Insbesondere Prozessanalysen verbaler Daten werden verstärkt gefordert und auch durchgeführt. Im vorliegenden Beitrag wird gezeigt, dass beiden Trends genüge getan werden kann, indem das Cognitive Mapping ( Axelrod, 1976 ) mit der Monotone Netzwerkanalyse ( Orth, 1998 ) kombiniert wird. Die Stärke beider Methoden liegt hierbei auf der Herausarbeitung von Strukturen von Argumentationen, die in Gruppendiskussionen angebracht werden. Der Ansatz ist außerdem geeignet, soziale Repräsentationen zu untersuchen und abzubilden.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

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