A Study on Human Learning Ability during Classification of Motion and Colour Visual Cues and Their Combination

Abstract The paper presents a study on the human learning process during the classification of stimuli, defined by motion and color visual cues and their combination. Because the classification dimension and the features that define each category are uncertain, we model the learning curves using Bayesian inference and more precisely the Normalized Conjunctive Consensus rule, and also on the base of the more efficient probabilistic Proportional Conflict Redistribution rule No 5 (pPCR5) defined within Dezert-Smarandache Theory (DSmT) of plausible and paradoxical reasoning. Our goal is to study how these rules succeed to model consistently both: human individual and group behaviour during the learning of the associations between the stimuli and the responses in categorization tasks varying by the amount of relevant stimulus information. The effect of age on this process is also evaluated.

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The Ecology of Social Learning in Animals and its Link with Intelligence

The Spanish Journal of Psychology ◽

10.1017/sjp.2016.100 ◽

2016 ◽

Vol 19 ◽

Cited By ~ 4

Author(s):

Carel van Schaik ◽

Sereina Graber ◽

Caroline Schuppli ◽

Judith Burkart

Keyword(s):

Social Learning ◽

Skill Acquisition ◽

Behavioral Ecology ◽

Cultural Intelligence ◽

Learning Ability ◽

Learning Abilities ◽

The Social ◽

In The Wild ◽

Broad Variety

AbstractClassical ethology and behavioral ecology did not pay much attention to learning. However, studies of social learning in nature reviewed here reveal the near-ubiquity of reliance on social information for skill acquisition by developing birds and mammals. This conclusion strengthens the plausibility of the cultural intelligence hypothesis for the evolution of intelligence, which assumes that selection on social learning abilities automatically improves individual learning ability. Thus, intelligent species will generally be cultural species. Direct tests of the cultural intelligence hypothesis require good estimates of the amount and kind of social learning taking place in nature in a broad variety of species. These estimates are lacking so far. Here, we start the process of developing a functional classification of social learning, in the form of the social learning spectrum, which should help to predict the mechanisms of social learning involved. Once validated, the categories can be used to estimate the cognitive demands of social learning in the wild.

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Deep Feature Fusion with Integration of Residual Connection and Attention Model for Classification of VHR Remote Sensing Images

Remote Sensing ◽

10.3390/rs11131617 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1617 ◽

Cited By ~ 5

Author(s):

Jicheng Wang ◽

Li Shen ◽

Wenfan Qiao ◽

Yanshuai Dai ◽

Zhilin Li

Keyword(s):

Remote Sensing ◽

Feature Fusion ◽

Learning Ability ◽

Remote Sensing Images ◽

Convolutional Network ◽

Fully Convolutional Network ◽

Attention Model ◽

Low Level ◽

Deep Feature

The classification of very-high-resolution (VHR) remote sensing images is essential in many applications. However, high intraclass and low interclass variations in these kinds of images pose serious challenges. Fully convolutional network (FCN) models, which benefit from a powerful feature learning ability, have shown impressive performance and great potential. Nevertheless, only classification results with coarse resolution can be obtained from the original FCN method. Deep feature fusion is often employed to improve the resolution of outputs. Existing strategies for such fusion are not capable of properly utilizing the low-level features and considering the importance of features at different scales. This paper proposes a novel, end-to-end, fully convolutional network to integrate a multiconnection ResNet model and a class-specific attention model into a unified framework to overcome these problems. The former fuses multilevel deep features without introducing any redundant information from low-level features. The latter can learn the contributions from different features of each geo-object at each scale. Extensive experiments on two open datasets indicate that the proposed method can achieve class-specific scale-adaptive classification results and it outperforms other state-of-the-art methods. The results were submitted to the International Society for Photogrammetry and Remote Sensing (ISPRS) online contest for comparison with more than 50 other methods. The results indicate that the proposed method (ID: SWJ_2) ranks #1 in terms of overall accuracy, even though no additional digital surface model (DSM) data that were offered by ISPRS were used and no postprocessing was applied.

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Classification of Gold-Bearing Particles Using Visual Cues and Cost-Sensitive Machine Learning

Mathematical Geosciences ◽

10.1007/s11004-015-9597-7 ◽

2015 ◽

Vol 47 (5) ◽

pp. 521-545 ◽

Cited By ~ 8

Author(s):

Tom Horrocks ◽

Daniel Wedge ◽

Eun-Jung Holden ◽

Peter Kovesi ◽

Nick Clarke ◽

...

Keyword(s):

Machine Learning ◽

Visual Cues ◽

Gold Bearing

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Middle Cerebral Artery Bifurcation Aneurysms: An Anatomic Classification Scheme for Planning Optimal Surgical Strategies

Operative Neurosurgery ◽

10.1227/neu.0000000000000250 ◽

2013 ◽

Vol 10 (1) ◽

pp. 145-155 ◽

Cited By ~ 3

Author(s):

Chad W. Washington ◽

Tao Ju ◽

Gregory J. Zipfel ◽

Ralph G. Dacey

Keyword(s):

Middle Cerebral Artery ◽

Cerebral Artery ◽

Visual Cues ◽

Classification Scheme ◽

Parent Vessel ◽

3 Dimensional ◽

Shape Pattern ◽

Angiographic Data ◽

The Relationship

Abstract BACKGROUND: Changing landscapes in neurosurgical training and increasing use of endovascular therapy have led to decreasing exposure in open cerebrovascular neurosurgery. To ensure the effective transition of medical students into competent practitioners, new training paradigms must be developed. OBJECTIVE: Using principles of pattern recognition, we created a classification scheme for middle cerebral artery (MCA) bifurcation aneurysms that allows their categorization into a small number of shape pattern groups. METHODS: Angiographic data from patients with MCA aneurysms between 1995 and 2012 were used to construct 3-dimensional models. Models were then analyzed and compared objectively by assessing the relationship between the aneurysm sac, parent vessel, and branch vessels. Aneurysms were then grouped on the basis of the similarity of their shape patterns in such a way that the in-class similarities were maximized while the total number of categories was minimized. For each category, a proposed clip strategy was developed. RESULTS: From the analysis of 61 MCA bifurcation aneurysms, 4 shape pattern categories were created that allowed the classification of 56 aneurysms (91.8%). The number of aneurysms allotted to each shape cluster was 10 (16.4%) in category 1, 24 (39.3%) in category 2, 7 (11.5%) in category 3, and 15 (24.6%) in category 4. CONCLUSION: This study demonstrates that through the use of anatomic visual cues, MCA bifurcation aneurysms can be grouped into a small number of shape patterns with an associated clip solution. Implementing these principles within current neurosurgery training paradigms can provide a tool that allows more efficient transition from novice to cerebrovascular expert.

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Classification of abnormal children: Discrimination learning ability

Journal of Autism and Developmental Disorders ◽

10.1007/bf02414817 ◽

1980 ◽

Vol 10 (4) ◽

pp. 405-415 ◽

Cited By ~ 4

Author(s):

C. William Deckner ◽

Richard L. Blanton

Keyword(s):

Discrimination Learning ◽

Learning Ability

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Actin Reorganization in Hippocampal Neurons May Play Roles in Early Impairment of Learning and Memory in Rats After Propofol Anesthesia

10.21203/rs.3.rs-30634/v1 ◽

2020 ◽

Author(s):

Xuena Zhang ◽

Jie Li ◽

ANSHI WU

Keyword(s):

Dendritic Spines ◽

Hippocampal Neurons ◽

Laser Scanning ◽

Learning Curves ◽

Learning Ability ◽

Male Rats ◽

Actin Reorganization ◽

Learning Evaluation ◽

Old Rats ◽

Learning Impairment

Abstract Background Perioperative neurocognitive disorder (PND) is a kind of neuronal complication especially observed in elderly patients. The present study was conducted to observe the changes of actin in hippocampus after propofol anesthesia, and to evaluate their roles in consequent learning impairment in both young (3-month-old) and aged (20-month-old) male rats.Methods Double-shuttle box was used to learning evaluation since1, 3, 7 or 14d after anesthesia. Hippocampi were removed after evaluations. F-actin content and the spines were observed through immunofluorescence and laser scanning confocal microscope (LSCM).Results In young rats, latency of escape response (LER) was prolonged within 3 days after anesthesia. However, LER was significantly prolonged until 7days after anesthesia in elderly rats. Moreover, the learning curves were also shift in old rats. Dendritic spines became smaller in anesthesia groups of aged rats within 3 days, where the F-actin contents were significantly increased until 14d post anesthesia.Conclusion Our results indicate that learning ability could be inhibited until after propofol anesthesia especially in old rats. The over-polymerization of actin and the consequent reorganization of dendrite spines in hippocampus may be responsible, which provides fresh evidence in aspect of synaptic plasticity for PND mechanism.

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INFORMATION AND MEMORY-BASED ANALYSIS FOR DECODING OF THE HUMAN LEARNING BETWEEN NORMAL AND VIRTUAL REALITY (VR) CONDITIONS

Fractals ◽

10.1142/s0218348x21501632 ◽

2021 ◽

Vol 29 (03) ◽

pp. 2150163

Author(s):

HAMIDREZA NAMAZI ◽

MOHAMMAD HOSSEIN BABINI ◽

KAMIL KUCA ◽

ONDREJ KREJCAR

Keyword(s):

Virtual Reality ◽

Shannon Entropy ◽

Mathematical Analysis ◽

Strong Correlation ◽

Hurst Exponent ◽

Human Learning ◽

Learning Ability ◽

Eeg Signals ◽

2D And 3D ◽

Electroencephalogram Eeg

In this paper, we investigated the learning ability of students in normal versus virtual reality (VR) watching of videos by mathematical analysis of electroencephalogram (EEG) signals. We played six videos in the 2D and 3D modes for nine subjects and calculated the Shannon entropy of recorded EEG signals to investigate how much their embedded information changes between these modes. We also calculated the Hurst exponent of EEG signals to compare the changes in the memory of signals. The analysis results showed that watching the videos in a VR condition causes greater information and memory in EEG signals. A strong correlation was obtained between the increment of information and memory of EEG signals. These increments also have been verified based on the answers that subjects gave to the questions about the content of videos. Therefore, we can say that when subjects watch a video in a VR condition, more information is transferred to their brains that cause increments in their memory.

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Application Research of Personal Learning Curve in Staff Skill Testing

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.3200 ◽

2012 ◽

Vol 433-440 ◽

pp. 3200-3205

Author(s):

Yan Wei Du ◽

Xian Long Xu

Keyword(s):

Learning Curve ◽

Operation Time ◽

Working Hours ◽

Scientific Basis ◽

Learning Curves ◽

Learning Ability ◽

Forming Process ◽

Personal Learning ◽

And Performance ◽

Definition Of

According to the characteristics of many varieties small batch production, the assembly workers with operating accurate, coordination and strong learning ability are needed in a manual assembly line. Some scientific operating skills test is used to evaluate staff in recruitment, working hours formulate and performance evaluation. The origins and definition of learning curve were introduced. The characteristics of operation skills were analyzed. The mirror painting instrument was used to test the learning ability of workers for new operation skills. Workers were asked to face the graphics within the mirror, and to depict the graphics using the testing pen. The mistake number and the operation time were recorded. Learning curves were drawn out. Everyone worker movement skills forming process was analyzed on the basis of the above data. The test process and analyze conclusion provide the scientific basis and the decision support for the enterprises.

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Feedback and human learning: Preliminary insights from disengaged students

Human Systems Management ◽

10.3233/hsm-201148 ◽

2021 ◽

pp. 1-13

Author(s):

Kongkiti Phusavat ◽

Zbigniew Pastuszak ◽

Achmad Nizar Hidayanto ◽

Jukka Majava

Keyword(s):

Human Learning ◽

Business Community ◽

Learning Ability ◽

Traditional Practices ◽

Constructive Feedback ◽

External Feedback ◽

Indirect Feedback ◽

Traditional Paradigm

BACKGROUND: How to reconnect the disengaged learners has been a major challenge for human learning. Motivating the disengaged learners through traditional interventions has not been effective. OBJECTIVE: The study aims to examine whether feedback from an external unit would be more persuasive for the disengaged learners. The perception on a lack of learning stems from poor attitude of learning, poor behavior, laziness, and lack of learning ability and attention. METHODS: A foreign business community has collaborated with two Bangkok Metropolitan Administration schools since 2016 on creating constructive and indirect feedback. There were 337 students from both schools participated in the survey. 163 students participated in the revised practices while 174 students attended the traditional practices. RESULTS: The results show the gap between the two groups on the effects from constructive and indirect feedback. The disengaged students from the revised pedagogy show that they are attracted to constructive feedback and indirect feedback more. CONCLUSIONS: The findings show that, unlike the traditional paradigm, the disengaged students are perceptive to external feedback. The findings show some consistency with previous studies. Integrating external feedback can attract the attention from the disengaged students which could potentially contribute to human learning.

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Residual Dense Network Based on Channel-Spatial Attention for the Scene Classification of a High-Resolution Remote Sensing Image

Remote Sensing ◽

10.3390/rs12111887 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1887 ◽

Cited By ~ 5

Author(s):

Xiaolei Zhao ◽

Jing Zhang ◽

Jimiao Tian ◽

Li Zhuo ◽

Jie Zhang

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Spatial Attention ◽

Remote Sensing Image ◽

Learning Ability ◽

Aerial Image ◽

Dense Network ◽

Scene Classification ◽

Training Requirements

The scene classification of a remote sensing image has been widely used in various fields as an important task of understanding the content of a remote sensing image. Specially, a high-resolution remote sensing scene contains rich information and complex content. Considering that the scene content in a remote sensing image is very tight to the spatial relationship characteristics, how to design an effective feature extraction network directly decides the quality of classification by fully mining the spatial information in a high-resolution remote sensing image. In recent years, convolutional neural networks (CNNs) have achieved excellent performance in remote sensing image classification, especially the residual dense network (RDN) as one of the representative networks of CNN, which shows a stronger feature learning ability as it fully utilizes all the convolutional layer information. Therefore, we design an RDN based on channel-spatial attention for scene classification of a high-resolution remote sensing image. First, multi-layer convolutional features are fused with residual dense blocks. Then, a channel-spatial attention module is added to obtain more effective feature representation. Finally, softmax classifier is applied to classify the scene after adopting data augmentation strategy for meeting the training requirements of the network parameters. Five experiments are conducted on the UC Merced Land-Use Dataset (UCM) and Aerial Image Dataset (AID), and the competitive results demonstrate that our method can extract more effective features and is more conducive to classifying a scene.

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