scholarly journals Generalization of motor learning in psychological space

2021 ◽  
Author(s):  
Eugene Poh ◽  
Naser Al-Fawakari ◽  
Rachel Tam ◽  
Jordan A. Taylor ◽  
Samuel D. McDougle
Keyword(s):  
Motor Learning ◽  
Human Behavior ◽  
Visuomotor Adaptation ◽  
Movement Kinematics ◽  
Top Down ◽  
Target Shape ◽  
Motor Behaviors ◽  
Inference Problems ◽  
Similarity Ratings ◽  
High Level

ABSTRACTTo generate adaptive movements, we must generalize what we have previously learned to novel situations. The generalization of learned movements has typically been framed as a consequence of neural tuning functions that overlap for similar movement kinematics. However, as is true in many domains of human behavior, situations that require generalization can also be framed as inference problems. Here, we attempt to broaden the scope of theories about motor generalization, hypothesizing that part of the typical motor generalization function can be characterized as a consequence of top-down decisions about different movement contexts. We tested this proposal by having participants make explicit similarity ratings over traditional contextual dimensions (movement directions) and abstract contextual dimensions (target shape), and perform a visuomotor adaptation generalization task where trials varied over those dimensions. We found support for our predictions across five experiments, which revealed a tight link between subjective similarity and motor generalization. Our findings suggest that the generalization of learned motor behaviors is influenced by both low-level kinematic features and high-level inferences.

CSim 2

2021 ◽  
Vol 43 (1) ◽  
pp. 1-46
Author(s):  
David Sanan ◽  
Yongwang Zhao ◽  
Shang-Wei Lin ◽  
Liu Yang
Keyword(s):  
Programming Languages ◽  
Concurrent Systems ◽  
Theorem Prover ◽  
Compositional Techniques ◽  
Machine Code ◽  
Top Down ◽  
Hol Theorem Prover ◽  
High Level ◽  
High Degree

To make feasible and scalable the verification of large and complex concurrent systems, it is necessary the use of compositional techniques even at the highest abstraction layers. When focusing on the lowest software abstraction layers, such as the implementation or the machine code, the high level of detail of those layers makes the direct verification of properties very difficult and expensive. It is therefore essential to use techniques allowing to simplify the verification on these layers. One technique to tackle this challenge is top-down verification where by means of simulation properties verified on top layers (representing abstract specifications of a system) are propagated down to the lowest layers (that are an implementation of the top layers). There is no need to say that simulation of concurrent systems implies a greater level of complexity, and having compositional techniques to check simulation between layers is also desirable when seeking for both feasibility and scalability of the refinement verification. In this article, we present CSim 2 a (compositional) rely-guarantee-based framework for the top-down verification of complex concurrent systems in the Isabelle/HOL theorem prover. CSim 2 uses CSimpl, a language with a high degree of expressiveness designed for the specification of concurrent programs. Thanks to its expressibility, CSimpl is able to model many of the features found in real world programming languages like exceptions, assertions, and procedures. CSim 2 provides a framework for the verification of rely-guarantee properties to compositionally reason on CSimpl specifications. Focusing on top-down verification, CSim 2 provides a simulation-based framework for the preservation of CSimpl rely-guarantee properties from specifications to implementations. By using the simulation framework, properties proven on the top layers (abstract specifications) are compositionally propagated down to the lowest layers (source or machine code) in each concurrent component of the system. Finally, we show the usability of CSim 2 by running a case study over two CSimpl specifications of an Arinc-653 communication service. In this case study, we prove a complex property on a specification, and we use CSim 2 to preserve the property on lower abstraction layers.

scholarly journals Pursuing Artful Movement Science in Music Performance: Single Subject Motor Analysis With Two Elite Pianists

2021 ◽  
pp. 003151252110034
Author(s):  
Craig Turner ◽  
Peter Visentin ◽  
Deanna Oye ◽  
Scott Rathwell ◽  
Gongbing Shan
Keyword(s):  
Motor Learning ◽  
Music Performance ◽  
Force Plate ◽  
Left Shoulder ◽  
Single Subject ◽  
Performance Variables ◽  
Motor Behaviors ◽  
Piano Students ◽  
Motor Strategy ◽  
The Right

Piano performance motor learning research requires more “artful” methodologies if it is to meaningfully address music performance as a corporeal art. To date, research has been sparse and it has typically constrained multiple performance variables in order to isolate specific phenomena. This approach has denied the fundamental ethos of music performance which, for elite performers, is an act of interpretation, not mere reproduction. Piano performances are intentionally manipulated for artistic expression. We documented motor movements in the complex task of performance of the first six measures of Chopin’s “Revolutionary” Etude by two anthropometrically different elite pianists. We then discussed their motor strategy selections as influenced by anthropometry and the composer’s musical directives. To quantify the joint angles of the trunk, shoulders, elbows, and wrists, we used a VICON 3 D motion capture system and biomechanical modeling. A Kistler force plate (1 N, Swiss) quantified center of gravity (COG) shifts. Changes in COG and trunk angles had considerable influence on the distal segments of the upper limbs. The shorter pianist used an anticipatory strategy, employing larger shifts in COG and trunk angles to produce dynamic stability as compensation for a smaller stature. Both pianists took advantage of low inertial left shoulder internal rotation and adduction to accommodate large leaps in the music. For the right arm, motor strategizing was confounded by rests in the music. These two cases illustrated, in principle, that expert pianists’ individualized motor behaviors can be explained as compensatory efforts to accommodate both musical goals and anthropometric constraints. Motor learning among piano students can benefit from systematic attention to motor strategies that consider both of these factors.

Learning human poses in natural scenes

2018 ◽  
Author(s):  
◽  
Guanghan Ning
Keyword(s):  
Computer Vision ◽  
Pose Estimation ◽  
The Body ◽  
Human Pose Estimation ◽  
Natural Scenes ◽  
Top Down ◽  
University Of Missouri ◽  
Single Person ◽  
Human Pose ◽  
High Level

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The task of human pose estimation in natural scenes is to determine the precise pixel locations of body keypoints. It is very important for many high-level computer vision tasks, including action and activity recognition, human-computer interaction, motion capture, and animation. We cover two different approaches for this task: top-down approach and bottom-up approach. In the top-down approach, we propose a human tracking method called ROLO that localizes each person. We then propose a state-of-the-art single-person human pose estimator that predicts the body keypoints of each individual. In the bottomup approach, we propose an efficient multi-person pose estimator with which we participated in a PoseTrack challenge [11]. On top of these, we propose to employ adversarial training to further boost the performance of single-person human pose estimator while generating synthetic images. We also propose a novel PoSeg network that jointly estimates the multi-person human poses and semantically segment the portraits of these persons at pixel-level. Lastly, we extend some of the proposed methods on human pose estimation and portrait segmentation to the task of human parsing, a more finegrained computer vision perception of humans.

scholarly journals Inferring Emotion Tags from Object Images Using Convolutional Neural Network

2020 ◽  
Vol 10 (15) ◽  
pp. 5333
Author(s):  
Anam Manzoor ◽  
Waqar Ahmad ◽  
Muhammad Ehatisham-ul-Haq ◽  
Abdul Hannan ◽  
Muhammad Asif Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Human Behavior ◽  
Real Life ◽  
Vital Role ◽  
Object Categories ◽  
Different Types ◽  
Gender Based ◽  
High Level ◽  
Human Behavior Analysis

Emotions are a fundamental part of human behavior and can be stimulated in numerous ways. In real-life, we come across different types of objects such as cake, crab, television, trees, etc., in our routine life, which may excite certain emotions. Likewise, object images that we see and share on different platforms are also capable of expressing or inducing human emotions. Inferring emotion tags from these object images has great significance as it can play a vital role in recommendation systems, image retrieval, human behavior analysis and, advertisement applications. The existing schemes for emotion tag perception are based on the visual features, like color and texture of an image, which are poorly affected by lightning conditions. The main objective of our proposed study is to address this problem by introducing a novel idea of inferring emotion tags from the images based on object-related features. In this aspect, we first created an emotion-tagged dataset from the publicly available object detection dataset (i.e., “Caltech-256”) using subject evaluation from 212 users. Next, we used a convolutional neural network-based model to automatically extract the high-level features from object images for recognizing nine (09) emotion categories, such as amusement, awe, anger, boredom, contentment, disgust, excitement, fear, and sadness. Experimental results on our emotion-tagged dataset endorse the success of our proposed idea in terms of accuracy, precision, recall, specificity, and F1-score. Overall, the proposed scheme achieved an accuracy rate of approximately 85% and 79% using top-level and bottom-level emotion tagging, respectively. We also performed a gender-based analysis for inferring emotion tags and observed that male and female subjects have discernment in emotions perception concerning different object categories.

scholarly journals Top-down and bottom-up processes during observation: Implications for motor learning

2012 ◽  
Vol 14 (sup1) ◽  
pp. S250-S256 ◽  
Author(s):  
James W. Roberts ◽  
Simon J. Bennett ◽  
Digby Elliott ◽  
Spencer J. Hayes
Keyword(s):  
Motor Learning ◽  
Top Down ◽  
Bottom Up

TOP-DOWN DESIGN OF BULK-SYNCHRONOUS PARALLEL PROGRAMS

2003 ◽  
Vol 13 (03) ◽  
pp. 389-400 ◽  
Author(s):  
YIFENG CHEN ◽  
J. W. SANDERS
Keyword(s):  
Program Development ◽  
Parallel Programs ◽  
Top Down ◽  
Bulk Synchronous Parallel ◽  
Global Synchrony ◽  
High Level

This paper studies top-down program development techniques for Bulk-Synchronous Parallelism. In that context a specification formalism LOGS, for 'the Logic of Global Synchrony', has been proposed for the specification and high-level development of BSP designs. This paper extends the use of LOGS to provide support for the protection of local variables in BSP programs, thus completing the link between specifications and programs.

scholarly journals Effectiveness of Growth Enhancement Support Scheme (GESS) in Input Delivery in Southwest Nigeria

2019 ◽  
pp. 1-11
Author(s):  
Ibitunde Ibidun Olatohun ◽  
Farinde Akinloye Jimoh ◽  
Adereti Francis Oke
Keyword(s):  
Sampling Technique ◽  
Small Scale ◽  
Growth Enhancement ◽  
Top Down ◽  
Chi Square ◽  
Southwestern Nigeria ◽  
The Government ◽  
Small Scale Farmers ◽  
High Level ◽  
Chi Square Analysis

The study identified the problems of access to inputs by the small-scale farmers; and analyzed the structure and operations of the Growth Enhancement Support Scheme (GESS) on input supply to small-scale farmers in Southwestern Nigeria with the view to investigate the effectiveness of GESS in South western Nigeria. A multistage sampling technique was employed in selecting 420 GESS farmers. The interview schedule was used to collect data which were subjected to descriptive and inferential analysis to test the hypothesis. Results showed that the mean age of the small-scale farmers was 49.57±10.49 years and a high level, 75.70 per cent were males. A higher percentage (55.80%) showed a high level of identified problems of access to inputs. Analysis of the structure and operations of GESS on input supply showed that GESS was structured and operated by the government among the various stakeholders using the top-down approach. Out of the nineteen GESS effectiveness indicators, none was effective at solving the problems of inputs delivery to the respondents. Chi-square analysis showed a significant association between the effectiveness of GESS and respondents' sex (χ2=46.159; p≤ 0.01). Correlation analysis showed a negative and significant relationship between the effectiveness of GESS and identified problems of access to inputs (r=-0.214, p≤0.001). It was concluded that GESS recorded a low level of effectiveness of GESS in the study area as a result of the high level of identified problems of access to agricultural inputs through GESS. The study therefore recommends that there should be better orientation for future likely programmes and a reorientation of the farmers about the GESS in which there will be more extensive sensitization and enlightenment, especially at the grassroots level, also that quantity of input supply be increased and that more inclusive participatory approach instead of top-down approach should be adopted for planning, execution and evaluation of the GESS programme.

scholarly journals Characterization of Feedback Neurons in the High-Level Visual Cortical Areas That Project Directly to the Primary Visual Cortex in the Cat

2021 ◽  
Vol 14 ◽  
Author(s):  
Huijun Pan ◽  
Shen Zhang ◽  
Deng Pan ◽  
Zheng Ye ◽  
Hao Yu ◽  
...  
Keyword(s):  
Information Processing ◽  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Information ◽  
Higher Order ◽  
Top Down ◽  
Cortical Areas ◽  
Area 21A ◽  
High Level ◽  
Visual Cortical

Previous studies indicate that top-down influence plays a critical role in visual information processing and perceptual detection. However, the substrate that carries top-down influence remains poorly understood. Using a combined technique of retrograde neuronal tracing and immunofluorescent double labeling, we characterized the distribution and cell type of feedback neurons in cat’s high-level visual cortical areas that send direct connections to the primary visual cortex (V1: area 17). Our results showed: (1) the high-level visual cortex of area 21a at the ventral stream and PMLS area at the dorsal stream have a similar proportion of feedback neurons back projecting to the V1 area, (2) the distribution of feedback neurons in the higher-order visual area 21a and PMLS was significantly denser than in the intermediate visual cortex of area 19 and 18, (3) feedback neurons in all observed high-level visual cortex were found in layer II–III, IV, V, and VI, with a higher proportion in layer II–III, V, and VI than in layer IV, and (4) most feedback neurons were CaMKII-positive excitatory neurons, and few of them were identified as inhibitory GABAergic neurons. These results may argue against the segregation of ventral and dorsal streams during visual information processing, and support “reverse hierarchy theory” or interactive model proposing that recurrent connections between V1 and higher-order visual areas constitute the functional circuits that mediate visual perception. Also, the corticocortical feedback neurons from high-level visual cortical areas to the V1 area are mostly excitatory in nature.

scholarly journals VALIDATION OF A SURVIVABLE PUBLISH-SUBSCRIBE SYSTEM

2014 ◽  
pp. 77-94
Author(s):  
Sankalp Singh ◽  
Adnan Agbaria ◽  
Fabrice Stevens ◽  
Tod Courtney ◽  
John F. Meyer ◽  
...  
Keyword(s):  
Top Down ◽  
Model Based ◽  
Trade Offs ◽  
Validation Procedure ◽  
High Level

We describe, with respect to high-level survivability requirements, the validation of a survivable publish subscribe system that is under development. We use a top-down approach that methodically breaks the task of validation into manageable tasks, and for each task, applies techniques best suited to its accomplishment. These efforts can be largely independent and use a variety of validation techniques, and the results, which complement and supplement each other, are seamlessly integrated to provide a convincing assurance argument. We also demonstrate the use of model-based validation techniques, as a part of the overall validation procedure, to guide the system’s design by exploring different configurations and evaluating trade-offs.

scholarly journals A-082 SPECT Cerebral Blood Flow Differences between Self-Reported Low and High Feelings of Pessimism

2020 ◽  
Vol 35 (6) ◽  
pp. 874-874
Author(s):  
Trinidad B ◽  
Stebbins L ◽  
Golden C ◽  
Amen D ◽  
Taylor D ◽  
...  
Keyword(s):  
Blood Flow ◽  
Clear Understanding ◽  
Symptom Checklist ◽  
Brain Areas ◽  
Motor Behaviors ◽  
Cognitive Reasoning ◽  
Parietal Lobes ◽  
Mean Differences ◽  
High Level ◽  
Increased Blood Flow

Abstract Objective To assess which brain areas, as measured by SPECT, are related to self-reported feelings of pessimism. Method Using a symptom checklist, participants were determined based on their self-reported feelings of pessimism. The participants were part of a large archival de-identified database. A total of 7,661 individuals were categorized into a low level of pessimism group (N = 3,495) and a high level of pessimism group (N = 4,166) who were primarily male (60.7%) with a mean age of 40.00 (SD = 15.69). Participants were placed in their respective groups based on whether their responses fell at or below the 25th percentile or at or above the 75th percentile of the sum of responses that indicated having problems related to pessimism. The two groups were then compared in 17 brain areas at baseline. Results Results from an Independent samples t-test showed mean differences in blood flow. Hyper-perfusion was seen in the group with high level of pessimism in the left Frontal t(7659) = −1.668, p < .007 and bilateral Parietal, left t(7659) = −1.333, p < .001 and right t(7659) = −1.159, p < .013. Conclusion Results indicate that individuals who report high levels of pessimism have an increased blood flow to several areas, increasing behavioral, cognitive, and emotional arousal. The increase of blood flow to the left frontal area suggests an over activation of cognitive reasoning and judgement, inhibiting a clear understanding of feelings of powerlessness and fear. The overwhelming feelings activate bilateral parietal lobes in carrying out motor behaviors to try and reduce feelings of hopelessness and fear.

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