Friend effects framework: Contrastive and hierarchical processing in cheerleader effects

Cognition ◽  
2021 ◽  
Vol 212 ◽  
pp. 104715
Author(s):  
Edwin J. Burns ◽  
Weiying Yang ◽  
Haojiang Ying
Keyword(s):  
Hierarchical Processing

Hierarchical Processing of Stimuli by Adults, 5-Year-Olds, and Monkeys

2004 ◽  
Author(s):  
Julie J. Neiworth ◽  
Amy Gleichman ◽  
Anne Olinick
Keyword(s):  
Hierarchical Processing

scholarly journals Cortical response to naturalistic stimuli is largely predictable with deep neural networks

2021 ◽  
Vol 7 (22) ◽  
pp. eabe7547
Author(s):  
Meenakshi Khosla ◽  
Gia H. Ngo ◽  
Keith Jamison ◽  
Amy Kuceyeski ◽  
Mert R. Sabuncu
Keyword(s):  
Brain Function ◽  
Deep Neural Networks ◽  
Neural Responses ◽  
Dynamic Interactions ◽  
Hierarchical Processing ◽  
Human Connectome Project ◽  
Neural Information ◽  
Neural Information Processing ◽  
Visual Interactions ◽  
High Level

Naturalistic stimuli, such as movies, activate a substantial portion of the human brain, invoking a response shared across individuals. Encoding models that predict neural responses to arbitrary stimuli can be very useful for studying brain function. However, existing models focus on limited aspects of naturalistic stimuli, ignoring the dynamic interactions of modalities in this inherently context-rich paradigm. Using movie-watching data from the Human Connectome Project, we build group-level models of neural activity that incorporate several inductive biases about neural information processing, including hierarchical processing, temporal assimilation, and auditory-visual interactions. We demonstrate how incorporating these biases leads to remarkable prediction performance across large areas of the cortex, beyond the sensory-specific cortices into multisensory sites and frontal cortex. Furthermore, we illustrate that encoding models learn high-level concepts that generalize to task-bound paradigms. Together, our findings underscore the potential of encoding models as powerful tools for studying brain function in ecologically valid conditions.

scholarly journals HPEFT for Hierarchical Heterogeneous Multi-DAG in a Multigroup Scan UPA System

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 498
Author(s):  
Yuzhong Li ◽  
Wenming Tang ◽  
Guixiong Liu
Keyword(s):  
Signal Processing ◽  
Scheduling Algorithm ◽  
Frame Rate ◽  
Hierarchical Processing ◽  
Finish Time ◽  
System A ◽  
Time Period ◽  
Upa System ◽  
Ultrasonic Phased Array ◽  
Earliest Finish Time

Multidirected acyclic graph (DAG) workflow scheduling is a key problem in the heterogeneous distributed environment in the distributed computing field. A hierarchical heterogeneous multi-DAG workflow problem (HHMDP) was proposed based on the different signal processing workflows produced by different grouping and scanning modes and their hierarchical processing in specific functional signal processing modules in a multigroup scan ultrasonic phased array (UPA) system. A heterogeneous predecessor earliest finish time (HPEFT) algorithm with predecessor pointer adjustment was proposed based on the improved heterogeneous earliest finish time (HEFT) algorithm. The experimental results denote that HPEFT reduces the makespan, ratio of the idle time slot (RITS), and missed deadline rate (MDR) by 3.87–57.68%, 0–6.53%, and 13–58%, respectively, and increases relative relaxation with respect to the deadline (RLD) by 2.27–8.58%, improving the frame rate and resource utilization and reducing the probability of exceeding the real-time period. The multigroup UPA instrument architecture in multi-DAG signal processing flow was also provided. By simulating and verifying the scheduling algorithm, the architecture and the HPEFT algorithm is proved to coordinate the order of each group of signal processing tasks for improving the instrument performance.

scholarly journals Distinct neural sources underlying visual word form processing as revealed by steady state visual evoked potentials (SSVEP)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fang Wang ◽  
Blair Kaneshiro ◽  
C. Benjamin Strauber ◽  
Lindsey Hasak ◽  
Quynh Trang H. Nguyen ◽  
...  
Keyword(s):  
Steady State ◽  
Word Recognition ◽  
Visual Word Recognition ◽  
Time Course ◽  
Signal To Noise Ratio ◽  
Visual Word ◽  
High Signal ◽  
Hierarchical Processing ◽  
Form Processing ◽  
Visual Evoked

AbstractEEG has been central to investigations of the time course of various neural functions underpinning visual word recognition. Recently the steady-state visual evoked potential (SSVEP) paradigm has been increasingly adopted for word recognition studies due to its high signal-to-noise ratio. Such studies, however, have been typically framed around a single source in the left ventral occipitotemporal cortex (vOT). Here, we combine SSVEP recorded from 16 adult native English speakers with a data-driven spatial filtering approach—Reliable Components Analysis (RCA)—to elucidate distinct functional sources with overlapping yet separable time courses and topographies that emerge when contrasting words with pseudofont visual controls. The first component topography was maximal over left vOT regions with a shorter latency (approximately 180 ms). A second component was maximal over more dorsal parietal regions with a longer latency (approximately 260 ms). Both components consistently emerged across a range of parameter manipulations including changes in the spatial overlap between successive stimuli, and changes in both base and deviation frequency. We then contrasted word-in-nonword and word-in-pseudoword to test the hierarchical processing mechanisms underlying visual word recognition. Results suggest that these hierarchical contrasts fail to evoke a unitary component that might be reasonably associated with lexical access.

scholarly journals Brain hierarchy score: Which deep neural networks are hierarchically brain-like?

2020 ◽  
Author(s):  
Soma Nonaka ◽  
Kei Majima ◽  
Shuntaro C. Aoki ◽  
Yukiyasu Kamitani
Keyword(s):  
Neural Networks ◽  
Image Recognition ◽  
High Performance ◽  
Deep Neural Networks ◽  
Recognition Performance ◽  
Brain Activity ◽  
Spatial Integration ◽  
Hierarchical Processing ◽  
Visual Areas ◽  
The Brain

SummaryAchievement of human-level image recognition by deep neural networks (DNNs) has spurred interest in whether and how DNNs are brain-like. Both DNNs and the visual cortex perform hierarchical processing, and correspondence has been shown between hierarchical visual areas and DNN layers in representing visual features. Here, we propose the brain hierarchy (BH) score as a metric to quantify the degree of hierarchical correspondence based on the decoding of individual DNN unit activations from human brain activity. We find that BH scores for 29 pretrained DNNs with varying architectures are negatively correlated with image recognition performance, indicating that recently developed high-performance DNNs are not necessarily brain-like. Experimental manipulations of DNN models suggest that relatively simple feedforward architecture with broad spatial integration is critical to brain-like hierarchy. Our method provides new ways for designing DNNs and understanding the brain in consideration of their representational homology.

scholarly journals Visual Object Multimodality Tracking Based on Correlation Filters for Edge Computing

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Guosheng Yang ◽  
Qisheng Wei
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Target Position ◽  
Correlation Filter ◽  
Estimation Accuracy ◽  
Visual Object ◽  
Correlation Filters ◽  
Data Set ◽  
Hierarchical Processing ◽  
Target Rotation

In recent years, visual object tracking has become a very active research field which is mainly divided into the correlation filter-based tracking and deep learning (e.g., deep convolutional neural network and Siamese neural network) based tracking. For target tracking algorithms based on deep learning, a large amount of computation is required, usually deployed on expensive graphics cards. However, for the rich monitoring devices in the Internet of Things, it is difficult to capture all the moving targets in each device in real time, so it is necessary to perform hierarchical processing and use tracking based on correlation filtering in insensitive areas to alleviate the local computing pressure. In sensitive areas, upload the video stream to a cloud computing platform with a faster computing speed to perform an algorithm based on deep features. In this paper, we mainly focus on the correlation filter-based tracking. In the correlation filter-based tracking, the discriminative scale space tracker (DSST) is one of the most popular and typical ones which is successfully applied to many application fields. However, there are still some improvements that need to be further studied for DSST. One is that the algorithms do not consider the target rotation on purpose. The other is that it is a very heavy computational load to extract the histogram of oriented gradient (HOG) features from too many patches centered at the target position in order to ensure the scale estimation accuracy. To address these two problems, we introduce the alterable patch number for target scale tracking and the space searching for target rotation tracking into the standard DSST tracking method and propose a visual object multimodality tracker based on correlation filters (MTCF) to simultaneously cope with translation, scale, and rotation in plane for the tracked target and to obtain the target information of position, scale, and attitude angle at the same time. Finally, in Visual Tracker Benchmark data set, the experiments are performed on the proposed algorithms to show their effectiveness in multimodality tracking.

Optimization and physical performance evaluation of electrospun nanofibrous mats of PLA, PCL and their blends

2020 ◽  
pp. 152808372094450
Author(s):  
Deepika Sharma ◽  
Bhabani K Satapathy
Keyword(s):  
Physical Performance ◽  
Process Parameters ◽  
Optimization Technique ◽  
Electrospinning Process ◽  
Sequential Optimization ◽  
Orifice Diameter ◽  
Solvent Ratio ◽  
Solvent Mixtures ◽  
Minimum Diameter ◽  
Hierarchical Processing

The optimization of process parameters such as applied voltage, orifice diameter, solvent system, and solvent ratio for electrospinning of neat polymers, polylactic acid (PLA) and poly (є-caprolactone) (PCL), to obtain uniform, randomly oriented nanofibers with minimum diameter variation and beaded structures has been critically discussed. The paper focuses on establishing a sequential optimization technique for arriving at a common set of electrospinning process parameters for individual polymers, such as, applied voltages, orifice diameters, solvent mixtures, solvent ratios, to be used in the fabrication of electrospun nanofibrous mats (ENMs) of blended polymers. In this study, the effect of variation of applied voltages, orifice diameters, solvent mixtures, solvent ratios, PLA/PCL blending ratios, solution concentration of blends and flow rate were reported via morphological analysis of electrospun nanofibers. The set of optimal process parameters obtained for both PLA and PCL were adopted for the fabrication of ENMs based on the PLA/PCL blends. The paper further deliberates on the physical performance of PLA/PCL based ENMs in acidic, basic and neutral release media. Thus, the study establishes a hierarchical processing optimization route for designing blended ENMs by following a set of variable electrospinning process parameters.

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