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News
August 25, 2026
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
August 24, 2026
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
August 21, 2026
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.

 

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Understanding emotions through biological motion in autism spectrum disorder: A systematic review

Research in Autism. 2026. Vol. 132. Article 202833.
Gavrilova E., Novoselova K., Myachykov A., Shtyrov Y.

Background

Body movements convey crucial insights into emotions. Although autistic individuals may process these cues differently, the specific factors influencing emotion-from-motion perception in autism remain poorly understood. This systematic review synthesizes current research and highlights key findings in this area of study.

Objectives

This systematic review aimed to assess autistic individuals’ ability to recognize emotions through biological motion (operationalized via point-light displays or avatars) and to detect task- and stimulus-related factors that may affect emotion recognition quality.

Design

Relevant studies were selected from PubMed and ScienceDirect databases. Sixteen publications were eligible for the final review following strict selection criteria. The results were assessed specifically with respect to the experimental paradigms, stimulus characteristics, and control tasks (a range of non-emotion-from-motion tasks included in the reviewed studies).

Results

Methodological synthesis reveals that empirical heterogeneity is systematically linked to experimental design. Paradigm choice critically shapes outcomes, with consistent group differences in verbal naming tasks but not in perceptual rating or matching tasks. Over a third of studies omitted specific emotion analysis, and stimulus parameter investigations were scarce and inconsistent. Control task data vary widely across studies. Although some research suggests emotion-specific deficits, the inconsistent findings and absence of standardized, well-matched control paradigms make it impossible to draw firm conclusions.

Conclusion

While difficulties in emotion-from-motion perception in autism are widely reported, they represent a profile highly sensitive to methodological context, not a uniform deficit. The field is hampered by methodological fragmentation and insufficient replication. Future research must prioritize cross-paradigm comparisons, open science, and systematic stimulus exploration to clarify the specific conditions underlying perceptual differences.

Research target: Psychology
Language: English
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Keywords: social cognitionresearch methodologyemotion recognitionbiological motionmotion processing
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