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🦇 Humans show bat-like skills using mouth-click echolocation
(... and where is the bat-man?)
https://medicalxpress.com/news/2026-02-humans-skills-mouth-click-echolocation.html

Humans show bat-like skills using mouth-click echolocation
It may sound like a scene from "Nosferatu," but research from the University of East Anglia shows that humans can use bat-like echolocation skills to judge the distance of objects. The new study reveals that, just like bats navigating in the dark, humans too can rely on the echoes of mouth clicks to gauge how far away objects are. While humans may not match the precision of these nocturnal navigators, the study, published in Experimental Brain Research, shows that with simple tools like mouth c…

From single scenes to extended scenarios: The role of the ventromedial prefrontal cortex in the construction of imagery-rich events https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0324764 "perceptual representations in the posterior neoco…

From single scenes to extended scenarios: The role of the ventromedial prefrontal cortex in the construction of imagery-rich events
Mental events are fundamental to daily cognition, including the recollection of past experiences, the anticipation of future scenarios, and engagement in imaginative, fictitious thought. Typically, these temporally extended mental events unfold within coherent spatial contexts, rich in naturalistic scenes and objects. However, there remains a significant gap in understanding how these events are represented in the brain. This study aimed to investigate the neural patterns involved in the constr…

Hierarchic-EEG2Text: Assessing EEG-To-Text Decoding across Hierarchical Abstraction Levels
Anupam Sharma, Harish Katti, Prajwal Singh, Shanmuganathan Raman, Krishna Miyapuram
https://arxiv.org/abs/2602.20932 https://arxiv.org/pdf/2602.20932 https://arxiv.org/html/2602.20932
arXiv:2602.20932v1 Announce Type: new
Abstract: An electroencephalogram (EEG) records the spatially averaged electrical activity of neurons in the brain, measured from the human scalp. Prior studies have explored EEG-based classification of objects or concepts, often for passive viewing of briefly presented image or video stimuli, with limited classes. Because EEG exhibits a low signal-to-noise ratio, recognizing fine-grained representations across a large number of classes remains challenging; however, abstract-level object representations may exist. In this work, we investigate whether EEG captures object representations across multiple hierarchical levels, and propose episodic analysis, in which a Machine Learning (ML) model is evaluated across various, yet related, classification tasks (episodes). Unlike prior episodic EEG studies that rely on fixed or randomly sampled classes of equal cardinality, we adopt hierarchy-aware episode sampling using WordNet to generate episodes with variable classes of diverse hierarchy. We also present the largest episodic framework in the EEG domain for detecting observed text from EEG signals in the PEERS dataset, comprising $931538$ EEG samples under $1610$ object labels, acquired from $264$ human participants (subjects) performing controlled cognitive tasks, enabling the study of neural dynamics underlying perception, decision-making, and performance monitoring.
We examine how the semantic abstraction level affects classification performance across multiple learning techniques and architectures, providing a comprehensive analysis. The models tend to improve performance when the classification categories are drawn from higher levels of the hierarchy, suggesting sensitivity to abstraction. Our work highlights abstraction depth as an underexplored dimension of EEG decoding and motivates future research in this direction.
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