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Qian CHU 褚乾

@qianchu.bsky.social

🧠PhD candidate @mpc.utoronto.ca NYU Shanghai 22' @slang.science memory + action + perception homo ludens

10 Posts82 Follower208 Folgt
Qian CHU 褚乾@qianchu.bsky.social · 2w ago

Rookie corresponding author here — Is this what people wake up to every day? These are junk emails I received over Saturday, all from predatory journals, and with bizarre wording like: “We expect a prompt response within 24 hours to avoid the need for re-asking.” Was it like this before the AI era?

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Qian CHU 褚乾@qianchu.bsky.social · last mo.

Looking for a Python toolbox to analyze your mobile eye-tracking data collected with @pupil-labs.bsky.social Neon? Meet PyNeon, our open-source toolbox designed to streamline the analysis of such novel data! Check out the paper in @psychonomicsociety.bsky.social's BRM: doi.org/10.3758/s134... ...

PyNeon: A Python package for the analysis of Neon multimodal mobile eye-tracking data - Behavior Research Methods

Mobile eye-tracking has revolutionized the study of human behavior and cognition by enabling researchers to record eye movements in the real world. However, the dynamic and multimodal nature of mobile eye-tracking data also introduces significant analytical challenges, including the alignment, integration, and interpretation of complex data. To fill these gaps, we present PyNeon, a versatile, community-oriented Python package designed to streamline the analysis of mobile eye-tracking, motion, and video data from the Neon eye-tracking system (Pupil Labs GmbH). We describe how PyNeon provides accessible APIs for reading, preprocessing, epoching, and exporting Neon data. Furthermore, it supports advanced video processing such as mapping between eye movement data and real-world coordinates and dynamic scanpath estimation. PyNeon presents an open-source and extendable framework for analyzing mobile eye-tracking data and forms the foundation for higher-level applications.

doi.org

Nanthia Suthana@suthanalab.bsky.social · 5mo ago

New lab paper! 🧠 Human hippocampal & MTL theta activity is linked to eye movements, but only during memory-guided navigation. Theta is also strongest during longer, more exploratory eye movements. plos.io/4dwJhR8 Huge congrats to Humza & team! 👏

Eye movements reflect memory-related theta activity in the human brain

Theta oscillations in the medial temporal lobe support memory, but how they relate to eye and body movements during human navigation is unclear. This study shows that theta power increases during sacc...

journals.plos.org

PLOS Biology@plosbiology.org · 5mo ago

How do MTL theta oscillations relate to eye & body movements during navigation? @suthanalab.bsky.social &co show that #theta power increases during #saccades under memory demands, linking exploratory gaze & planning to memory‑related dynamics during #navigation @plosbiology.org 🧪 plos.io/4dwJhR8

Experimental design and task. Top left: Intracranial electroencephalographic activity, eye and body movements were recorded as participants freely walked around the room. Wall-mounted motion-tracking cameras recorded the position of on-body reflective markers. Participants also wore an eye-tracking headset to monitor saccadic eye movements. A snapshot from the eye-facing camera is shown in the bottom right. For illustrative purposes, an experimenter is shown wearing the full setup. Top right: MRI of an example participant with an implanted RNS System. Purple dots indicate the location of four electrode contacts in the left medial temporal lobe (MTL). The top left inset shows an X-ray used to localize electrode positions. Bottom left: The environment contained 20 visible wall-mounted signs and three invisible circular target locations (0.7 m diameter). At the start of the task, participants freely explored the room to locate the invisible targets; each time a target was reached, an auditory tone signaled success, allowing them to gradually learn and remember these locations through experience. The task then alternated between two conditions: “visually-cued” navigation, during which participants navigated to a wall-mounted sign (e.g., “Blue 3”), and “memory-cued” navigation, during which they recalled and navigated to the previously learned invisible targets (e.g., “T”).
@alexlepauvre.bsky.social · last yr.

Excited to share that our iEEG dataset from the @arc-cogitate.bsky.social is publicly available! 📄Paper: nature.com/articles/s41... 📁Data: arc-cogitate.com/data-release 💻Code: github.com/Cogitate-con... Huge thanks to the team & co-first authors Alia Seedat, Urszula Gorska and Jay Jeschke🙌

Open multi-center intracranial electroencephalography dataset with task probing conscious visual perception - Scientific Data

Scientific Data - Open multi-center intracranial electroencephalography dataset with task probing conscious visual perception

nature.com