š New paper out!
We show training improves motion categorization but doesn't reduce (or even worsens) misperceptionsāexplained via model combining efficient coding implicit categorization increased encoding precision
journals.plos.org/ploscompbiā¦
So in 2007, physicists wrote a paper that made the headlines: according to their calculations, human coin flips arenāt 50/50 - more like 51/49.
Why is that, and did students in Amsterdam really flip 350,000 coins to find out?
š§µ
We've open-sourced the egocentric eye tracking dataset from our recent papers ā via @Facebook@RealityLabs GitHub.
~26 hrs of user gaze and head movement data across 9 everyday tasks: driving, shopping, LEGO, Pokemon GO, more.
github.com/facebookresearch/ā¦
h/t @tsmurdison
Iām very excited to share that my graduate work is now online in @ScienceMagazine today!
With generous help from my mentor @yuji_ikegaya and my amazing teammates, we investigated a top-down pathway for volitional heart rate regulation!
science.org/doi/10.1126/scieā¦
All-day AR would benefit from AI models that understand a person's context & eye tracking could be key for task recognition. Yet past work - including our own research.facebook.com/public⦠- hasn't found much added value from gaze in addition to computer vision & egocentric video 2/
Got Butterflies in your Stomach?šµāš«I am super excited to share the first major study of my postdoc @visceral_mind! We report a multidimensional mental health signature of stomach-brain coupling in the largest sample to date š§µšbiorxiv.org/content/10.1101/ā¦
New paper alert! @RealityLabs
Eye gaze in everyday life contains multi-scale temporal dependencies across objects (1-7 fixations into past, depending on task). Akin to natural language.
Key to foundation models for visual understanding in mixed reality
dl.acm.org/doi/10.1145/36499ā¦
But object-part-based image segmentation is just starting to gain traction, and universal segmentation (segmenting and labelling all image pixels) is still a challenge. So this is one major bottleneck for aligning the model input state space with that of human active vision. 5/
Once the input state space is well-aligned with human action & vision, and appropriate models that can represent long-term dependencies are used, we believe that multiple problems in contextual AI may be solved convergently by a single (gaze-based) visual foundation model. 6/