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SUMMARY:Spatio-temporal visual statistical learning in context - Ivan Tomi
 c
DTSTART:20251212T140000Z
DTEND:20251212T150000Z
UID:TALK242029@talks.cam.ac.uk
CONTACT:Adam Triabhall
DESCRIPTION:This week we will discuss and debate a very recent preprint by
  Garber and Fiser (2026).\n\nAbstract: “Visual Statistical Learning (VSL
 ) is classically investigated in a restricted format\, either as temporal 
 or spatial VSL\, and void of any effect or bias due to context. However\, 
 in real-world environments\, spatial patterns unfold over time\, leading t
 o a fundamental intertwining between spatial and temporal regularities. In
  addition\, their interpretation is heavily influenced by contextual infor
 mation through internal biases encoded at different scales. Using a novel 
 spatio-temporal VSL setup\, we explored this interdependence between time\
 , space\, and biases by moving spatially defined patterns in and out of pa
 rticipants' views over time in the presence or absence of occluders. First
 \, we replicated the classical VSL results in such a mixed setup. Next\, w
 e obtained evidence that purely temporal statistics can be used for learni
 ng spatial patterns through internal inference. Finally\, we found that mo
 tion-defined and occlusion-related context jointly and strongly modulated 
 which temporal and spatial regularities were automatically learned from th
 e same visual input. Overall\, our findings expand the conceptualization o
 f VSL from a mechanistic recorder of low-level spatial and temporal co-occ
 urrence statistics of single visual elements to a complex interpretive pro
 cess that integrates low-level spatio-temporal information with higher-lev
 el internal biases to infer the general underlying structure of the enviro
 nment” (Garber & Fiser\, 2026).\n\nReference: Garber\, D.\, & Fiser\, J.
  (2026). Spatio-temporal visual statistical learning in context. Cognition
 \, 266\, 106324. https://doi.org/10.1016/j.cognition.2025.106324
LOCATION:https://cam-ac-uk.zoom.us/j/92612577704?pwd=MUtqMjVQdXNmUTVIYjRkM
 G1NUW9GZz09
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