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SUMMARY:Causal inference during motion perception\, and its neural basis -
  Ralf Haefner\, Department for Brain &amp\; Cognitive Sciences\, Universit
 y of Rochester
DTSTART:20240624T090000Z
DTEND:20240624T100000Z
UID:TALK217015@talks.cam.ac.uk
CONTACT:Samuel Eckmann
DESCRIPTION:Short abstract: \nIn this talk I’ll present our work on expl
 aining motion perception as hierarchical causal inference. I’ll describe
  the intuitions behind the theory and show new psychophysics data task tha
 t quantitatively tests our theory. I’ll also describe our work in progre
 ss on using neural responses to test our theory\, as well as Bayesian mode
 ls of behavior in general.\n\nLong abstract:\nIf motion is always defined 
 relative to a reference frame\, what is the brain's reference frame for th
 e perception of a moving object? A century of psychophysical studies has p
 rovided us with seemingly conflicting evidence about motion perception in 
 a variety of reference frames: from egocentric\, to world-centric\, to ref
 erence frames defined by other moving objects. We present a hierarchical B
 ayesian model which describes how observed retinal velocities give rise to
  perceived velocities. The hierarchically recurring generative model motif
  represents each perceived object's motion in its natural reference frame 
 which reflects the causal structure of the world. The degeneracy of object
  motion and reference frame motion is broken by a spike and slab prior ref
 lecting the fact that most objects are exactly stationary in their natural
  reference frame. Data from three new psychophysical experiments quantitat
 ively confirm key predictions of our model.\nFinally\, I will present a st
 epwise method for generating neural predictions from our\, and other\, Bay
 esian models of the brain\, and for comparing them against each other usin
 g neural data. Interestingly\, a neural circuit implementing a generalized
  version of divisive normalization can generate the center-surround tuning
  curves predicted by causal inference.\n\nRelated manuscripts:\nShivkumar\
 , S.\, DeAngelis\, G. C.\, & Haefner\, R. M. (2023). Hierarchical motion p
 erception as causal inference. https://doi.org/10.1101/2023.11.18.567582\n
 Lengyel\, G.\, Shivkumar\, S.\, & Haefner\, R. M. (2023). A General Method
  for Testing Bayesian Models using Neural Data. UniReps: The First Worksho
 p on Unifying Representations in Neural Models. https://openreview.net/for
 um?id=oWJP0NhcY7
LOCATION:CBL Seminar Room\, Engineering Department\, 4th floor Baker build
 ing
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