COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |
University of Cambridge > Talks.cam > Computational Neuroscience > Causal inference during motion perception, and its neural basis
Causal inference during motion perception, and its neural basisAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Samuel Eckmann. Short abstract: In this talk I’ll present our work on explaining motion perception as hierarchical causal inference. I’ll describe the intuitions behind the theory and show new psychophysics data task that quantitatively tests our theory. I’ll also describe our work in progress on using neural responses to test our theory, as well as Bayesian models of behavior in general. Long abstract: If motion is always defined relative to a reference frame, what is the brain’s reference frame for the perception of a moving object? A century of psychophysical studies has provided us with seemingly conflicting evidence about motion perception in a variety of reference frames: from egocentric, to world-centric, to reference frames defined by other moving objects. We present a hierarchical Bayesian 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 reflecting the fact that most objects are exactly stationary in their natural reference frame. Data from three new psychophysical experiments quantitatively confirm key predictions of our model. Finally, I will present a stepwise method for generating neural predictions from our, and other, Bayesian models of the brain, and for comparing them against each other using neural data. Interestingly, a neural circuit implementing a generalized version of divisive normalization can generate the center-surround tuning curves predicted by causal inference. Related manuscripts: Shivkumar, S., DeAngelis, G. C., & Haefner, R. M. (2023). Hierarchical motion perception as causal inference. https://doi.org/10.1101/2023.11.18.567582 Lengyel, G., Shivkumar, S., & Haefner, R. M. (2023). A General Method for Testing Bayesian Models using Neural Data. UniReps: The First Workshop on Unifying Representations in Neural Models. https://openreview.net/forum?id=oWJP0NhcY7 This talk is part of the Computational Neuroscience series. This talk is included in these lists:
Note that ex-directory lists are not shown. |
Other listsMax Cam Wolfson College Education Society Davido 2020Other talksEQUALITY-BASED FORMULATION FOR NON-SMOOTH VIBRATING SYSTEMS Quantum Information Active Surveillance of Neglected Tropical Diseases: A Fresh Push to Identify Reservoir Hosts Flows generated by stochastic differential equations with reflection Benchmarking and developing models for molecule-surface potential energy surfaces. The difficulties and opportunities arising from experimental data which is too detailed and too sensitive Homogenisation of resonators via a two-scale transform, and generalisations |