scholarly journals The development of Bayesian integration in sensorimotor estimation

2018 ◽  
Vol 18 (12) ◽  
pp. 8 ◽  
Author(s):  
Claire Chambers ◽  
Taegh Sokhey ◽  
Deborah Gaebler-Spira ◽  
Konrad Paul Kording
Keyword(s):  
Bayesian Integration

Population Vectors Can Provide Near Optimal Integration of Information

2017 ◽  
Vol 29 (8) ◽  
pp. 2021-2029
Author(s):  
Josue Orellana ◽  
Jordan Rodu ◽  
Robert E. Kass
Keyword(s):  
Bayesian Inference ◽  
Point Process ◽  
Neural Mechanism ◽  
Input Neuron ◽  
Tuning Curves ◽  
Optimal Integration ◽  
Bayesian Integration ◽  
Combine Information ◽  
Input Spike ◽  
Combine Evidence

Much attention has been paid to the question of how Bayesian integration of information could be implemented by a simple neural mechanism. We show that population vectors based on point-process inputs combine evidence in a form that closely resembles Bayesian inference, with each input spike carrying information about the tuning of the input neuron. We also show that population vectors can combine information relatively accurately in the presence of noisy synaptic encoding of tuning curves.

Bayesian integration during sensorimotor estimation in elite athletes

2022 ◽  
Vol 81 ◽  
pp. 102895
Author(s):  
Osmar Pinto Neto ◽  
Victor Curty ◽  
Leonardo Crespim ◽  
Deanna M. Kennedy
Keyword(s):  
Elite Athletes ◽  
Bayesian Integration

scholarly journals Bayesian integration in setting classification criterion

IBRO Reports ◽  
2019 ◽  
Vol 6 ◽  
pp. S204
Author(s):  
Heeseung Lee ◽  
Sang-Hun Lee
Keyword(s):  
Classification Criterion ◽  
Bayesian Integration

scholarly journals The material-weight illusion disappears or inverts in objects made of two materials

2019 ◽  
Vol 121 (3) ◽  
pp. 996-1010 ◽  
Author(s):  
Vivian C. Paulun ◽  
Gavin Buckingham ◽  
Melvyn A. Goodale ◽  
Roland W. Fleming
Keyword(s):  
Bayesian Inference ◽  
Weight Distribution ◽  
Weight Perception ◽  
Sensory Information ◽  
Common Mechanism ◽  
Potential Candidate ◽  
Bayesian Integration ◽  
The Common ◽  
Weight Illusion

The material-weight illusion (MWI) occurs when an object that looks heavy (e.g., stone) and one that looks light (e.g., Styrofoam) have the same mass. When such stimuli are lifted, the heavier-looking object feels lighter than the lighter-looking object, presumably because well-learned priors about the density of different materials are violated. We examined whether a similar illusion occurs when a certain weight distribution is expected (such as the metal end of a hammer being heavier), but weight is uniformly distributed. In experiment 1, participants lifted bipartite objects that appeared to be made of two materials (combinations of stone, Styrofoam, and wood) but were manipulated to have a uniform weight distribution. Most participants experienced an inverted MWI (i.e., the heavier-looking side felt heavier), suggesting an integration of incoming sensory information with density priors. However, a replication of the classic MWI was found when the objects appeared to be uniformly made of just one of the materials ( experiment 2). Both illusions seemed to be independent of the forces used when the objects were lifted. When lifting bipartite objects but asked to judge the weight of the whole object, participants experienced no illusion ( experiment 3). In experiment 4, we investigated weight perception in objects with a nonuniform weight distribution and again found evidence for an integration of prior and sensory information. Taken together, our seemingly contradictory results challenge most theories about the MWI. However, Bayesian integration of competing density priors with the likelihood of incoming sensory information may explain the opposing illusions. NEW & NOTEWORTHY We report a novel weight illusion that contradicts all current explanations of the material-weight illusion: When lifting an object composed of two materials, the heavier-looking side feels heavier, even when the true weight distribution is uniform. The opposite (classic) illusion is found when the same materials are lifted in two separate objects. Identifying the common mechanism underlying both illusions will have implications for perception more generally. A potential candidate is Bayesian inference with competing priors.

Neuromorphic VLSI Bayesian integration synapse

2015 ◽  
Vol 51 (3) ◽  
pp. 207-209 ◽  
Author(s):  
A. Aggarwal
Keyword(s):  
Bayesian Integration ◽  
Neuromorphic Vlsi

scholarly journals Bayesian Integration of Face and Low-Level Cues for Foveated Video Coding

2008 ◽  
Vol 18 (12) ◽  
pp. 1727-1740 ◽  
Author(s):  
G. Boccignone ◽  
A. Marcelli ◽  
P. Napoletano ◽  
G. Di Fiore ◽  
G. Iacovoni ◽  
...  
Keyword(s):  
Video Coding ◽  
Low Level ◽  
Bayesian Integration
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