当前位置:网站首页>[day39 literature extensive reading] a Bayesian perspective on magnetic estimation
[day39 literature extensive reading] a Bayesian perspective on magnetic estimation
2022-07-05 23:47:00 【Yu Adzuki】
Read the literature :
Petzschner, F. H., Glasauer, S., & Stephan, K. E. (2015). A Bayesian perspective on magnitude estimation. trends in cognitive sciences, 19(5), 285-293.
Links to Literature :
Abstract
we discuss a unifying Bayesian framework for understanding biases in magnitude estimation and enable a re-interpretation of a range of established psychophysical findings.
Theories of magnitude estimation
Bayesian accounts of magnitude estimation have the potential to provide a more general explanation that covers a wide set of behavioral characteristics and transcends any specific modality.
A Bayesian framework for magnitude estimation
In Beyesian framework, a generative model combines a priori information (prior) with noisy sensory input (likelihood), weighing the two information sources by their relative uncertainty to produce biased magnitude judgments (posterior).
→ The higher the uncertainty (variance) of the prior, the more posterior depends on the likelihood.
Links between classical and generative models of magnitude estimation
Regarding magnitude estimation as Bayesian perceptual inference is capable of reconciling the seemingly incompatible theories about perceptual function fπ→s by Weber-Fechner (logarithmic form) and Stevens (power-function form).
The utility of a Bayesian framework for future psychophysical and neuroimaging studies
Larger quantities in the non-temporal domain (such as space) are associated with longer durations, which may because the statistical co-occurrence of different magnitudes reliably reflects properties ofvthe physical world that link different magnitudes (such as time and space) and a global prior reflecting these probabilistic relations might lead to coupled estimates of different physical magnitudes.
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