A nonparametric Bayesian approach to adaptive sampling of psychometric functions
| Autor: | Stephan Poppe, Juergen Jost, Tobias Elze |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2009 |
| Předmět: |
Adaptive sampling
Computer science business.industry General Neuroscience Speech recognition lcsh:QP351-495 Experimental data Monotonic function Stimulus (physiology) Machine learning computer.software_genre lcsh:RC321-571 Visual processing Cellular and Molecular Neuroscience Nonparametric inference lcsh:Neurophysiology and neuropsychology Time course Nonparametric bayesian Artificial intelligence business computer lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry |
| Zdroj: | BMC Neuroscience, Vol 10, Iss Suppl 1, p P353 (2009) |
| ISSN: | 1471-2202 |
| Popis: | As an example for the third point, we refer to an ongoing research debate about the time course for early visual processing. For the detection and discrimination impairment of a stimulus by a closely followed second stimulus, two competing models exist (U-shaped vs. monotonically increasing, see [3]) that had been related to special types of experiments each. Recent experimental results, however, demonstrate severe violations of these experimental assumptions [4,5], but the question whether any of these two models is appropriate at all remains open. A suitable nonparametric inference might help to find better descriptions of the experimental data and finally to develop new models with only a few parameters. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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