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Stanley Klein, Dennis Levi; Problems with modeling detection and identification of signals in noise. Journal of Vision 2007;7(15):73. doi: https://doi.org/10.1167/7.15.73.
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© ARVO (1962-2015); The Authors (2016-present)
A powerful method for peeking into the black box of perception is to measure thresholds for detecting and identifying patterns in noise. A typical experiment varies two parameters: the rms strength of the external noise (Next) and the criterion at which threshold is measured (d'). The Lu/Dosher Perceptual Template Model (PTM) and the Barlow/Pelli Equivalent Noise Model (ENM) claim that the TvN (threshold vs. noise) curve is well fit by a separable function of the form: Th(Next,d')= CRF(d') (1+(Next /Neq)^p)^(1/p) where the contrast response function behaves as CRF(d')∼d'^(1/t) for d'Neq we found t∼1.5±0.3. This finding that the CRF shape depends on Next means that all separable models are inadequate. We also found that Neq was very close to the noise detection threshold.
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