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Zhong-Lin Lu, Barbara A. Dosher; Using external noise methods to isolate mechanisms of attention/perceptual learning. Journal of Vision 2002;2(7):67. doi: 10.1167/2.7.67.
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© ARVO (1962-2015); The Authors (2016-present)
We proposed a theoretical framework to distinguish three mechanisms underlying performance improvements in visual attention (1–5) and perceptual learning (6,7): stimulus enhancement, external noise exclusion via template retuning, and reduction of contrast gain-control or multiplicative noise. Measuring TVCs (threshold vs external noise contrast) at multiple performance levels under joint manipulations of external noise and attention/training can characterize the nonlinearities in the perceptual system and distinguish mechanisms and their mixtures (7,8). In visual attention, several pure cases of template retuning (2–4) and stimulus enhancement (1,4,5 have been reported. In perceptual learning in visual periphery, we found a mixture of stimulus enhancement and template retuning (6). Gold et al.(9) replicated our results with other stimuli; but instead concluded that perceptual learning only changed the signal not the noise. Our conclusions were based on a multiple-TVC constraint on nonlinearity, while Gold et al's were based on double-pass response consistency (10). However, the double pass method only assesses the internal to external noise ratio. We show that response consistency is a function only of the ratio. Mathematically, neither the multiple-TVC nor the double-pass method can distinguish stimulus enhancement from internal additive noise reduction, nor a mixture of stimulus enhancement + external noise exclusion from the changes only in signal claimed by Gold et al. We discuss empirical results, the mathematical properties of multiple-TVCs and measures of response consistency, their relation to different classes of observer models, and to performance signatures of attention and perceptual learning.
LuDosher VR'98. 2.
DosherLu PsychSci'99. 3.
DosherLu VR'00. 4.
LuDosher JEPHPP'00. 5.
Lu VR'00. 6.
DosherLu PNAS'98. 7.
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Gold Nature'99. 10.
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