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Mark Georgeson, Stuart Wallis; Seeing light vs dark lines: psychophysical performance is based on separate channels, limited by noise and uncertainty. Journal of Vision 2008;8(6):821. doi: https://doi.org/10.1167/8.6.821.
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© ARVO (1962-2015); The Authors (2016-present)
Visual detection performance (d') is usually an accelerating function of stimulus contrast, which could imply a smooth, threshold-like nonlinearity in the sensory response. Alternatively, Pelli (1985 Journal of the Optical Society of America A 2 1508–1532) developed the ‘uncertainty model’ in which responses were linear with contrast, but the observer was uncertain about which of many noisy channels contained the signal. Such internal uncertainty effectively adds noise to weak signals, and predicts the nonlinear psychometric function. We re-examined these ideas by plotting psychometric functions (as z-scores, with high precision) for two observers (SAW, PRM). The task was to detect a single, vertical, blurred line at the fixation point, or to identify its polarity (light vs dark). Detection of a known polarity was nearly linear for SAW but very nonlinear for PRM. Randomly interleaving light and dark trials reduced detection performance and rendered it more nonlinear for SAW, but had little effect for PRM. These effects occurred for both single-interval and 2AFC procedures. The whole pattern of results was well predicted by our Monte Carlo simulation of Pelli's model, with only two free parameters - the levels of uncertainty and noise. SAW (highly practised) had very low uncertainty. PRM (with little prior practice) had much greater uncertainty, resulting in lower contrast sensitivity, nonlinear performance, and no effect of external (polarity) uncertainty. For SAW, identification was about √2 better than detection, implying statistically independent channels for stimuli of opposite polarity, rather than an opponent (light-dark) channel. These findings strongly suggest that noise and uncertainty, rather than sensory nonlinearity, limit visual detection, and we conjecture that uncertainty decreases with intensive practice.
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