Seeing light vs dark lines: psychophysical performance is based on separate channels, limited by noise and uncertainty

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) for two observers (SAW, PRM) with high precision. The task was to detect a single, vertical, blurred line at the fixation point, or 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 performance and rendered it non-linear for SAW, but had little effect for PRM. This 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. 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 v2 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.

Two-B or not two-B? Design patterns for hybrid metaheuristics

Real world search problems, characterised by nonlinearity, noise and multidimensionality, are often best solved by hybrid algorithms. Techniques embodying different necessary features are triggered at specific iterations, in response to the current state of the problem space. In the existing literature, this alternation is managed either statically (through pre-programmed policies) or dynamically, at the cost of high coupling with algorithm inner representation. We extract two design patterns for hybrid metaheuristic search algorithms, the All-Seeing Eye and the Commentator patterns, which we argue should be replaced by the more flexible and loosely coupled Simple Black Box (Two-B) and Utility-based Black Box (Three-B) patterns that we propose here. We recommend the Two-B pattern for purely fitness based hybridisations and the Three-B pattern for more generic search quality evaluation based hybridisations.