Visual signal detection. III. On Bayesian use of prior knowledge and cross correlation.
Experimental results are presented demonstrating that humans can make effective use of prior knowledge for detecting and identifying visual signals in static noise. The signals were selected from an orthogonal Hadamard set. There was a marked drop in detection performance when observers did not know which signal was present. The drop was in excellent quantitative agreement with that predicted by the theory of signal detectability. The statistical efficiency of the human observers was 33% in both cases (detection with and without prior knowledge). When interpreted in terms of channel uncertainty, the detection results demonstrated an upper limit of 10 orthogonal, uncertain channels. The statistical efficiency for the Hadamard signal-identification task was 40%. All the results are consistent with the standard theory of signal detectability based on a Bayesian maximum a posteriori probability decision strategy using cross correlation (or matched filtering) of expected signal profiles with those present in the display.
D001288:Attention; D001499:Bayes Theorem; D004193:Discrimination Learning; D006801:Humans; D010364:Pattern Recognition, Visual; D011601:Psychophysics; D012718:Set, Psychology; D014796:Visual Perception