The effect of notched noise on flicker detection and discrimination

Abstract

Flicker perception was investigated using two-alternative forced-choice detection and discrimination tasks with four different types of external noise: (1) broadband noise, (2) 5-Hz notched-noise--broadband noise with a 5-Hz band centered on the signal frequency removed, (3) 10-Hz notched-noise, and (4) no external noise. The signal was a burst of 10-Hz sinusoidal flicker presented in one of two observation intervals. In discrimination experiments, a pedestal--sinusoidal flicker with the same frequency, duration, and phase as the signal--was added to both observation intervals. With no noise, observers' performance first improved with increasing pedestal modulation, before deteriorating in accordance with Weber's Law, producing the typical "dipper" shaped plot of signal versus pedestal modulation. Noise affects performance, but the dipper effect persisted in each type of noise. The results exclude three models: the ideal-observer in which the pedestal improves performance by specifying the signal exactly; off-frequency-looking models in which the dipper depends on detection by channels tuned to temporal frequencies different from that of the signal; and strict energy detectors. Our data are consistent with signal processing by a single mechanism with an expansive non-linearity for near-threshold signal modulations (with an exponent of six) and a compressive "Weberian" non-linearity for high modulations.

Correlated and uncorrelated invisible temporal white noise alters mesopic rod signaling

“…To the best of our knowledge, no previous study has measured rod psychometric functions in the presence of extrinsic noise for comparison to our findings. However, the mesopic rod data parallel the findings of the Weibull psychometric slopes estimated in studies of photopic cone vision measured in the presence of extrinsic noise [20][21][22][23][24][25][26][27][28][29][30][31][32], and as reviewed by Baker and Messe [75]. The standard explanations for steeper slopes in the absence of noise are provided by the mechanism uncertainty model [69], nonlinear signal transduction [83,84], or both [82].…”

“…In addition to thresholds, psychometric slopes provide information related to changes in the detection mechanism [12,13] and for estimating variability [12][13][14][15][16][17][18][19]. Extrinsic noise acts to increase the spread of the psychometric function (i.e., the psychometric slope is shallower) [16][17][18] under photopic illumination [20][21][22][23][24][25][26][27][28][29][30][31][32] and the slope is steeper for luminance stimuli (detected via the MC pathway) than for red-green chromatic stimuli (detected via the PC pathway) [33]. While extrinsic noise has been used to study postreceptoral processing of cone signals [34], the effect of extrinsic noise on rod thresholds and psychometric slopes is largely unknown.…”

A common signal detection model accounts for both perception and discrimination of the watercolor effect

“…This is rarely done as the task is heroic. Instead, an underlying response function is typically assumed and the data fit by estimating its free parameters (for example, see Foley & Legge, 1981;Legge & Foley, 1980;Smithson, Henning, MacLeod, & Stockman, 2009 for recent examples). The scale's interpretation as a perceptual scale depends on the assumption that equally discriminable differences are perceptually equal.…”

Probability summation—a critique

“…(A linear function gives no facilitation at all.) This result has been replicated [38] in the frequency domain. Expanding the range of applicable phenomena in this way leads to a more reliable and comprehensive theory.…”