Abstract
White pixel noise is widely used to estimate the level of internal noise in a system by injecting external variance into the detecting mechanism. Recent work (Baker and Meese, 2012) has provided psychophysical evidence that such noise masks might also cause suppression that could invalidate estimates of internal noise. Here we measure neural population responses directly, using steady-state visual evoked potentials, elicited by target stimuli embedded in different mask types. Sinusoidal target gratings of 1 c/deg flickered at 5 Hz, and were shown in isolation, or with superimposed orthogonal grating masks or 2D white noise masks, flickering at 7 Hz. Compared with responses to a blank screen, the Fourier amplitude at the target frequency increased monotonically as a function of target contrast when no mask was present. Both orthogonal and white noise masks caused rightward shifts of the contrast response function, providing evidence of contrast gain control suppression. We also calculated within-observer amplitude variance across trials. This increased in proportion to the target response, implying signal-dependent (i.e., multiplicative) noise at the system level, the implications of which we discuss for behavioral tasks. This measure of variance was reduced by both mask types, consistent with the changes in mean target response. An alternative variety of noise, which we term zero-dimensional noise, involves trial-by-trial jittering of the target contrast. This type of noise produced no gain control suppression, and increased the amplitude variance across trials.
Highlights
Physical implementations of signal transduction systems suffer from degraded information transmission owing to internal noise
We show that broadband white noise masks have a powerful suppressive effect, very similar to that of narrowband orthogonal grating masks
When a high contrast (30 dB) orthogonal mask was added at a higher temporal frequency (7 Hz), this shifted the contrast response function to the right. This is a classic contrast gain control effect, consistent with those reported in previous state visual evoked potential (SSVEP) (Brown et al, 1999; Busse et al, 2009; Tsai et al, 2012), fMRI (Brouwer and Heeger, 2011), and neuronal recordings (Morrone et al, 1982; Carandini and Heeger, 1994; Freeman et al, 2002; Busse et al, 2009; Sit et al, 2009)
Summary
Physical implementations of signal transduction systems suffer from degraded information transmission owing to internal noise. This is true both for electronic systems, such as amplifiers, and for biological sensory systems like the human visual system. The standard method for estimating internal noise is to assess how task performance degrades in varying levels of external noise (Pelli, 1981; Lu and Dosher, 2008). The external noise level at which performance starts to become poorer is referred to as the “equivalent internal noise,” as it is the point at which the external noise is equal in magnitude to the internal noise. Various techniques exist for estimating this value, including fitting computational models (Lu and Dosher, 2008) and using Bayesian adaptive methods (Lesmes et al, 2006)
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