Early visual processing analyses fine and coarse image features separately. Here we show that motion signals derived from fine and coarse analyses are combined in rather a surprising way: Coarse and fine motion sensors representing the same direction of motion inhibit one another and an imbalance can reverse the motion perceived. Observers judged the direction of motion of patches of filtered two-dimensional noise, centered on 1 and 3 cycles/deg. When both sets of noise were present and only the 3 cycles/deg noise moved, judgments were reversed at short durations. When both sets of noise moved, judgments were correct but sensitivity was impaired. Reversals and impairments occurred both with isotropic noise and with orientation-filtered noise. The reversals and impairments could be simulated in a model of motion sensing by adding a stage in which the outputs of motion sensors tuned to 1 and 3 cycles/deg and the same direction of motion were subtracted from one another. The subtraction model predicted and we confirmed in experiments with orientation-filtered noise that if the 1 cycle/deg noise flickered and the 3 cycles/deg noise moved, the 1 cycle/deg noise appeared to move in the opposite direction to the 3 cycles/deg noise even at long durations.

^{2}. The stimuli were presented in white mode at the center of the monitor screen in a square of 20 cm per side and were viewed at a distance of 143 cm subtending an area of 8° × 8°. The remainder of the screen was at mean luminance. The display spatial resolution was 64 pixels per degree of visual angle.

*n*

_{1}and

*n*

_{2}are the filtered Gaussian noises (anisotropic or isotropic) with peak spatial frequencies of 1 and 3 cycles/deg, respectively (the equations of the filters are given below);

*L*

_{0}is the mean luminance, in cd/m

^{2};

*σ*

_{ xy}is the spatial standard deviation, in deg (

*σ*

_{ xy}= 2°);

*m*is the Michelson contrast as a function of time given by

*m*(

*t*) = exp {−

*t*

^{2}/ (2

*σ*

_{ t}

^{2})}, where

*σ*

_{ t}is the temporal standard deviation;

*υ*

_{1}and

*υ*

_{2}are the velocities of each noise, in deg/s; and

*m*

_{1}and

*m*

_{2}are the contrasts which are chosen to ensure that both filtered noises have equal contrast energy.

*σ*

_{ u}and

*σ*

_{ v}were obtained by the following equations

*B*= 1 octave (full width at half-height);

*α*= 30° (full width at half-height); and the center frequency

*ρ*

_{0}of the filter was 1 cycle/deg for the low frequency noise and 3 cycles/deg for the high frequency noise. Then, we need to calculate the contrast (

*m*

_{1}and

*m*

_{2}) for the filtered images

*I*to equate both images (low and high) in energy. To obtain the value of

*m*, we only need to know the root mean square contrast (

*c*

_{RMS}) value for each noise; in this case, the RMS contrast was 0.0374. The equation (Serrano-Pedraza & Sierra-Vazquez, 2006) used was

*I*

_{0}= 128, where

*σ*, of the Gaussian filter was obtained by the equation

*B*= 1 octave (full width at half-height) and the center frequency

*ρ*

_{0}of the filter was 1 cycle/deg for the low frequency noise and 3 cycles/deg for the high frequency noise. The contrast

*m*of each noise was calculated as described above and the RMS contrast was 0.0748 for each noise (low and high).

*σ*

_{ t}∈ {12.5, 17.67, 25, 35.35, 50, 70.71, 100} ms, the simple stimuli were displayed with a standard deviation of

*σ*

_{ t}∈ {12.5, 25, 50, 100} ms. The temporal envelope was truncated to obtain the overall duration of 500 ms. The motion direction, left or right, was randomized and the observer's task was to indicate, by pressing a mouse button, the direction they saw on each presentation. A new trial was initiated only after the observer's response, thus the experiment proceeded at a pace determined by the observer. For each stimulus and duration, 25 presentations were required. No feedback about the correctness of responses was provided.

*σ*

_{ t}∈ {12.5, 25, 50, 100} ms, stimuli of the second type were displayed with a standard deviation of

*σ*

_{ t}∈ {100, 200} ms. The temporal envelope was truncated to obtain the overall duration of 1 s.

*ρ*

_{0}∈ {1, 3} cycles/deg. In the model below, we use the terms LF for the 1 cycle/deg sensors and HF for the 3 cycles/deg sensors. The function

*γ*is the gain of the sensor, where

*γ*(1) = 0.15 and

*γ*(3) = 1. The spreads of the Gaussian function

*σ*

_{ x}and

*σ*

_{ y}were obtained by the equations

*B*= 1 octave (full width at half-height) and

*α*= 30° (full width at half-height). The locations of the sensors were

*x*′ ∈ {−2°, −1°, 0°, 1°, 2°} and

*y*′ ∈ {−1.5°, 0°, 1.5°}. The total number of locations was a combination of

*x*′ ×

*y*′ = 15 locations. The orientations of the sensors were

*θ*

_{0}∈ {−60°, −30°, 0°, 30°, 60°, 90°}. The model uses a quadrature pair of sensors

*f*

_{1}and

*f*

_{2}. For

*f*

_{1}(

*x*,

*y*), the phase was

*φ*

_{0}=

*π*/2 rad; and for

*f*

_{2}(

*x*,

*y*), the phase was

*φ*

_{0}= 0 rad.

*h*

_{1}(

*t*) and

*h*

_{2}(

*t*), were a quadrature pair. The equation of the slower function

*h*

_{2}(

*t*) was taken from Adelson and Bergen (1985)

*k*= 0.09 and

*n*= 3. The faster function,

*h*

_{1}(

*t*), was the quadrature pair of

*h*

_{2}(

*t*), calculated in the frequency domain by using the Hilbert transform (Watson & Ahumada, 1985).

*i*and orientation

*j*was calculated from the inner product of the stimulus with the sensor spatial weighting function and the convolution of the stimulus with the temporal impulse response function

*m*orientations with a cosine weighting