All Pulfrich-like illusions can be explained without joint encoding of motion and disparity.

The final step was to build a neuronal model, and show that it experienced the illusion. We modelled a neuronal population constructed of neurons which either encoded motion, or depth (not both), and showed that a very simple way of "reading out" this activity, so as to convert it to a perception of depth, would be subject to the Pulfrich illusion. We also examined other evidence which had been put forward in support of the joint motion/depth idea, such as the illusion of swirling motion which occurs in dynamic noise with an interocular delay. We found that this, too, could be experienced by a brain which encoded motion and depth entirely separately. So, while there certainly are primate neurons which jointly encode motion and depth (notably in MT), there is no reason to suppose that these play a privileged role in supporting the Pulfrich effect and related illusions.
This series of three papers (Read & Cumming 2005abc) has recently attracted some criticism from Ning Qian and Ralph Freeman, in a paper entitled "Pulfrich phenomena are coded effectively by a joint motion-disparity process" (J Vis, 9(5): 1-16). My take on it is that we are all basically in agreement, but the situation is obscured by the lack of a clear agreed definition of "joint" vs "separate" encoding of motion and disparity. For example, we said that to be called a motion detector, a cell not only had to be tuned to speed, it also had to respond differently to opposite directions of motion, whereas Qian & Freeman required only speed tuning. I want to clear up one other point. Qian & Freeman say that our model is "non-causal", apparently because it responds to the disparity between a stimulus in one eye and a stimulus which arrives in the other eye at a later time. At the time that stimulus 1 occurs, stimulus 2 is still in the future. However, at the time the neuron responds to the disparity between the two stimuli, both stimuli have already occurred. Thus, the model is firmly causal. Indeed, our derivation of its properties explicitly sets the temporal kernel to zero for future times.
ReadCumming05c.pdf
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DateJanuary 16, 2012
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AuthorRead JCA, Cumming BG