Reconciling multiplicative physiological noise and additive psychophysical noise K May, J Solomon |
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Division of Optometry and Visual Science, City University London, United Kingdom
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| In many psychophysical models of contrast discrimination, the contrast signal undergoes nonlinear transduction, and corruption with additive (i.e., stimulus-invariant) Gaussian noise. But physiological noise is often found to be multiplicative (variance proportional to response). A simple Bayesian decoding model of spiking neurons accommodates both findings, showing Poisson-based multiplicative noise at the physiological level, but additive Gaussian noise at the psychophysical level. If the model neurons' contrast-response functions are evenly spaced along the log-contrast axis, the decoded log-contrast has a stimulus-invariant, approximately Gaussian, distribution. At the psychophysical level, this model is equivalent to a log transducer with stimulus-invariant Gaussian noise. A slight manipulation of the neurons' pattern of spacing along the contrast axis makes the model behave much like a Legge-Foley transducer with stimulus-invariant noise. But is the noise on the model's internal signal really stimulus-invariant? It depends on the (arbitrary) choice of units in which we express the model's decoded contrast. We suggest that the transducer in some psychophysical models is just a transform of the stimulus contrast that allows us to express the internal signal in units such that the noise is stimulus-invariant. In this case, the argument that the noise is stimulus-invariant at the psychophysical level is circular. |
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