A Logarithmic, Scale-Invariant Representation of Speed in Macaque Middle Temporal Area Accounts for Speed Discrimination Performance

Human speed discrimination thresholds follow Weber's law over a large range of reference (i.e., pedestal) speeds, that is, the just-noticeable-difference in speed scales in proportion to the reference speed. We analyzed the neural representation of speed information in macaque middle temporal visual area (MT) to determine whether this representation can account for the basic form of psychophysical data. Based on theoretical considerations, we hypothesized: (1) that the speed tuning curves of MT neurons should be bell-shaped (Gaussian) as a function of the logarithm of speed, (2) that the set of speed-tuning curves should be approximately scale-invariant, (3) that the distribution of speed preferences should be approximately uniform in log speed, and (4) that response variability should be independent of speed preference. Our quantitative analysis of data from 501 MT neurons shows that the neural representation of speed approximately obeys these constraints, with modest deviations particularly at slow speeds. We then used the MT data to predict how speed discrimination thresholds should depend on pedestal speed. The shape of this prediction matches very closely to that of human psychophysical data, accounting for constant Weber fractions over a large range of intermediate speeds as well as a marked departure from Weber's law at slow speeds. Moreover, we show that deviations of the MT representation from the above constraints are important for predicting how psychophysical thresholds depart from Weber's law at slow speeds. These findings support the notion that a logarithmic, approximately scale-invariant representation of speed in area MT limits perceptual speed discrimination.