Inverted Encoding Models of Human Population Response Conflate Noise and Neural Tuning Width.

Channel-encoding models offer the ability to bridge different scales of neuronal measurement by interpreting population responses, typically measured with BOLD imaging in humans, as linear sums of groups of neurons (channels) tuned for visual stimulus properties. Inverting these models to form predicted channel responses from population measurements in humans seemingly offers the potential to infer neuronal tuning properties. Here, we test the ability to make inferences about neural tuning width from inverted encoding models. We examined contrast invariance of orientation selectivity in human V1 (both sexes) and found that inverting the encoding model resulted in channel response functions that became broader with lower contrast, thus apparently violating contrast invariance. Simulations showed that this broadening could be explained by contrast-invariant single-unit tuning with the measured decrease in response amplitude at lower contrast. The decrease in response lowers the signal-to-noise ratio of population responses that results in poorer population representation of orientation. Simulations further showed that increasing signal to noise makes channel response functions less sensitive to underlying neural tuning width, and in the limit of zero noise will reconstruct the channel function assumed by the model regardless of the bandwidth of single units. We conclude that our data are consistent with contrast-invariant orientation tuning in human V1. More generally, our results demonstrate that population selectivity measures obtained by encoding models can deviate substantially from the behavior of single units because they conflate neural tuning width and noise and are therefore better used to estimate the uncertainty of decoded stimulus properties.SIGNIFICANCE STATEMENT It is widely recognized that perceptual experience arises from large populations of neurons, rather than a few single units. Yet, much theory and experiment have examined links between single units and perception. Encoding models offer a way to bridge this gap by explicitly interpreting population activity as the aggregate response of many single neurons with known tuning properties. Here we use this approach to examine contrast-invariant orientation tuning of human V1. We show with experiment and modeling that due to lower signal to noise, contrast-invariant orientation tuning of single units manifests in population response functions that broaden at lower contrast, rather than remain contrast-invariant. These results highlight the need for explicit quantitative modeling when making a reverse inference from population response profiles to single-unit responses.