Many cognitive neuroscience theories assume that changes in behavior arise from changes in the tuning properties of neurons (e.g., Dosher & Lu 1998, Ling, Liu, & Carrasco 2009). However, direct tests of these theories with electrophysiology are rarely feasible with humans. Non-invasive functional magnetic resonance imaging (fMRI) produces voxel tuning, but each voxel aggregates hundreds of thousands of neurons, and voxel tuning modulation is a complex mixture of the underlying neural responses. We developed a pair of statistical tools to address this problem, which we refer to as NeuroModulation Modeling (NMM). NMM advances fMRI analysis methods, inferring the response of neural subpopulations by leveraging modulations at the voxel-level to differentiate between different forms of neuromodulation. One tool uses hierarchical Bayesian modeling and model comparison while the other tool uses a non-parametric slope analysis. We tested the validity of NMM by applying it to fMRI data collected from participants viewing orientation stimuli at high- and low-contrast, which is known from electrophysiology to cause multiplicative scaling of neural tuning (e.g., Sclar & Freeman 1982). In seeming contradiction to ground truth, increasing contrast appeared to cause an additive shift in orientation tuning of voxel-level fMRI data. However, NMM indicated multiplicative gain rather than an additive shift, in line with single-cell electrophysiology. Beyond orientation, this approach could be applied to determine the form of neuromodulation in any fMRI experiment, provided that the experiment tests multiple points along a stimulus dimension to which neurons are tuned (e.g., direction of motion, isoluminant hue, pitch, etc.).

Representational theories predict that brain regions contribute to cognition according to the information they represent (e.g., simple versus complex), contradicting the traditional notion that brain regions are specialized for cognitive functions (e.g., perception versus memory). In support of representational accounts, substantial evidence now attests that the Medial Temporal Lobe (MTL) is not specialized solely for long-term declarative memory, but underpins other functions including perception and future-imagining for complex stimuli and events. However, a complementary prediction has been less well explored, namely that the cortical locus of declarative memory may fall outside the MTL if the to-be-remembered content is sufficiently simple. Specifically, the locus should coincide with the optimal neural code for the representations being retrieved. To test this prediction, we manipulated the complexity of the to-be-remembered representations in a recognition memory task. First, participants in the scanner viewed novel 3D objects and scenes, and we used multivariate analyses to identify regions in the ventral visual-MTL pathway that preferentially coded for either simple features of the stimuli, or complex conjunctions of those features. Next, in a separate scan, we tested recognition memory for these stimuli and performed neuroimaging contrasts that revealed two memory signals ‒ feature memory and conjunction memory. Feature memory signals were found in visual cortex, while conjunction memory signals emerged in MTL. Further, the regions optimally representing features via preferential feature-coding coincided with those exhibiting feature memory signals. These findings suggest that representational content, rather than cognitive function, is the primary organizing principle in the ventral visual-MTL pathway.