Abstract
Life used to be simpler for sensory neuroscientists. Some measurement of neural activity, be it single-unit activity or increase in BOLD response, was measured against systematic variation of a stimulus and the resulting tuning functions presented and interpreted. But as the field discovered signal in the pattern of responses across voxels in a BOLD measurement or dynamic structure hidden within the activity of a population of neurons, computational techniques to extract features not easily discernible from raw measurement increasingly began to intervene between measurement and data presentation and interpretation. I will discuss one particular technique, the inverted encoding model, and how it extracts model responses rather than stimulus representations and what challenges that makes for interpretation of results.