AI Behavioral Evaluation Should Be Grounded in Psychophysics Across Marr’s Levels

This position paper argues that AI evaluation research is fragmented across incompatible frameworks (e.g., adversarial robustness, attention probing, bias analysis, corruption tolerance, interpretability), each operating at a different explanatory level without a shared grammar to make this explicit. We propose unifying these efforts under \textbf{AI psychophysics}, a behavioral evaluation methodology grounded in classical psychophysics and Marr's three levels of analysis. The core contribution is a formalization showing that all these threads instantiate the same psychometric function $\psi(\mathbf{S})=p(\mathbf{J}|\mathbf{S})$, but at different levels of explanatory granularity (computational, algorithmic, implementational). We present a taxonomy of 21 evaluation paradigms organized by this framework and show that the absence of a shared grammar causes category errors, where metrics from one level are routinely used to support claims at another. We conclude with a concrete research agenda for standardizing AI evaluation around this framework.