Stochastic characterization of small-scale algorithms for human sensory processing.

Human sensory processing can be viewed as a functional H mapping a stimulus vector s into a decisional variable r. We currently have no direct access to r; rather, the human makes a decision based on r in order to drive subsequent behavior. It is this (typically binary) decision that we can measure. For example, there may be two external stimuli s([0]) and s([1]), mapped onto r([0]) and r([1]) by the sensory apparatus H; the human chooses the stimulus associated with largest r. This kind of decisional transduction poses a major challenge for an accurate characterization of H. In this article, we explore a specific approach based on a behavioral variant of reverse correlation techniques, where the input s contains a target signal corrupted by a controlled noisy perturbation. The presence of the target signal poses an additional challenge because it distorts the otherwise unbiased nature of the noise source. We consider issues arising from both the decisional transducer and the target signal, their impact on system identification, and ways to handle them effectively for system characterizations that extend to second-order functional approximations with associated small-scale cascade models.