Assistant Professor, Justin Gardner, Department of Psychology, Stanford University
Title: Optimality and heuristics in perceptual neuroscience
Abstract: Optimality considerations have been a core driver of developments in sensory neuroscience. For example, using signal detection theory, ideal observer models can be formulated which set the upper limits of performance on detection tasks. Statistical decision theory prescribes optimal solutions to sensory inference problems in which sensory and prior information are both uncertain. At the same time, human behavior has often been shown to take short-cuts to optimality in the form of heuristic behaviors which approximate optimal models using incomplete information and/or simpler computations. Indeed, in our own work, we have found that human observers can use heuristics like switching between the prior and likelihood to approximate optimal Bayesian computation. I will discuss the aim of building perceptual theory that uses optimality to set the computational goals for perceptual behavior but, through consideration of ecological, computational, and energetic constraints, incorporates how these optimal goals can be achieved through heuristic approximation. Building such theory for human perception is critical for the goal of understanding the underlying neural computations that implement complex perceptual behavior.