Event Details

In models of perception under uncertainty, the terms “optimal” and “probabilistic” are often used interchangeably. I will argue that they have distinct meanings and are associated with different experimental designs. I will then present two recent studies: a) a complex visual search task with simple stimuli, in which optimality, not simplicity seems to govern behavior; b) a visual categorization task under ambiguity, in which human and monkey behavior is probabilistic, but  not necessarily optimal. In the latter task, I also show how the computation can be implemented by a plausible neural network, using a coding scheme known as probabilistic population coding. Finally, I will discuss V1 population recordings from monkeys performing this task.