Various approaches to online inference - human behavior and theoretical models
Abstract: In natural settings, we make decisions based on streams of partial and noisy information. Arguably, we summarize the perceived information into a probabilistic model of the world, which we can exploit to make predictions and decisions. This talk will explore such 'mental models' in the context of idealized tasks that can be carried out in the laboratory and modeled quantitatively. I shall describe results from behavioral experiments on human subjects, and propose several theoretical approaches that may capture the sub-optimal and noisy behavior quantified in humans. These will range from phenomenological to normative approaches. Overall, the analyses indicate that humans may simplify the internal process that leads to a prediction or decision at the cost of using subjective, reduced, and noisy representations of their environment.