Doctoral Defense of Jozsef Arato
The Department of Cognitive Science cordially invites you to the public defense of the PhD thesis of
Active learning as a link between environmental statistics and the development of internal representations
Supervisor: József Fiser
Secondary Supervisor: Gergely Csibra
External Advisor: Constantin Rothkopf
Although it is known that facing a dynamically changing sensory stream, people’s perceptual decisions could be influenced not only by individual past stimuli, but also by extracted summary statistics of the stimuli, the effects of these long-term influences are underexplored. In the present thesis, I explored the impact of past stimulus statistics on two distinct types of visual decisions. In the first line of research, in Chapters 2-3, I focused on visual explorative decisions via eye-movements and investigated whether hidden statistical structures of complex scenes could influence visual exploration. I found that spatial regularities of visual stimuli influenced explorative eye-movement patterns, that this effect emerged over time, and it could predict the success in learning the underlying structure of the input. These findings suggest a strong relationship between visual exploration and learning, during which the two processes continuously influence each other. I also showed how this relationship depended on the explicit vs. implicit nature of the task. In the second line of research, in Chapters 4-5, I explored long-term statistical influences in perceptual decision making. To this end, I tested the influence of past probabilities of appearance on discrimination judgments about ambiguous stimuli. I found that statistics of past stimulus strongly influenced perceptual decisions independently of the well-documented short-term sequential effects. This past influence depended on the change-dynamics between long-term and recent stimulus probabilities, sometimes resulting in locally irrational biases. Taken together, the results in these two research domains are consistent with a framework, in which past stimulus statistics are perpetually and automatically built into complex internal representations, which in turn, depending on the task and type of regularity, can dramatically influence visual decisions.