Our research focuses on two levels of computation. Models of high-level computation aim at understanding the representation that humans use to learn about their environment. The way information is represented constrains both the ways new information can be acquired and how learned information can be exploited for achieving various (e.g. behavioral) goals. Since learning has to be performed on high-dimensional, noisy and ambiguous stimuli, probabilistic models are adequate tools as these models can handle all of these issues. Furthermore, Bayesian probabilistic models provide a normative theory for learning, which enables us to compare model performance with human data. We test theories by analyzing behavior of humans in experiments: by following participants’ eye movement we analyze how learning affects the design of efficient movement strategies.
Our investigations in low-level computations address how neurons deal with the problems imposed by the extremely rich stimuli. Optimal inference and learning requires that neurons also represent the uncertainty related to the inferred features of the environment besides the actual values of the features. The focus is on how a proper representation can be built and how these principles affect neural responses. Probabilistic models are used to model evoked and spontaneous activities in the visual system.
- Cognitive tomography
- Darkness sheds light on neural activity
- Sources of variability
- Learning the bricks of vision
- Linking response variability to perceptual inference