- Visual statistical learning in humans and animals
- Probabilistic modeling of visual perception and statistical learning
- The Sampling Hypothesis: implementing probabilistic learning and inference in the cortex
- The effects of sequential perception and learning
- Emergence of visual constancies and invariances
- Active learning and teaching
Currently, we are expanding this line of research to four new directions. First, we are exploring hierarchical SL to trace the emergence of abstract hierarchical internal representations (Garber & Fiser in prep.). Second, we expand our SL framework to the auditory domain (Szabo, Markus & Fiser in prep) and link it to multimodal cue integration (Reguly, Nagy, Markus, & Fiser in prep). Third, we investigate the role of SL in active learning (Nagy, Arato, Rothkopf & Fiser in prep). Finally, we study SL in autistic patients and higher species other than humans to further clarify the origin of humans’ remarkable advantage in SL.
As a second step, we demonstrated that, contrary to the classic view of early visual coding with fixed receptive fields, humans encode orientation information of the visual input in a dynamic context by continuously combining sensory information with expectations derived from earlier experiences (Christensen, Bex & Fiser, 2015). Moreover, we provided evidence that orientation and position information of small contour segments are encoded independently and in a different manner, and they are combined together by using their uncertainty according to the rules of optimal cue combination (Christensen, Bex & Fiser, 2019). This suggests that uncertainty of information is represented already at the earliest level of visual processing in accordance with the fully Bayesian proposal.
Currently, we use our framework to develop experimental paradigms that can provide behavioral hallmarks on a fully Bayesian treatment of sensory information in the brain (Koblinger, Fiser & Lengyel, in prep). We are testing whether humans build up their internal representations in a coarse-to-fine manner (Fiser, Orban, Aslin & Lengyel, in prep), how this framework gives a probabilistic interpretation to contextual effects in scenes (Orban, Lengyel & Fiser, in prep), and how the dependence of the capacity of visual working memory on the number of items on a display is just a special case of a more general dependency on the complexity of the input as specified by prior experience (Lengyel, Orban, Fiser, in prep).
Currently, we are expanding the sampling framework to different modalities and species, and to dynamically changing environments. In addition, we investigate whether the framework provides correct predictions not only for normally reared but also visually deprived animals confirming the role of visual experience in developing internal representations (Savin, Chui, Lengyel & Fiser, in prep). We also developing behavioral paradigms to identify hallmarks of sampling-based probabilistic computation in the brain (Koblinger, Fiser & Lengyel, in prep). Finally, we explore the computational consequences of resource-limited approximate implementation of the sampling framework in the brain (Fiser & Koblinger, 2021).
Currently, we are exploring computational models that can capture this decision making behavior and tie those models to structure learning and sampling-based implementations (Szabo & Fiser, in prep.). We also investigate how these results relate to active learning task-switching, and the exploration-exploitation trade-off (Vieira & Fiser, in prep).
Currently, we are investigating how 2- and 3-dimensional size invariance emerges through statistical learning processes (Nagy & Fiser, in prep; Nagy, McKenzie & Fiser, in prep) and how the emergence of size invariance and size constancy could be jointly modelled (Garber & Fiser, in prep).
Optimal perception, optimal learning and optimal teaching are three successively more complex levels of performing a task well under uncertainty. In optimal perception, both the sensory input and all the relevant constraints of processing this input (typically related to the physical context) are clearly specified. In optimal learning, the constraints are not fully and explicitly specified, some of them need to be acquired based on the input and additional constraints. In optimal teaching, a subset of the relevant (unspecified) constraints are related to other individuals rather than to the physical environment. We are exploring the additional unique features required as an agent progresses across these three levels, and the extent to which humans and animals possess these characteristics (Stanciu, Lengyel & Fiser, in preparation).