Fiser J. & Garami L. (2023) Modality-independent biases in temporal processing. Journal of Vision 23 (9), 5430-5430 [Abstract]
The brain encodes dynamic sensory information along different modalities effectively and accurately into structured representations by relying on various biases of different complexities. While the ultimate representation is multimodal, the biases used for encoding have been defined at the level of individual modalities despite a growing body of evidence showing that integration is already present […]
Garber D. & Fiser J. (2023) The effect of consolidation and explicitness on learning and transferring higher-level structural knowledge in vision. Journal of Vision 23 (9), 5308-5308 [Abstract]
While studies on visual statistical learning focus on how specific chunks based on co-occurrence of observable elements are learned, they typically neglect exploring the role of knowledge about the higher-level structure of these chunks in learning. We studied this role of structural knowledge by investigating how first being exposed to only horizontal or vertical shape-pairs […]
Magas D. & Fiser J. (2023) Probabilistic encoding and well-calibratedness of long-term episodic memory. VSS 2023, Journal of Vision 23 (9), 5325-5325 [Abstract]
Accumulating behavioral and neural evidence suggests that incoming sensory input is represented and combined with generalized semantic knowledge in a fundamentally probabilistic way during perceptual processes. Recently we provided evidence that human perceptual decision making is fully probabilistic (encodes uncertainty of all internal variables) rather than task-dependently probabilistic (encoding uncertainty only at the level of […]
Yang C., Lisitsyn D. & Fiser J. (2012) Testing the nature of the representation for binocular rivalry. VSS 2012, Journal of Vision 12 (9), 204-204 [Abstract]
Recently, several studies proposed a probabilistic framework for explaining the phenomenon of binocular rivalry, as an alternative to the classic bottom-up or eye-dominant interpretation of it. According to this framework, perception is generated from the observer’s internal model of the visual world, based on sampling-based probabilistic representations and computations in the cortex. To test the […]
Cui M., Katz DB., Fontanini A. & Fiser J. (2010) The flow of expected and unexpected sensory information through the distributed forebrain network, Frontiers in Systems Neuroscience. Conference Abstract: Computational and systems neuroscience, 2010.
Forebrain taste information processing is accomplished mainly by three reciprocally connected forebrain regions -primary gustatory cortex (GC), (basolateral) amygdala (AM), and orbitofrontal cortex (OFC)- loosely characterized as the neural sources of sensory, palatability-related, and cognitive information, respectively. It has been proposed that the perception of complex taste stimuli involves an intricate flow of information between […]
Orbán G., Berkes P., Lengyel M. & Fiser J. (2008) Relating evoked and spontaneous cortical activities in a generative modeling framework. Sloan-Swartz Meeting of Theoretical Neurobiology 2008, Princeton, NJ [Abstract]
Recently we proposed a computational framework in which we assumed that the visual cortex implicitly implements a generative model of the natural visual environment and performs its functions such as recognition and discrimination by inferring the underlying external causes of the visual input. In the present work, we test this framework by relating synthetic and […]
Popovic M., Lisitsyn D., Lengyel M. & Fiser J. (2012) Time to decide: sampling based representation of uncertainty in human vision. VSS 2012, Journal of Vision 12 (9), 616-616 [Abstract]
Growing behavioral evidence suggests that animals and humans represent uncertainty about both high and low-level sensory stimuli in the brain for probabilistic inference and learning. One proposal about the nature of the neural basis of this representation of uncertainty suggests that instantaneous membrane potentials of cortical sensory neurons correspond to statistical samples from a probability […]
Berkes P., David SV., Fritz J., Shamma SA. & Fiser J. (2010) Neural activity as samples from a probabilistic representation: evidence from the auditory cortex., Frontiers in Systems Neuroscience. Conference Abstract: Computational and systems neuroscience, 2010.
In the past years, there has been a paradigm shift in the field of cognitive neuroscience as a number of behavioral studies demonstrated that animals and humans can take into account statistical uncertainties of task, reward, and their own behavior, in order to achieve optimal task performance. These results have been interpreted in terms of […]
Orbán G., Berkes P., Lengyel M. & Fiser J. (2008) Looking for hallmarks of generative models in the visual cortex. COSYNE 2008, Salt Lake Ctiy, UT [Abstract]
A recently emerging computational framework of the visual cortex assumes that it implements a generative model of (natural) visual input. According to this view, the visual cortex implicitly embodies a statistical model of how external causes (the latent variables of the model) combine to form the visual input (the observed variables of the model). Given […]
Ledley J., MacKenzie K. & Fiser J. (2012) Coding object size based rules in 3D visual scenes. VSS 2012, Journal of Vision 12 (9), 806-806 [Abstract]
Learning abstract rules in the auditory and visual domains is customarily investigated with the AAB vs. ABB paradigm where each scene contains three auditory events or visual objects and either identity or an attribute of these items, such as the size of the objects, follows a same-same-different (i.e. AAB) pattern during a training period. In […]