Berkes P., White BL. & Fiser J. (2010) Sparseness is not actively optimized in V1. COSYNE 2010, Salt Lake City, UT [Abstract]
Sparse coding is a powerful idea in computational neuroscience referring to the general principle that the cortex exploits the benefits of representing every stimulus by a small subset of neurons. Advantages of sparse coding include reduced dependencies, improved detection of co-activation of neurons, and a more efficient encoding of visual information. Computational models based on […]
Orbán G., Lengyel M. & Fiser J. (2011) Statistically optimal effects of uncertainty in scene segmentation on human learning. COSYNE 2011, Nature Precedings (2011) [Abstract]
High-throughput neuroscience presents unique challenges for exploratory data analysis. Clustering often helps experimenters make sense of data, but model-based clustering techniques, including Dirichlet-process mixture models, have difficulty when differing subsets of dimensions are best explained by differing clusterings. As a result, they can be misled by irrelevant dimensions, they easily miss structure that dimensionality reduction […]
Turner RE., Berkes P. & Fiser J. (2011) Learning complex tasks with probabilistic population codes. COSYNE 2011, Nature Precedings (2011) [Abstract]
Recent psychophysical experiments imply that the brain employs a neural representation of the uncertainty in sensory stimuli and that probabilistic computations are supported by the cortex. Several candidate neural codes for uncertainty have been posited including Probabilistic Population Codes (PPCs). PPCs support various versions of probabilistic inference and marginalisation in a neurally plausible manner. However, […]
Berkes P., Turner R. & Fiser J. (2011) The army of one (sample): the characteristics of sampling-based probabilistic neural representations. COSYNE 2011, Nature Precedings (2011) [Abstract]
There is growing evidence that humans and animals represent the uncertainty associated with sensory stimuli and utilize this uncertainty during planning and decision making in a statistically optimal way. Recently, a nonparametric framework for representing probabilistic information has been proposed whereby neural activity encodes samples from the distribution over external variables. Although such sample-based probabilistic […]
Selig G., Lisitsyn D., Bex P. & Fiser J. (2011) The diagnostic features used for recognizing faces under natural conditions. VSS 2011, Journal of Vision 11 (11), 614-614 [Abstract]
Classical studies of face perception have used stimulus sets with standardized pose, feature locations and extremely impoverished information content. It is unclear how the results of these studies translate to natural perception, where faces are typically encountered in a wide variety of viewpoints and conditions. To address this issue, we used a 2-AFC coherence paradigm, […]
Fiser J., Orbán G. & Lengyel M. (2011) Uncertainty in scene segmentation: statistically optimal effects on learning visual representations. VSS 2011, Journal of Vision 11 (11), 994-994 [Abstract]
A number of recent psychophysical studies have argued that human behavioral processing of sensory inputs is best captured by probabilistic computations. Due to conflicting cues, real scenes are ambiguous and support multiple hypotheses of scene interpretation, which require handling uncertainty. The effects of this inherent perceptual uncertainty have been well-characterized on immediate perceptual decisions, but […]
Popovic M., Lisitsyn D., Berkes P., Lengyel M. & Fiser J. (2011) Uncertainty representation of low-level visual attributes. VSS 2011, Journal of Vision 11 (11), 807-807 [Abstract]
There is increasing behavioral evidence that humans represent uncertainty about sensory stimuli in a way that it is suitable for decision making and learning in a statistically optimal manner. Do such representations of uncertainty exist for low-level visual stimuli, and furthermore, are they probabilistic in nature? We tested whether subjective assessment of the orientation uncertainty […]
MacKenzie KJ., Aslin RN. & Fiser J. (2011) The interaction between chunking and stimulus complexity in infant visual statistical learning. VSS 2011, Journal of Vision 11 (11), 459-459 [Abstract]
Human infants are known to learn statistical regularities of the sensory environment implicitly in various perceptual domains. Visual statistical leaning studies have illustrated that this learning is highly sophisticated and well_approximated by optimal probabilistic chunking of the unfamiliar input. However, the emergence and unitization of such perceptual chunks at an early age and their relation […]
Fiser J., Berkes P., Orban G. & Lengyel M. (2011) Probabilistic computation: a possible functional role for spontaneous activity in the cortex. ECVP 2011, Perception 40, 53-53 [Abstract]
Although a number of recent behavioral studies implied that the brain maintains probabilistic internal models of the environment for perception, motor control, and higher order cognition, the neural correlates of such models has not been characterized so far. To address this issue, we introduce a new framework with two key ingredients: the “sampling hypothesis” and […]
Popovic M., Lisitsyn D., Lengyel M. & Fiser J. (2011) Simultaneous representation of uncertainty about multiple low-level visual elements. EVCP 2011, Perception 40, 190-190 [Abstract]
Recent findings suggest that humans represent uncertainty for statistically optimal decision making and learning. However, it is unknown whether such representations of uncertainty extend to multiple low-level elements of visual stimuli, although this would be crucial for optimal probabilistic representations. We examined how subjects’ subjective assessment of uncertainty about the orientations of multiple elements in […]