2019 | 2018 | 2017 | 2016 | 2015 | 2013 | 2012 | 2011 | 2010
2009 | 2008 | 2007 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000
2003
Fiser J., Bex PJ. & Makous W. (2003) Contrast conservation in human vision. Vision Research 43 (25), 2637-2648
Weliky M., Fiser J., Hunt RH. & Wagner DN. (2003) Coding of natural scenes in primary visual cortex. Neuron 37 (4), 703-718
2010
Fiser J., Berkes P., Orbán G. & Lengyel M. (2010) Statistically optimal perception and learning: from behavior to neural representations. Trends in cognitive sciences 14 (3), 119-130 [Highly Cited Paper]
Human perception has recently been characterized as statistical inference based on noisy and ambiguous sensory inputs. Moreover, suitable neural representations of uncertainty have been identified that could underlie such probabilistic computations. In this review, we argue that learning an internal model of the sensory environment is another key aspect of the same statistical inference procedure and thus perception and learning need to be treated jointly. We review evidence for statistically optimal learning in humans and animals, and re-evaluate possible neural representations of uncertainty based on their potential to support statistically optimal learning. We propose that spontaneous activity can have a functional role in such representations leading to a new, sampling-based, framework of how the cortex represents information and uncertainty.
2002
Fiser J. & Aslin RN. (2002) Statistical learning of new visual feature combinations by infants. Proceedings of the National Academy of Sciences 99 (24), 15822-15826
Fiser J. & Aslin RN. (2002) Statistical learning of higher-order temporal structure from visual shape sequences.. Journal of Experimental Psychology: Learning, Memory, and Cognition 28 (3), 458
2008
Orbán G., Fiser J., Aslin RN. & Lengyel M. (2008) Bayesian learning of visual chunks by human observers. Proceedings of the National Academy of Sciences 105 (7), 2745-2750
2010
Fiser J., Berkes P., Orbán G. & Lengyel M. (2010) Statistically optimal perception and learning: from behavior to neural representations. Trends in cognitive sciences 14 (3), 119-130 [Highly Cited Paper]
Human perception has recently been characterized as statistical inference based on noisy and ambiguous sensory inputs. Moreover, suitable neural representations of uncertainty have been identified that could underlie such probabilistic computations. In this review, we argue that learning an internal model of the sensory environment is another key aspect of the same statistical inference procedure and thus perception and learning need to be treated jointly. We review evidence for statistically optimal learning in humans and animals, and re-evaluate possible neural representations of uncertainty based on their potential to support statistically optimal learning. We propose that spontaneous activity can have a functional role in such representations leading to a new, sampling-based, framework of how the cortex represents information and uncertainty.
2012
Janacsek K., Fiser J. & Nemeth D. (2012) The best time to acquire new skills: age-related differences in implicit sequence learning across the human lifespan. Developmental science 15 (4), 496-505
McIlreavy L., Fiser J. & Bex PJ. (2012) Impact of simulated central scotomas on visual search in natural scenes. Optometry and vision science: official publication of the American Academy of Optometry
White B., Abbott LF. & Fiser J. (2012) Suppression of cortical neural variability is stimulus-and state-dependent. Journal of neurophysiology 108 (9), 2383-2392
2009
Fiser J. (2009) Perceptual learning and representational learning in humans and animals. Learning & behavior 37 (2), 141-153
Fiser J. (2009) The other kind of perceptual learning. Learning & Perception 1 (1), 69-87
In the present review we discuss an extension of classical perceptual learning called the observational learning paradigm. We propose that studying the process how humans develop internal representation of their environment requires modifications of the original perceptual learning paradigm which lead to observational learning. We relate observational learning to other types of learning, mention some recent developments that enabled its emergence, and summarize the main empirical and modeling findings that observational learning studies obtained. We conclude by suggesting that observational learning studies have the potential of providing a unified framework to merge human statistical learning, chunk learning and rule learning.
Berkes P., Turner RE. & Sahani M. (2009) A structured model of video reproduces primary visual cortical organisation, PLoS Computational Biology, 2009. 5(9): e1000495
Zito T., Wilbert N., Wiskott L. & Berkes P. (2009) Modular toolkit for data processing (MDP): a Python data processing framework. Frontiers in Neuroinformatics 2:8
2020
Avarguès-Weber A., Finke V., Nagy M., Szabó T., d’Amaro D., Dyer A.G. & Fiser J (2020) Different mechanisms underlie implicit visual statistical learning in honey bees and humans, PNAS 117 (41) 25923-25934
Arató J., Rothkopf C. A. & Fiser J. (2020) Learning in the eyes: specific changes in gaze patterns track explicit and implicit visual learning, bioRxiv 2020.08.03.234039
2011
Berkes P., Orbán G., Lengyel M. & Fiser J. (2011) Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science 331 (6013), 83-87 [Highly Cited Paper]
The brain maintains internal models of its environment to interpret sensory inputs and to prepare actions. Although behavioral studies have demonstrated that these internal models are optimally adapted to the statistics of the environment, the neural underpinning of this adaptation is unknown. Using a Bayesian model of sensory cortical processing, we related stimulus-evoked and spontaneous neural activities to inferences and prior expectations in an internal model and predicted that they should match if the model is statistically optimal. To test this prediction, we analyzed visual cortical activity of awake ferrets during development. Similarity between spontaneous and evoked activities increased with age and was specific to responses evoked by natural scenes. This demonstrates the progressive adaptation of internal models to the statistics of natural stimuli at the neural level.
Roser ME., Fiser J., Aslin RN. & Gazzaniga MS. (2011) Right hemisphere dominance in visual statistical learning. Journal of cognitive neuroscience 23 (5), 1088-1099
2010
Fiser J., Berkes P., Orbán G. & Lengyel M. (2010) Statistically optimal perception and learning: from behavior to neural representations. Trends in cognitive sciences 14 (3), 119-130 [Highly Cited Paper]
Human perception has recently been characterized as statistical inference based on noisy and ambiguous sensory inputs. Moreover, suitable neural representations of uncertainty have been identified that could underlie such probabilistic computations. In this review, we argue that learning an internal model of the sensory environment is another key aspect of the same statistical inference procedure and thus perception and learning need to be treated jointly. We review evidence for statistically optimal learning in humans and animals, and re-evaluate possible neural representations of uncertainty based on their potential to support statistically optimal learning. We propose that spontaneous activity can have a functional role in such representations leading to a new, sampling-based, framework of how the cortex represents information and uncertainty.
2009
Fiser J. (2009) Perceptual learning and representational learning in humans and animals. Learning & behavior 37 (2), 141-153
Fiser J. (2009) The other kind of perceptual learning. Learning & Perception 1 (1), 69-87
In the present review we discuss an extension of classical perceptual learning called the observational learning paradigm. We propose that studying the process how humans develop internal representation of their environment requires modifications of the original perceptual learning paradigm which lead to observational learning. We relate observational learning to other types of learning, mention some recent developments that enabled its emergence, and summarize the main empirical and modeling findings that observational learning studies obtained. We conclude by suggesting that observational learning studies have the potential of providing a unified framework to merge human statistical learning, chunk learning and rule learning.
Berkes P., Turner RE. & Sahani M. (2009) A structured model of video reproduces primary visual cortical organisation, PLoS Computational Biology, 2009. 5(9): e1000495
Zito T., Wilbert N., Wiskott L. & Berkes P. (2009) Modular toolkit for data processing (MDP): a Python data processing framework. Frontiers in Neuroinformatics 2:8
2012
Janacsek K., Fiser J. & Nemeth D. (2012) The best time to acquire new skills: age-related differences in implicit sequence learning across the human lifespan. Developmental science 15 (4), 496-505
McIlreavy L., Fiser J. & Bex PJ. (2012) Impact of simulated central scotomas on visual search in natural scenes. Optometry and vision science: official publication of the American Academy of Optometry
White B., Abbott LF. & Fiser J. (2012) Suppression of cortical neural variability is stimulus-and state-dependent. Journal of neurophysiology 108 (9), 2383-2392
2007
Fiser J., Scholl BJ. & Aslin RN. (2007) Perceived object trajectories during occlusion constrain visual statistical learning. Psychonomic bulletin & review 14 (1), 173-178
Visual statistical learning of shape sequences was examined in the context of occluded object trajectories. In a learning phase, participants viewed a sequence of moving shapes whose trajectories and speed profiles elicited either a bouncing or a streaming percept: The sequences consisted of a shape moving toward and then passing behind an occluder, after which two different shapes emerged from behind the occluder. At issue was whether statistical learning linked both object transitions equally, or whether the percept of either bouncing or streaming constrained the association between pre- and postocclusion objects. In familiarity judgments following the learning, participants reliably selected the shape pair that conformed to the bouncing or streaming bias that was present during the learning phase. A follow-up experiment demonstrated that differential eye movements could not account for this finding. These results suggest that sequential statistical learning is constrained by the spatiotemporal perceptual biases that bind two shapes moving through occlusion, and that this constraint thus reduces the computational complexity of visual statistical learning.
2010
Fiser J., Berkes P., Orbán G. & Lengyel M. (2010) Statistically optimal perception and learning: from behavior to neural representations. Trends in cognitive sciences 14 (3), 119-130 [Highly Cited Paper]
Human perception has recently been characterized as statistical inference based on noisy and ambiguous sensory inputs. Moreover, suitable neural representations of uncertainty have been identified that could underlie such probabilistic computations. In this review, we argue that learning an internal model of the sensory environment is another key aspect of the same statistical inference procedure and thus perception and learning need to be treated jointly. We review evidence for statistically optimal learning in humans and animals, and re-evaluate possible neural representations of uncertainty based on their potential to support statistically optimal learning. We propose that spontaneous activity can have a functional role in such representations leading to a new, sampling-based, framework of how the cortex represents information and uncertainty.
2009
Fiser J. (2009) Perceptual learning and representational learning in humans and animals. Learning & behavior 37 (2), 141-153
Fiser J. (2009) The other kind of perceptual learning. Learning & Perception 1 (1), 69-87
In the present review we discuss an extension of classical perceptual learning called the observational learning paradigm. We propose that studying the process how humans develop internal representation of their environment requires modifications of the original perceptual learning paradigm which lead to observational learning. We relate observational learning to other types of learning, mention some recent developments that enabled its emergence, and summarize the main empirical and modeling findings that observational learning studies obtained. We conclude by suggesting that observational learning studies have the potential of providing a unified framework to merge human statistical learning, chunk learning and rule learning.
Berkes P., Turner RE. & Sahani M. (2009) A structured model of video reproduces primary visual cortical organisation, PLoS Computational Biology, 2009. 5(9): e1000495
Zito T., Wilbert N., Wiskott L. & Berkes P. (2009) Modular toolkit for data processing (MDP): a Python data processing framework. Frontiers in Neuroinformatics 2:8
2003
Fiser J., Bex PJ. & Makous W. (2003) Contrast conservation in human vision. Vision Research 43 (25), 2637-2648
Weliky M., Fiser J., Hunt RH. & Wagner DN. (2003) Coding of natural scenes in primary visual cortex. Neuron 37 (4), 703-718
2012
Janacsek K., Fiser J. & Nemeth D. (2012) The best time to acquire new skills: age-related differences in implicit sequence learning across the human lifespan. Developmental science 15 (4), 496-505
McIlreavy L., Fiser J. & Bex PJ. (2012) Impact of simulated central scotomas on visual search in natural scenes. Optometry and vision science: official publication of the American Academy of Optometry
White B., Abbott LF. & Fiser J. (2012) Suppression of cortical neural variability is stimulus-and state-dependent. Journal of neurophysiology 108 (9), 2383-2392
2007
Fiser J., Scholl BJ. & Aslin RN. (2007) Perceived object trajectories during occlusion constrain visual statistical learning. Psychonomic bulletin & review 14 (1), 173-178
Visual statistical learning of shape sequences was examined in the context of occluded object trajectories. In a learning phase, participants viewed a sequence of moving shapes whose trajectories and speed profiles elicited either a bouncing or a streaming percept: The sequences consisted of a shape moving toward and then passing behind an occluder, after which two different shapes emerged from behind the occluder. At issue was whether statistical learning linked both object transitions equally, or whether the percept of either bouncing or streaming constrained the association between pre- and postocclusion objects. In familiarity judgments following the learning, participants reliably selected the shape pair that conformed to the bouncing or streaming bias that was present during the learning phase. A follow-up experiment demonstrated that differential eye movements could not account for this finding. These results suggest that sequential statistical learning is constrained by the spatiotemporal perceptual biases that bind two shapes moving through occlusion, and that this constraint thus reduces the computational complexity of visual statistical learning.
2012
Janacsek K., Fiser J. & Nemeth D. (2012) The best time to acquire new skills: age-related differences in implicit sequence learning across the human lifespan. Developmental science 15 (4), 496-505
McIlreavy L., Fiser J. & Bex PJ. (2012) Impact of simulated central scotomas on visual search in natural scenes. Optometry and vision science: official publication of the American Academy of Optometry
White B., Abbott LF. & Fiser J. (2012) Suppression of cortical neural variability is stimulus-and state-dependent. Journal of neurophysiology 108 (9), 2383-2392
2010
Fiser J., Berkes P., Orbán G. & Lengyel M. (2010) Statistically optimal perception and learning: from behavior to neural representations. Trends in cognitive sciences 14 (3), 119-130 [Highly Cited Paper]
Human perception has recently been characterized as statistical inference based on noisy and ambiguous sensory inputs. Moreover, suitable neural representations of uncertainty have been identified that could underlie such probabilistic computations. In this review, we argue that learning an internal model of the sensory environment is another key aspect of the same statistical inference procedure and thus perception and learning need to be treated jointly. We review evidence for statistically optimal learning in humans and animals, and re-evaluate possible neural representations of uncertainty based on their potential to support statistically optimal learning. We propose that spontaneous activity can have a functional role in such representations leading to a new, sampling-based, framework of how the cortex represents information and uncertainty.
2009
Fiser J. (2009) Perceptual learning and representational learning in humans and animals. Learning & behavior 37 (2), 141-153
Fiser J. (2009) The other kind of perceptual learning. Learning & Perception 1 (1), 69-87
In the present review we discuss an extension of classical perceptual learning called the observational learning paradigm. We propose that studying the process how humans develop internal representation of their environment requires modifications of the original perceptual learning paradigm which lead to observational learning. We relate observational learning to other types of learning, mention some recent developments that enabled its emergence, and summarize the main empirical and modeling findings that observational learning studies obtained. We conclude by suggesting that observational learning studies have the potential of providing a unified framework to merge human statistical learning, chunk learning and rule learning.
Berkes P., Turner RE. & Sahani M. (2009) A structured model of video reproduces primary visual cortical organisation, PLoS Computational Biology, 2009. 5(9): e1000495
Zito T., Wilbert N., Wiskott L. & Berkes P. (2009) Modular toolkit for data processing (MDP): a Python data processing framework. Frontiers in Neuroinformatics 2:8
2002
Fiser J. & Aslin RN. (2002) Statistical learning of new visual feature combinations by infants. Proceedings of the National Academy of Sciences 99 (24), 15822-15826
Fiser J. & Aslin RN. (2002) Statistical learning of higher-order temporal structure from visual shape sequences.. Journal of Experimental Psychology: Learning, Memory, and Cognition 28 (3), 458
2002
Fiser J. & Aslin RN. (2002) Statistical learning of new visual feature combinations by infants. Proceedings of the National Academy of Sciences 99 (24), 15822-15826
Fiser J. & Aslin RN. (2002) Statistical learning of higher-order temporal structure from visual shape sequences.. Journal of Experimental Psychology: Learning, Memory, and Cognition 28 (3), 458