Bayesian learning of visual chunks by human observers

By Gergo Orbán, József Fiser, Richard N. Aslin, Máté Lengyel. 2008

276 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

On the Binding Problem in Artificial Neural Networks (2020, background): “Indeed, evidence suggests that human object perception is based on similar principles (Orbán et al., 2008; Chater, 1996).”
Intentions and Motor Representations: the Interface Challenge (2017, background): “In recent years, evidence has accumulated that Bayesian inference and learning models can serve as powerful and biologically plausible models and successfully explain a range of phenomena including perceptual learning (Orbán et al. 2008; Maloney & Mamassian 2009) and causal learning in adults and in”
Optimal Behavioral Hierarchy (2014, background): “Some encouragement for this possibility comes from previous work in which related formal principles have been proposed to underlie learning in other domains, including vision [34,35], working memory [36,37], language [38], and others [39,40].”
Statistical learning of language: Theory, validity, and predictions of a statistical learning account of language acquisition (2015, unknown): “This is accomplished by competition between the stored units, although different models achieve this competition using different processes (e.g., Frank et al., 2010; Orbán et al., 2008; Perruchet & Vinter, 1998).”
A Bayesian framework for word segmentation: exploring the effects of context. (2009, unknown): “Indeed, the approach that we have taken here is complemented by a recent model of statistical learning for visual stimuli which was directly motivated by this kind of ideal observer analysis (Orbán et al., 2008).”
Exercising Your Brain: A Review of Human Brain Plasticity and Training-Induced Learning (2008, unknown): “In the music domain for instance, Schellenberg (2004) assessed the effect of music lessons on IQ.”
Reinforcement learning, efficient coding, and the statistics of natural tasks (2015, background): “In this connection, it is perhaps no coincidence that the work that we have reviewed on action hierarchy [26 ], which deals with the placement of boundaries between subtasks, rests upon the same mathematical foundations as recent work on object identification in vision [19], or that the same formal ”
Copying and Evolution of Neuronal Topology (2008, background): “A Bayesian account can be given of performance in some tasks [93].”
Neural Evidence of Statistical Learning: Efficient Detection of Visual Regularities Without Awareness (2009, unknown): “However, the extraction of regularities is an important component of other types of learning, including classification learning of associations between cues and outcomes (e.g., Knowlton, Squire, & Gluck, 1994), motor learning of sequences of manual responses (e.g., Nissen & Bullemer, 1987 ), and ru”