Statistical learning of higher-order temporal structure from visual shape sequences.

By József Fiser, Richard N. Aslin. 2002

630 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Developmental and Educational Psychology, Representation & Self-Supervised Learning

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Citations in the corpus

Generating Videos with Scene Dynamics (2016, background): “Conceptually, our work is related to studies into fundamental roles of time in computer vision [30, 12, 2, 7, 24].”
Seeing the Arrow of Time (2014, result): “In contrast to quite extensive research on the statistical properties of the spatial structure in images [6], there has been far less investigation of temporal structure (one of the exceptions to this rule includes [7]).”
Serial dependence in visual perception (2011, neutral): “Further, unlike statistical learning effects, serial dependence in perception does not rely on repeated displays, implicit or explicit learning, or long exposure durations 32,33 .”
Bayesian learning of visual chunks by human observers (2008, result): “Results To determine whether human performance is best described by the AL or the BCL, we compared the behavior of these models to the results of previously published experiments of chunk formation by humans (20, 22).”
The roles of information deficits and identity threat in the prevalence of misperceptions (2019, background): “While the findings from psychology are well documented, the testing paradigm is typically very abstract (e.g. Fiser and Aslin 2002).”
Children's imitation of causal action sequences is influenced by statistical and pedagogical evidence. (2011, background): “…are surprisingly sophisticated in their use of statistical information such as conditional probabilities in a range of domains, from phonology (Saffran, Aslin, & Newport, 1996), to visual perception (Fiser & Aslin, 2002; Kirkham, Slemmer, & Johnson, 2002), to word meaning (Xu & Tenenbaum, 2007).”
Variability and Detection of Invariant Structure (2002, background): “Learners can use transitional probabilities to segment rapid streams of tones (Saffran, Johnson, Aslin, & Newport, 1999) as well as temporal sequences of visual shapes (Fiser & Aslin, 2002).”
Neural Evidence of Statistical Learning: Efficient Detection of Visual Regularities Without Awareness (2009, neutral): “Similar to previous studies of statistical learning (e.g., Fiser & Aslin, 2002; Saffran et al., 1996), the structured blocks were constructed by assigning without replacement each of the 12 glyphs in the structured set to one of four ‘‘triplets’’: a subsequence of three glyphs that always appeared…”