Statistical learning of higher-order temporal structure from visual shape sequences.
Abstract
In 3 experiments, the authors investigated the ability of observers to extract the probabilities of successive shape co-occurrences during passive viewing. Participants became sensitive to several temporal-order statistics, both rapidly and with no overt task or explicit instructions. Sequences of shapes presented during familiarization were distinguished from novel sequences of familiar shapes, as well as from shape sequences that were seen during familiarization but less frequently than other shape sequences, demonstrating at least the extraction of joint probabilities of 2 consecutive shapes. When joint probabilities did not differ, another higher-order statistic (conditional probability) was automatically computed, thereby allowing participants to predict the temporal order of shapes. Results of a single-shape test documented that lower-order statistics were retained during the extraction of higher-order statistics. These results suggest that observers automatically extract multiple statistics of temporal events that are suitable for efficient associative learning of new temporal features.
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- Implicit learning and statistical learning: one phenomenon, two approachesPerruchet, Pierre, Pacton, Sebastien · 2006 · Trends in Cognitive Sciences
- Generating Videos with Scene DynamicsCarl Vondrick, Hamed Pirsiavash, Antonio B. Torralba · 2016 · arXiv (Cornell University)
- 19 The Moral Mind: How Five Sets of Innate Intuitions Guide the Development of Many Culture‐Specific Virtues, and Perhaps Even ModulesHaidt, Jonathan, Joseph, Craig · 2008 · The Innate Mind, Volume 3
- Neural representations of events arise from temporal community structureSchapiro, Anna C, Rogers, Timothy T, Cordova, Natalia I, Turk-Browne, Nicholas B, Botvinick, Matthew M · 2013 · Nature Neuroscience
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Statistical learning of higher-order temporal structure from visual shape sequences. https://app.cochat.ai/papers/10.1037//0278-7393.28.3.458
- The paper
- Fiser, J., & Aslin, R. Ν. (2002). Statistical learning of higher-order temporal structure from visual shape sequences. Journal of Experimental Psychology Learning Memory and Cognition. https://doi.org/10.1037//0278-7393.28.3.458
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.