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Updated: Jun 12, 2025

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Published on: January 23, 2017
The order of stimuli matters when learning second-order transitional probabilities.
Laura Lazartigues1, Fabien Mathy2, Carlos Aguilar3
1University Lille, CNRS, UMR 9193 - SCALab - Sciences Cognitives et Sciences Affectives, F-59000, Lille, France. Laura.lazartigues@univ-lille.fr.
Statistical learning can process complex sequences, but learning second-order transitional probabilities (TPs) involving stimulus order is challenging for human cognition. This study investigated how the brain learns these complex associations.
Area of Science:
- Cognitive psychology
- Neuroscience
- Computational modeling
Background:
- Sequence processing relies on understanding stimulus order and transitional probabilities (TPs).
- Second-order TPs, where two stimuli predict a third, are crucial but less understood.
- General learning mechanisms' ability to extract complex sequential information requires further investigation.
Purpose of the Study:
- To investigate statistical learning of second-order transitional probabilities (TPs).
- To determine if the order of stimulus presentation within pairs affects learning.
- To identify challenges in learning second-order TPs and their interaction with stimulus order.
Main Methods:
- Utilized eight visuomotor sequences governed by second-order TPs.
- Recorded response times (RTs) during learning and switch phases with reversed TPs.
- Conducted two experiments to isolate learning of order versus learning of TPs.
Main Results:
- RTs decreased during learning and increased during TP reversal, suggesting some learning of order variations.
- Most participants found learning second-order TPs difficult.
- Difficulty stemmed from learning TPs alongside stimulus order, not just order effects.
Conclusions:
- Statistical learning can acquire complex associations, demonstrating cognitive flexibility.
- Learning second-order transitional probabilities presents a significant challenge for human cognition.
- Future research should explore factors influencing the learning of complex sequential information.
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