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The formation of structurally relevant units in artificial grammar learning
Pierre Perruchet1, Annie Vinter, Chantal Pacteau
1Université de Bourgogne, LEAD/CNRS, Faculté des Sciences, Dijon, France. pierre.perruchet@u-bourgogne.fr
Summary
Adults learned to segment grammatical strings more consistently after training, showing improved pattern recognition. This suggests learning enhances the ability to parse complex sensory input into meaningful units.
Area of Science:
- Cognitive Psychology
- Computational Linguistics
- Artificial Intelligence
Background:
- Understanding how humans parse sensory input is crucial for developmental theories.
- Previous research suggests implicit learning plays a role in segmenting structured data.
Purpose of the Study:
- To investigate how familiarization with a finite state grammar affects cognitive segmentation.
- To determine if training leads to larger cognitive units or more consistent segmentation.
Main Methods:
- 78 adult participants segmented strings from a finite state grammar before and after a familiarization phase.
- Familiarization tasks included rote learning, matching, and rule discovery.
- A computational model (PARSER) was used to simulate results.
Main Results:
- Participants did not form larger cognitive units but reliably decreased the number of different units used.
- Segmentation consistency increased, aligning more closely with the grammar's structure.
- The PARSER model successfully replicated the observed pattern of results.
Conclusions:
- Training enhances the consistency of sensory input segmentation, aligning it with underlying grammatical structures.
- Principles of associative memory and learning underpin this improved parsing ability.
- Findings have implications for understanding how children develop the capacity to parse complex information.