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Exploiting multiple sources of information in learning an artificial language: human data and modeling
Pierre Perruchet1, Barbara Tillmann
1CNRS-UMR 5022, University of Bourgogne CNRS-UMR 5020, University of Lyon 1.
Cognitive Science
|May 14, 2011
Summary
This study reveals how statistical structure, initial word-likeness, and contextual cues interact to influence word discovery in speech. These findings highlight the power of general learning principles in early language acquisition.
Area of Science:
- Cognitive Science
- Psycholinguistics
- Computational Linguistics
Background:
- Early language acquisition involves segmenting continuous speech into meaningful units.
- Understanding word segmentation mechanisms is crucial for explaining language development.
- Previous models have focused on specific statistical cues, but the interplay of multiple factors is less understood.
Purpose of the Study:
- To investigate the combined effects of statistical structure, initial word-likeness, and contextual information on word discovery.
- To compare the performance of different computational models in explaining human word segmentation behavior.
- To determine if general learning principles can account for complex word segmentation phenomena.
Main Methods:
- An experiment was conducted with adult participants exposed to a continuous artificial speech stream.
- Participants' ability to discover word-like units was assessed under varying conditions.
- Computational models, including PARSER, transitional probability-based approaches, and Minimum Description Length (MDL) models like INCDROP, were evaluated against the experimental data.
Main Results:
- Statistical structure, initial word-likeness, and contextual information significantly and interactively influence word discovery.
- The PARSER model demonstrated a superior ability to account for the observed data compared to other models.
- PARSER's success is attributed to its reliance on general-purpose learning principles without requiring ad-hoc modifications.
Conclusions:
- Nonspecific cognitive processes, particularly associative learning and memory, play a substantial role in early word segmentation.
- The findings support a broader view of the capabilities of general learning mechanisms in language acquisition.
- This research provides a more comprehensive account of word discovery by integrating multiple sources of information and robust computational modeling.
