Learning to perceive and recognize a second language: the L2LP model revised.

Area of Science:

• Psycholinguistics
• Computational Linguistics
• Second Language Acquisition

Background:

• The Second Language Linguistic Perception (L2LP) model computationally explains second language (L2) speech perception and recognition.
• Previous implementations faced challenges validating L2 learning scenarios with fewer L2 phonemic categories than native language (L1).

Purpose of the Study:

• To test a revised L2LP model accounting for perceptual category adjustments in L2 learners.
• To investigate whether meaning-driven lexical access or learner awareness drives changes in phonemic perception.
• To compare sequential versus interactive processing models in L2 phonological representation.

Main Methods:

• Computational modeling of L2 speech perception using phonetic, phonological, and psycholinguistic principles.
• Simulations testing a revised L2LP model against empirical L2 phoneme perception data.
• Comparative analysis of sequential and interactive processing stages within the L2LP framework.

Main Results:

• The revised L2LP model successfully predicts L2 phoneme perception development driven by lexical meaning.
• Meaning-driven category updating accurately models L2 learning scenarios with reduced phonemic contrasts.
• Both sequential and interactive model versions learn minimal pairs but diverge on phonological representations.

Conclusions:

• The revised L2LP model offers a more accurate account of L2 speech perception acquisition, emphasizing meaning-driven category recalibration.
• Lexical meaning, not just phonemic awareness, is crucial for adjusting perceptual categories in L2 learners.
• The study contributes to understanding L2 acquisition and general speech processing mechanisms.