Acquiring a new language requires individuals to simultaneously and gradually learn linguistic attributes on multiple levels. Here, we investigated how this learning process changes the neural encoding of natural speech by assessing the encoding of the linguistic feature hierarchy in second-language listeners. Electroencephalography (EEG) signals were recorded from native Mandarin speakers with varied English proficiency and from native English speakers while they listened to audio-stories in English. We measured the temporal response functions (TRFs) for acoustic, phonemic, phonotactic, and semantic features in individual participants and found a main effect of proficiency on linguistic encoding. This effect of second-language proficiency was particularly prominent on the neural encoding of phonemes, showing stronger encoding of “new” phonemic contrasts (i.e., English contrasts that do not exist in Mandarin) with increasing proficiency. Overall, we found that the nonnative listeners with higher proficiency levels had a linguistic feature representation more similar to that of native listeners, which enabled the accurate decoding of language proficiency. This result advances our understanding of the cortical processing of linguistic information in second-language learners and provides an objective measure of language proficiency.

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Word count (excluding abstract, title page, references and methods): 6094. Acknowledgements:The authors would like to thank Michael Broderick for his help with the semantic dissimilarity analysis. The authors also thank Adam Soussana and Ghislain de Labbey for their help with a pilot version of this experiment. AbstractAcquiring a new language requires a simultaneous and gradual learning of multiple levels of linguistic attributes. Here, we investigated how this process changes the neural encoding of natural speech by assessing the encoding of the linguistic feature hierarchy in second-language listeners. Electroencephalography (EEG) signals were recorded during English story listening from native Mandarin speakers with varied English proficiency and from native English speakers. The neural encoding of acoustic, phonemic, phonotactic, and semantic features was measured in individual participants. We found that linguistic feature representation in nonnative listeners progressively converged to that of native listeners with proficiency, which enabled accurate decoding of language proficiency. This effect of second-language proficiency was particularly prominent on the neural encoding of phonemes, showing stronger encoding of "new" phonemic contrasts (i.e. English contrasts that do not exist in Mandarin) with increasing proficiency. This detailed view advances our understanding of the cortical processing of linguistic information in second-language learners and provides an objective measure of language proficiency. be affected differently by proficiency, with some of them becoming more native-like than others for proficient L2 users. Part of the evidence comes from electro-and magneto-encephalography (EEG and MEG respectively) research, which showed the effect of proficiency at the levels of phonemes 13 , syntax 14,15 , and semantics 16 . These studies measured the changes in well-known event-related potentials, such as the MMN, N400, and P600. These approaches, however, use unnatural speech stimuli (e.g., isolated syllables or violative speech sentences) which do not fully and realistically activate the specialized speech cortex [17][18][19] . In addition, these approaches consider various levels of speech perception independently and in isolation. Language learning, on the other hand, involves simultaneous acquisition of novel phonetic contrasts 20,21 , new syllabic structures (phonotactics) 22 , and new words. A more complete view of the neural basis of language learning therefore requires a joint study of multiple levels of the linguistic hierarchy to advance our understanding of L2 perception by informing us on the precise effect of proficiency on the cortical processing strategies that underpin sound and language perception 23-25 .Previous effort in using naturalistic speech stimuli to study language proficiency showed a modulation of EEG phase-synchronization during naturalistic speech listening both at sub-cortical (FFR 26,27 ) and cortical levels (gamma EEG synchrony 28,29 ). Specifically, stronger synchron...

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Fig. 1. CaiTI is a conversational "AI therapist" that screens and assesses the day-to-day functioning of the occupant across 37 dimensions and provides appropriate psychotherapeutic interventions and empathic validations depending on the physical and mental status of the occupant. Despite the global mental health crisis, access to screenings, professionals, and treatments remains high. In collaboration with licensed psychotherapists, we propose a Conversational AI Therapist with psychotherapeutic Interventions (CaiTI), a platform that leverages large language models (LLM)s and smart devices to enable better mental health self-care. CaiTI can screen the day-to-day functioning using natural and psychotherapeutic conversations. CaiTI leverages reinforcement learning to provide personalized conversation flow. CaiTI can accurately understand and interpret user responses. When the user needs further attention during the conversation, CaiTI can provide conversational psychotherapeutic interventions, including cognitive behavioral therapy (CBT) and motivational interviewing (MI). Leveraging the datasets prepared by the licensed psychotherapists, we experiment and microbenchmark various LLMs' performance in tasks along CaiTI's conversation flow and discuss their strengths and weaknesses. With the psychotherapists, we implement CaiTI and conduct 14-day and 24-week studies. The study results, validated by therapists, demonstrate that CaiTI can converse with users naturally, accurately understand and interpret user responses, and provide psychotherapeutic interventions