UNASSIGNED: Large language model (LLM)-powered services are gaining popularity in various applications due to their exceptional performance in many tasks, such as sentiment analysis and answering questions. Recently, research has been exploring their potential use in digital health contexts, particularly in the mental health domain. However, implementing LLM-enhanced conversational artificial intelligence (CAI) presents significant ethical, technical, and clinical challenges. In this viewpoint paper, we discuss 2 challenges that affect the use of LLM-enhanced CAI for individuals with mental health issues, focusing on the use case of patients with depression: the tendency to humanize LLM-enhanced CAI and their lack of contextualized robustness. Our approach is interdisciplinary, relying on considerations from philosophy, psychology, and computer science. We argue that the humanization of LLM-enhanced CAI hinges on the reflection of what it means to simulate \"human-like\" features with LLMs and what role these systems should play in interactions with humans. Further, ensuring the contextualization of the robustness of LLMs requires considering the specificities of language production in individuals with depression, as well as its evolution over time. Finally, we provide a series of recommendations to foster the responsible design and deployment of LLM-enhanced CAI for the therapeutic support of individuals with depression.

In the realm of language education, the influence of learners\' personality traits on their educational outcomes within novel instructional frameworks has gained prominence, prompting an exploration into the effects of ambiguity tolerance on grammar acquisition among English as a Foreign Language (EFL) students. This study investigates the impact of learners\' personality traits on their learning outcomes in innovative instructional models, such as flipped and blended classes. A sample of 120 EFL students was divided into four comparative groups and two control groups based on their proficiency and ambiguity tolerance (AT) scores. The study utilized a Learning Management System (LMS) to deliver instruction to the different groups. The blended group received a combination of online and face-to-face instruction, while the flipped group received online instruction using the flipped approach. The control group received only face-to-face instruction. After a semester of instruction, a posttest on grammar learning was administered. The findings showed that the blended group performed better than the flipped and face-to-face groups in terms of grammar learning. The study also found no significant differences in grammar learning between high AT and low AT participants in the flipped and blended classes. However, high AT students in the face-to-face class demonstrated higher levels of success in grammar learning compared to low AT students.

BACKGROUND: The Stroke Self-Efficacy Questionnaire (SSEQ) measures the self-confidence of the individual in functional activities after a stroke. The SSEQ is a self-report scale with 13 items that assess self-efficacy after a stroke in several functional domains.
OBJECTIVE: The purpose was to translate the Stroke Self-Efficacy Questionnaire into Urdu Language and to find out the validity and reliability of Urdu SSEQ among stroke patients.
METHODS: The cross-cultural validation study design was used. Following COSMIN guidelines, forward and backward translation protocols were adopted. After pilot testing on 10 stroke patients, the final Urdu version was drafted. A sample of 110 stroke patients was used to evaluate the validity and reliability of the SSEQ-U. Content and Concurrent validity were determined. The intraclass correlation coefficient and Cronbach\'s alpha were used to measure internal consistency and test-retest reliability. Data analysis was performed using SPSS 25.
RESULTS: The final version was drafted after application on 10 stroke patients. Content validity was analyzed by a content validity index ranging from 0.87 to 1. The internal consistency was calculated by Cronbach\'s alpha (α > 0.80). Test-retest reliability was determined by the Intra-class correlation coefficient (ICC2,1=0.956). Concurrent validity was determined by correlations with other scales by using the Spearman correlation coefficient; moderate to strong correlations (positive and negative) were found with the Functional Independence Measure (r = 0.76), Beck Depression Inventory (r=-0.54), Short Form of 12-item Scale (r = 0.68) and Fall Efficacy Scale (r = 0.82) with p < 0.05.
CONCLUSIONS: The Urdu version was linguistically acceptable and accurate for stroke survivors for determining self-efficacy. It showed good content and concurrent validity, internal consistency and test-retest reliability.

When processing language, the brain is thought to deploy specialized computations to construct meaning from complex linguistic structures. Recently, artificial neural networks based on the Transformer architecture have revolutionized the field of natural language processing. Transformers integrate contextual information across words via structured circuit computations. Prior work has focused on the internal representations (\"embeddings\") generated by these circuits. In this paper, we instead analyze the circuit computations directly: we deconstruct these computations into the functionally-specialized \"transformations\" that integrate contextual information across words. Using functional MRI data acquired while participants listened to naturalistic stories, we first verify that the transformations account for considerable variance in brain activity across the cortical language network. We then demonstrate that the emergent computations performed by individual, functionally-specialized \"attention heads\" differentially predict brain activity in specific cortical regions. These heads fall along gradients corresponding to different layers and context lengths in a low-dimensional cortical space.

OBJECTIVE: Predictive coding theories posit that autism is characterized by an over-adjustment to prediction errors, resulting in frequent updates of prior beliefs. Atypical weighting of prediction errors is generally considered to negatively impact the construction of stable models of the world, but may also yield beneficial effects. In a novel associative learning paradigm, we investigated whether unexpected events trigger faster learning updates in favour of subtle but fully predictive cues in autistic children compared to their non-autistic counterparts. We also explored the relationship between children\'s language proficiency and their predictive performances.
METHODS: Anticipatory fixations and explicit predictions were recorded during three associative learning tasks with deterministic or probabilistic contingencies. One of the probabilistic tasks was designed so that a fully predictive but subtle cue was overshadowed by a less predictive salient one.
RESULTS: Both autistic and non-autistic children based their learning on the salient cue, and, contrary to our predictions, showed no signs of updating in favour of the subtle cue. While both groups demonstrated associative learning, autistic children made less accurate explicit predictions than their non-autistic peers in all tasks. Explicit prediction performances were positively correlated with language proficiency in non-autistic children, but no such correlation was observed in autistic children.
CONCLUSIONS: These results suggest no over-adjustment to prediction errors in autistic children and highlight the need to control for general performance in cue-outcome associative learning in predictive processing studies. Further research is needed to explore the nature of the relationship between predictive processing and language development in autism.

This study investigates the phenomena of semantic drift through the lenses of language and situated simulation (LASS) and the word frequency effect (WFE) within a timed word association task. Our primary objectives were to determine whether semantic drift can be identified over the short time (25 seconds) of a free word association task (a predicted corollary of LASS), and whether more frequent terms are generated earlier in the process (as expected due to the WFE). Respondents were provided with five cue words (tree, dog, quality, plastic and love), and asked to write as many associations as they could. We hypothesized that terms generated later in the task (fourth time quartile, the last 19-25 seconds) would be semantically more distant (cosine similarity) from the cue word than those generated earlier (first quartile, the first 1-7 seconds), indicating semantic drift. Additionally, we explored the WFE by hypothesizing that earlier generated words would be more frequent and less diverse. Utilizing a dataset matched with GloVe 300B word embeddings, BERT and WordNet synsets, we analysed semantic distances among 1569 unique term pairs for all cue words across time. Our results supported the presence of semantic drift, with significant evidence of within-participant, semantic drift from the first to fourth time (LASS) and frequency (WFE) quartiles. In terms of the WFE, we observed a notable decrease in the diversity of terms generated earlier in the task, while more unique terms (greater diversity and relative uniqueness) were generated in the 4th time quartile, aligning with our hypothesis that more frequently used words dominate early stages of a word association task. We also found that the size of effects varied substantially across cues, suggesting that some cues might invoke stronger and more idiosyncratic situated simulations. Theoretically, our study contributes to the understanding of LASS and the WFE. It suggests that semantic drift might serve as a scalable indicator of the invocation of language versus simulation systems in LASS and might also be used to explore cognition within word association tasks more generally. The findings also add a temporal and relational dimension to the WFE. Practically, our research highlights the utility of word association tasks in understanding semantic drift and the diffusion of word usage over a sub-minute task, arguably the shortest practically feasible timeframe, offering a scalable method to explore group and individual changes in semantic relationships, whether via the targeted diffusion of influence in a marketing campaign, or seeking to understand differences in cognition more generally. Possible practical uses and opportunities for future research are discussed.

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A qualitative study was carried out with five professionals in a pediatric intensive care unit in 2022. Semi-structured interviews were used to find out how they felt about patients from their own culture, and about the role of their mother tongue in hospital care. Caregivers find it difficult to bring their own culture to the fore when caring for their patients. A description of the obstacles they encounter, as well as the advantages, is included.

Considerable work has investigated similarities between the processing of music and language, but it remains unclear whether typical, genuine music can influence speech processing via cross-domain priming. To investigate this, we measured ERPs to musical phrases and to syntactically ambiguous Chinese phrases that could be disambiguated by early or late prosodic boundaries. Musical primes also had either early or late prosodic boundaries and we asked participants to judge whether the prime and target have the same structure. Within musical phrases, prosodic boundaries elicited reduced N1 and enhanced P2 components (relative to the no-boundary condition) and musical phrases with late boundaries exhibited a closure positive shift (CPS) component. More importantly, primed target phrases elicited a smaller CPS compared to non-primed phrases, regardless of the type of ambiguous phrase. These results suggest that prosodic priming can occur across domains, supporting the existence of common neural processes in music and language processing.

A key aspect of linguistic communication involves semantic reference to objects. Presently, we investigate neural responses at objects when reference is disrupted, e.g., \"The connoisseur tasted *that wine\"… vs. \"…*that roof…\" Without any previous linguistic context or visual gesture, use of the demonstrative determiner \"that\" renders interpretation at the noun as incoherent. This incoherence is not based on knowledge of how the world plausibly works but instead is based on grammatical rules of reference. Whereas Event-Related Potential (ERP) responses to sentences such as \"The connoisseur tasted the wine …\" vs. \"the roof\" would result in an N400 effect, it is unclear what to expect for doubly incoherent \"…*that roof…\". Results revealed an N400 effect, as expected, preceded by a P200 component (instead of predicted P600 effect). These independent ERP components at the doubly violated condition support the notion that semantic interpretation can be partitioned into grammatical vs. contextual constructs.