Do They Know It's Christmash? Lexical Knowledge Directly Impacts Speech Perception.

We recently reported strong, replicable (i.e., replicated) evidence for lexically mediated compensation for coarticulation (LCfC; Luthra et al., 2021), whereby lexical knowledge influences a prelexical process. Critically, evidence for LCfC provides robust support for interactive models of cognition that include top-down feedback and is inconsistent with autonomous models that allow only feedforward processing. McQueen, Jesse, and Mitterer (2023) offer five counter-arguments against our interpretation; we respond to each of those arguments here and conclude that top-down feedback provides the most parsimonious explanation of extant data.

Automated translation accurately translates recorded pediatric neurosurgery clinic conversations between Spanish and English.

OBJECTIVE: The purpose of this study is to analyze an automated voice to text translation device by reporting the translation accuracy for recorded pediatric neurosurgery clinic conversations, classifying errors in translation according to their impact on overall understanding, and comparing the incidence of these errors in English to Spanish vs. Spanish to English conversations. METHODS: English and Spanish speaking patients at a single academic health system's outpatient pediatric neurosurgery clinic had their conversations recorded. These recordings were played back to a Google Pixel handheld smartphone with Live Translate voice to text translation software. A certified medical interpreter evaluated recordings for incidence of minor errors, errors impacting understanding, and catastrophic errors affecting patient-provider relationship or care. Two proportion t-testing was used to compare these outcomes. RESULTS: 50 patient visits were recorded: 40 English recordings translated to Spanish and 10 Spanish recordings translated to English. The mean transcript length was 4244 ± 992 words. The overall accuracy was 98.2% ± 0.5%. On average, 46 words were missed in translation (1.09% error rate), 31 understanding-altering translation errors (0.73% error rate), and 0 catastrophic errors were made. There was no significant difference in English to Spanish or vice versa. CONCLUSION: Voice to text translation devices using automatic speech recognition accurately translate recorded clinic conversations between Spanish and English with high accuracy and low incidence of errors impacting medical care or understanding. Further study should investigate additional languages, assess patient preferences and potential concerns with respect to device use, and compare these devices directly to medical interpreters in live clinic settings.

Gender differences in the functional language networks at birth: a resting-state fNIRS study.

Numerous studies reported inconsistent results concerning gender influences on the functional organization of the brain for language in children and adults. However, data for the gender differences in the functional language networks at birth are sparse. Therefore, we investigated gender differences in resting-state functional connectivity in the language-related brain regions in newborns using functional near-infrared spectroscopy. The results revealed that female newborns demonstrated significantly stronger functional connectivities between the superior temporal gyri and middle temporal gyri, the superior temporal gyri and the Broca's area in the right hemisphere, as well as between the right superior temporal gyri and left Broca's area. Nevertheless, statistical analysis failed to reveal functional lateralization of the language-related brain areas in resting state in both groups. Together, these results suggest that the onset of language system might start earlier in females, because stronger functional connectivities in the right brain in female neonates were probably shaped by the processing of prosodic information, which mainly constitutes newborns' first experiences of speech in the womb. More exposure to segmental information after birth may lead to strengthened functional connectivities in the language system in both groups, resulting in a stronger leftward lateralization in males and a more balanced or leftward dominance in females.

Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study.

BACKGROUND: Dermatologic patient education materials (PEMs) are often written above the national average seventh- to eighth-grade reading level. ChatGPT-3.5, GPT-4, DermGPT, and DocsGPT are large language models (LLMs) that are responsive to user prompts. Our project assesses their use in generating dermatologic PEMs at specified reading levels. OBJECTIVE: This study aims to assess the ability of select LLMs to generate PEMs for common and rare dermatologic conditions at unspecified and specified reading levels. Further, the study aims to assess the preservation of meaning across such LLM-generated PEMs, as assessed by dermatology resident trainees. METHODS: The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. We prompted ChatGPT-3.5, GPT-4, DermGPT, and DocsGPT to "Create a patient education handout about [condition] at a [FKRL]" to iteratively generate 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs, evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 dermatology resident trainees. RESULTS: The current American Academy of Dermatology PEMs had an average (SD) FKRL of 9.35 (1.26) and 9.50 (2.3) for common and rare diseases, respectively. For common diseases, the FKRLs of LLM-produced PEMs ranged between 9.8 and 11.21 (unspecified prompt), between 4.22 and 7.43 (fifth-grade prompt), and between 5.98 and 7.28 (seventh-grade prompt). For rare diseases, the FKRLs of LLM-produced PEMs ranged between 9.85 and 11.45 (unspecified prompt), between 4.22 and 7.43 (fifth-grade prompt), and between 5.98 and 7.28 (seventh-grade prompt). At the fifth-grade reading level, GPT-4 was better at producing PEMs for both common and rare conditions than ChatGPT-3.5 (P=.001 and P=.01, respectively), DermGPT (P<.001 and P=.03, respectively), and DocsGPT (P<.001 and P=.02, respectively). At the seventh-grade reading level, no significant difference was found between ChatGPT-3.5, GPT-4, DocsGPT, or DermGPT in producing PEMs for common conditions (all P>.05); however, for rare conditions, ChatGPT-3.5 and DocsGPT outperformed GPT-4 (P=.003 and P<.001, respectively). The preservation of meaning analysis revealed that for common conditions, DermGPT ranked the highest for overall ease of reading, patient understandability, and accuracy (14.75/15, 98%); for rare conditions, handouts generated by GPT-4 ranked the highest (14.5/15, 97%). CONCLUSIONS: GPT-4 appeared to outperform ChatGPT-3.5, DocsGPT, and DermGPT at the fifth-grade FKRL for both common and rare conditions, although both ChatGPT-3.5 and DocsGPT performed better than GPT-4 at the seventh-grade FKRL for rare conditions. LLM-produced PEMs may reliably meet seventh-grade FKRLs for select common and rare dermatologic conditions and are easy to read, understandable for patients, and mostly accurate. LLMs may play a role in enhancing health literacy and disseminating accessible, understandable PEMs in dermatology.

Meta predictive learning model of languages in neural circuits.

Large language models based on self-attention mechanisms have achieved astonishing performances, not only in natural language itself, but also in a variety of tasks of different nature. However, regarding processing language, our human brain may not operate using the same principle. Then, a debate is established on the connection between brain computation and artificial self-supervision adopted in large language models. One of most influential hypotheses in brain computation is the predictive coding framework, which proposes to minimize the prediction error by local learning. However, the role of predictive coding and the associated credit assignment in language processing remains unknown. Here, we propose a mean-field learning model within the predictive coding framework, assuming that the synaptic weight of each connection follows a spike and slab distribution, and only the distribution, rather than specific weights, is trained. This meta predictive learning is successfully validated on classifying handwritten digits where pixels are input to the network in sequence, and moreover, on the toy and real language corpus. Our model reveals that most of the connections become deterministic after learning, while the output connections have a higher level of variability. The performance of the resulting network ensemble changes continuously with data load, further improving with more training data, in analogy with the emergent behavior of large language models. Therefore, our model provides a starting point to investigate the connection among brain computation, next-token prediction, and general intelligence.

Speech, voice, and language outcomes following deep brain stimulation: A systematic review.

BACKGROUND: Deep brain stimulation (DBS) reliably ameliorates cardinal motor symptoms in Parkinson's disease (PD) and essential tremor (ET). However, the effects of DBS on speech, voice and language have been inconsistent and have not been examined comprehensively in a single study. OBJECTIVE: We conducted a systematic analysis of literature by reviewing studies that examined the effects of DBS on speech, voice and language in PD and ET. METHODS: A total of 675 publications were retrieved from PubMed, Embase, CINHAL, Web of Science, Cochrane Library and Scopus databases. Based on our selection criteria, 90 papers were included in our analysis. The selected publications were categorized into four subcategories: Fluency, Word production, Articulation and phonology and Voice quality. RESULTS: The results suggested a long-term decline in verbal fluency, with more studies reporting deficits in phonemic fluency than semantic fluency following DBS. Additionally, high frequency stimulation, left-sided and bilateral DBS were associated with worse verbal fluency outcomes. Naming improved in the short-term following DBS-ON compared to DBS-OFF, with no long-term differences between the two conditions. Bilateral and low-frequency DBS demonstrated a relative improvement for phonation and articulation. Nonetheless, long-term DBS exacerbated phonation and articulation deficits. The effect of DBS on voice was highly variable, with both improvements and deterioration in different measures of voice. CONCLUSION: This was the first study that aimed to combine the outcome of speech, voice, and language following DBS in a single systematic review. The findings revealed a heterogeneous pattern of results for speech, voice, and language across DBS studies, and provided directions for future studies.

Evaluating the strengths and weaknesses of large language models in answering neurophysiology questions.

Large language models (LLMs), like ChatGPT, Google's Bard, and Anthropic's Claude, showcase remarkable natural language processing capabilities. Evaluating their proficiency in specialized domains such as neurophysiology is crucial in understanding their utility in research, education, and clinical applications. This study aims to assess and compare the effectiveness of Large Language Models (LLMs) in answering neurophysiology questions in both English and Persian (Farsi) covering a range of topics and cognitive levels. Twenty questions covering four topics (general, sensory system, motor system, and integrative) and two cognitive levels (lower-order and higher-order) were posed to the LLMs. Physiologists scored the essay-style answers on a scale of 0-5 points. Statistical analysis compared the scores across different levels such as model, language, topic, and cognitive levels. Performing qualitative analysis identified reasoning gaps. In general, the models demonstrated good performance (mean score = 3.87/5), with no significant difference between language or cognitive levels. The performance was the strongest in the motor system (mean = 4.41) while the weakest was observed in integrative topics (mean = 3.35). Detailed qualitative analysis uncovered deficiencies in reasoning, discerning priorities, and knowledge integrating. This study offers valuable insights into LLMs' capabilities and limitations in the field of neurophysiology. The models demonstrate proficiency in general questions but face challenges in advanced reasoning and knowledge integration. Targeted training could address gaps in knowledge and causal reasoning. As LLMs evolve, rigorous domain-specific assessments will be crucial for evaluating advancements in their performance.

Inclusive Research Environments for Deaf and Hard of Hearing English Speakers.

In recent years, an increasing number of deaf and hard of hearing (D/HH) undergraduates have chosen to study in STEM fields and pursue careers in research. Yet, very little research has been undertaken on the barriers and inclusive experiences often faced by D/HH undergraduates who prefer to use spoken English in research settings, instead of American Sign Language (ASL). To identify barriers and inclusive strategies, we studied six English speaking D/HH undergraduate students working in research laboratories with their eight hearing mentors, and their three hearing peers sharing their experiences. Three researchers observed the interactions between all three groups and conducted interviews and focus groups, along with utilizing the Communication Assessment Self-Rating Scale (CASS). The main themes identified in the findings were communication and environmental barriers in research laboratories, creating accessible and inclusive laboratory environments, communication strategies, and self-advocating for effective communication. Recommendations for mentors include understanding the key elements of creating an inclusive laboratory environment for English speaking D/HH students and effectively demonstrating cultural competence to engage in inclusive practices.

Language-adaptive artificial intelligence: assessing CHATGPT'S answer to frequently asked questions on total hip arthroplasty questions.

ChatGPT is reported to be an acceptable tool to answer a majority of frequently asked patient questions. ChatGPT also converses in other languages including Urdu, which offers immense potential for the education of Pakistani patients. Therefore, this study evaluated ChatGPT's Urdu answers to the ten most frequently asked questions on Total Hip Arthroplasty, which were then rated by an expert. Out of 10 answers in English, 9 (90%) were satisfactory requiring minimal clarification and 1 (10%) was satisfactory requiring moderate clarification. In both Roman and Nastaliq script Urdu, 1 (10%) answer was satisfactory requiring moderate clarification, while 9 (90%) were unsatisfactory requiring substantial clarification. In conclusion, as opposed to ChatGPT English responses, Urdu responses were much less rigorous, generic, and lacked scientific rigor. We have a long way to go before Pakistani patients with limited English language skills could benefit from AI chatbots like ChatGPT.

It Costs To Exist: Acceptability Judgments of the Temporal Concord of the Auxiliary Verbs You and Hui in Taiwan Mandarin.

The mechanisms underlying the processing of the temporal reference of a sentence are still unexplored. Most of the previous psycholinguistic studies used the temporal concord violation between deictic time adverbs and tense marking on the verb to investigate this issue. They found that processing past tense marking is more difficult than non-past tense, indicated by lower accuracy rates and/or longer reaction time. However, it is not clear whether this complexity is due to tense marking or the temporal reference it denotes. This paper examines this issue with a judgment acceptability experiment in Taiwan Mandarin, which is analyzed as a tenseless language. The two modal auxiliary verbs you and hui were placed after deictic past time adverbs (grammatical with you but not with hui) and deictic future time adverbs (grammatical with hui but not with you). The temporal concord violation of the auxiliary verb you led to higher acceptability rates but longer reaction time than hui, reflecting higher processing difficulties. This paper argues that these complexities are due to the existential-assertive meaning of you, which interplays with the meaning of the event described by the verb rendering the situation more or less likely to occur in the future. The computation of the temporal concord of hui, displaying a future sense meaning, is more straightforward and therefore easier to process. This suggests that the mechanisms responsible for temporal reference processing are of different nature depending on the semantics of the temporal marker in the sentence.

Mechanisms of Concept Verbalization in the Ethnolinguistic Context.

This article investigates the verbalization mechanisms of the 'family' concept within the Kazakh, Russian, and English linguistic cultures. The research aims to examine the verbal representation mechanisms of the 'family' concept within the linguistic worldviews of the aforementioned cultures. The research material comprises dictionary definitions of the primary lexemes as presented in explanatory dictionaries and synonym dictionaries, proverbs and sayings, phraseological units, and data derived from an associative experiment. The employed analysis methods include component analysis, the descriptive method, the experimental method (psycholinguistic experiment), and the statistical method. This article furnishes a thorough analysis of the linguistic representation methods of the 'family' concept, illuminating its intricate and multidimensional nature. The authors endeavored to identify the concept's structure and describe linguistic units via the interpretation of semantic components. Based on the data procured from the psycholinguistic experiment, the components and layers of the 'family' concept, identified during the analysis, substantiate the theory that this concept plays a fundamental role in the shaping of society and individuals.

Solving Bongard Problems With a Visual Language and Pragmatic Constraints.

More than 50 years ago, Bongard introduced 100 visual concept learning problems as a challenge for artificial vision systems. These problems are now known as Bongard problems. Although they are well known in cognitive science and artificial intelligence, only very little progress has been made toward building systems that can solve a substantial subset of them. In the system presented here, visual features are extracted through image processing and then translated into a symbolic visual vocabulary. We introduce a formal language that allows representing compositional visual concepts based on this vocabulary. Using this language and Bayesian inference, concepts can be induced from the examples that are provided in each problem. We find a reasonable agreement between the concepts with high posterior probability and the solutions formulated by Bongard himself for a subset of 35 problems. While this approach is far from solving Bongard problems like humans, it does considerably better than previous approaches. We discuss the issues we encountered while developing this system and their continuing relevance for understanding visual cognition. For instance, contrary to other concept learning problems, the examples are not random in Bongard problems; instead they are carefully chosen to ensure that the concept can be induced, and we found it helpful to take the resulting pragmatic constraints into account.

Learning the Meanings of Function Words From Grounded Language Using a Visual Question Answering Model.

Interpreting a seemingly simple function word like "or," "behind," or "more" can require logical, numerical, and relational reasoning. How are such words learned by children? Prior acquisition theories have often relied on positing a foundation of innate knowledge. Yet recent neural-network-based visual question answering models apparently can learn to use function words as part of answering questions about complex visual scenes. In this paper, we study what these models learn about function words, in the hope of better understanding how the meanings of these words can be learned by both models and children. We show that recurrent models trained on visually grounded language learn gradient semantics for function words requiring spatial and numerical reasoning. Furthermore, we find that these models can learn the meanings of logical connectives and and or without any prior knowledge of logical reasoning as well as early evidence that they are sensitive to alternative expressions when interpreting language. Finally, we show that word learning difficulty is dependent on the frequency of models' input. Our findings offer proof-of-concept evidence that it is possible to learn the nuanced interpretations of function words in a visually grounded context by using non-symbolic general statistical learning algorithms, without any prior knowledge of linguistic meaning.

Language barriers during vaccination practice, the point of view of healthcare providers.

Background: Language barriers are one of the main obstacles faced by migrants in accessing healthcare services. A compromised communication between migrants and Healthcare Providers in vaccination setting can result in increased vaccine hesitancy and decreased vaccine uptake. The objective of the current study is to investigate Healthcare Providers' perceptions about linguistic barriers faced during both routinary vaccination practice and the extraordinary vaccination program for Ukrainian refugees in the Local Health Authorities of Bologna and Romagna (Italy). Methods: A cross-sectional study was conducted through the administration of a questionnaire examining Healthcare Providers' perceptions. A descriptive analysis and a multiple logistic regression model were adopted to analyze the collected data. Results: Language barriers resulted as an obstacle to informed consent and to doctor-patient relationship. The strategies adopted were perceived as helpful in increasing vaccination adherence, despite communication difficulties were still experienced during refugees' vaccinations. Results suggest that the implementation of translated material and the use of professional interpreters may represent important strategies to overcome linguistic barriers, along with Healthcare Providers' training. Healthcare Providers' opinions could assist the implementation of new tools capable of countering language barriers. Conclusions: The current study represents an example of providers' involvement in understanding the complexities behind the issue of language barriers in vaccination practice.

One Cue's Loss Is Another Cue's Gain-Learning Morphophonology Through Unlearning.

A word often expresses many different morphological functions. Which part of a word contributes to which part of the overall meaning is not always clear, which raises the question as to how such functions are learned. While linguistic studies tacitly assume the co-occurrence of cues and outcomes to suffice in learning these functions (Baer-Henney, Kügler, & van de Vijver, 2015; Baer-Henney & van de Vijver, 2012), error-driven learning suggests that contingency rather than contiguity is crucial (Nixon, 2020; Ramscar, Yarlett, Dye, Denny, & Thorpe, 2010). In error-driven learning, cues gain association strength if they predict a certain outcome, and they lose strength if the outcome is absent. This reduction of association strength is called unlearning. So far, it is unclear if such unlearning has consequences for cue-outcome associations beyond the ones that get reduced. To test for such consequences of unlearning, we taught participants morphophonological patterns in an artificial language learning experiment. In one block, the cues to two morphological outcomes-plural and diminutive-co-occurred within the same word forms. In another block, a single cue to only one of these two outcomes was presented in a different set of word forms. We wanted to find out, if participants unlearn this cue's association with the outcome that is not predicted by the cue alone, and if this allows the absent cue to be associated with the absent outcome. Our results show that if unlearning was possible, participants learned that the absent cue predicts the absent outcome better than if no unlearning was possible. This effect was stronger if the unlearned cue was more salient. This shows that unlearning takes place even if no alternative cues to an absent outcome are provided, which highlights that learners take both positive and negative evidence into account-as predicted by domain general error-driven learning.

Cross-cultural adaptation and validation of the Quality of Life in School (QoLS) Questionnaire into Brazilian Portuguese.

The school is fundamental for the development of societies and caring for the student is part of the educational process. Reflections on collective health allowed the expansion of the vision of the concept of quality of life considering different social spaces and indicators. Thus, the aim of this study was to assess of some psychometric Properties of the Quality of Life in School instrument into Brazilian Portuguese (QoLS-BR) among elementary school students. The processes of translation, content evaluation, focus group and Confirmatory Factor Analysis (CFA) were carried out. Reproducibility analysis was performed by administering QoLS-BR to 30 students. The sample used for Internal Consistency and CFA comprised 434 students with a mean age of 12.31 years. High indices of language clarity, practical relevance, theoretical relevance, internal consistency, and reproducibility were obtained. In the AFC, adjustments were not necessary in the QoLS-BR model with four factors (RMSEA=0.065; TLI=0.959; CFI=0.962; SRMR=0.080) indicating that the indices were adequate when investigating all four domains. QoLS-BR has adequate psychometric indicators for investigating the quality of life in school.

A systematic review of culturally adapted physical activity interventions for culturally and linguistically diverse children and adolescents.

OBJECTIVE: Limited evidence shows culturally and linguistically diverse (CALD) children and adolescents are less active, compared to the general population. It is unclear, how physical activity interventions have been adapted for CALD children and adolescents to enhance engagement. This study aimed to review culturally adapted physical activity interventions targeting CALD children and adolescents. METHODS: All studies recruited children and adolescents (i.e., aged ≥5 to <18 years old) from CALD backgrounds, targeted physical activity, and included cultural adaptations. Cultural adaptations were defined as surface structures (i.e., observable characteristics of a targeted population) or deep structures (i.e., rooted in core ethnic values derived from individual cultures. RESULTS: Twenty studies were included. Ten studies used a combination of surface and deep structure adaptations. Of these 10 studies, 3 found a significant between-group difference in physical activity favouring the intervention group. Among studies (n = 6) that used surface structure adaptations (e.g., language adjustments to information sheets, consent forms, and resources), 1 found a significant intervention effect on physical activity. With studies (n = 4) that used deep structure adaptations (e.g., incorporating traditional songs and dances relevant to cultural groups), 1 study found a significant intervention effect on physical activity. CONCLUSION: A small number of studies found significant changes to increase physical activity levels. We found there is a lack of consistent evidence indicating that incorporating surface and/or deep structure adaptations result in significant changes in physical activity. Future research should focus on establishing higher quality methodology when developing culturally adapted interventions for CALD populations.

The role of handwriting in English word acquisition among elementary students.

In order to investigate whether handwriting has an advantage in learning word form, sound, and meaning, this study randomly selected 40 elementary school student participants (20 males, 20 females, aged 11.4 ± 1.34 years). Using an experimental approach, we compared the learning outcomes of word sound matching, word meaning matching, and word form judgment tasks under two conditions: handwriting and visual learning. After three consecutive days of learning and testing, we found that handwriting generally outperformed visual learning in terms of accuracy and response time in word form, sound, and meaning learning. Additionally, we observed differences in the timing of significant discrepancies in learning outcomes between the two methods across the three tasks. Specifically, in terms of accuracy, discrepancies first appeared in the word sound matching task on the first day, followed by the word form judgment task, and lastly the word meaning matching task. Regarding response time, significant differences between learning methods first emerged in the word form judgment task, followed by the word sound and word meaning tasks. Thus, combining accuracy and response time data, we conclude that handwriting is more advantageous than visual learning for word acquisition, with a differential impact on word form, sound, and meaning, where word form and sound are prioritized over meaning.

Detection of Language Lateralization Using Spectral Analysis of EEG.

PURPOSE: Language lateralization relies on expensive equipment and can be difficult to tolerate. We assessed if lateralized brain responses to a language task can be detected with spectral analysis of electroencephalography (EEG). METHODS: Twenty right-handed, neurotypical adults (28 ± 10 years; five males) performed a verb generation task and two control tasks (word listening and repetition). We measured changes in EEG activity elicited by tasks (the event-related spectral perturbation [ERSP]) in the theta, alpha, beta, and gamma frequency bands in two language (superior temporal and inferior frontal [ST and IF]) and one control (occipital [Occ]) region bilaterally. We tested whether language tasks elicited (1) changes in spectral power from baseline (significant ERSP) at any region or (2) asymmetric ERSPs between matched left and right regions. RESULTS: Left IF beta power (-0.37±0.53, t = -3.12, P = 0.006) and gamma power in all regions decreased during verb generation. Asymmetric ERSPs (right > left) occurred between the (1) IF regions in the beta band (right vs. left difference of 0.23±0.37, t(19) = -2.80, P = 0.0114) and (2) ST regions in the alpha band (right vs. left difference of 0.48±0.63, t(19) = -3.36, P = 0.003). No changes from baseline or hemispheric asymmetries were noted in language regions during control tasks. On the individual level, 16 (80%) participants showed decreased left IF beta power from baseline, and 16 showed ST alpha asymmetry. Eighteen participants (90%) showed one of these two findings. CONCLUSIONS: Spectral EEG analysis detects lateralized responses during language tasks in frontal and temporal regions. Spectral EEG analysis could be developed into a readily available language lateralization modality.