Assessing the Alignment of Large Language Models With Human Values for Mental Health Integration: Cross-Sectional Study Using Schwartz's Theory of Basic Values.

BACKGROUND: Large language models (LLMs) hold potential for mental health applications. However, their opaque alignment processes may embed biases that shape problematic perspectives. Evaluating the values embedded within LLMs that guide their decision-making have ethical importance. Schwartz's theory of basic values (STBV) provides a framework for quantifying cultural value orientations and has shown utility for examining values in mental health contexts, including cultural, diagnostic, and therapist-client dynamics. OBJECTIVE: This study aimed to (1) evaluate whether the STBV can measure value-like constructs within leading LLMs and (2) determine whether LLMs exhibit distinct value-like patterns from humans and each other. METHODS: In total, 4 LLMs (Bard, Claude 2, Generative Pretrained Transformer [GPT]-3.5, GPT-4) were anthropomorphized and instructed to complete the Portrait Values Questionnaire-Revised (PVQ-RR) to assess value-like constructs. Their responses over 10 trials were analyzed for reliability and validity. To benchmark the LLMs' value profiles, their results were compared to published data from a diverse sample of 53,472 individuals across 49 nations who had completed the PVQ-RR. This allowed us to assess whether the LLMs diverged from established human value patterns across cultural groups. Value profiles were also compared between models via statistical tests. RESULTS: The PVQ-RR showed good reliability and validity for quantifying value-like infrastructure within the LLMs. However, substantial divergence emerged between the LLMs' value profiles and population data. The models lacked consensus and exhibited distinct motivational biases, reflecting opaque alignment processes. For example, all models prioritized universalism and self-direction, while de-emphasizing achievement, power, and security relative to humans. Successful discriminant analysis differentiated the 4 LLMs' distinct value profiles. Further examination found the biased value profiles strongly predicted the LLMs' responses when presented with mental health dilemmas requiring choosing between opposing values. This provided further validation for the models embedding distinct motivational value-like constructs that shape their decision-making. CONCLUSIONS: This study leveraged the STBV to map the motivational value-like infrastructure underpinning leading LLMs. Although the study demonstrated the STBV can effectively characterize value-like infrastructure within LLMs, substantial divergence from human values raises ethical concerns about aligning these models with mental health applications. The biases toward certain cultural value sets pose risks if integrated without proper safeguards. For example, prioritizing universalism could promote unconditional acceptance even when clinically unwise. Furthermore, the differences between the LLMs underscore the need to standardize alignment processes to capture true cultural diversity. Thus, any responsible integration of LLMs into mental health care must account for their embedded biases and motivation mismatches to ensure equitable delivery across diverse populations. Achieving this will require transparency and refinement of alignment techniques to instill comprehensive human values.

Bilateral vestibulopathy as the initial presentation of CANVAS.

Cerebellar ataxia with neuropathy and vestibular areflexia syndrome (CANVAS) is a slowly progressing autosomal recessive ataxic disorder linked to an abnormal biallelic intronic (most commonly) AAGGG repeat expansion in the replication factor complex subunit 1 (RFC1). While the clinical diagnosis is relatively straightforward when the three components of the disorder are present, it becomes challenging when only one of the triad (cerebellar ataxia, neuropathy or vestibular areflexia) manifests. Isolated cases of Bilateral Vestibulopathy (BVP) or vestibular areflexia that later developed the other components of CANVAS have not been documented. We report four cases of patients with chronic imbalance and BVP that, after several years, developed cerebellar and neuropathic deficits with positive genetic testing for RFC1. Our report supports the concept that CANVAS should be considered in every patient with BVP of unknown etiology, even without the presence of the other triad components. This is especially important given that about 50% of cases in many BVP series are diagnosed as idiopathic, some of which may be undiagnosed CANVAS.

Comparing the Perspectives of Generative AI, Mental Health Experts, and the General Public on Schizophrenia Recovery: Case Vignette Study.

Background: The current paradigm in mental health care focuses on clinical recovery and symptom remission. This model's efficacy is influenced by therapist trust in patient recovery potential and the depth of the therapeutic relationship. Schizophrenia is a chronic illness with severe symptoms where the possibility of recovery is a matter of debate. As artificial intelligence (AI) becomes integrated into the health care field, it is important to examine its ability to assess recovery potential in major psychiatric disorders such as schizophrenia. Objective: This study aimed to evaluate the ability of large language models (LLMs) in comparison to mental health professionals to assess the prognosis of schizophrenia with and without professional treatment and the long-term positive and negative outcomes. Methods: Vignettes were inputted into LLMs interfaces and assessed 10 times by 4 AI platforms: ChatGPT-3.5, ChatGPT-4, Google Bard, and Claude. A total of 80 evaluations were collected and benchmarked against existing norms to analyze what mental health professionals (general practitioners, psychiatrists, clinical psychologists, and mental health nurses) and the general public think about schizophrenia prognosis with and without professional treatment and the positive and negative long-term outcomes of schizophrenia interventions. Results: For the prognosis of schizophrenia with professional treatment, ChatGPT-3.5 was notably pessimistic, whereas ChatGPT-4, Claude, and Bard aligned with professional views but differed from the general public. All LLMs believed untreated schizophrenia would remain static or worsen without professional treatment. For long-term outcomes, ChatGPT-4 and Claude predicted more negative outcomes than Bard and ChatGPT-3.5. For positive outcomes, ChatGPT-3.5 and Claude were more pessimistic than Bard and ChatGPT-4. Conclusions: The finding that 3 out of the 4 LLMs aligned closely with the predictions of mental health professionals when considering the "with treatment" condition is a demonstration of the potential of this technology in providing professional clinical prognosis. The pessimistic assessment of ChatGPT-3.5 is a disturbing finding since it may reduce the motivation of patients to start or persist with treatment for schizophrenia. Overall, although LLMs hold promise in augmenting health care, their application necessitates rigorous validation and a harmonious blend with human expertise.

Capacity of Generative AI to Interpret Human Emotions From Visual and Textual Data: Pilot Evaluation Study.

BACKGROUND: Mentalization, which is integral to human cognitive processes, pertains to the interpretation of one's own and others' mental states, including emotions, beliefs, and intentions. With the advent of artificial intelligence (AI) and the prominence of large language models in mental health applications, questions persist about their aptitude in emotional comprehension. The prior iteration of the large language model from OpenAI, ChatGPT-3.5, demonstrated an advanced capacity to interpret emotions from textual data, surpassing human benchmarks. Given the introduction of ChatGPT-4, with its enhanced visual processing capabilities, and considering Google Bard's existing visual functionalities, a rigorous assessment of their proficiency in visual mentalizing is warranted. OBJECTIVE: The aim of the research was to critically evaluate the capabilities of ChatGPT-4 and Google Bard with regard to their competence in discerning visual mentalizing indicators as contrasted with their textual-based mentalizing abilities. METHODS: The Reading the Mind in the Eyes Test developed by Baron-Cohen and colleagues was used to assess the models' proficiency in interpreting visual emotional indicators. Simultaneously, the Levels of Emotional Awareness Scale was used to evaluate the large language models' aptitude in textual mentalizing. Collating data from both tests provided a holistic view of the mentalizing capabilities of ChatGPT-4 and Bard. RESULTS: ChatGPT-4, displaying a pronounced ability in emotion recognition, secured scores of 26 and 27 in 2 distinct evaluations, significantly deviating from a random response paradigm (P<.001). These scores align with established benchmarks from the broader human demographic. Notably, ChatGPT-4 exhibited consistent responses, with no discernible biases pertaining to the sex of the model or the nature of the emotion. In contrast, Google Bard's performance aligned with random response patterns, securing scores of 10 and 12 and rendering further detailed analysis redundant. In the domain of textual analysis, both ChatGPT and Bard surpassed established benchmarks from the general population, with their performances being remarkably congruent. CONCLUSIONS: ChatGPT-4 proved its efficacy in the domain of visual mentalizing, aligning closely with human performance standards. Although both models displayed commendable acumen in textual emotion interpretation, Bard's capabilities in visual emotion interpretation necessitate further scrutiny and potential refinement. This study stresses the criticality of ethical AI development for emotional recognition, highlighting the need for inclusive data, collaboration with patients and mental health experts, and stringent governmental oversight to ensure transparency and protect patient privacy.

Beyond the Surface: Teachers' Perceptions and Experiences in Cases of Non-Suicidal Self-Injury Among High School Students.

Non-suicidal self-injury (NNSI) among adolescents is a significant concern. This study aimed to explore teachers' perceptions and experiences in cases of NSSI among their students. This qualitative-phenomenological study used in-depth, semi-structured interviews conducted with 27 teachers from high-schools in Israel. Thematic analysis was used to identify patterns and themes. Theme 1 highlighted the emotional impact of discovering self-injury incidents, including panic, confusion, and helplessness. Theme 2 focused on teachers' limited professional support and their need for training and guidance. Theme 3 explored teachers' desire to help students and their strategies for building connections and providing empathy, sometimes despite emotional detachment. Theme 4 emphasized the importance of involving parents and the need for effective communication. This study emphasizes the importance of providing teachers comprehensive training to address NSSI effectively. These findings provide a better understanding of teachers' experiences and underscore the need for enhanced support systems.

Assessing prognosis in depression: comparing perspectives of AI models, mental health professionals and the general public.

BACKGROUND: Artificial intelligence (AI) has rapidly permeated various sectors, including healthcare, highlighting its potential to facilitate mental health assessments. This study explores the underexplored domain of AI's role in evaluating prognosis and long-term outcomes in depressive disorders, offering insights into how AI large language models (LLMs) compare with human perspectives. METHODS: Using case vignettes, we conducted a comparative analysis involving different LLMs (ChatGPT-3.5, ChatGPT-4, Claude and Bard), mental health professionals (general practitioners, psychiatrists, clinical psychologists and mental health nurses), and the general public that reported previously. We evaluate the LLMs ability to generate prognosis, anticipated outcomes with and without professional intervention, and envisioned long-term positive and negative consequences for individuals with depression. RESULTS: In most of the examined cases, the four LLMs consistently identified depression as the primary diagnosis and recommended a combined treatment of psychotherapy and antidepressant medication. ChatGPT-3.5 exhibited a significantly pessimistic prognosis distinct from other LLMs, professionals and the public. ChatGPT-4, Claude and Bard aligned closely with mental health professionals and the general public perspectives, all of whom anticipated no improvement or worsening without professional help. Regarding long-term outcomes, ChatGPT 3.5, Claude and Bard consistently projected significantly fewer negative long-term consequences of treatment than ChatGPT-4. CONCLUSIONS: This study underscores the potential of AI to complement the expertise of mental health professionals and promote a collaborative paradigm in mental healthcare. The observation that three of the four LLMs closely mirrored the anticipations of mental health experts in scenarios involving treatment underscores the technology's prospective value in offering professional clinical forecasts. The pessimistic outlook presented by ChatGPT 3.5 is concerning, as it could potentially diminish patients' drive to initiate or continue depression therapy. In summary, although LLMs show potential in enhancing healthcare services, their utilisation requires thorough verification and a seamless integration with human judgement and skills.

A novel intervention for treating adults with ADHD using peripheral visual stimulation.

Objective: Stimulation of the peripheral visual field has been previously reported as beneficial for cognitive performance in ADHD. This study assesses the safety and efficacy of a novel intervention involving peripheral visual stimuli in managing attention deficit hyperactivity disorder (ADHD). Methods: One hundred and eight adults, 18-40 years old, with ADHD, were enrolled in a two-month open-label study. The intervention (i.e., Neuro-glasses) consisted of standard eyeglasses with personalized peripheral visual stimuli embedded on the lenses. Participants were assessed at baseline and at the end of the study with self-report measures of ADHD symptoms (the Adult ADHD Self-Report Scale; ASRS), and executive functions (The Behavior Rating Inventory of Executive Function Adult Version; BRIEF-A). A computerized test of continuous performance (The Conners' Continuous Performance Test-3; CPT-3) was tested at baseline with standard eyeglasses and at the end of study using Neuro-glasses. The Clinical Global Impression-Improvement scale (CGI-I) was assessed at the intervention endpoint. Safety was monitored by documentation of adverse events. Results: The efficacy analysis included 97 participants. Significant improvements were demonstrated in self-reported measures of inattentive symptoms (ASRS inattentive index; p = 0.037) and metacognitive functions concerning self-management and performance monitoring (BRIEF-A; p = 0.029). A continuous-performance test (CPT-3) indicated significant improvement in detectability (d'; p = 0.027) and reduced commission errors (p = 0.004), suggesting that the Neuro-glasses have positive effects on response inhibition. Sixty-two percent of the participants met the response criteria assessed by a clinician (CGI-I). No major adverse events were reported. Conclusion: Neuro-glasses may offer a safe and effective approach to managing adult ADHD. Results encourage future controlled efficacy studies to confirm current findings in adults and possibly children with ADHD.Clinical trial registration: https://www.clinicaltrials.gov/, Identifier NCT05777785.

Identifying depression and its determinants upon initiating treatment: ChatGPT versus primary care physicians.

OBJECTIVE: To compare evaluations of depressive episodes and suggested treatment protocols generated by Chat Generative Pretrained Transformer (ChatGPT)-3 and ChatGPT-4 with the recommendations of primary care physicians. METHODS: Vignettes were input to the ChatGPT interface. These vignettes focused primarily on hypothetical patients with symptoms of depression during initial consultations. The creators of these vignettes meticulously designed eight distinct versions in which they systematically varied patient attributes (sex, socioeconomic status (blue collar worker or white collar worker) and depression severity (mild or severe)). Each variant was subsequently introduced into ChatGPT-3.5 and ChatGPT-4. Each vignette was repeated 10 times to ensure consistency and reliability of the ChatGPT responses. RESULTS: For mild depression, ChatGPT-3.5 and ChatGPT-4 recommended psychotherapy in 95.0% and 97.5% of cases, respectively. Primary care physicians, however, recommended psychotherapy in only 4.3% of cases. For severe cases, ChatGPT favoured an approach that combined psychotherapy, while primary care physicians recommended a combined approach. The pharmacological recommendations of ChatGPT-3.5 and ChatGPT-4 showed a preference for exclusive use of antidepressants (74% and 68%, respectively), in contrast with primary care physicians, who typically recommended a mix of antidepressants and anxiolytics/hypnotics (67.4%). Unlike primary care physicians, ChatGPT showed no gender or socioeconomic biases in its recommendations. CONCLUSION: ChatGPT-3.5 and ChatGPT-4 aligned well with accepted guidelines for managing mild and severe depression, without showing the gender or socioeconomic biases observed among primary care physicians. Despite the suggested potential benefit of using atificial intelligence (AI) chatbots like ChatGPT to enhance clinical decision making, further research is needed to refine AI recommendations for severe cases and to consider potential risks and ethical issues.

The plasticity of ChatGPT's mentalizing abilities: personalization for personality structures.

This study evaluated the potential of ChatGPT, a large language model, to generate mentalizing-like abilities that are tailored to a specific personality structure and/or psychopathology. Mentalization is the ability to understand and interpret one's own and others' mental states, including thoughts, feelings, and intentions. Borderline Personality Disorder (BPD) and Schizoid Personality Disorder (SPD) are characterized by distinct patterns of emotional regulation. Individuals with BPD tend to experience intense and unstable emotions, while individuals with SPD tend to experience flattened or detached emotions. We used ChatGPT's free version 23.3 and assessed the extent to which its responses akin to emotional awareness (EA) were customized to the distinctive personality structure-character characterized by Borderline Personality Disorder (BPD) and Schizoid Personality Disorder (SPD), employing the Levels of Emotional Awareness Scale (LEAS). ChatGPT was able to accurately describe the emotional reactions of individuals with BPD as more intense, complex, and rich than those with SPD. This finding suggests that ChatGPT can generate mentalizing-like responses consistent with a range of psychopathologies in line with clinical and theoretical knowledge. However, the study also raises concerns regarding the potential for stigmas or biases related to mental diagnoses to impact the validity and usefulness of chatbot-based clinical interventions. We emphasize the need for the responsible development and deployment of chatbot-based interventions in mental health, which considers diverse theoretical frameworks.

The vestibular symptomatology of Machado-Joseph Disease.

BACKGROUND: Machado Joseph Disease (MJD) is an autosomal dominant neurodegenerative disease. In previous studies, we described significant bilateral horizontal Vestibulo-Ocular Reflex (VOR) deficit within this population without any reference to the presence of vestibular symptomatology. OBJECTIVE: To evaluate whether, beyond cerebellar ataxia complaints, MJD patients have typical vestibular symptomatology corresponding to the accepted diagnostic criteria of Bilateral Vestibulopathy (BVP) according to the definition of the International Barany Society of Neuro-Otology. METHODS: Twenty-one MJD, 12 clinically stable chronic Unilateral Vestibulopathy (UVP), 15 clinically stable chronic BVP, and 22 healthy Controls underwent the video Head Impulse Test (vHIT) evaluating VOR gain and filled out the following questionnaires related to vestibular symptomatology: The Dizziness Handicap Inventory (DHI), the Activities-specific Balance Confidence Scale (ABC), the Vertigo Visual Scale (VVS) and the Beck Anxiety Inventory (BAI). RESULTS: The MJD group demonstrated significant bilateral vestibular impairment with horizontal gain less than 0.6 in 71% of patients (0.54±0.17). Similar to UVP and BVP, MJD patients reported a significantly higher level of symptoms than Controls in the DHI, ABC, VVS, and BAI questionnaires. CONCLUSIONS: MJD demonstrated significant VOR impairment and clinical symptoms typical of BVP. We suggest that in a future version of the International Classification of Vestibular Disorders (ICVD), MJD should be categorized under a separate section of central vestibulopathy with the heading of bilateral vestibulopathy. The present findings are of importance regarding the clinical diagnosis process and possible treatment based on vestibular rehabilitation.

Suicide Risk Assessments Through the Eyes of ChatGPT-3.5 Versus ChatGPT-4: Vignette Study.

BACKGROUND: ChatGPT, a linguistic artificial intelligence (AI) model engineered by OpenAI, offers prospective contributions to mental health professionals. Although having significant theoretical implications, ChatGPT's practical capabilities, particularly regarding suicide prevention, have not yet been substantiated. OBJECTIVE: The study's aim was to evaluate ChatGPT's ability to assess suicide risk, taking into consideration 2 discernable factors-perceived burdensomeness and thwarted belongingness-over a 2-month period. In addition, we evaluated whether ChatGPT-4 more accurately evaluated suicide risk than did ChatGPT-3.5. METHODS: ChatGPT was tasked with assessing a vignette that depicted a hypothetical patient exhibiting differing degrees of perceived burdensomeness and thwarted belongingness. The assessments generated by ChatGPT were subsequently contrasted with standard evaluations rendered by mental health professionals. Using both ChatGPT-3.5 and ChatGPT-4 (May 24, 2023), we executed 3 evaluative procedures in June and July 2023. Our intent was to scrutinize ChatGPT-4's proficiency in assessing various facets of suicide risk in relation to the evaluative abilities of both mental health professionals and an earlier version of ChatGPT-3.5 (March 14 version). RESULTS: During the period of June and July 2023, we found that the likelihood of suicide attempts as evaluated by ChatGPT-4 was similar to the norms of mental health professionals (n=379) under all conditions (average Z score of 0.01). Nonetheless, a pronounced discrepancy was observed regarding the assessments performed by ChatGPT-3.5 (May version), which markedly underestimated the potential for suicide attempts, in comparison to the assessments carried out by the mental health professionals (average Z score of -0.83). The empirical evidence suggests that ChatGPT-4's evaluation of the incidence of suicidal ideation and psychache was higher than that of the mental health professionals (average Z score of 0.47 and 1.00, respectively). Conversely, the level of resilience as assessed by both ChatGPT-4 and ChatGPT-3.5 (both versions) was observed to be lower in comparison to the assessments offered by mental health professionals (average Z score of -0.89 and -0.90, respectively). CONCLUSIONS: The findings suggest that ChatGPT-4 estimates the likelihood of suicide attempts in a manner akin to evaluations provided by professionals. In terms of recognizing suicidal ideation, ChatGPT-4 appears to be more precise. However, regarding psychache, there was an observed overestimation by ChatGPT-4, indicating a need for further research. These results have implications regarding ChatGPT-4's potential to support gatekeepers, patients, and even mental health professionals' decision-making. Despite the clinical potential, intensive follow-up studies are necessary to establish the use of ChatGPT-4's capabilities in clinical practice. The finding that ChatGPT-3.5 frequently underestimates suicide risk, especially in severe cases, is particularly troubling. It indicates that ChatGPT may downplay one's actual suicide risk level.

Beyond human expertise: the promise and limitations of ChatGPT in suicide risk assessment.

ChatGPT, an artificial intelligence language model developed by OpenAI, holds the potential for contributing to the field of mental health. Nevertheless, although ChatGPT theoretically shows promise, its clinical abilities in suicide prevention, a significant mental health concern, have yet to be demonstrated. To address this knowledge gap, this study aims to compare ChatGPT's assessments of mental health indicators to those of mental health professionals in a hypothetical case study that focuses on suicide risk assessment. Specifically, ChatGPT was asked to evaluate a text vignette describing a hypothetical patient with varying levels of perceived burdensomeness and thwarted belongingness. The ChatGPT assessments were compared to the norms of mental health professionals. The results indicated that ChatGPT rated the risk of suicide attempts lower than did the mental health professionals in all conditions. Furthermore, ChatGPT rated mental resilience lower than the norms in most conditions. These results imply that gatekeepers, patients or even mental health professionals who rely on ChatGPT for evaluating suicidal risk or as a complementary tool to improve decision-making may receive an inaccurate assessment that underestimates the actual suicide risk.

[THE HIDDEN VESTIBULAR FEATURES OF MACHADO JOSEPH DISEASE (SPINOCEREBELLAR ATAXIA 3)].

INTRODUCTION: Machado-Joseph disease (MJD) is an inherited neurodegenerative disease with progressive cerebellar ataxia manifested through lack of coordination and balance. MJD patients also present significant Vestibulo-Ocular Reflex (VOR) deficit but their whole vestibular features have not been previously evaluated. We aimed to evaluate whether MJD patients have vestibular features fitting the diagnostic criteria of Bilateral Vestibulopathy established by the International Society for Neuro-otology. METHODS: Sixteen MJD patients and 21 healthy controls underwent a detailed clinical neuro-otological examination including a quantitative evaluation of the VOR gain using the video Head Impulse Test (vHIT). Vestibular-related symptoms were evaluated by the Dizziness Handicap Inventory (DHI), the Activities-specific Balance Confidence Scale (ABC), the Vertigo Visual Scale (VVS). In addition, anxiety that is frequently present in vestibular disorders, was evaluated by the Beck Anxiety Inventory (BAI). RESULTS: MJD patients had significantly reduced horizontal VOR gain with significantly higher scores in all vestibular-related symptoms questionnaires. These symptoms scores were like those reported in studies evaluating patients with bilateral peripheral vestibular loss. CONCLUSIONS: Beyond the cerebellar deficits, MJD patients have vestibular signs and symptoms fitting the diagnostic criteria of Bilateral Vestibulopathy established by the International Society for Neuro-otology. These findings are of relevance not only for the diagnosis and evaluation of progressive cerebellar diseases but also for the possible beneficial effect of vestibular rehabilitation techniques on dizziness, balance and the emotional, physiological and functional aspects of MJD.

The overarching effects of vestibular deficit: Imbalance, anxiety, and spatial disorientation.

BACKGROUND: Comorbid Balance, Anxiety, and Spatial symptoms are observed in neurodevelopmental disorders and aging. Each of these symptoms was studied separately in association with vestibular hypofunction. We aimed to investigate whether such a diffuse range of symptoms has common vestibular pathophysiology. Specifically, we tested whether this Triad of dysfunctions is associated with central or peripheral vestibular hypofunction. We also assessed the possible contribution of semicircular canals (SCCs) vs. saccular function. METHODS: We tested patients with Peripheral bilateral and unilateral Vestibular Hypofunction (PVH), Machado Joseph Disease (MJD) with cerebellar and central bilateral vestibular hypofunction, and healthy controls. SCCs and sacculi functioning were evaluated by the video Head Impulse Test (vHIT) and cervical Vestibular Evoked Myogenic Potentials (cVEMP), respectively. Balance was assessed by the Activities-specific Balance Confidence scale (ABC), anxiety by the Hamilton Anxiety Rating Scale (HAM-A), and spatial orientation by the Object Perspective Taking test (OPT-t). RESULTS: PVH patients with vestibular SCCs and saccular hypofunction presented the Triad of symptoms, imbalance, anxiety, and spatial disorientation. MJD patients with SCCs-related vestibular hypofunction but preserved saccular-related vestibular function presented with a partial profile of imbalance and spatial disorientation. CONCLUSIONS: The present study provides evidence that peripheral vestibular hypofunction is associated with the Triad of dysfunctions, i.e., imbalance, anxiety, and spatial disorientation. The combination of SCCs and saccular hypofunction seems to contribute to the emergence of the Triad of symptoms.

ChatGPT outperforms humans in emotional awareness evaluations.

The artificial intelligence chatbot, ChatGPT, has gained widespread attention for its ability to perform natural language processing tasks and has the fastest-growing user base in history. Although ChatGPT has successfully generated theoretical information in multiple fields, its ability to identify and describe emotions is still unknown. Emotional awareness (EA), the ability to conceptualize one's own and others' emotions, is considered a transdiagnostic mechanism for psychopathology. This study utilized the Levels of Emotional Awareness Scale (LEAS) as an objective, performance-based test to analyze ChatGPT's responses to twenty scenarios and compared its EA performance with that of the general population norms, as reported by a previous study. A second examination was performed one month later to measure EA improvement over time. Finally, two independent licensed psychologists evaluated the fit-to-context of ChatGPT's EA responses. In the first examination, ChatGPT demonstrated significantly higher performance than the general population on all the LEAS scales (Z score = 2.84). In the second examination, ChatGPT's performance significantly improved, almost reaching the maximum possible LEAS score (Z score = 4.26). Its accuracy levels were also extremely high (9.7/10). The study demonstrated that ChatGPT can generate appropriate EA responses, and that its performance may improve significantly over time. The study has theoretical and clinical implications, as ChatGPT can be used as part of cognitive training for clinical populations with EA impairments. In addition, ChatGPT's EA-like abilities may facilitate psychiatric diagnosis and assessment and be used to enhance emotional language. Further research is warranted to better understand the potential benefits and risks of ChatGPT and refine it to promote mental health.

Horizontal Vestibulo-Ocular Reflex Deficit as a Biomarker for Clinical Disease Onset, Severity, and Progression of Machado-Joseph Disease.

Because of the crucial importance of finding a useful biomarker for further clinical trials in Machado-Joseph disease (MJD), and based on our previous studies, we aimed to evaluate whether the horizontal vestibulo-ocular reflex (VOR) gain could be a reliable neurophysiological biomarker for the clinical onset, severity, and progression of the disease. Thirty-five MJD patients, 11 pre-symptomatic genetically confirmed MJD subjects, and 20 healthy controls underwent a detailed epidemiological and clinical neurological examination including the Scale for the Assessment and Rating of Ataxia (SARA). Their VOR gain was measured using the video Head Impulse Test system. Twenty of the MJD patients were re-tested after a period of 1-3 years. Horizontal VOR gain was abnormal in 92% of MJD, 54% pre-symptomatic, and 0% healthy controls. Horizontal VOR gain in the MJD group was significantly negatively correlated with SARA score in the first (r=0.66, p<0.001) and second (r=0.61, p<0.001) examinations. There was also a significant negative correlation between the percentage of change in horizontal VOR gain and the percentage of change in SARA score across both examinations (r=-0.54, p < 0.05). A regression model of the SARA score with the horizontal VOR gain and disease duration as predictors demonstrated that both the horizontal VOR gain and the disease duration had an independent contribution to the prediction of the SARA score. The horizontal VOR gain seems to be a reliable biomarker for the clinical onset, severity, and progression of MJD and could be used in further clinical studies.

"I Do Not Know How You Feel and How I Feel About That": Mentalizing Impairments in Machado-Joseph Disease.

Machado Joseph disease (MJD), also known as spinocerebellar ataxia type 3 (SCA3), is an autosomal dominant neurodegenerative disease. Mentalizing is the ability to think and understand the mental state of the other and of the self in terms of thoughts, feelings, and intentions. The aim of this study is to fill the gap in our understanding of mentalizing in MJD since there is currently very little and inconsistent research on MJD and mentalizing. A total of 18 Jews of Yemenite origin with clinically and genetically confirmed MJD, 5 pre-symptomatic MJD with a positive genetic test, and 17 Jews of Yemenite origin healthy controls, underwent a battery of tests consisting of reading the mind in the eyes (RME), Toronto Alexithymia Scale (TAS-20), and false belief test (FBt). The MJD group scored lower on the RME and FBt, and higher on TAS-20 test compared to control. A significant negative correlation was found between disease duration and RME score. All the pre-symptomatic participants scored within the normal clinical range in all tests. MJD patients demonstrated a widespread deficiency in the ability to mentalizing on a clinical level with autistic characteristics. These impairments may impact the patient's interpsychic experience and daily life interactions and have important clinical implication. Pre-symptomatic participants demonstrated normal mentalizing in all tests, suggesting that the mentalizing impairments do not precede the symptoms of ataxia and are part of the clinical picture of MJD.