When a person listens to natural speech, the relation between features of the speech signal and the corresponding evoked electroencephalogram (EEG) is indicative of neural processing of the speech signal. Using linguistic representations of speech, we investigate the differences in neural processing between speech in a native and foreign language that is not understood. We conducted experiments using three stimuli: a comprehensible language, an incomprehensible language, and randomly shuffled words from a comprehensible language, while recording the EEG signal of native Dutch-speaking participants. We modeled the neural tracking of linguistic features of the speech signals using a deep-learning model in a match-mismatch task that relates EEG signals to speech, while accounting for lexical segmentation features reflecting acoustic processing. The deep learning model effectively classifies coherent versus nonsense languages. We also observed significant differences in tracking patterns between comprehensible and incomprehensible speech stimuli within the same language. It demonstrates the potential of deep learning frameworks in measuring speech understanding objectively.

BACKGROUND: Brain-computer interfaces can enable communication for people with paralysis by transforming cortical activity associated with attempted speech into text on a computer screen. Communication with brain-computer interfaces has been restricted by extensive training requirements and limited accuracy.
METHODS: A 45-year-old man with amyotrophic lateral sclerosis (ALS) with tetraparesis and severe dysarthria underwent surgical implantation of four microelectrode arrays into his left ventral precentral gyrus 5 years after the onset of the illness; these arrays recorded neural activity from 256 intracortical electrodes. We report the results of decoding his cortical neural activity as he attempted to speak in both prompted and unstructured conversational contexts. Decoded words were displayed on a screen and then vocalized with the use of text-to-speech software designed to sound like his pre-ALS voice.
RESULTS: On the first day of use (25 days after surgery), the neuroprosthesis achieved 99.6% accuracy with a 50-word vocabulary. Calibration of the neuroprosthesis required 30 minutes of cortical recordings while the participant attempted to speak, followed by subsequent processing. On the second day, after 1.4 additional hours of system training, the neuroprosthesis achieved 90.2% accuracy using a 125,000-word vocabulary. With further training data, the neuroprosthesis sustained 97.5% accuracy over a period of 8.4 months after surgical implantation, and the participant used it to communicate in self-paced conversations at a rate of approximately 32 words per minute for more than 248 cumulative hours.
CONCLUSIONS: In a person with ALS and severe dysarthria, an intracortical speech neuroprosthesis reached a level of performance suitable to restore conversational communication after brief training. (Funded by the Office of the Assistant Secretary of Defense for Health Affairs and others; BrainGate2 ClinicalTrials.gov number, NCT00912041.).

UNASSIGNED: The consensus in scientific literature is that each child undergoes a unique linguistic development path, albeit with shared developmental stages. Some children excel or lag behind their peers in language skills. Consequently, a key challenge in language acquisition research is pinpointing factors influencing individual differences in language development.
UNASSIGNED: We observed children longitudinally from 3 to 24 months of life to explore early predictors of vocabulary size. Based on the productive vocabulary size of children at 24 months, 30 children met our sample selection criteria: 10 late talkers and 10 early talkers, and we compared them with 10 typical talkers. We evaluated interactive behaviors at 3, 6, 9 and 12 months, considering vocal production, gaze at mother\'s face, and gestural production during mother-child interactions, and we considered mothers\' report of children\'s actions and gestures and receptive-vocabulary size at 15 and 18 months.
UNASSIGNED: Results indicated early precursors of language outcome at 24 months identifiable as early as 3 months in vocal productions, 6 months for gaze at mother\'s face and 12 months for gestural productions.
UNASSIGNED: Our research highlights both theoretical and practical implications. Theoretically, identifying the early indicators of belonging to the group of late or early talkers underscores the significant role of this developmental period for future studies. On a practical note, our findings emphasize the crucial need for early investigations to identify predictors of vocabulary development before the typical age at which lexical delay is identified.

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UNASSIGNED: Identifying hate speech (HS) is a central concern within online contexts. Current methods are insufficient for efficient preemptive HS identification. In this study, we present the results of an analysis of automatic HS identification applied to popular alt-right YouTube videos.
UNASSIGNED: This essay describes methodological challenges of automatic HS detection. The case study concerns data on a formative segment of contemporary radical right discourse. Our purpose is twofold. (1) To outline an interdisciplinary mixed-methods approach for using automated identification of HS. This bridges the gap between technical research on the one hand (such as machine learning, deep learning, and natural language processing, NLP) and traditional empirical research on the other. Regarding alt-right discourse and HS, we ask: (2) What are the challenges in identifying HS in popular alt-right YouTube videos?
UNASSIGNED: The results indicate that effective and consistent identification of HS communication necessitates qualitative interventions to avoid arbitrary or misleading applications. Binary approaches of hate/non-hate speech tend to force the rationale for designating content as HS. A context-sensitive qualitative approach can remedy this by bringing into focus the indirect character of these communications. The results should interest researchers within social sciences and the humanities adopting automatic sentiment analysis and for those analysing HS and radical right discourse.
UNASSIGNED: Automatic identification or moderation of HS cannot account for an evolving context of indirect signification. This study exemplifies a process whereby automatic hate speech identification could be utilised effectively. Several methodological steps are needed for a useful outcome, with both technical quantitative processing and qualitative analysis being vital to achieve meaningful results. With regard to the alt-right YouTube material, the main challenge is indirect framing. Identification demands orientation in the broader discursive context and the adaptation towards indirect expressions renders moderation and suppression ethically and legally precarious.

BACKGROUND: Psychosis involves a distortion of thought content, which is partly reflected in anomalous ways in which words are semantically connected into utterances in speech. We sought to explore how these linguistic anomalies are realized through putative circuit-level abnormalities in the brain\'s semantic network.
METHODS: Using a computational large-language model, Bidirectional Encoder Representations from Transformers (BERT), we quantified the contextual expectedness of a given word sequence (perplexity) across 180 samples obtained from descriptions of 3 pictures by patients with first-episode schizophrenia (FES) and controls matched for age, parental social status, and sex, scanned with 7 T ultra-high field functional magnetic resonance imaging (fMRI). Subsequently, perplexity was used to parametrize a spectral dynamic causal model (DCM) of the effective connectivity within (intrinsic) and between (extrinsic) 4 key regions of the semantic network at rest, namely the anterior temporal lobe, the inferior frontal gyrus (IFG), the posterior middle temporal gyrus (MTG), and the angular gyrus.
RESULTS: We included 60 participants, including 30 patients with FES and 30 controls. We observed higher perplexity in the FES group, indicating that speech was less predictable by the preceding context among patients. Results of Bayesian model comparisons showed that a DCM including the group by perplexity interaction best explained the underlying patterns of neural activity. We observed an increase of self-inhibitory effective connectivity within the IFG, as well as reduced self-inhibitory tone within the pMTG, in the FES group. An increase in self-inhibitory tone in the IFG correlated strongly and positively with inter-regional excitation between the IFG and posterior MTG, while self-inhibition of the posterior MTG was negatively correlated with this interregional excitation.
CONCLUSIONS: Our design did not address connectivity in the semantic network during tasks that selectively activated the semantic network, which could corroborate findings from this resting-state fMRI study. Furthermore, we do not present a replication study, which would ideally use speech in a different language.
CONCLUSIONS: As an explanation for peculiar speech in psychosis, these results index a shift in the excitatory-inhibitory balance regulating information flow across the semantic network, confined to 2 regions that were previously linked specifically to the executive control of meaning. Based on our approach of combining a large language model with causal connectivity estimates, we propose loss in semantic control as a potential neurocognitive mechanism contributing to disorganization in psychosis.

UNASSIGNED: Machine learning (ML) algorithms have been heralded as promising solutions to the realization of assistive systems in digital healthcare, due to their ability to detect fine-grain patterns that are not easily perceived by humans. Yet, ML algorithms have also been critiqued for treating individuals differently based on their demography, thus propagating existing disparities. This paper explores gender and race bias in speech-based ML algorithms that detect behavioral and mental health outcomes.
UNASSIGNED: This paper examines potential sources of bias in the data used to train the ML, encompassing acoustic features extracted from speech signals and associated labels, as well as in the ML decisions. The paper further examines approaches to reduce existing bias via using the features that are the least informative of one\'s demographic information as the ML input, and transforming the feature space in an adversarial manner to diminish the evidence of the demographic information while retaining information about the focal behavioral and mental health state.
UNASSIGNED: Results are presented in two domains, the first pertaining to gender and race bias when estimating levels of anxiety, and the second pertaining to gender bias in detecting depression. Findings indicate the presence of statistically significant differences in both acoustic features and labels among demographic groups, as well as differential ML performance among groups. The statistically significant differences present in the label space are partially preserved in the ML decisions. Although variations in ML performance across demographic groups were noted, results are mixed regarding the models\' ability to accurately estimate healthcare outcomes for the sensitive groups.
UNASSIGNED: These findings underscore the necessity for careful and thoughtful design in developing ML models that are capable of maintaining crucial aspects of the data and perform effectively across all populations in digital healthcare applications.

This paper discusses a neglected aspect of the historiography of aphasia, the role that Pavlovian conditioning played in Alexander Luria\'s and Wilder Penfield\'s understanding of the acquisition, expression, and loss of spoken and written speech. Luria was born into a bourgeois family in Tzarist Russia and pursued his research on speech and aphasia under the Soviet regime. Luria\'s work was condemned in the last years of Stalin\'s rule, but it received international acclaim in the West after Stalin\'s death. Penfield was conversant with Pavlov\'s writing having had a working relationship with one of Pavlov\'s foremost students, Boris Babkin, who came to McGill University and later to the Montreal Neurological Institute after being jailed and exiled from the Soviet Union for lack of revolutionary fervor. Both Luria and Penfield, the latter as early as 1935, saw in Pavlovian conditioning mediated by specific areas of the human cerebral cortex the basic neurophysiological mechanism underlying speech and thought, and in Penfield\'s\' case, memory, perception, self-awareness, and purposeful behavior. It is concluded that Luria and Penfield independently arrived at a general hypothesis, based on Pavlovian conditioning, that united the localization of speech, the syndromes caused by damage to speech-competent regions, and the putative neurophysiological mechanisms that they believed to underlie speech and higher cortical functions.

The speech amplitude envelope carries important acoustic information required for speech intelligibility and contains sensory cues (amplitude rise times, ARTs) that play a key role in both sensory rhythm perception and neural speech encoding. Individual differences in children\'s sensitivity to ARTs have been related to the development of children\'s phonological processing skills across languages by the Temporal Sampling theory. Impaired processing of ARTs also characterises children with dyslexia. However, different ART tasks have been employed in different studies, in different languages, and at different ages. Here, we compare the sensitivity of three frequently used ART tasks (based on synthetic syllables, sine tones, and speech-shaped noise) in a longitudinal study of English-speaking children with and without dyslexia. Children\'s ability to discriminate rising frequency, duration, and intensity was also tested. ART discrimination in all 3 tasks was significantly inter-related, but different relations to phonology and literacy were found for different ART tasks at different ages. In particular, the often-used sine tone and speech-shaped noise ART tasks showed greater sensitivity in older children, while the synthetic syllable task (/ba/ rise) showed greater sensitivity in younger children. Sensitivity to rising frequency was also related to phonology and literacy across ages. The data are interpreted with respect to the Temporal Sampling theory of developmental dyslexia.

UNASSIGNED: Depression and its components significantly impact dementia prediction and severity, necessitating reliable objective measures for quantification.
UNASSIGNED: We investigated associations between emotion-based speech measures (valence, arousal, and dominance) during picture descriptions and depression dimensions derived from the geriatric depression scale (GDS, dysphoria, withdrawal-apathy-vigor (WAV), anxiety, hopelessness, and subjective memory complaint).
UNASSIGNED: Higher WAV was associated with more negative valence (estimate = -0.133, p = 0.030). While interactions of apolipoprotein E (APOE) 4 status with depression dimensions on emotional valence did not reach significance, there was a trend for more negative valence with higher dysphoria in those with at least one APOE4 allele (estimate = -0.404, p = 0.0846). Associations were similar irrespective of dementia severity.
UNASSIGNED: Our study underscores the potential utility of speech biomarkers in characterizing depression dimensions. In future research, using emotionally charged stimuli may enhance emotional measure elicitation. The role of APOE on the interaction of speech markers and depression dimensions warrants further exploration with greater sample sizes.
UNASSIGNED: Participants reporting higher apathy used more negative words to describe a neutral picture.Those with higher dysphoria and at least one APOE4 allele also tended to use more negative words.Our results suggest the potential use of speech biomarkers in characterizing depression dimensions.