This study aimed to understand how visually degraded conditions affect the intelligibility of isolated signs in sign language, and how these conditions influence perceived difficulty. Twenty-nine fluent users of Swedish Sign Language viewed 100 isolated signs presented under two types of visual degradation: spectral degradation (pixelation) and background noise (salt-and-pepper noise), each across five levels of degradation. Participants identified each sign and rated the perceived difficulty. Generalized linear mixed models were used to evaluate the effect of level of degradation on intelligibility and perceived difficulty, separately and for each type of degradation. Psychometric functions were estimated, and generalized linear mixed models were used to analyse the relationship between intelligibility and perceived difficulty. Both types of degradation significantly reduced intelligibility and increased perceived difficulty. Psychometric curves showed that intelligibility plateaued around 70% accuracy, mirroring results from speech-in-noise research. Correct responses were consistently rated as easier to perceive, and an interaction between degradation level and response accuracy was observed only in the spectral degradation condition, suggesting distinct perceptual processing mechanisms. Visual degradations affect sign language intelligibility in ways comparable to auditory distortions in speech, with differences between degradation types suggesting distinct perceptual strategies. These findings have implications for theories about linguistic processing in perceptually challenging conditions and call for further investigation into cognitive and linguistic factors that influence sign perception in degraded conditions.

Few studies have examined which acoustic features of speech can be used to distinguish between different emotions, and how combinations of acoustic parameters contribute to identification of emotions. The aim of the present study was to investigate which acoustic parameters in Swedish speech are most important for differentiation between, and identification of, the emotions anger, fear, happiness, sadness, and surprise in Swedish sentences. One-way ANOVAs were used to compare acoustic parameters between the emotions and both simple and multiple logistic regression models were used to examine the contribution of different acoustic parameters to differentiation between emotions. Results showed differences between emotions for several acoustic parameters in Swedish speech: surprise was the most distinct emotion, with significant differences compared to the other emotions across a range of acoustic parameters, while anger and happiness did not differ from each other on any parameter. The logistic regression models showed that fear was the best-predicted emotion while happiness was most difficult to predict. Frequency- and spectral-balance-related parameters were best at predicting fear. Amplitude- and temporal-related parameters were most important for surprise, while a combination of frequency-, amplitude- and spectral balance-related parameters are important for sadness. Assuming that there are similarities between acoustic models and how listeners infer emotions in speech, results suggest that individuals with hearing loss, who lack abilities of frequency detection, may compared to normal hearing individuals have difficulties in identifying fear in Swedish speech. Since happiness and fear relied primarily on amplitude- and spectral-balance-related parameters, detection of them are probably facilitated more by hearing aid use.

Objective: Earlier research on fatigue in post-COVID-19 condition (PCC) has mainly studied subjective fatigue, either over a prolonged period (trait fatigue) or in relation to a certain situation (state fatigue) in the form of perceived fatigability. Another aspect of state fatigue, cognitive performance fatigability, defined as a decline in performance over time in cognitively demanding tasks, has not been the focus of PCC research. We aimed to examine performance fatigability and the associations between these three fatigue domains. Method: Thirty-four individuals with PCC and 36 controls who recovered from COVID-19 without PCC performed cognitive tests repeated twice during the test session to assess performance fatigability. Self-reports were used to assess perceived fatigability (repeated Visual Analogue Scales) and trait fatigue (Fatigue Severity Scale, Multidimensional Fatigue Inventory). Results: Analysis of covariance showed significant interactions in two tests measuring processing speed: coding (p < .001, eta(2)(p) = .17) and a speed composite score derived from Color-Word Interference Test (p = .031, eta(2)(p) = .08), where the controls improved their performance more on the repeated tests compared with the PCC group, indicating performance fatigability in the PCC group. For the PCC group, no correlations were found between fatigue domains (r < 0.34). Conclusions: The PCC group exhibited performance fatigability in measures of processing speed. There were no correlations between fatigue domains, suggesting that the domains might represent separate constructs. The results highlight the need to differentiate between various types of fatigue in research and clinical practice and to assess performance fatigability in addition to using self-reports to obtain a comprehensive understanding of fatigue and fatigability.

Previous research has shown deficits in vocal emotion recognition in sub-populations of individuals with hearing loss. As emotion recognition is an essential ability that affects social interaction, and in extension, can impact well-being, understanding vocal emotion recognition difficulties is a high priority research topic. Furthermore, it has been shown that although hearing aids improves word recognition, it does not improve emotion recognition. To explore emotion recognition and the effect of amplification in individuals with hearing loss, we examined recognition of vocal emotions expressed both verbally and non-verbally in middle-aged to older individuals with and without linear amplification (similar amplification across sound levels). Twenty-one experienced hearing aid users with bilateral mild-to-moderate hearing loss, and 20 individuals with normal hearing performed a vocal emotion recognition task on sentences and non-verbal vocalizations. The hearing loss group had poorer emotion recognition for both sentences (F(1,38)=15.24, p <0.001, η2p=0.29, without linear amplification, and F(1, 38)= 5.62, p=0.023, η2p=0.13, with linear amplification) and non-verbal vocalizations(F(1,38)= 25.18, p <0.001, η2p=0.40, without linear amplification, and F(1, 38)= 10.30, p=0.003, η2p=0.21, with linear amplification). However, linear amplification significantly improved the recognition of happiness (p < 0.001), which is distinguished by frequency parameters, for sentences. For non-verbal vocalizations, recognition of fear (p = 0.004) and anger (p = 0.004), were improved by linear amplification. Patterns of confusion were similar for the two groups, which may suggest that both groups perceived the emotions similarly, but that the degree of perceptual precision was lower in the hearing loss group. In sum, hearing loss negatively impacts vocal emotion recognition, but linear amplification can enhance recognition for some emotions.

Evidence suggests that large-scale brain network organization may differ between individuals with limited sensory input (e.g. deaf individuals) and those without sensory impairment. These neural differences may manifest as behavioural differences, such as enhanced visual attention in deaf individuals. Using independent component analyses (ICA) on resting-state fMRI data, we showed that the attention network was more widespread in the hearing compared to the deaf group. This suggests that the organization of the attention network is different in deaf and hearing adults. 

Deaf signers and hearing non-signers have previously been shown to recruit partially different brain regions during simple arithmetic. In light of the triple code model, the differences were interpreted as relating to stronger recruitment of the verbal system of numerical processing, that is, left angular and inferior frontal gyrus, in hearing non-signers, and of the quantity system of numerical processing, that is, right horizontal intraparietal sulcus, for deaf signers. The main aim of the present study was to better understand similarities and differences in the neural correlates supporting arithmetic in deaf compared to hearing individuals. Twenty-nine adult deaf signers and 29 hearing non-signers were enrolled in an functional magnetic resonance imaging study of simple and difficult subtraction and multiplication. Brain imaging data were analyzed using whole-brain analysis, region of interest analysis, and functional connectivity analysis. Although the groups were matched on age, gender, and nonverbal intelligence, the deaf group performed generally poorer than the hearing group in arithmetic. Nevertheless, we found generally similar networks to be involved for both groups, the only exception being the involvement of the left inferior frontal gyrus. This region was activated significantly stronger for the hearing compared to the deaf group but showed stronger functional connectivity with the left superior temporal gyrus in the deaf, compared to the hearing, group. These results lend no support to increased recruitment of the quantity system in deaf signers. Perhaps the reason for performance differences is to be found in other brain regions not included in the original triple code model.

Previous research has shown deficits in vocal emotion recognition in sub-populations of individuals with hearing loss, making this a high priority research topic. However, previousresearch has only examined vocal emotion recognition using verbal material, in which emotions are expressed through emotional prosody. There is evidence that older individualswith hearing loss suffer from deficits in general prosody recognition, not specific to emotionalprosody. No study has examined the recognition of non-verbal vocalization, which constitutes another important source for the vocal communication of emotions. It might be thecase that individuals with hearing loss have specific difficulties in recognizing emotionsexpressed through prosody in speech, but not non-verbal vocalizations. We aim to examinewhether vocal emotion recognition difficulties in middle- aged-to older individuals with sensorineural mild-moderate hearing loss are better explained by deficits in vocal emotion recognition specifically, or deficits in prosody recognition generally by including both sentencesand non-verbal expressions. Furthermore a, some of the studies which have concluded thatindividuals with mild-moderate hearing loss have deficits in vocal emotion recognition abilityhave also found that the use of hearing aids does not improve recognition accuracy in thisgroup. We aim to examine the effects of linear amplification and audibility on the recognitionof different emotions expressed both verbally and non-verbally. Besides examining accuracy for different emotions we will also look at patterns of confusion (which specific emotionsare mistaken for other specific emotion and at which rates) during both amplified and nonamplified listening, and we will analyze all material acoustically and relate the acoustic content to performance. Together these analyses will provide clues to effects of amplification onthe perception of different emotions. For these purposes, a total of 70 middle-aged-olderindividuals, half with mild-moderate hearing loss and half with normal hearing will perform acomputerized forced-choice vocal emotion recognition task with and without amplification

If the brain is deprived of input from one or more senses during development, functional and structural reorganization of the deprived regions takes place. However, little is known about how sensory deprivation affects large-scale brain networks. In the present study, we use data-driven independent component analysis (ICA) to characterize large-scale brain networks in 15 deaf early signers and 24 hearing non-signers based on resting-state functional MRI data. We found differences between the groups in independent components representing the left lateralized control network, the default network, the ventral somatomotor network, and the attention network. In addition, we showed stronger functional connectivity for deaf compared to hearing individuals from the middle and superior temporal cortices to the cingulate cortex, insular cortex, cuneus and precuneus, supramarginal gyrus, supplementary motor area, and cerebellum crus 1, and stronger connectivity for hearing non-signers to hippocampus, middle and superior frontal gyri, pre- and postcentral gyri, and cerebellum crus 8. These results show that deafness induces large-scale network reorganization, with the middle/superior temporal cortex as a central node of plasticity. Cross-modal reorganization may be associated with behavioral adaptations to the environment, including superior ability in some visual functions such as visual working memory and visual attention, in deaf signers.