Cognitive Tasks Research Articles

Sociotechnical feasibility of natural language processing-driven tools in clinical trial eligibility prescreening for Alzheimer's disease and related dementias.

Alzheimer's disease and related dementias (ADRD) affect over 55 million globally. Current clinical trials suffer from low recruitment rates, a challenge potentially addressable via natural language processing (NLP) technologies for researchers to effectively identify eligible clinical trial participants. This study investigates the sociotechnical feasibility of NLP-driven tools for ADRD research prescreening and analyzes the tools' cognitive complexity's effect on usability to identify cognitive support strategies. A randomized experiment was conducted with 60 clinical research staff using three prescreening tools (Criteria2Query, Informatics for Integrating Biology and the Bedside [i2b2], and Leaf). Cognitive task analysis was employed to analyze the usability of each tool using the Health Information Technology Usability Evaluation Scale. Data analysis involved calculating descriptive statistics, interrater agreement via intraclass correlation coefficient, cognitive complexity, and Generalized Estimating Equations models. Leaf scored highest for usability followed by Criteria2Query and i2b2. Cognitive complexity was found to be affected by age, computer literacy, and number of criteria, but was not significantly associated with usability. Adopting NLP for ADRD prescreening demands careful task delegation, comprehensive training, precise translation of eligibility criteria, and increased research accessibility. The study highlights the relevance of these factors in enhancing NLP-driven tools' usability and efficacy in clinical research prescreening. User-modifiable NLP-driven prescreening tools were favorably received, with system type, evaluation sequence, and user's computer literacy influencing usability more than cognitive complexity. The study emphasizes NLP's potential in improving recruitment for clinical trials, endorsing a mixed-methods approach for future system evaluation and enhancements.

Effects of combined cognitive and resistance training on physical and cognitive performance and psychosocial well-being of older adults ≥65: study protocol for a randomised controlled trial

IntroductionWith increasing life expectancy of older adult population, maintaining independence and well-being in later years is of paramount importance. This study aims to investigate the impact of three distinct interventions:...

Brain Activation for Social Cognition and Emotion Processing Tasks in Borderline Personality Disorder: A Meta-Analysis of Neuroimaging Studies.

The present meta-analysis summarizes brain activation for social cognition and emotion-processing tasks in borderline personality disorder (BPD). We carried out two meta-analyses to elaborate on commonalities and potential differences between the two types of tasks. In the first meta-analysis, we implemented a more liberal strategy for task selection (including social and emotional content). The results confirmed previously reported hyperactivations in patients with BPD in the bilateral amygdala and prefrontal cortex and hypoactivations in bilateral inferior frontal gyri. When applying a stricter approach to task selection, focusing narrowly on social cognition tasks, we only found activation in prefrontal areas, particularly in the anterior cingulate and ventromedial prefrontal cortex. We review the role of these areas in social cognition in healthy adults, suggesting that the observed BPD hyperactivations may reflect an overreliance on self-related thought in social cognition.

Different learning aberrations relate to delusion-like beliefs with different contents.

The prediction error account of delusions has had success. However, its explanation of delusions with different contents has been lacking. Persecutory delusions and paranoia are the common unfounded beliefs that others have harmful intentions towards us. Other delusions include believing that one's thoughts or actions are under external control, or that events in the world have specific personal meaning. We compare learning on two different cognitive tasks, probabilistic reversal learning (PRL) and Kamin blocking, that have relationships to paranoid and non-paranoid delusion-like beliefs, respectively. We find that Clinical High-Risk status alone does not result in different behavioral results on the PRL task but that an individual's level of paranoia is associated with excessive switching behavior. During the Kamin blocking task, paranoid individuals learned inappropriately about the blocked cue. However, they also had decreased learning about the control cue, suggesting more general learning impairments. Non-paranoid delusion-like belief conviction (but not paranoia) was associated with aberrant learning about the blocked cue but intact learning about the control cue, suggesting specific impairments in learning related to cue combination. We fit task-specific computational models separately to behavioral data to explore how latent parameters vary within individuals between tasks, and how they can explain symptom-specific effects. We find that paranoia is associated with low learning rates on the PRL task as well as the blocking task. Non-paranoid delusion-like belief conviction was instead related to parameters controlling the degree and direction of similarity between cue updating during simultaneous cue presentation. These results suggest that paranoia and other delusion-like beliefs involve dissociable deficits in learning and belief updating, which - given the transdiagnostic status of paranoia - may have differential utility in predicting psychosis.

Analysis of functional hop test with dual task on injured and uninjured athletes

Purpose: Maladaptive neuroplasticity may develop after injury. However, most of the test applications in the return to the sport process mainly focus on the motor end of the sensorimotor system. In this study, we aimed to examine the performance outputs of the functional hop tests with the dual task methodology.
 Methods: Triple hop for distance (THD), crossover hop for distance (CHD) and 6-meter hop for timed (6MHT) tests were done. For the cognitive task the backward digit span test of the Wechsler intelligence scale was preferred. Nineteen athletes with a history of unilateral lower extremity injuries were assigned to the previously injured group (PIG), and 20 athletes with no previous injury were assigned to the control group (CG).
 Results: There were no significant differences between the results of the cognitive task levels and Mini Mental State Examination scores of the athletes in our study (p>0.05). PIG athletes showed significantly lower jump performances compared to CG athletes; in the injured extremity side, differences were found between the groups' THD, CHD, and 6MHT values during a single task (p

A Multimodal Exertional Test for concussion: a pilot study in healthy athletes.

Exertional tests have become a promising tool to assist clinicians in the management of concussions, however require expensive equipment, extensive spaces, and specialized clinician expertise. As such, we developed a test with minimal resource requirements encompassing key elements of sport and physical activity. The purpose of this study was to pilot test the Multimodal Exertional Test (MET) protocol in a sample of healthy interuniversity athletes. The MET comprises four stages, each featuring three distinct tasks. The test begins with engaging in squats, alternating reverse lunges, and hip hinges (Stage 1). The next stage progressively evolves into executing these tasks within specified time limits (Stage 2). Following this, the test advances to a stage that incorporates cognitive tasks (Stage 3), and the final stage demands greater levels of physical exertion, cognition, and multi-directional movements (Stage 4). Heart rate (HR) was obtained during each stage of the MET and participants' symptom severity scores were recorded following each task. Fourteen healthy interuniversity athletes (n = 8 female, n = 6 male) participated in the study. HR was obtained for 10 of the 14 athletes (females: n = 6, males: n = 4). Increases in average and maximum HR were identified between pre-MET and Stage 1, and between Stages 3 and 4. Consistent with the tasks in each stage, there were no increases in average and maximum HR observed between MET Stages 1 to 3. Female athletes exhibited higher average and maximum HRs compared to male athletes during all four stages. All 14 athletes reported minimal changes in symptom severity following each task. Among healthy athletes, the MET elicits an increase in average and maximum HR throughout the protocol without symptom provocation. Female athletes exhibit higher HRs during all four stages in comparison to male athletes.

The effects of irrelevant speech on physiological stress, cognitive performance, and subjective experience – Focus on heart rate variability

Irrelevant speech impairs cognitive performance, especially in tasks requiring verbal short-term memory. Working on these tasks during irrelevant speech can also cause a physiological stress reaction. The aim of this study was to examine heart rate variability (HRV) as a non-invasive and easy-to-use stress measure in an irrelevant speech paradigm. Thirty participants performed cognitive tasks (n-back and serial recall) during two sound conditions: irrelevant speech (50 dB) and quiet (33 dB steady-state noise). The influence of conditions as well as presentation orders of conditions were examined on performance, subjective experience, and physiological stress. Working during irrelevant speech compared to working during quiet reduced performance, namely accuracy, in the serial recall task. It was more annoying, heightened the perceived workload, and lowered acoustic satisfaction. It was related to higher physiological stress by causing faster heart rate and changes in HRV frequency-domain analysis (LF, HF and LF/HF). The order of conditions showed some additional effects. When speech was the first condition, 3-back performance was less accurate, and serial recall response times were longer, heart rate was faster, and successive heart beats had less variability (lower RMSSD) during speech than during quiet. When quiet was the first condition, heart rate was faster and reaction times in 3-back were slower during quiet than during speech. The negative effect of irrelevant speech was clear in experience, performance, and physiological stress. The study shows that HRV can be used as a physiological stress measure in irrelevant speech studies.

The non-visual effects of correlated color temperature on the alertness, cognition, and mood of fatigued individuals during the afternoon

Railway dispatchers are prone to mental fatigue due to prolonged periods of intense concentration. As the main or even the only light source in the enclosed control centers, artificial lighting has non-visual effects on human alertness, cognition, and mood. To investigate whether external environmental cues could counteract performance decline induced by endogenous cardiac exhaustion, the current study modulated four correlated color temperature (CCT) levels and developed one visual task and six cognitive tasks representing basic and advanced cognitive functions, including sustained attention, response inhibition, working memory, risk decision-making, executive control, and emotion perception. A within-subjects design was employed to collect subjective and behavioral responses under both normal and fatigued mental antecedent states. Electroencephalogram (EEG) with alpha and theta bands as well as four fatigue-related algorithms including θ/β, α/β, (θ+α)/β, and (θ+α)/(α+β) were analyzed to explore the underlying neural mechanisms behind these responses. The results indicated a complex interactive pattern of CCT and mental antecedent state on alertness, cognition, and mood. Higher CCT had a supportive effect on subjective alertness and positive mood, independent of mental antecedent state. However, it exhibited a slight performance-impairing effect on sustained attention tasks. Conversely, higher CCT demonstrated a significant enhancement in advanced cognitive functions for fatigued individuals. EEG data revealed alpha oscillations increased, but theta activity and (θ + α)/β suppressed at 12,000 K, suggesting higher CCT partially alleviated the decline in alertness and cognition induced by mental fatigue. To develop an appropriate photobiological lighting scheme for fatigue intervention, larger and longer-term studies are needed in the future to measure a range of confounding factors and individual differences.

The Language Network Reliably “Tracks” Naturalistic Meaningful Nonverbal Stimuli

Abstract The language network, comprised of brain regions in the left frontal and temporal cortex, responds robustly and reliably during language comprehension but shows little or no response during many nonlinguistic cognitive tasks (e.g., Fedorenko & Blank, 2020). However, one domain whose relationship with language remains debated is semantics—our conceptual knowledge of the world. Given that the language network responds strongly to meaningful linguistic stimuli, could some of this response be driven by the presence of rich conceptual representations encoded in linguistic inputs? In this study, we used a naturalistic cognition paradigm to test whether the cognitive and neural resources that are responsible for language processing are also recruited for processing semantically rich nonverbal stimuli. To do so, we measured BOLD responses to a set of ∼5-minute-long video and audio clips that consisted of meaningful event sequences but did not contain any linguistic content. We then used the intersubject correlation (ISC) approach (Hasson et al., 2004) to examine the extent to which the language network “tracks” these stimuli, that is, exhibits stimulus-related variation. Across all the regions of the language network, meaningful nonverbal stimuli elicited reliable ISCs. These ISCs were higher than the ISCs elicited by semantically impoverished nonverbal stimuli (e.g., a music clip), but substantially lower than the ISCs elicited by linguistic stimuli. Our results complement earlier findings from controlled experiments (e.g., Ivanova et al., 2021) in providing further evidence that the language network shows some sensitivity to semantic content in nonverbal stimuli.

A GRU–CNN model for auditory attention detection using microstate and recurrence quantification analysis

Attention as a cognition ability plays a crucial role in perception which helps humans to concentrate on specific objects of the environment while discarding others. In this paper, auditory attention detection (AAD) is investigated using different dynamic features extracted from multichannel electroencephalography (EEG) signals when listeners attend to a target speaker in the presence of a competing talker. To this aim, microstate and recurrence quantification analysis are utilized to extract different types of features that reflect changes in the brain state during cognitive tasks. Then, an optimized feature set is determined by employing the processes of significant feature selection based on classification performance. The classifier model is developed by hybrid sequential learning that employs Gated Recurrent Units (GRU) and Convolutional Neural Network (CNN) into a unified framework for accurate attention detection. The proposed AAD method shows that the selected feature set achieves the most discriminative features for the classification process. Also, it yields the best performance as compared with state-of-the-art AAD approaches from the literature in terms of various measures. The current study is the first to validate the use of microstate and recurrence quantification parameters to differentiate auditory attention using reinforcement learning without access to stimuli.

Postnatal hypofunction of N-methyl-D-aspartate receptors alters perforant path synaptic plasticity and filtering and impairs dentate gyrus-mediated spatial discrimination.

Transient hypofunction of the NMDA receptor represents a convergence point for the onset and further development of psychiatric disorders, including schizophrenia. Although the cumulative evidence indicates dysregulation of the hippocampal formation in schizophrenia, the integrity of the synaptic transmission and plasticity conveyed by the somatosensorial inputs to the dentate gyrus, the perforant pathway synapses, have barely been explored in this pathological condition. We identified a series of synaptic alterations of the lateral and medial perforant paths in animals postnatally treated with the NMDA antagonist MK-801. This dysregulation suggests decreased cognitive performance, for which the dentate gyrus is critical. We identified alterations in the synaptic properties of the lateral and medial perforant paths to the dentate gyrus synapses in slices from MK-801-treated animals. Altered glutamate release and decreased synaptic strength precede an impairment in the induction and expression of long-term potentiation (LTP) and CB1 receptor-mediated long-term depression (LTD). Remarkably, by inhibiting the degradation of 2-arachidonoylglycerol (2-AG), an endogenous ligand of the CB1 receptor, we restored the LTD in animals treated with MK-801. Additionally, we showed for the first time, that spatial discrimination, a cognitive task that requires dentate gyrus integrity, is impaired in animals exposed to transient hypofunction of NMDA receptors. Dysregulation of glutamatergic transmission and synaptic plasticity from the entorhinal cortex to the dentate gyrus has been demonstrated, which may explain the cellular dysregulations underlying the altered cognitive processing in the dentate gyrus associated with schizophrenia.

Brain imaging of a gamified cognitive flexibility task in young and older adults

Brain imaging of a gamified cognitive flexibility task in young and older adults

Structural, Functional, and Genetic Changes Surrounding Electrodes Implanted in the Brain.

ConspectusImplantable neurotechnology enables monitoring and stimulating of the brain signals responsible for performing cognitive, motor, and sensory tasks. Electrode arrays implanted in the brain are increasingly used in the clinic to treat a variety of sources of neurological diseases and injuries. However, the implantation of a foreign body typically initiates a tissue response characterized by physical disruption of vasculature and the neuropil as well as the initiation of inflammation and the induction of reactive glial states. Likewise, electrical stimulation can induce damage to the surrounding tissue depending on the intensity and waveform parameters of the applied stimulus. These phenomena, in turn, are likely influenced by the surface chemistry and characteristics of the materials employed, but further information is needed to effectively link the biological responses observed to specific aspects of device design. In order to inform improved design of implantable neurotechnology, we are investigating the basic science principles governing device-tissue integration. We are employing multiple techniques to characterize the structural, functional, and genetic changes that occur in the cells surrounding implanted electrodes. First, we have developed a new "device-in-slice" technique to capture chronically implanted electrodes within thick slices of live rat brain tissue for interrogation with single-cell electrophysiology and two-photon imaging techniques. Our data revealed several new observations of tissue remodeling surrounding devices: (a) there was significant disruption of dendritic arbors in neurons near implants, where losses were driven asymmetrically on the implant-facing side. (b) There was a significant loss of dendritic spine densities in neurons near implants, with a shift toward more immature (nonfunctional) morphologies. (c) There was a reduction in excitatory neurotransmission surrounding implants, as evidenced by a reduction in the frequency of excitatory postsynaptic currents (EPSCs). Lastly, (d) there were changes in the electrophysiological underpinnings of neuronal spiking regularity. In parallel, we initiated new studies to explore changes in gene expression surrounding devices through spatial transcriptomics, which we applied to both recording and stimulating arrays. We found that (a) device implantation is associated with the induction of hundreds of genes associated with neuroinflammation, glial reactivity, oligodendrocyte function, and cellular metabolism and (b) electrical stimulation induces gene expression associated with damage or plasticity in a manner dependent upon the intensity of the applied stimulus. We are currently developing computational analysis tools to distill biomarkers of device-tissue interactions from large transcriptomics data sets. These results improve the current understanding of the biological response to electrodes implanted in the brain while producing new biomarkers for benchmarking the effects of novel electrode designs on responses. As the next generation of neurotechnology is developed, it will be increasingly important to understand the influence of novel materials, surface chemistries, and implant architectures on device performance as well as the relationship with the induction of specific cellular signaling pathways.

Explaining the predictions of kernel SVM models for neuroimaging data analysis

Machine learning methods have shown great performance in many areas, including neuroimaging data analysis. However, model performance is only one objective in neuroimaging analysis. Gaining insight from the data is also critical in this field, such as identifying regions where detected signals are relevant to cognitive and diagnostic tasks. To fulfill this need, enabling the explainability of a model’s decision-making process is critical. Predictions of complex machine learning models are notoriously difficult to explain. This limits the use of complex models like kernel support vector machines (SVM) in neuroimaging analysis. Recently, several permutation-based methods have been developed to explain these complex models. However, the explanation results are affected by class-irrelevant features like suppressor variables and high background noise variables. This problem may also happen when explaining linear models. One possible reason is that the permutation process will produce unrealistic data instances when features are not independent, e.g. correlated. These unrealistic data instances will influence the explanation results. In neuroimaging analysis, the activation pattern, the estimated weight of the assumed generative model corresponding to the current classifier, is used to deal with this problem for linear models. This method does not rely on a permutation process but rather on the available data information. In this paper, we propose a novel method of Explanation through Activation Pattern (EAP) to explain the SVM models with different types of kernels for neuroimaging data analysis. Our method can generate a global feature importance score by estimating the activation pattern of kernel SVM models. We evaluate our method against three popular methods on both simulation datasets and a publicly available EEG/MEG dataset on visual tasks. The experimental results demonstrate that the proposed EAP method can provide explanations with low computational cost and is less affected by class-irrelevant features than the other three methods. In the experiment using the MEG/EEG dataset of visual tasks, the proposed EAP method provides agreement results with the brain’s electrical activity patterns reported in the literature on the visual tasks EEG/MEG data and is significantly faster than the other explanation methods.

Testing the bilingual advantage for executive function: insights from hearing children who are native signers

ABSTRACT Bimodal bilingualism involves the use of a sign language and a spoken language, and offers a unique opportunity to explore the cognitive effects of growing up bilingual. The aim of this study was to investigate the relationship between bimodal bilingualism and executive function (EF) in hearing children who are native users of a sign language. We studied three groups of children: bimodal bilinguals (users of a sign language and a spoken language), unimodal bilinguals (users of two spoken languages), and monolinguals, performing three cognitive tasks to measure different components of EF: visuospatial working memory (Odd One Out task), cognitive flexibility (Children’s Colored Trails Test) and conflict resolution (Simon task). All groups of children obtained similar scores on the Odd One Out task and the Children’s Colored Trails Test. Bimodal bilinguals displayed distinct patterns of conflict resolution in comparison to monolingual children: bimodal bilinguals had better overall accuracy and slower overall reaction time in the Simon task. The subsequent analysis did not find a straightforward trade-off between speed and accuracy. These results suggest that bimodal language experience and/or visual-spatial language usage may explain the small bimodal bilingual advantage in the Simon task that we observed in hearing children who are native signers.

An EEG Dataset of Neural Signatures in a Competitive Two-Player Game Encouraging Deceptive Behavior

Studying deception is vital for understanding decision-making and social dynamics. Recent EEG research has deepened insights into the brain mechanisms behind deception. Standard methods in this field often rely on memory, are vulnerable to countermeasures, yield false positives, and lack real-world relevance. Here, we present a comprehensive dataset from an EEG-monitored competitive, two-player card game designed to elicit authentic deception behavior. Our extensive dataset contains EEG data from 12 pairs (N = 24 participants with role switching), controlled for age, gender, and risk-taking, with detailed labels and annotations. The dataset combines standard event-related potential and microstate analyses with state-of-the-art decoding approaches of four scenarios: spontaneous/instructed truth-telling and lying. This demonstrates game-based methods’ efficacy in studying deception and sets a benchmark for future research. Overall, our dataset represents a unique resource with applications in cognitive neuroscience and related fields for studying deception, competitive behavior, decision-making, inter-brain synchrony, and benchmarking of decoding frameworks in a difficult, high-level cognitive task.

Assessment of dynamic cognitive performance under different thermal conditions by multiple physiological signals

A method to evaluate cognitive performance under different thermal conditions by multiple physiological signals is proposed. Heart rate (HR), heart rate variability (RMSSD), skin conductance level (SCL), skin conductance response (SCR) magnitude and SCR total frequency were measured by a multichannel electrophysiological signal monitor, which were interpreted as thermal stress indicators that can characterize cognitive performance of subjects during cognitive stimulation tasks. In the experiment, the indoor air temperature is the independent variable, ranging from 18 °C to 33 °C, with a step length of three degrees. Other indoor environmental quality (IEQ) parameters are controlled variables. In each experiment, subjects subjectively voted on thermal sensation and cognitive load, and performed cognitive stimulation tasks to evaluate cognitive performance. The study found that different physiological indicators have different “thermosensitivity” zones, indicating variations in thermal attributes. Moreover, the influence mechanism of thermal conditions on the effectiveness levels of three different cognitive functions, namely identification, decision and action, were found to be diverse. Additionally, this paper utilized machine learning algorithms to construct fatigue loss function and residual interference function for cognitive performance, effectively correcting cognitive performance errors caused by fatigue and task superimposition. The feasibility of predicting cognitive performance and thermal comfort by multiple physiological indicators is validated in this study.

Clinical and neuropsychological correlates of theta-band functional excitation-inhibition ratio in autism: An EEG study

ObjectiveHow abnormal brain signaling impacts cognition in autism spectrum disorder (ASD) remained elusive. This study aimed to investigate the local and global brain signaling in ASD indicated by theta-band functional excitation-inhibition (fE/I) ratio and explored psychophysiological relationships between fE/I, cognitive deficits, and ASD symptomatology. MethodsA total of 83 ASD and typically developing (TD) individuals participated in this study. Participants’ interference control and set-shifting abilities were assessed. Resting-state electroencephalography (EEG) was used for estimating theta-band fE/I ratio. ResultsASD individuals (n = 31 without visual EEG abnormality; n = 22 with visual EEG abnormality) generally performed slower in a cognitive task tapping interference control and set-maintenance abilities, but only ASD individuals with visually abnormal EEG performed significantly slower than their TD counterparts (Bonferroni-corrected ps < .001). Heightened theta-band fE/I ratios at the whole-head level, left and right hemispheres were observed in the ASD subgroup without visual EEG abnormality only (Bonferroni-corrected ps < .001), which remained highly significant when only data from medication-naïve participants were analyzed. In addition, higher left hemispheric fE/I ratios in ASD individuals without visual EEG abnormality were significantly correlated with faster interference control task performance, in turn faster reaction time was significantly associated with less severe restricted, repetitive behavior (Bonferroni-corrected ps ≤ .0017). ConclusionsDifferential theta-band fE/I within the ASD population. Heightened theta-band fE/I in ASD without visual EEG abnormality may be associated with more efficient filtering of distractors and a less severe ASD symptom manifestation. SignificanceBrain signaling, indicated by theta-band fE/I, was different in ASD subgroups. Only ASD with visually-normal EEG showed heightened theta-band fE/I, which was associated with faster processing of visual distractors during a cognitive task. More efficient distractor filtering was associated with less restricted, repetitive behaviors.

The Obese Taste Bud study: Objectives and study design.

Taste modifies eating behaviour, impacting body weight and potentially obesity development. The Obese Taste Bud (OTB) Study is a prospective cohort study launched in 2020 at the University of Leipzig Obesity Centre in cooperation with the HI-MAG Institute. OTB will test the hypothesis that taste cell homeostasis and taste perception are linked to obesity. Here, we provide the study design, data collection process and baseline characteristics. Participants presenting overweight, obesity or normal weight undergo taste and smell tests, anthropometric, and taste bud density (TBD) assessment on Day 1. Information on physical and mental health, eating behaviour, physical activity, and dental hygiene are obtained, while biomaterial (saliva, tongue swap, blood) is collected in the fasted state. Further blood samples are taken during a glucose tolerance test. A stool sample is collected at home prior to Day 2, on which a taste bud biopsy follows dental examination. A subsample undergoes functional magnetic resonance imaging while exposed to eating-related cognitive tasks. Follow-up investigations after conventional weight loss interventions and bariatric surgery will be included. Initial results show that glycated haemoglobin levels and age are negatively associated with TBD, while an unfavourable metabolic profile, current dieting, and vegan diet are related to taste perception. Olfactory function negatively correlates with age and high-density lipoprotein cholesterol. Initial findings suggest that metabolic alterations are relevant for taste and smell function and TBD. By combining omics data from collected biomaterial with physiological, metabolic and psychological data related to taste perception and eating behaviour, the OTB study aims to strengthen our understanding of taste perception in obesity.

Influence of visual feedback and cognitive challenge on the age-related changes in force steadiness.

Force steadiness can be influenced by visual feedback as well as presence of a cognitive tasks and potentially differs with age and sex. This study determined the impact of altered visual feedback on force steadiness in the presence of a difficult cognitive challenge in young and older men and women. Forty-nine young (19-30yr; 25 women, 24 men) and 25 older (60-85yr; 15 women; 10 men) performed low force (5% of maximum) static contractions with the elbow flexor muscles in the presence and absence of a cognitive challenge (counting backwards by 13) either with low or high visual feedback gain. The cognitive challenge reduced force steadiness (increased force fluctuation amplitude) particularly in women (cognitive challenge × sex: P < 0.05) and older individuals (cognitive challenge × age: P < 0.05). Force steadiness improved with high-gain visual feedback compared with low-gain visual feedback (P < 0.01) for all groups (all interactions: P > 0.05). Manipulation of visual feedback had no influence on the reduced force steadiness in presence of the cognitive challenge for all groups (all P > 0.05). These findings indicate that older individuals and women have greater risk of impaired motor performance of the upper extremity if steadiness is required during a low-force static contraction. Manipulation of visual feedback had minimal effects on the reduced force steadiness in presence of a difficult cognitive challenge.