Psychophysical measures of sensitivity to facial expression of emotion.

We report the development of two simple, objective, psychophysical measures of the ability to discriminate facial expressions of emotion that vary in intensity from a neutral facial expression and to discriminate between varying intensities of emotional facial expression. The stimuli were created by morphing photographs of models expressing four basic emotions, anger, disgust, happiness, and sadness with neutral expressions. Psychometric functions were obtained for 15 healthy young adults using the Method of Constant Stimuli with a two-interval forced-choice procedure. Individual data points were fitted by Quick functions for each task and each emotion, allowing estimates of absolute thresholds and slopes. The tasks give objective and sensitive measures of the basic perceptual abilities required for perceiving and interpreting emotional facial expressions

Age-related changes in short-interval intracortical facilitation and dexterity

Functional changes in the primary motor cortex might contribute to the age-related decline infine motor control. We measured short-interval intracortical facilitation (SICF) in an intrinsichand muscle with paired-pulse transcranial magnetic stimulation at interstimulus intervals(ISIs) of 1.5, 2.5, and 4.5 ms in young and old subjects and examined its association withdexterity. We found age-related effects in SICF, with greater facilitation in old than youngsubjects at the 1.5-ms ISI and greater facilitation in young than old subjects at the 2.5-ms ISI.SICF at the 2.5-ms ISI was positively correlated with performance on a task that requiredcoordinated and dextrous use of both hands, suggesting that this measure indicates a capacityfor executing demanding manual tasks

VISTO: An open-source device to measure exposure time in psychological experiments

The study of higher cognitive processes often relies on the manipulation of bottom-up stimulus characteristics such as exposure time. While several software exist that can schedule the onset and offset time of a visual stimulus, the actual exposure time depends on several factors that are not easy to control, resulting in undesired variability within and across studies. Here we present VISTO, a simple device built on the Arduino platform that allows one to measure the exact onset and offset of a visual stimulus, and to test its synchronization with a trigger signal. The device is used to measure the profile of luminance waveforms in arbitrary analog/digital (AD) units, and the implications of these luminance profiles are discussed based on a model of information accumulation from visual exposure. Moreover, VISTO can be calibrated to match the brightness of each experimental monitor. VISTO allows for control of stimulus timing presentation, both in classical laboratory settings and in more complex settings as technology allows to use new display devices or acquisition equipment. In sum, VISTO allows one to: • measure the profile of luminance curves. • determine the exposure time of a visual stimulus. • measure the synchronization between a trigger signal and a visual stimulus

Ultracold collision properties of metastable alkaline-earth atoms

Ultra-cold collisions of spin-polarized 24Mg,40Ca, and 88Sr in the metastable 3P2 excited state are investigated. We calculate the long-range interaction potentials and estimate the scattering length and the collisional loss rate as a function of magnetic field. The estimates are based on molecular potentials between 3P2 alkaline-earth atoms obtained from ab initio atomic and molecular structure calculations. The scattering lengths show resonance behavior due to the appearance of a molecular bound state in a purely long-range interaction potential and are positive for magnetic fields below 50 mT. A loss-rate model shows that losses should be smallest near zero magnetic field and for fields slightly larger than the resonance field, where the scattering length is also positive.Comment: 4 pages, 4 figure

Digital Peer-Supported Self-Management Intervention Codesigned by People With Long COVID: Mixed Methods Proof-of-Concept Study.

BACKGROUND: There are around 1.3 million people in the United Kingdom with the devastating psychological, physical, and cognitive consequences of long COVID (LC). UK guidelines recommend that LC symptoms be managed pragmatically with holistic support for patients' biopsychosocial needs, including psychological, emotional, and physical health. Self-management strategies, such as pacing, prioritization, and goal setting, are vital for the self-management of many LC symptoms. OBJECTIVE: This paper describes the codevelopment and initial testing of a digital intervention combining peer support with positive psychology approaches for self-managing the physical, emotional, psychological, and cognitive challenges associated with LC. The objectives of this study were to (1) codesign an intervention with and for people with LC; (2) test the intervention and study methods; (3) measure changes in participant well-being, self-efficacy, fatigue, and loneliness; and (4) understand the types of self-management goals and strategies used by people with LC. METHODS: The study used a pre-post, mixed methods, pragmatic, uncontrolled design. Digital intervention content was codeveloped with a lived-experience group to meet the needs uncovered during the intervention development and logic mapping phase. The resulting 8-week digital intervention, Hope Programme for Long COVID, was attended by 47 participants, who completed pre- and postprogram measures of well-being, self-efficacy, fatigue, and loneliness. Goal-setting data were extracted from the digital platform at the end of the intervention. RESULTS: The recruitment rate (n=47, 83.9%) and follow-up rate (n=28, 59.6%) were encouraging. Positive mental well-being (mean difference 6.5, P<.001) and self-efficacy (mean difference 1.1, P=.009) improved from baseline to postcourse. All goals set by participants mapped onto the 5 goal-oriented domains in the taxonomy of everyday self-management strategies (TEDSS). The most frequent type of goals was related to activity strategies, followed by health behavior and internal strategies. CONCLUSIONS: The bespoke self-management intervention, Hope Programme for Long COVID, was well attended, and follow-up was encouraging. The sample characteristics largely mirrored those of the wider UK population with LC. Although not powered to detect statistically significant changes, the preliminary data show improvements in self-efficacy and positive mental well-being. Our next trial (ISRCTN: 11868601) will use a nonrandomized waitlist control design to further examine intervention efficacy

Imagery or meaning? Evidence for a semantic origin of category-specific brain activity in metabolic imaging

Category-specific brain activation distinguishing between semantic word types has imposed challenges on theories of semantic representations and processes. However, existing metabolic imaging data are still ambiguous about whether these category-specific activations reflect processes involved in accessing the semantic representation of the stimuli, or secondary processes such as deliberate mental imagery. Further information about the response characteristics of category-specific activation is still required. Our study for the first time investigated the differential impact of word frequency on functional magnetic resonance imaging (fMRI) responses to action-related words and visually related words, respectively. First, we corroborated previous results showing that action-relatedness modulates neural responses in action-related areas, while word imageability modulates activation in object processing areas. Second, we provide novel results showing that activation negatively correlated with word frequency in the left fusiform gyrus was specific for visually related words, while in the left middle temporal gyrus word frequency effects emerged only for action-related words. Following the dominant view in the literature that effects of word frequency mainly reflect access to lexico-semantic information, we suggest that category-specific brain activation reflects distributed neuronal ensembles, which ground language and concepts in perception-action systems of the human brain. Our approach can be applied to any event-related data using single-stimulus presentation, and allows a detailed characterization of the functional role of category-specific activation patterns

Smell and taste symptom-based predictive model for COVID-19 diagnosis.

BackgroundThe presentation of coronavirus 2019 (COVID-19) overlaps with common influenza symptoms. There is limited data on whether a specific symptom or collection of symptoms may be useful to predict test positivity.MethodsAn anonymous electronic survey was publicized through social media to query participants with COVID-19 testing. Respondents were questioned regarding 10 presenting symptoms, demographic information, comorbidities, and COVID-19 test results. Stepwise logistic regression was used to identify predictors for COVID-19 positivity. Selected classifiers were assessed for prediction performance using receiver operating characteristic (ROC) curve analysis.ResultsA total of 145 participants with positive COVID-19 testing and 157 with negative results were included. Participants had a mean age of 39 years, and 214 (72%) were female. Smell or taste change, fever, and body ache were associated with COVID-19 positivity, and shortness of breath and sore throat were associated with a negative test result (p &lt; 0.05). A model using all 5 diagnostic symptoms had the highest accuracy with a predictive ability of 82% in discriminating between COVID-19 results. To maximize sensitivity and maintain fair diagnostic accuracy, a combination of 2 symptoms, change in sense of smell or taste and fever was found to have a sensitivity of 70% and overall discrimination accuracy of 75%.ConclusionSmell or taste change is a strong predictor for a COVID-19-positive test result. Using the presence of smell or taste change with fever, this parsimonious classifier correctly predicts 75% of COVID-19 test results. A larger cohort of respondents will be necessary to refine classifier performance