Greenhouse gas and aerosol contributions to the observed global and regional changes in extreme temperature changes

<p>This study carried out an updated detection and attribution analysis of extreme temperature changes for 1951-2015. Four extreme temperature indices (warm extremes: annual maximum daily maximum/minimum temperatures; cold extremes: annual minimum daily maximum/minimum temperatures) were used considering global, continental (6 domains), and subcontinental (33 domains) scales. HadEX3 observations were compared with CMIP6 multi-model simulations using an optimal fingerprinting technique. Response patterns of extreme indices (fingerprints) to anthropogenic (ANT), greenhouse gas (GHG), anthropogenic aerosol (AA), and natural (NAT) forcings were estimated from corresponding CMIP6 forced simulations. Pre-industrial control simulations (CTL) were also used to estimate the internal variability. Results from two-signal detection analysis where the observations are simultaneously regressed onto ANT and NAT fingerprints reveal that ANT signals are robustly detected in separation from NAT in global and most continental regions for all extreme indices. At subcontinental scale, ANT detection occurs especially in warm extremes (more than 60% of regions). Results from three-signal detection analysis where observations are simultaneously regressed onto GHG, AA, and NAT fingerprints show that GHG signals are detected and separated from other external forcings over global, most continental, and several subcontinental (more than 60%) domains in warm extremes. In addition, AA influences are jointly detected in warm extremes over global, Europe and Asia. The detected GHG forcings are found to explain most of the observed warming while AA forcings contribute to the observed cooling for the early decades over globe, Europe, and Asia with a slight warming over Europe during recent decades. Overall, improved detection occurs compared to previous studies, especially in cold extremes, which is due to the use of extended period which increases signal-to-noise ratios.</p>

Understanding communicative intentions in schizophrenia using an error analysis approach

Patients with schizophrenia (SCZ) have a core impairment in the communicative-pragmatic domain, characterized by severe difficulties in correctly inferring the speaker’s communicative intentions. While several studies have investigated pragmatic performance of patients with SCZ, little research has analyzed the errors committed in the comprehension of different communicative acts. The present research investigated error patterns in 24 patients with SCZ and 24 healthy controls (HC) during a task assessing the comprehension of different communicative acts, i.e., sincere, deceitful and ironic, and their relationship with the clinical features of SCZ. We used signal detection analysis to quantify participants’ ability to correctly detect the speakers’ communicative intention, i.e., sensitivity, and their tendency to wrongly perceive a communicative intention when not present, i.e., response bias. Further, we investigated the relationship between sensitivity and response bias, and the clinical features of the disorder, namely symptom severity, pharmacotherapy, and personal and social functioning. The results showed that the ability to infer the speaker’s communicative intention is impaired in SCZ, as patients exhibited lower sensitivity, compared to HC, for all the pragmatic phenomena evaluated, i.e., sincere, deceitful, and ironic communicative acts. Further, we found that the sensitivity measure for irony was related to disorganized/concrete symptoms. Moreover, patients with SCZ showed a stronger response bias for deceitful communicative acts compared to HC: when committing errors, they tended to misattribute deceitful intentions more often than sincere and ironic ones. This tendency to misattribute deceitful communicative intentions may be related to the attributional bias characterizing the disorder.

Anthropogenic Greenhouse Gas and Aerosol Contributions to Extreme Temperature Changes during 1951–2015

This study conducted a detection and attribution analysis of the observed global and regional changes in extreme temperatures during 1951–2015. HadEX3 observations were compared with multimodel simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6) using an optimal fingerprinting technique. Annual maximum daily maximum and minimum temperatures (TXx and TNx; warm extremes) and annual minimum daily maximum and minimum temperatures (TXn and TNn; cold extremes) over land were analyzed considering global, continental, and subcontinental scales. Response patterns (fingerprints) of extreme temperatures to anthropogenic (ANT), greenhouse gases (GHG), aerosols (AA), and natural (NAT) forcings were obtained from CMIP6 forced simulations. The internal variability ranges were estimated from preindustrial control simulations. A two-signal detection analysis where the observations are regressed onto ANT and NAT fingerprints simultaneously reveals that ANT signals are robustly detected in separation from NAT over global and all continental domains (North and South America, Europe, Asia, and Oceania) for most of the extreme indices. ANT signals are also detected over many subcontinental regions, particularly for warm extremes (more than 60% of 33 subregions). A three-signal detection analysis that considers GHG, AA, and NAT fingerprints simultaneously demonstrates that GHG signals are detected in isolation from other external forcings over global, continental, and several subcontinental domains especially for warm extremes, explaining most of the observed warming. Moreover, AA influences are detected for warm extremes over Europe and Asia, indicating significant offsetting cooling contributions. Overall, human influences are detected more frequently, compared to previous studies, particularly for cold extremes, due to the extended period and the improved spatial coverage of observations.

Sounds like a fight: Listeners map spontaneous nonverbal vocalizations to behavioral contexts

When we hear another person laugh or scream, can we tell the kind of situation they are in – whether they are playing or fighting? If nonverbal expressions vary systematically across behavioral contexts, perceivers might be sensitive to these mappings and consequently be able to tell the contexts from others’ vocalizations. Here, we test the prediction that listeners can infer production contexts from vocalizations by examining listeners’ ability to match spontaneous nonverbal vocalizations to the behavioral contexts in which they were produced. In a preregistered experiment, listeners (N = 3120) matched 200 nonverbal vocalizations to one of 10 contexts using yes/no response options. Using signal detection analysis, we show that listeners were accurate at matching vocalizations to nine of the behavioral contexts. We also found that listeners’ performance was more accurate for vocalizations produced in negative as compared to positive contexts. These results indicate that perceivers can accurately infer contextual information from nonverbal vocalizations, demonstrating that listeners are sensitive to systematic associations between vocalizations and behavioral contexts.

Effects of Emotional Valence and Concreteness on Children’s Recognition Memory

There are considerable gaps in our knowledge of how children develop abstract language. In this paper, we tested the Affective Embodiment Account, which proposes that emotional information is more essential for abstract than concrete conceptual development. We tested the recognition memory of 7- and 8-year-old children, as well as a group of adults, for abstract and concrete words which differed categorically in valence (negative, neutral, and positive). Word valence significantly interacted with concreteness in hit rates of both children and adults, such that effects of valence were only found in memory for abstract words. The pattern of valence effects differed for children and adults: children remembered negative words more accurately than neutral and positive words (a negativity effect), whereas adults remembered negative and positive words more accurately than neutral words (a negativity effect and a positivity effect). In addition, signal detection analysis revealed that children were better able to discriminate negative than positive words, regardless of concreteness. The findings suggest that the memory accuracy of 7- and 8-year-old children is influenced by emotional information, particularly for abstract words. The results are in agreement with the Affective Embodiment Account and with multimodal accounts of children’s lexical development.

Seeing the Bigger Picture: Susceptibility to, and Detection of, Deception

An extended time window was used to examine susceptibility to, and detection of, deception in rugby union. High- and low-skilled rugby players judged the final running direction of an opponent “cutting” left or right, with or without a deceptive sidestep. Each trial was occluded at one of eight time points relative to the footfall after the initial (genuine or fake) reorientation. Based on response accuracy, the results were separated into deception susceptibility and deception detection windows. Signal-detection analysis was used to calculate the discriminability of genuine and deceptive actions (d') and the response bias (c). High-skilled players were less susceptible to deception and better able to detect when they had been deceived, accompanied by a reduced bias toward perceiving all actions as genuine. By establishing the time window in which players become deceived, it will now be possible to identify the kinematic sources that drive deception.

Empirically defining treatment response and remission in body dysmorphic disorder using a short self-report instrument

Determining response or remission status in body dysmorphic disorder (BDD) usually requires a lengthy interview with a trained clinician. This study sought to establish empirically derived cut-offs to define treatment response and remission in BDD using a brief self-reported instrument, the Appearance Anxiety Inventory (AAI). Results from three clinical trials of BDD were pooled to create a sample of 123 individuals who had received cognitive behaviour therapy for BDD, delivered via the internet. The AAI was compared to gold-standard criteria for response and remission in BDD, based on the clinician-administered Yale-Brown Obsessive Compulsive Scale, modified for BDD (BDD-YBOCS), and evaluated using signal detection analysis. The results showed that a ≥40% reduction on the AAI best corresponded to treatment response, with a sensitivity of 0.71 and a specificity of 0.84. A score ≤13 at post-treatment was the optimal cut-off in determining full or partial remission from BDD, with a sensitivity of 0.75 and a specificity of 0.88. These findings provide benchmarks for using the AAI in BDD treatment evaluation when resource-intensive measures administered by clinicians are not feasible.