No abstract

The kinematics of peoples' body movements provide useful cues about emotional states: for example, angry movements are typically fast and sad movements slow. Unlike the body movement literature, studies of facial expressions have focused on spatial, rather than kinematic, cues. This series of experiments demonstrates that speed comprises an important facial emotion expression cue. In Experiments 1a-1c we developed (N ϭ 47) and validated (N ϭ 27) an emotion-induction procedure, and recorded (N ϭ 42) posed and spontaneous facial expressions of happy, angry, and sad emotional states. Our novel analysis pipeline quantified the speed of changes in distance between key facial landmarks. We observed that happy expressions were fastest, sad were slowest, and angry expressions were intermediate. In Experiment 2 (N ϭ 67) we replicated our results for posed expressions and introduced a novel paradigm to index communicative emotional expressions. Across Experiments 1 and 2, we demonstrate differences between posed, spontaneous, and communicative expression contexts. Whereas mouth and eyebrow movements reliably distinguished emotions for posed and communicative expressions, only eyebrow movements were reliable for spontaneous expressions. In Experiments 3 and 4 we manipulated facial expression speed and demonstrated a quantifiable change in emotion recognition accuracy. That is, in a discovery (N ϭ 29) and replication sample (N ϭ 41), we showed that speeding up facial expressions promotes anger and happiness judgments, and slowing down expressions encourages sad judgments. This influence of kinematics on emotion recognition is dissociable from the influence of spatial cues. These studies demonstrate that the kinematics of facial movements provide added value, and an independent contribution to emotion recognition.

Over the past two decades, there have been increasing discussions around which terms should be used to talk about autism. Whilst these discussions have largely revolved around the suitability of identity‐first language and person‐first language, more recently this debate has broadened to encompass other autism‐related terminology (e.g., ‘high‐functioning’). To date, academic studies have not investigated the language preferences of autistic individuals outside of the United Kingdom or Australia, nor have they compared levels of endorsement across countries. Hence, the current study adopted a mixed‐methods approach, employing both quantitative and qualitative techniques, to explore the linguistic preferences of 654 English‐speaking autistic adults across the globe. Despite variation in levels of endorsement between countries, we found that the most popular terms were similar—the terms ‘Autism’, ‘Autistic person’, ‘Is autistic’, ‘Neurological/Brain Difference’, ‘Differences’, ‘Challenges’, ‘Difficulties’, ‘Neurotypical people’, and ‘Neurotypicals’ were consistently favored across countries. Despite relative consensus across groups, both our quantitative and qualitative data demonstrate that there is no universally accepted way to talk about autism. Our thematic analysis revealed the reasons underlying participants’ preferences, generating six core themes, and illuminated an important guiding principle—to respect personal preferences. These findings have significant implications for informing practice, research and language policy worldwide.

Here, we address flaws in existing approaches to farmer behavioral change which place undue attention on the individual. Rather, we argue for a more distributed understanding of farmer decision‐making behavior, which includes all relevant actors within a farmers’ “ring of confidence” in projects.

To date, studies have not established whether autistic and non-autistic individuals differ in emotion recognition from facial motion cues when matched in terms of alexithymia. Here, autistic and non-autistic adults (N = 60) matched on age, gender, non-verbal reasoning ability and alexithymia, completed an emotion recognition task, which employed dynamic point light displays of emotional facial expressions manipulated in terms of speed and spatial exaggeration. Autistic participants exhibited significantly lower accuracy for angry, but not happy or sad, facial motion with unmanipulated speed and spatial exaggeration. Autistic, and not alexithymic, traits were predictive of accuracy for angry facial motion with unmanipulated speed and spatial exaggeration. Alexithymic traits, in contrast, were predictive of the magnitude of both correct and incorrect emotion ratings.