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The study of visual representations of emotion - the way facial expressions look in the “mind’s eye” – is a burgeoning field. However, to date, studies have not yet investigated accompanying features, such as the precision or differentiation, of visual emotion representations. Here, we introduce ExpressionMap, a novel tool for indexing these features.

Background: In recent years, there have been increasing discussions surrounding the appropriate terminology to talk about autism. Initially, this debate revolved around the use of person-first language (e.g., person with autism) vs. identity-first language (e.g., autistic person) but has recently expanded to other autism-related terms (e.g., deficits). However, to date, studies investigating autism-related language preferences have been limited to English-speaking countries, and little is known about preferences in other languages. This study addresses this gap by investigating the language preferences of French-speaking autistic adults.Methods: 541 French-speaking autistic adults (formal diagnosis and self-identified) completed an online survey where they selected terms they preferred to use to talk about: 1) the nomenclature of autism; 2) an autistic person; 3) someone’s autistic identity; 4) autism more broadly; 5) the abilities of autistic people; and 6) people without a diagnosis of autism. Participants also revealed more about their language preferences via an open-text response. Results: The most preferred terms were “Autisme”, “Personne autiste”, “Autiste”, “Est Autiste”, “Différence neurologique/cérébrale”, “Différences”, “Difficultés”, “Personne neurotypique”, “Neurotypique” and “Personne non-autiste”. To better understand these preferences, participants’ open comments were analyzed, revealing further support for identity-first language and the social model of disability, and a preference for simple, precise and validated terms. Conclusions: These results are consistent with autism terminology preferences in English-speaking countries and provide additional insight into the reasons underlying these preferences. Such work has implications for informing the language of researchers, clinicians and other professionals in the field, as well as the general public.

Funder: Trinidad and Tobago - UWI Research Development Impact Fund, The scale of data produced during the SARS-CoV-2 pandemic has been unprecedented, with more than 13 million sequences shared publicly at the time of writing. This wealth of sequence data provides important context for interpreting local outbreaks. However, placing sequences of interest into national and international context is difficult given the size of the global dataset. Often outbreak investigations and genomic surveillance efforts require running similar analyses again and again on the latest dataset and producing reports. We developed civet (cluster investigation and virus epidemiology tool) to aid these routine analyses and facilitate virus outbreak investigation and surveillance. Civet can place sequences of interest in the local context of background diversity, resolving the query into different 'catchments' and presenting the phylogenetic results alongside metadata in an interactive, distributable report. Civet can be used on a fine scale for clinical outbreak investigation, for local surveillance and cluster discovery, and to routinely summarise the virus diversity circulating on a national level. Civet reports have helped researchers and public health bodies feedback genomic information in the appropriate context within a timeframe that is useful for public health.

Some people can effortlessly pinpoint which emotion they are experiencing, for instance easily identifying butterflies in their tummy as excitement rather than nervousness, whilst others confuse their emotions. This ability to make fine-grained distinctions between emotional states- termed emotional granularity- is important for psychological adjustment and mental health. Existing tasks assessing emotional granularity require participants to translate emotional experiences into words and therefore rely heavily on language ability. In this paper, we discuss this limitation (amongst others) and introduce our novel EmoMap paradigm for assessing emotional granularity, which does not require labelling of emotional state.

Some people are exceptional at navigating the social world of emotional expressions, while others struggle. Here we ask whether the way we feel “on the inside” influences the way we expect emotions to be expressed in the “outside world” and subsequently our ability to read others’ emotional expressions. Across multiple experiments, incorporating discovery and replication samples, we develop EmoMap (N=271) and ExpressionMap (N=98; replication N=193) to map individuals’ experiences of emotions and visual representations of others’ emotions. We illustrate an association between these maps: those with precise and distinct experiences of emotion also have precise and distinct visual representations of emotion. Finally (N=193), we construct the Inside Out Model of Emotion Recognition, which explains 61.2% of the variance in emotion recognition from dynamic cues and illuminates multiple pathways to emotion recognition difficulties. These findings may have important implications for understanding emotion recognition in numerous clinical populations.

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 UK 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.