Trait irritability in adults is unrelated to face emotion identification

2022 ◽  
Vol 185 ◽  
pp. 111290
Author(s):  
Christen M. Deveney ◽  
Goretty Chavez ◽  
Lynandrea Mejia
2019 ◽  
Vol 28 (2) ◽  
pp. 501-514
Author(s):  
Deborah A. Hwa-Froelich ◽  
Hisako Matsuo

Purpose Pragmatic language is important for social communication across all settings. Children adopted internationally (CAI) may be at risk of poorer pragmatic language because of adverse early care, delayed adopted language development, and less ability to inhibit. The purpose of this study was to compare pragmatic language performance of CAI from Asian and Eastern European countries with a nonadopted group of children who were of the same age and from similar socioeconomic backgrounds as well as explore the relationship among emotion identification, false belief understanding, and inhibition variables with pragmatic language performance. Method Using a quasi-experimental design, 35 four-year-old CAI (20 Asian, 15 Eastern European) and 33 children who were not adopted were included in this study. The children's pragmatic language, general language, and social communication (emotion identification of facial expressions, false belief understanding, inhibition) were measured. Comparisons by region of origin and adoption experience were completed. We conducted split-half correlation analyses and entered significant correlation variables into simple and backward regression models. Results Pragmatic language performance differed by adoption experience. The adopted and nonadopted groups demonstrated different correlation patterns. Language performance explained most of the pragmatic language variance. Discussion Because CAI perform less well than their nonadopted peers on pragmatic communication measures and different variables are related to their pragmatic performance, speech-language pathologists may need to adapt assessment and intervention practices for this population.


2007 ◽  
Author(s):  
Angela N. Fellner ◽  
Gerald Matthews ◽  
Gregory J. Funke ◽  
Amanda K. Emo ◽  
Moshe Zeidner ◽  
...  

2021 ◽  
pp. 102755
Author(s):  
Winson F.Z. Yang ◽  
Gianina Toller ◽  
Suzanne Shdo ◽  
Sonja A. Kotz ◽  
Jesse Brown ◽  
...  

2017 ◽  
Vol 12 (5) ◽  
pp. 839-847 ◽  
Author(s):  
Camilla L. Nord ◽  
Sophie Forster ◽  
D. Chamith Halahakoon ◽  
Ian S. Penton-Voak ◽  
Marcus R. Munafò ◽  
...  

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Pragati Patel ◽  
Raghunandan R ◽  
Ramesh Naidu Annavarapu

AbstractMany studies on brain–computer interface (BCI) have sought to understand the emotional state of the user to provide a reliable link between humans and machines. Advanced neuroimaging methods like electroencephalography (EEG) have enabled us to replicate and understand a wide range of human emotions more precisely. This physiological signal, i.e., EEG-based method is in stark comparison to traditional non-physiological signal-based methods and has been shown to perform better. EEG closely measures the electrical activities of the brain (a nonlinear system) and hence entropy proves to be an efficient feature in extracting meaningful information from raw brain waves. This review aims to give a brief summary of various entropy-based methods used for emotion classification hence providing insights into EEG-based emotion recognition. This study also reviews the current and future trends and discusses how emotion identification using entropy as a measure to extract features, can accomplish enhanced identification when using EEG signal.


2017 ◽  
Vol 10 (5) ◽  
pp. 29-40
Author(s):  
Sarfaraz Masood ◽  
Jeevan Singh Nayal ◽  
Ravi Kumar Jain ◽  
M. N. Doja ◽  
Musheer Ahmad

2012 ◽  
Author(s):  
Tova Most ◽  
Hilit Michaelis

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