Comparing internal representations of facial expression kinematics between autistic and non-autistic adults

2021 ◽  
Author(s):  
Connor Tom Keating ◽  
Sophie Sowden ◽  
Jennifer Cook
Keyword(s):  
High Speed ◽  
Internal Representation ◽  
Expression Recognition ◽  
Internal Representations ◽  
Dynamic Stimuli ◽  
Facial Motion ◽  
Recent Developments ◽  
Autistic Adults ◽  
Point Light ◽  
Full Face

Recent developments suggest that autistic individuals may require static and dynamic angry expressions to be of higher emotional intensity in order for them to be successfully identified. In the case of dynamic stimuli, autistic individuals require angry facial motion to have a higher speed. Therefore, it is plausible that autistic individuals do not have a ‘deficit’ in angry expression recognition, but rather their internal representation of these expressions is characterized by very high-speed movement. In this (pre-registered) study, 25 autistic and 25 non-autistic adults matched on age, gender, non-verbal reasoning and alexithymia completed a novel emotion-based task which employed dynamic displays of happy, angry and sad point light facial (PLF) expressions. On each trial, participants moved a slider to manipulate the speed of a PLF stimulus such that it moved at a speed that, in their ‘mind’s eye’, was typical of happy, angry or sad expressions. Results showed that participants attributed the highest speeds to angry, then happy, then sad, facial motion. Participants increased the speed of angry and happy expressions by 41% and 27% respectively and decreased the speed of sad expressions by 18%. This suggests that participants have ‘caricatured’ internal representations of emotion, wherein emotion-related kinematic cues are over-emphasized. There were no differences between autistic and non-autistic individuals in the speeds attributed to full-face and partial-face (those only showing the eyes or mouth) angry, happy and sad facial motion respectively. Consequently, we find no evidence that autistic adults possess atypical fast internal representations of angry expressions.

scholarly journals Differences Between Autistic and Non-Autistic Adults in the Recognition of Anger from Facial Motion Remain after Controlling for Alexithymia

2021 ◽  
Author(s):  
Connor T. Keating ◽  
Dagmar S. Fraser ◽  
Sophie Sowden ◽  
Jennifer L. Cook
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Recognition Task ◽  
Reasoning Ability ◽  
Emotional Facial Expressions ◽  
Motion Cues ◽  
Facial Motion ◽  
Lower Accuracy ◽  
Autistic Adults ◽  
Point Light

AbstractTo 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.

scholarly journals Differences Between Autistic and Non-autistic Adults in the Recognition of Anger From Dynamic Expressions Remain After Controlling for Alexithymia.

2020 ◽  
Author(s):  
Connor Tom Keating ◽  
Sophie L Sowden ◽  
Dagmar S Fraser ◽  
Jennifer L Cook
Keyword(s):  
Emotion Recognition ◽  
Recognition Task ◽  
Autistic Traits ◽  
Future Research ◽  
Control Group ◽  
Expression Recognition ◽  
Verbal Reasoning ◽  
Speed Level ◽  
Autistic Adults ◽  
Point Light

Abstract BackgroundFor many years, research has suggested that autistic individuals have difficulties recognising the emotions of other people. However, a burgeoning literature argues that these difficulties may be better explained by co-occurring alexithymia rather than autistic characteristics. Importantly, extant studies in this field have focused on the recognition of emotion from static images. Here we investigated whether there are differences with respect to emotion recognition from dynamic facial stimuli between autistic and non-autistic groups matched on alexithymia.Methods29 control and 31 autistic adults, matched on age, gender, non-verbal reasoning ability and alexithymia, completed a facial emotion recognition task which employed dynamic point light displays of happy, angry and sad facial expressions. Stimuli were manipulated such that expressions were reproduced at 50%, 100% and 150% of their normal speed and spatial extent. ResultsThe ASD group exhibited significantly lower emotion recognition accuracy for angry, but not happy or sad, expressions at the normal (100%) spatial and speed level. Whilst the control group exhibited increasing accuracy across all levels of the speed manipulation, the ASD group only showed improvement from the 100% to 150% level. Non-verbal reasoning and level of autistic traits (and not age, gender or alexithymia) were significant predictors of accuracy for angry videos at the 100% spatial and speed level. LimitationsDue to COVID-19 restrictions, only 22 members of the ASD group completed the ADOS-2 assessments and 7 of those who did, scored below threshold for an autism or ASD diagnosis. Therefore, our ASD group may display less frequent or lower intensity autistic behaviours than would typically be seen in an ASD population. The TAS, which has recently been questioned for its construct validity, was used to measure alexithymia. ConclusionsSince our participants were matched on alexithymia, and we identified that level of autistic traits (and not alexithymic traits) was a significant predictor of the accuracy of angry expression recognition at the normal level, we conclude that a difficulty with recognising angry expressions is relevant to autism and cannot be explained by alexithymia. Future research should elucidate why autistic individuals exhibit differences with angry expressions in particular.

Application of Advanced Electrochemical Methods with Nanomaterial-based Electrodes as Powerful Tools for Trace Analysis of Drugs and Toxic Compounds

2019 ◽  
Vol 15 (2) ◽  
pp. 143-151 ◽  
Author(s):  
Parviz Norouzi ◽  
Bagher Larijani ◽  
Taher Alizadeh ◽  
Eslam Pourbasheer ◽  
Mostafa Aghazadeh ◽  
...  
Keyword(s):  
Trace Analysis ◽  
High Speed ◽  
Portable Devices ◽  
Great Opportunity ◽  
Toxic Compounds ◽  
High Speed Analysis ◽  
Recent Developments ◽  
Sensitivity And Selectivity ◽  
Trace Measurement ◽  
Health Application

Background: The new progress in electronic devices has provided a great opportunity for advancing electrochemical instruments by which we can more easily solve many problems of interest for trace analysis of compounds, with a high degree of accuracy, precision, sensitivity, and selectivity. On the other hand, in recent years, there is a significant growth in the application of nanomaterials for the construction of nanosensors due to enhanced chemical and physical properties arising from discrete modified nanomaterial-based electrodes or microelectrodes. Objective: Combination of the advanced electrochemical system and nanosensors make these devices very suitable for the high-speed analysis, as motioning and portable devices. This review will discuss the recent developments and achievements that have been reported for trace measurement of drugs and toxic compounds for environment, food and health application.

AlGaN/GaN HEMTs: RECENT DEVELOPMENTS AND FUTURE DIRECTIONS

2008 ◽  
Vol 18 (04) ◽  
pp. 913-922 ◽  
Author(s):  
SIDDHARTH RAJAN ◽  
UMESH K. MISHRA ◽  
TOMÁS PALACIOS
Keyword(s):  
Field Effect ◽  
High Speed ◽  
Digital Circuits ◽  
Field Effect Transistors ◽  
Rf Power ◽  
Power Performance ◽  
Future Directions ◽  
Recent Developments ◽  
Gan Hemts ◽  
State Of Art

This paper provides an overview of recent work and future directions in Gallium Nitride transistor research. We discuss the present status of Ga -polar AlGaN / GaN HEMTs and the innovations that have led to record RF power performance. We describe the development of N -polar AlGaN / GaN HEMTs with microwave power performance comparable with state-of-art Ga -polar AlGaN / GaN HEMTs. Finally we will discuss how GaN -based field effect transistors could be promising for a less obvious application: low-power high-speed digital circuits.

scholarly journals Error Performance Estimation of Modulated Retroreflective Transdermal Optical Wireless Links with Diversity under Generalized Pointing Errors

Telecom ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 167-180
Author(s):  
George K. Varotsos ◽  
Hector E. Nistazakis ◽  
Konstantinos Aidinis ◽  
Fadi Jaber ◽  
Mohd Nasor ◽  
...  
Keyword(s):  
High Speed ◽  
Signal To Noise Ratio ◽  
Spatial Diversity ◽  
Performance Estimation ◽  
Optical Wireless ◽  
Wireless Data ◽  
Error Performance ◽  
Pointing Errors ◽  
Implanted Medical Devices ◽  
Recent Developments

Recent developments in both optical wireless communication (OWC) systems and implanted medical devices (IMDs) have introduced transdermal optical wireless (TOW) technology as a viable candidate for extremely high-speed in-body to out-of-body wireless data transmissions, which are growing in demand for many vital biomedical applications, including telemetry with medical implants, health monitoring, neural recording and prostheses. Nevertheless, this emerging communication modality is primarily hindered by skin-induced attenuation of the propagating signal bit carrier along with its stochastic misalignment-induced fading. Thus, by considering a typical modulated retroreflective (MRR) TOW system with spatial diversity and optimal combining (OC) for signal reception in this work, we focus, for the first time in the MRR TOW literature, on the stochastic nature of generalized pointing errors with non-zero boresight (NZB). Specifically, under these circumstances, novel analytical mathematical expressions were derived for the total average bit error rate (BER) of various system configurations. Their results revealed significant outage performance enhancements when spatial diversity was utilized. Moreover, taking into consideration the total transdermal pathloss along with the effects of stochastic NZB pointing errors, the critical average signal-to-noise ratio (SNR) metric was evaluated for typical power spectral-density values.

Micro-Expression Recognition using 3D - CNN

2020 ◽  
pp. 5-13
Author(s):  
Vishal Dubey ◽  
◽  
◽  
◽  
Bhavya Takkar ◽  
...  
Keyword(s):  
Emotional Response ◽  
High Speed ◽  
Actual State ◽  
Time Interval ◽  
Short Time Interval ◽  
Expression Recognition ◽  
Specific Emotion ◽  
3D Cnn ◽  
Short Time ◽  
Micro Expression

Micro-expression comes under nonverbal communication, and for a matter of fact, it appears for minute fractions of a second. One cannot control micro-expression as it tells about our actual state emotionally, even if we try to hide or conceal our genuine emotions. As we know that micro-expressions are very rapid due to which it becomes challenging for any human being to detect it with bare eyes. This subtle-expression is spontaneous, and involuntary gives the emotional response. It happens when a person wants to conceal the specific emotion, but the brain is reacting appropriately to what that person is feeling then. Due to which the person displays their true feelings very briefly and later tries to make a false emotional response. Human emotions tend to last about 0.5 - 4.0 seconds, whereas micro-expression can last less than 1/2 of a second. On comparing micro-expression with regular facial expressions, it is found that for micro-expression, it is complicated to hide responses of a particular situation. Micro-expressions cannot be controlled because of the short time interval, but with a high-speed camera, we can capture one's expressions and replay them at a slow speed. Over the last ten years, researchers from all over the globe are researching automatic micro-expression recognition in the fields of computer science, security, psychology, and many more. The objective of this paper is to provide insight regarding micro-expression analysis using 3D CNN. A lot of datasets of micro-expression have been released in the last decade, we have performed this experiment on SMIC micro-expression dataset and compared the results after applying two different activation functions.

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