When do we see that others misrepresent how they feel? detecting deception from emotional faces with direct and indirect measures

A Utilização da Imitação Facial em Tarefas de Reconhecimento de Expressões Emocionais

Resumo A imitação facial é um comportamento involuntário capaz de facilitar a transmissão de informações não verbais relevantes em diferentes contextos sociais. Este estudo teve por objetivo analisar a capacidade de reconhecimento de expressões emocionais enquanto o observador tensiona a própria face ou imita a face-alvo. A hipótese utilizada foi a de que indivíduos que tensionam a própria face terão menor probabilidade de acertos na execução das tarefas de reconhecimento de expressões emocionais e aqueles que imitam a expressão terão uma maior probabilidade de acertos na execução das mesmas tarefas. A amostra foi composta por 30 participantes, divididos em dois grupos experimentais: o Grupo Imitação (GI) e o Grupo Ruído (GR), ambos com 18 participantes do sexo feminino e 12 do sexo masculino. O experimento consistiu em apresentar fotos de atores expressando facialmente uma emoção básica por 10 segundos. Neste período, os participantes deveriam, então, observar ou intervir facialmente, imitando ou tensionando a própria face (de acordo com o grupo alocado, Imitação ou Ruído). Após os 10 segundos executando a instrução (observar, imitar ou interferir), o participante deveria responder - entre as opções alegria, tristeza, nojo, raiva, surpresa e medo - a emoção correspondente à imagem. Os resultados apresentaram diferenças significativas quando comparadas as tarefas de tensionar ou imitar a face-alvo, sugerindo que a alteração da própria face do observador pode influenciar durante o desempenho de uma tarefa de reconhecimento de emoções em faces.

Interpersonal Consequences of Deceptive Expressions of Sadness

Emotional expressions evoke predictable responses from observers; displays of sadness are commonly met with sympathy and help from others. Accordingly, people may be motivated to feign emotions to elicit a desired response. In the absence of suspicion, we predicted that emotional and behavioral responses to genuine (vs. deceptive) expressers would be guided by empirically valid cues of sadness authenticity. Consistent with this hypothesis, untrained observers (total N = 1,300) reported less sympathy and offered less help to deceptive (vs. genuine) expressers of sadness. This effect was replicated using both posed, low-stakes, laboratory-created stimuli, and spontaneous, real, high-stakes emotional appeals to the public. Furthermore, lens models suggest that sympathy reactions were guided by difficult-to-fake facial actions associated with sadness. Results suggest that naive observers use empirically valid cues to deception to coordinate social interactions, providing novel evidence that people are sensitive to subtle cues to deception.

Driver Emotions Recognition Based on Improved Faster R-CNN and Neural Architectural Search Network

It is critical for intelligent vehicles to be capable of monitoring the health and well-being of the drivers they transport on a continuous basis. This is especially true in the case of autonomous vehicles. To address the issue, an automatic system is developed for driver’s real emotion recognizer (DRER) using deep learning. The emotional values of drivers in indoor vehicles are symmetrically mapped to image design in order to investigate the characteristics of abstract expressions, expression design principles, and an experimental evaluation is conducted based on existing research on the design of driver facial expressions for intelligent products. By substituting a custom-created CNN features learning block with the base 11 layers CNN model in this paper for the development of an improved faster R-CNN face detector that detects the driver’s face at a high frame per second (FPS). Transfer learning is performed in the NasNet large CNN model in order to recognize the driver’s various emotions. Additionally, a custom driver emotion recognition image dataset is being developed as part of this research task. The proposed model, which is a combination of an improved faster R-CNN and transfer learning in NasNet-Large CNN architecture for DER based on facial images, enables greater accuracy than previously possible for DER based on facial images. The proposed model outperforms some recently updated state-of-the-art techniques in terms of accuracy. The proposed model achieved the following accuracy on various benchmark datasets: JAFFE 98.48%, CK+ 99.73%, FER-2013 99.95%, AffectNet 95.28%, and 99.15% on a custom-developed dataset.

The Limits of Conscious Deception Detection: When Reliance on False Deception Cues Contributes to Inaccurate Judgments

People are generally too trusting, which decreases their ability to detect deceit. This suggests that distrust could enhance our deception detection abilities. Yet, a state of distrust may induce deliberative conscious thought. This mode of thinking has been related to worse complex decision making. Hence, we investigate whether contextual distrust decreases the ability to detect deceit via the stronger reliance on consciously held beliefs about which cues betray deception. In two studies, participants were asked to judge videos of either deceiving or truth telling targets. Contextual distrust was manipulated by asking participants to squint their eyes (distrust) or to round their eyes (trust) while watching the videos. Participants’ judgments of targets being deceptive or truthful were measured (Studies 1 and 2) and they were asked on what basis they made these judgments (Study 2). Results showed that distrust especially hampers the detection of truth, which is partly due to more reliance on false beliefs about deception cues. These results corroborate the idea that deliberative conscious information processing may hinder truth detection, while intuitive information processing may facilitate it.