Disrupted cognitive and affective empathy network interactions in autistic children viewing social animation.

Empathy can be divided into two core components, cognitive empathy (CE) and affective empathy (AE), mediated by distinct neural networks. Deficient empathy is a central feature of autism spectrum conditions (ASCs), but it is unclear if this deficit results from disruption solely within empathy networks or from disrupted functional integration between CE and AE networks. To address this issue, we measured functional connectivity (FC) patterns both within and between empathy networks in autistic children (4-8 years, n = 31) and matched typically developing (TD) children (n = 26) using near-infrared spectroscopy during the presentation of an animated story evoking CE and AE. Empathy and social communication ability were also assessed using the Empathy Quotient/Systemizing Quotient (EQ/SQ) and Social Responsiveness Scale, respectively. The results showed that the FC in the AE network of autistic children did not differ from the TD group across conditions; however, the ASC group showed weaker FC in the CE network under the CE condition and weaker FC between networks when processing AE information, the latter of which was negatively correlated with EQ scores in ASC. The empathy defect in ASC may involve abnormal integration of CE and AE network activities under AE conditions.

Autistic spectrum traits detection and early screening: A machine learning based eye movement study.

PROBLEM: Autism spectrum disorders (ASD) are pervasive neurodevelopmental disorders and generally accompanied by social disorders, verbal or nonverbal communication defects, inability to concentrate and other negative symptoms that affect the autistic person's normal life. However, traditional screening methods are time-consuming and public health resources are limited. METHODS: This study proposed a novel technique that combined eye-movement data and machine learning algorithms for predicting autistic traits. We converted raw eye movement data into features, trained and tested a model for early screening. FINDINGS: In the preliminary experiment, 107 participants (average age = 24.84 ± 5.24 years) wore HTC Vive to watch a VR scene for 15-20 s. We explored eight classification models, among which the ensemble model performed best, with 0.73 accuracy, 0.68 precision, 0.81 recall, 0.74 F1-score, and an area under the curve of 0.90. And in the test experiment, 22 participants (average age = 12.68 ± 7.61 years) diagnosed as ASD took the experiment and the ensemble model showed a recall of 0.77. Eye movement data is an effectively distinguishable tool and we find that the proportion of time to observe figure and animal region continuously can distinguish participants with obvious and unobvious autistic traits effectively in the model. CONCLUSION: This study focuses on the detection of autistic traits, and proposes a more objective and faster method for undertaking early screening, which provides possibilities to save precious time to intervene and alleviate its symptoms before making a definite diagnosis.

Young children with autism show atypical prefrontal cortical responses to humanoid robots: An fNIRS study.

BACKGROUND: Previous behavioral studies have found that children with autism spectrum disorder (ASD) show greater interest in humanoid robots than in humans. However, the neural mechanism underlying this is not clear. This study compared brain activation patterns between children with ASD and neurotypical children while they watched videos with robots and humans. METHOD: We recruited 45 children with ASD and 53 neurotypical children aged 4-6 years and recorded their neural activity in the dorsolateral prefrontal cortex (DLPFC) using a functional near-infrared spectroscopy (fNIRS) device when the two groups interacted with a robot or a human in a video. RESULTS: First, neural activity in the right DLPFC in children with ASD was significantly lower in the robot condition than in the human condition. Neural activity in the right DLPFC in children with ASD was also significantly lower than that of neurotypical children in the robot condition. Second, the neural activity in the left DLPFC between the human and robot conditions was negatively correlated in children with ASD, while it was positively correlated in neurotypical children. Moreover, neural activity in the left DLPFC in children with ASD was significantly correlated with the ADOS scores in both conditions. CONCLUSIONS: While neurotypical children showed comparable neural activity to humanoid robots and human beings, the children with ASD showed significantly different neural activity under those two conditions. Children with ASD may need more selective attention resources for human interaction than for robot interaction. It is also much more difficult for children with ASD to neglect the attraction of robots. Neural activity of the left DLPFC of children with ASD is correlated with their symptoms, which maybe a possible indicator for early diagnosis. Neural activity of the right DLPFC guided their atypical reactions and engagements with robots. Our study contributes to the current understanding of the neural mechanisms responsible for the different behavioral reactions in children with ASD toward robots and humans.

Risk perceptions and DUI decisions of drivers in different legal environments: New evidence on differential deterrence from a Chinese sample.

OBJECTIVES: Research on the deterrent effects of driving-under-the-influence (DUI) laws has been limited in China, which has criminalized drunk driving since May 2011 yet the effectiveness of this legislation remains unclear. Primary studies are needed to confirm whether government reports of reductions in DUI rates since then can indeed match changes in driver perceptions of DUI risk, and if so, be attributed to what specific components of the DUI legal environments. Based on the classical theory of deterrence and recent advances in differential deterrence, this study adopted a conjoint experiment from a previous US study that simulated the decision-making process of potential drinking drivers, and evaluated how DUI sanctions and enforcement practices contributed differentially to the three components of deterrence (i.e., certainty, swiftness, and severity of punishment). Key individual characteristics and nonlegal factors, as suggested by differential deterrence research to moderate the impact of DUI laws, were also considered. METHODS: A Web-based conjoint experiment was conducted on a sample of 109 college students from two major universities in Shenzhen, China. Participants were randomly assigned to blocks of hypothetical scenarios composed of different levels of DUI enforcement and penalties, and asked to choose from a pair of scenarios each time, in which they were more likely to drink and drive. They also answered questions adapted from previous studies that measured key individual factors in relation to differential deterrence, such as informal sanction threat, moral inhibition, and personal and vicarious experiences with punishment. Such individual differences were accounted for in both a conventional two-level mixed logit aggregate model and a Hierarchical Bayes model. RESULTS: Consistent with prior findings in Western countries, DUI enforcement intensity, was found to be the strongest deterrent to potential drinking drivers in China. License suspension, as an administrative punishment that can be swiftly implemented, was also effective in deterring the Chinese drivers, who however were much more likely to fear the revocation of their licenses rather than a 6-month suspension only. Meanwhile, they were notably deterred by the possibility of being in jail for 1-3 days, let alone for 1-2 months. Altogether, enforcement, license suspension and jail penalties accounted for more than 75 percent of attribute impact on drivers' decision to drink and drive, whereas fine penalty and license points had almost no effect. On the other hand, nonlegal factors such as informal sanction threat and vicarious experiences were found to have significantly moderated the deterrent effects of DUI laws. CONCLUSIONS: Overall, this study quantified the unique effects of perceived certainty, swiftness, and severity of DUI punishment in the Chinese context, and supported the usefulness of conjoint experiments for examining risk perceptions and DUI decisions in different legal environments. It also provided new empirical evidence on differential deterrence and pointed out the need of determining for which subsets of individuals and under what conditions can legal sanctions successfully deter potential offenders. Such research will help researchers and policy makers better understand the role of deterrence, for more effective policy development related to DUI as well as other important traffic safety issues.

Effects of Increasing the Negativity of Implicit Outcome Expectancies on Internet Gaming Impulsivity.

Outcome expectancies have been found to play important roles in addictive behaviors. Research has shown that implicit outcome expectancies (OE) were significantly correlated with Internet gaming behaviors among players with Internet gaming disorder (IGD). However, few empirical studies have further examined the relationship between implicit OE and players with IGD. This study first strengthened the implicit association between Internet games and negative outcomes using an evaluative conditioning paradigm (EC) and then examined the effects of increasing the negativity of implicit OE on Internet gaming impulsivity. Thirty-nine college students who were diagnosed as players with IGD participated in the study. Manipulation checks showed that after the EC was introduced, participants associated Internet gaming stimuli more closely with negative outcomes than with positive outcomes. After the implicit OE were effectively altered to be negative, players with IGD performed better in the delay discounting paradigm, showing a lower impulsivity with respect to Internet gaming.

Internet addiction, problematic internet use, nonproblematic internet use among Chinese adolescents: Individual, parental, peer, and sociodemographic correlates.

Internet addiction has been typically conceptualized as either a continuous construct or a dichotomous construct. Limited research has differentiated adolescents with problematic Internet use (PIU) from the Internet addiction group (IA) and/or nonproblematic Internet use group (NPIU) and examined the potential correlates. To fill this gap, based on data obtained from 956 Chinese adolescents (11-19 years, 47% male), this study examined if adolescents with PIU is a distinctive group from the IA and NPIU. This study also examined factors from different ecological levels that may differentiate among the three groups, including individual, parental, peer, and sociodemographic factors. Results indicated that IA, PIU, and NPIU differed significantly on scores of Young's Diagnostic Questionnaire (YDQ). Critical factors emerging from different ecological levels could differentiate between PIU and NPIU and between IA and NPIU. Such findings suggest that PIU may represent a distinct, intermediate group of Internet users. The potential theoretical and practical implications of identifying PIU were also discussed. (PsycINFO Database Record