Challenges for Children and Adolescents with Autism and Their Families during the Covid-19 Pandemic

The restrictions and lockdown measures implemented in response to the Covid-19 pandemic have posed a pronounced risk to the mental health of people with autism and their families. We discuss specific difficulties affecting children and adolescents with autism and their families during the pandemic, drawing on a broad range of recently published research in many countries and regions. A global perspective is adopted to provide an overview of the state of affairs and identify good practices. We consider challenges in four areas: research and practice related to autism, challenges facing children and adolescents with autism, specific difficulties in their education and care, and challenges facing their parents and families. Based on the reviewed work and our experience in group support sessions with parents of children with autism, we conclude that supporting families affected by autism should emphasize promoting the parents\u27 well-being and coping potential to improve their resilience

BCI Control of Heuristic Search Algorithms

The ability to develop Brain-Computer Interfaces (BCI) to Intelligent Systems would offer new perspectives in terms of human supervision of complex Artificial Intelligence (AI) systems, as well as supporting new types of applications. In this article, we introduce a basic mechanism for the control of heuristic search through fNIRS-based BCI. The rationale is that heuristic search is not only a basic AI mechanism but also one still at the heart of many different AI systems. We investigate how users’ mental disposition can be harnessed to influence the performance of heuristic search algorithm through a mechanism of precision-complexity exchange. From a system perspective, we use weighted variants of the A? algorithm which have an ability to provide faster, albeit suboptimal solutions. We use recent results in affective BCI to capture a BCI signal, which is indicative of a compatible mental disposition in the user. It has been established that Prefrontal Cortex (PFC) asymmetry is strongly correlated to motivational dispositions and results anticipation, such as approach or even risk-taking, and that this asymmetry is amenable to Neurofeedback (NF) control. Since PFC asymmetry is accessible through fNIRS, we designed a BCI paradigm in which users vary their PFC asymmetry through NF during heuristic search tasks, resulting in faster solutions. This is achieved through mapping the PFC asymmetry value onto the dynamic weighting parameter of the weighted A* (WA*) algorithm. We illustrate this approach through two different experiments, one based on solving 8-puzzle configurations, and the other on path planning. In both experiments, subjects were able to speed up the computation of a solution through a reduction of search space in WA?. Our results establish the ability of subjects to intervene in heuristic search progression, with effects which are commensurate to their control of PFC asymmetry: this opens the way to new mechanisms for the implementation of hybrid cognitive systems

Anger-based BCI Using fNIRS Neurofeedback

Functional near-infrared spectroscopy (fNIRS) holds increasing potential for Brain-Computer Interfaces (BCI) due to its portability, ease of application, robustness to movement artifacts, and relatively low cost. The use of fNIRS to support the development of affective BCI has received comparatively less attention, despite the role played by the prefrontal cortex in affective control, and the appropriateness of fNIRS to measure prefrontal activity. We present an active, fNIRS-based neurofeedback (NF) interface, which uses differential changes in oxygenation between the left and right sides of the dorsolateral prefrontal cortex to operationalize BCI input. The system is activated by users generating a state of anger, which has been previously linked to increased left prefrontal asymmetry. We have incorporated this NF interface into an experimental platform adapted from a virtual 3D narrative, in which users can express anger at a virtual character perceived as evil, causing the character to disappear progressively. Eleven subjects used the system and were able to successfully perform NF despite minimal training. Extensive analysis confirms that success was associated with the intent to express anger. This has positive implications for the design of affective BCI based on prefrontal asymmetry

A Mechanism Design Approach to Measure Awareness

In this paper, we study protocols that allow to discern conscious and unconscious decisions of human beings; i.e., protocols that measure awareness. Consciousness is a central research theme in Neuroscience and AI, which remains, to date, an obscure phenomenon of human brains. Our starting point is a recent experiment, called Post Decision Wagering (PDW) (Persaud, McLeod, and Cowey 2007), that attempts to align experimenters' and subjects' objectives by leveraging financial incentives. We note a similarity with mechanism design, a research area which aims at the design of protocols that reconcile often divergent objectives through incentive-compatibility. We look at the issue of measuring awareness from this perspective. We abstract the setting underlying the PDW experiment and identify three factors that could make it ineffective: rationality, risk attitude and bias of subjects. Using mechanism design tools, we study the barrier between possibility and impossibility of incentive compatibility with respect to the aforementioned characteristics of subjects. We complete this study by showing how to use our mechanisms to potentially get a better understanding of consciousness

Why do cats bring you rodents, and what does this have to do with party games and well-being?

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