“Late-onset” ADHD symptoms in young adulthood: Is this ADHD?

Objective: We investigated whether “late-onset” ADHD that emerges in adolescence/adulthood is similar in risk factor profile to: (1) child-onset ADHD, but emerges later because of scaffolding/compensation from childhood resources; and (2) depression, because it typically onsets in adolescence/adulthood and shows symptom and genetic overlaps with ADHD. Methods: We examined associations between late-onset ADHD and ADHD risk factors, cognitive tasks, childhood resources and depression risk factors in a population-based cohort followed-up to age 25 years ( N=4224–9764). Results: Parent-rated late-onset ADHD was like child-onset persistent ADHD in associations with ADHD polygenic risk scores and cognitive task performance, although self-rated late-onset ADHD was not. Late-onset ADHD was associated with higher levels of childhood resources than child-onset ADHD and did not show strong evidence of association with depression risk factors. Conclusions: Late-onset ADHD shares characteristics with child-onset ADHD when parent-rated, but differences for self-reports require investigation. Childhood resources may delay the onset of ADHD.

Relevant Physiological Indicators for Assessing Workload in Conditionally Automated Driving, Through Three-Class Classification and Regression

In future conditionally automated driving, drivers may be asked to take over control of the car while it is driving autonomously. Performing a non-driving-related task could degrade their takeover performance, which could be detected by continuous assessment of drivers' mental load. In this regard, three physiological signals from 80 subjects were collected during 1 h of conditionally automated driving in a simulator. Participants were asked to perform a non-driving cognitive task (N-back) for 90 s, 15 times during driving. The modality and difficulty of the task were experimentally manipulated. The experiment yielded a dataset of drivers' physiological indicators during the task sequences, which was used to predict drivers' workload. This was done by classifying task difficulty (three classes) and regressing participants' reported level of subjective workload after each task (on a 0–20 scale). Classification of task modality was also studied. For each task, the effect of sensor fusion and task performance were studied. The implemented pipeline consisted of a repeated cross validation approach with grid search applied to three machine learning algorithms. The results showed that three different levels of mental load could be classified with a f1-score of 0.713 using the skin conductance and respiration signals as inputs of a random forest classifier. The best regression model predicted the subjective level of workload with a mean absolute error of 3.195 using the three signals. The accuracy of the model increased with participants' task performance. However, classification of task modality (visual or auditory) was not successful. Some physiological indicators such as estimates of respiratory sinus arrhythmia, respiratory amplitude, and temporal indices of heart rate variability were found to be relevant measures of mental workload. Their use should be preferred for ongoing assessment of driver workload in automated driving.

The Specificity of Cognitive-Motor Dual-Task Interference on Balance in Young and Older Adults

Standing upright on stable and unstable surfaces requires postural control. Postural control declines as humans age, presenting greater risk of fall-related injury and other negative health outcomes. Secondary cognitive tasks can further impact balance, which highlights the importance of coordination between cognitive and motor processes. Past research indicates that this coordination relies on executive function (EF; the ability to control, maintain, and flexibly direct attention to achieve goals), which coincidentally declines as humans age. This suggests that secondary cognitive tasks requiring EF may exert a greater influence on balance compared to non-EF secondary tasks, and this interaction could be exaggerated among older adults. In the current study, we had younger and older adults complete two Surface Stability conditions (standing upright on stable vs. unstable surfaces) under varying Cognitive Load; participants completed EF (Shifting, Inhibiting, Updating) and non-EF (Processing Speed) secondary cognitive tasks on tablets, as well as a single task control scenario with no secondary cognitive task. Our primary balance measure of interest was sway area, which was measured with an array of wearable inertial measurement unit sensors. Replicating prior work, we found a main effect of Surface Stability with less sway on stable surfaces compared to unstable surfaces, and we found an interaction between Age and Surface Stability with older adults exhibiting significantly greater sway selectively on unstable surfaces compared to younger adults. New findings revealed a main effect of Cognitive Load on sway, with the single task condition having significantly less sway than two of the EF conditions (Updating and Shifting) and the non-EF condition (Processing Speed). We also found an interaction of Cognitive Load and Surface Stability on postural control, where Surface Stability impacted sway the most for the single task and two of the executive function conditions (Inhibition and Shifting). Interestingly, Age did not interact with Cognitive Load, suggesting that both age groups were equally impacted by secondary cognitive tasks, regardless the presence or type of secondary cognitive task. Taken together, these patterns suggest that cognitive demands vary in their impact on posture control across stable vs. unstable surfaces, and that EF involvement may not be the driving mechanism explaining cognitive-motor dual-task interference on balance.

CAPTCHA for crowdsourced image annotation: directions and efficiency analysis

Purpose Image annotation plays an important role in image retrieval process, especially when it comes to content-based image retrieval. In order to compensate the intrinsic weakness of machines in performing cognitive task of (human-like) image annotation, leveraging humans’ knowledge and abilities in the form of crowdsourcing-based annotation have gained momentum. Among various approaches for this purpose, an innovative one is integrating the annotation process into the CAPTCHA workflow. In this paper, the current state of the research works in the field and experimental efficiency analysis of this approach are investigated.Design/methodology/approach At first, and with the aim of presenting a current state report of research studies in the field, a comprehensive literature review is provided. Then, several experiments and statistical analyses are conducted to investigate how CAPTCHA-based image annotation is reliable, accurate and efficient.Findings In addition to study of current trends and best practices for CAPTCHA-based image annotation, the experimental results demonstrated that despite some intrinsic limitations on leveraging the CAPTCHA as a crowdsourcing platform, when the challenge, i.e. annotation task, is selected and designed appropriately, the efficiency of CAPTCHA-based image annotation can outperform traditional approaches. Nonetheless, there are several design considerations that should be taken into account when the CAPTCHA is used as an image annotation platform.Originality/value To the best of the authors’ knowledge, this is the first study to analyze different aspects of the titular topic through exploration of the literature and experimental investigation. Therefore, it is anticipated that the outcomes of this study can draw a roadmap for not only CAPTCHA-based image annotation but also CAPTCHA-mediated crowdsourcing and even image annotation.

First-Year Students' Self-Perceived Effects of Music on Two Tasks

This chapter presents results from a study with first-year university students completing online courses during the COVID-19 pandemic. The goal of the study is to further understand how the same genre of music might impact the completion of two very different assessment tasks. Students in the study participated in two different virtual “study halls” in order to study for their semester final assessments. While further research is needed, results do highlight the need for students to be aware of which type of music to listen to while studying and specifically what kind of cognitive task they are completing.

Developing Conversational Agents for Use in Criminal Investigations

The adoption of artificial intelligence (AI) systems in environments that involve high risk and high consequence decision-making is severely hampered by critical design issues. These issues include system transparency and brittleness, where transparency relates to (i) the explainability of results and (ii) the ability of a user to inspect and verify system goals and constraints; and brittleness, (iii) the ability of a system to adapt to new user demands. Transparency is a particular concern for criminal intelligence analysis, where there are significant ethical and trust issues that arise when algorithmic and system processes are not adequately understood by a user. This prevents adoption of potentially useful technologies in policing environments. In this article, we present a novel approach to designing a conversational agent (CA) AI system for intelligence analysis that tackles these issues. We discuss the results and implications of three different studies; a Cognitive Task Analysis to understand analyst thinking when retrieving information in an investigation, Emergent Themes Analysis to understand the explanation needs of different system components, and an interactive experiment with a prototype conversational agent. Our prototype conversational agent, named Pan, demonstrates transparency provision and mitigates brittleness by evolving new CA intentions. We encode interactions with the CA with human factors principles for situation recognition and use interactive visual analytics to support analyst reasoning. Our approach enables complex AI systems, such as Pan, to be used in sensitive environments, and our research has broader application than the use case discussed.

The Effect of Cognitive Task on Postural Control Dynamic Regularity of Athletes With Chronic Ankle Instability When Standing on an Unstable Surface

Background and Objectives: Postural control disorder is a common complication in patients with Chronic Ankle Instability (CAI). The present study aimed to investigate the effect of dual cognitive task on postural control behavior with regard to the Center of Pressure (CoP) signal regularity while standing on an unstable surface in athletes with CAI. Methods: In the present study, 58 men participated in two groups of healthy and patients with CAI. The CoP signal was examined in 4 different unstable states on the wobble board located at the center of the force plate. The regularity of the signals recorded from the force plate was investigated using sample entropy in two directions: anterior-posterior and medial-lateral. Results: In both groups, there was a significant difference in CoP’s sample entropy signal when performing a cognitive task with a postural task (P<0.001). There was a significant difference between the two groups in the cognitive task and the single task in the anteroposterior direction while standing on two legs. Conclusion: During dual tasks, the patients with CAI have a more dynamic regularity in the CoP signal than their normal counterparts. In the dual-task condition, more irregularities are observed in the CoP signal of healthy individuals. In unstable conditions, patients with CAI decrease the adaptability of postural control behavior with increasing CoP signal regularity.

Dissociable influences of reward and punishment on adaptive cognitive control

To invest effort into any cognitive task, people must be sufficiently motivated. Whereas prior research has focused primarily on how the cognitive control required to complete these tasks is motivated by the potential rewards for success, it is also known that control investment can be equally motivated by the potential negative consequence for failure. Previous theoretical and experimental work has yet to examine how positive and negative incentives differentially influence the manner and intensity with which people allocate control. Here, we develop and test a normative model of control allocation under conditions of varying positive and negative performance incentives. Our model predicts, and our empirical findings confirm, that rewards for success and punishment for failure should differentially influence adjustments to the evidence accumulation rate versus response threshold, respectively. This dissociation further enabled us to infer how motivated a given person was by the consequences of success versus failure.