Control Task Performance Research Articles

Enhancing air traffic control: A transparent deep reinforcement learning framework for autonomous conflict resolution

Rising traffic demands, advancements in automation, and communication breakthroughs are opening new design frontiers for future air traffic controllers (ATCs). This article introduces a deep reinforcement learning (DRL) controller to support conflict resolution in autonomous free flight. While DRL has made significant strides in this domain, there is scant focus in existing research on the explainability and safety of DRL controllers, especially their robustness against adversarial attacks. To tackle these concerns, we have engineered a fully transparent DRL framework that: (i) separates the intertwined Q-value learning model into distinct modules for safety-awareness and efficiency (target attainment); and (ii) incorporates data from nearby intruders directly, thereby obviating the need for central control. Our simulated experiments demonstrate that this bifurcation of safety-awareness and efficiency not only enhances performance in free flight control tasks but also substantially boosts practical explainability. Moreover, the safety-oriented Q-learning component offers detailed insights into environmental risk factors. To assess resilience against adversarial attacks, we present a novel attack strategy that induces both safety-centric and efficiency-centric disruptions. This adversary aims to degrade safety/efficiency by targeting the agent at select time intervals. Our experimental results reveal that this targeted approach can provoke as many collisions as a uniform attack strategy — that is, attacking at every opportunity — while engaging the agent four times less frequently, shedding light on the potential and limitations of DRL in future ATC system designs. The source code is available to the public at https://github.com/WLeiiiii/Gym-ATC-Attack-Project.

Trajectory tracking control based on deep reinforcement learning and ensemble random network distillation for robotic manipulator

Abstract In general, the trajectory tracking of robotic manipulator is exceptionally challenging due to the complex and strongly coupled mechanical architecture. In this paper, precise track control of the robotic manipulator is formulated as a dense reward problem for reinforcement learning(RL). A deep RL(DRL) approach combining the soft actor-critic (SAC) algorithm and ensemble random network distillation (ERND) is proposed to address the tracking control problem for robotic manipulator. Firstly, an ERND model is designed, consisting of a module list of multiple RND models. Each RND model obtains the error by learning the target features and the predicted features of the environment. The resulting error serves as internal rewards that drive the robotic agent to explore unknown and unpredictable environmental states. The ensemble model obtains the total internal reward by summing the internal rewards of each RND model, thereby obtaining more accurately reflecting the characteristics of the manipulator in tracking control tasks and improving control performance. Secondly, combining the SAC algorithm with ERND facilitates more robust exploration capabilities in environments with input saturation and joint angle constraints, thereby enabling faster learning of effective policies and enhancing the performance and efficiency of robotic manipulator tracking control tasks. Finally, the simulation results demonstrate that the robotic manipulator tracking control task is effectively completed in dense reward problems through the combination of the SAC algorithm and ERND.

Learning to Control Complex Robots Using High-Dimensional Body-Machine Interfaces.

When individuals are paralyzed from injury or damage to the brain, upper body movement and function can be compromised. While the use of body motions to interface with machines has shown to be an effective noninvasive strategy to provide movement assistance and to promote physical rehabilitation, learning to use such interfaces to control complex machines is not well understood. In a five session study, we demonstrate that a subset of an uninjured population is able to learn and improve their ability to use a high-dimensional Body-Machine Interface (BoMI), to control a robotic arm. We use a sensor net of four inertial measurement units, placed bilaterally on the upper body, and a BoMI with the capacity to directly control a robot in six dimensions. We consider whether the way in which the robot control space is mapped from human inputs has any impact on learning. Our results suggest that the space of robot control does play a role in the evolution of human learning: specifically, though robot control in joint space appears to be more intuitive initially, control in task space is found to have a greater capacity for longer-term improvement and learning. Our results further suggest that there is an inverse relationship between control dimension couplings and task performance.

Brain activations elicited during task-switching generalize beyond the task: A partial least squares correlation approach to combine fMRI signals and cognition.

An underlying hypothesis for broad transfer from cognitive training is that the regional brain signals engaged during the training task are related to the transfer tasks. However, it is unclear whether the brain activations elicited from a specific cognitive task can generalize to performance of other tasks, esp. in normal aging where cognitive training holds much promise. In this large dual-site functional magnetic resonance imaging (fMRI) study, we aimed to characterize the neurobehavioral correlates of task-switching in normal aging and examine whether the task-switching-related fMRI-blood-oxygen-level-dependent (BOLD) signals, engaged during varieties of cognitive control, generalize to other tasks of executive control and general cognition. We therefore used a hybrid blocked and event-related fMRI task-switching paradigm to investigate brain regions associated with multiple types of cognitive control on 129 non-demented older adults (65-85 years). This large dataset provided a unique opportunity for a data-driven partial least squares-correlation approach to investigate the generalizability of multiple fMRI-BOLD signals associated with task-switching costs to other tasks of executive control, general cognition, and demographic characteristics. While some fMRI signals generalized beyond the scanned task, others did not. Results indicate right middle frontal brain activation as detrimental to task-switching performance, whereas inferior frontal and caudate activations were related to faster processing speed during the fMRI task-switching, but activations of these regions did not predict performance on other tasks of executive control or general cognition. However, BOLD signals from the right lateral occipital cortex engaged during the fMRI task positively predicted performance on a working memory updating task, and BOLD signals from the left post-central gyrus that were disengaged during the fMRI task were related to slower processing speed in the task as well as to lower general cognition. Together, these results suggest generalizability of these BOLD signals beyond the scanned task. The findings also provided evidence for the general slowing hypothesis of aging as most variance in the data were explained by low processing speed and global low BOLD signal in older age. As processing speed shared variance with task-switching and other executive control tasks, it might be a possible basis of generalizability between these tasks. Additional results support the dedifferentiation hypothesis of brain aging, as right middle frontal activations predicted poorer task-switching performance. Overall, we observed that the BOLD signals related to the fMRI task not only generalize to the performance of other executive control tasks, but unique brain predictors of out-of-scanner performance can be identified.

Generalization Enhancement of Visual Reinforcement Learning through Internal States.

Visual reinforcement learning is important in various practical applications, such as video games, robotic manipulation, and autonomous navigation. However, a major challenge in visual reinforcement learning is the generalization to unseen environments, that is, how agents manage environments with previously unseen backgrounds. This issue is triggered mainly by the high unpredictability inherent in high-dimensional observation space. To deal with this problem, techniques including domain randomization and data augmentation have been explored; nevertheless, these methods still cannot attain a satisfactory result. This paper proposes a new method named Internal States Simulation Auxiliary (ISSA), which uses internal states to improve generalization in visual reinforcement learning tasks. Our method contains two agents, a teacher agent and a student agent: the teacher agent has the ability to directly access the environment's internal states and is used to facilitate the student agent's training; the student agent receives initial guidance from the teacher agent and subsequently continues to learn independently. From another perspective, our method can be divided into two phases, the transfer learning phase and traditional visual reinforcement learning phase. In the first phase, the teacher agent interacts with environments and imparts knowledge to the vision-based student agent. With the guidance of the teacher agent, the student agent is able to discover more effective visual representations that address the high unpredictability of high-dimensional observation space. In the next phase, the student agent autonomously learns from the visual information in the environment, and ultimately, it becomes a vision-based reinforcement learning agent with enhanced generalization. The effectiveness of our method is evaluated using the DMControl Generalization Benchmark and the DrawerWorld with texture distortions. Preliminary results indicate that our method significantly improves generalization ability and performance in complex continuous control tasks.

Combat work of firing artillery units in modern armed conflicts

The article reveals the structure of the control system of artillery units, considers the way so fire control of artillery units in armed conflicts during the five-day war in Transcaucasia in 2008, Nagorno-Karabakh in 2020, during the Special Military Operation in Ukraine in 2022 and the if impaction the effectiveness of artillery use. Modern reconnaissance means, reconnaissance-fire and strike complexes, their capabilities in fire control are presented, the actions of artillery units in the performance of fire control tasks represented.

Multiplexed Levels of Cognitive Control through Delta and Theta Neural Oscillations.

Cognitive control allows behavior to be guided according to environmental contexts and internal goals. During cognitive control tasks, fMRI analyses typically reveal increased activation in frontal and parietal networks, and EEG analyses reveal increased amplitude of neural oscillations in the delta/theta band (2-3, 4-7 Hz) in frontal electrodes. Previous studies proposed that theta-band activity reflects the maintenance of rules associating stimuli to appropriate actions (i.e., the rule set), whereas delta synchrony is specifically associated with the control over the context for when to apply a set of rules (i.e., the rule abstraction). We tested these predictions using EEG and fMRI data collected during the performance of a hierarchical cognitive control task that manipulated the level of abstraction of task rules and their set-size. Our results show a clear separation of delta and theta oscillations in the control of rule abstraction and of stimulus-action associations, respectively, in distinct frontoparietal association networks. These findings support a model by which frontoparietal networks operate through dynamic, multiplexed neural processes.

Optimizing Human-Robot Teaming Performance through Q-Learning-Based Task Load Adjustment and Physiological Data Analysis.

The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during task execution may vary. To ensure high human-robot teaming (HRT) performance, it is crucial to predict performance without negatively affecting task execution. Therefore, to predict performance indirectly, significant factors affecting human performance, such as engagement and task load (i.e., amount of cognitive, physical, and/or sensory resources required to perform a particular task), must be considered. Hence, we propose a framework to predict and maximize the HRT performance. For the prediction of task performance during the development phase, our methodology employs features extracted from physiological data as inputs. The labels for these predictions-categorized as accurate performance or inaccurate performance due to high/low task load-are meticulously crafted using a combination of the NASA TLX questionnaire, records of human performance in quality control tasks, and the application of Q-Learning to derive task-specific weights for the task load indices. This structured approach enables the deployment of our model to exclusively rely on physiological data for predicting performance, thereby achieving an accuracy rate of 95.45% in forecasting HRT performance. To maintain optimized HRT performance, this study further introduces a method of dynamically adjusting the robot's speed in the case of low performance. This strategic adjustment is designed to effectively balance the task load, thereby enhancing the efficiency of human-robot collaboration.

Clinical and neuropsychological correlates of theta-band functional excitation-inhibition ratio in autism: An EEG study

ObjectiveHow abnormal brain signaling impacts cognition in autism spectrum disorder (ASD) remained elusive. This study aimed to investigate the local and global brain signaling in ASD indicated by theta-band functional excitation-inhibition (fE/I) ratio and explored psychophysiological relationships between fE/I, cognitive deficits, and ASD symptomatology. MethodsA total of 83 ASD and typically developing (TD) individuals participated in this study. Participants’ interference control and set-shifting abilities were assessed. Resting-state electroencephalography (EEG) was used for estimating theta-band fE/I ratio. ResultsASD individuals (n = 31 without visual EEG abnormality; n = 22 with visual EEG abnormality) generally performed slower in a cognitive task tapping interference control and set-maintenance abilities, but only ASD individuals with visually abnormal EEG performed significantly slower than their TD counterparts (Bonferroni-corrected ps < .001). Heightened theta-band fE/I ratios at the whole-head level, left and right hemispheres were observed in the ASD subgroup without visual EEG abnormality only (Bonferroni-corrected ps < .001), which remained highly significant when only data from medication-naïve participants were analyzed. In addition, higher left hemispheric fE/I ratios in ASD individuals without visual EEG abnormality were significantly correlated with faster interference control task performance, in turn faster reaction time was significantly associated with less severe restricted, repetitive behavior (Bonferroni-corrected ps ≤ .0017). ConclusionsDifferential theta-band fE/I within the ASD population. Heightened theta-band fE/I in ASD without visual EEG abnormality may be associated with more efficient filtering of distractors and a less severe ASD symptom manifestation. SignificanceBrain signaling, indicated by theta-band fE/I, was different in ASD subgroups. Only ASD with visually-normal EEG showed heightened theta-band fE/I, which was associated with faster processing of visual distractors during a cognitive task. More efficient distractor filtering was associated with less restricted, repetitive behaviors.

Attentional Tunneling in Pilots During a Visual Tracking Task With a Head Mounted Display.

We examined whether active head aiming with a Helmet Mounted Display (HMD) can draw the pilot's attention away from a primary flight task. Furthermore, we examined whether visual clutter increases this effect. Head up display symbology can result in attentional tunneling, and clutter makes it difficult to identify objects. Eighteen military pilots had to simultaneously perform an attitude control task while flying in clouds and a head aiming task in a fixed-base flight simulator. The former consisted of manual compensation for roll disturbances of the aircraft, while the latter consisted of keeping a moving visual target inside a small or large head-referenced circle. A "no head aiming" condition served as a baseline. Furthermore, all conditions were performed with or without visual clutter. Head aiming led to deterioration of the attitude control task performance and an increase of the amount of roll-reversal errors (RREs). This was even the case when head aiming required minimal effort. Head aiming accuracy was significantly lower when the roll disturbances in the attitude control task were large compared to when they were small. Visual clutter had no effect on both tasks. We suggest that active head aiming of HMD symbology can cause attentional tunneling, as expressed by an increased number of RREs and less accuracy on a simultaneously performed attitude control task. This study improves our understanding in the perceptual and cognitive effects of (military) HMDs, and has implications for operational use and possibly (re)design of HMDs.

Construction Health Hazard Control Innovations: A Web-Based Intervention to Change Perceptions

ABSTRACT A theory-based intervention strategy to improve perceptions of construction health hazard control innovations was developed and tested in the following trades: masonry (n = 90), concrete (n = 52), and asphalt roofing (n = 105). A web-based intervention was designed to target constructs of the Prevention through Design Adoption Readiness Model (PtD ARM) and included information about the health impacts of construction health hazards; side-by-side videos of both conventional and innovative tools; information about control effectiveness, productivity, and task performance. The interventions were evaluated using a pre-test/posttest, within-subjects experimental design, with control groups. Posttest data were collected immediately, three months, and six months following the intervention. A validated survey instrument was used to collect data regarding dependent variables: health knowledge (HK), perceived ease of use (PEOU), perceived impacts on productivity (PU), perceived worker susceptibility to health effects (SUS), perceived health-effect severity (SEV), adoption readiness (AR), and actual use (AU). Open-ended questions generated qualitative data regarding benefits and barriers to adoption of the innovations and were analyzed using Content Analysis. Gain-score ANOVA found significant improvements in PEOU and PU in all intervention groups. In addition, significant improvements in HK, risk, and AR were found in the asphalt roofing intervention group.

The vice and virtue of conflict in boards: A longitudinal study into the effect of conflicts and cohesion on the task performance of supervisory school boards in the Netherlands

AbstractThe increased diversity and autonomy of schools calls for a more professional approach to school governance. In response, attention has been directed to the structure of school boards in the Netherlands. However, previous research indicates that the effectiveness of governance is not so much about structure as it is about behaviour. Conflict, in particular, appears to be an important underexposed factor in the effective behaviour of school boards. In a longitudinal research design, a survey using Likert‐scale items was distributed in 2016 and 2017 to executive directors of schools in the Netherlands to gauge their assessment of conflicts within the supervisory school board. Hierarchical regression analyses and moderator analyses were used to test the hypotheses. An increase in task‐related conflicts were found to have a clear positive effect on the task performance of supervisory boards. An increase in numbers of relational conflicts had a negative effect on the control task performance, while an increase in process‐related conflicts had a negative effect on the advisory task performance. The cohesion within a supervisory board proved to be an important moderator of the effect of task‐related conflicts and relationship conflicts on the control task performance. The results from this research underline the need for a multidimensional conceptualisation of conflict in supervisory boards. The need for further research on the interaction between cohesion and conflicts is underscored. Boards should make room for substantive debate and even for the inevitable friction and irritation that is sometimes inherent in being effective in managing schools.

Latent state-trait and latent growth curve modeling of inhibitory control.

The reliability of inhibitory control task performance as well as the existence of an underlying unitary inhibitory construct have been questioned. The present study is the first to use a trait and state decomposition approach to formally quantify the reliability of inhibitory control and to examine its hierarchical structure. N = 150 participants carried out antisaccade, Eriksen flanker, go/nogo, Simon, stop-signal, and Stroop tasks on three occasions. By applying latent state-trait modeling and latent growth-curve modeling, reliability was estimated and divided into the amount of variance explained by trait effects and trait changes (consistency) and the amount of variance explained by situational effects and effects of Situation × Person interaction (occasion specificity). Mean reaction times for all tasks revealed excellent reliabilities (.89-.99). Importantly, on average, 82% of variance was accounted for by consistency while specificity was rather small. Although primary inhibitory variables revealed lower reliabilities (.51-.85), the majority of explained variance was again trait determined. Trait changes were observed for most variables and were strongest when comparing the first occasion to later ones. In addition, in some variables, those improvements were particularly high in initially underperforming subjects. An analysis of the construct of inhibition on trait level showed that communality between tasks was low. We conclude that most variables in inhibitory control tasks are mainly affected by stable trait effects, but there is only little evidence of a common, underlying inhibitory control construct at trait level. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Associations Between Preterm Birth, Inhibitory Control-Implicated Brain Regions and Tracts, and Inhibitory Control Task Performance in Children: Consideration of Socioeconomic Context.

Preterm birth (PTB) is associated with increased risk for unfavorable outcomes such as deficits in attentional control and related brain structure alterations. Crucially, PTB is more likely to occur within the context of poverty. The current study examined associations between PTB and inhibitory control (IC) implicated brain regions/tracts and task performance, as well as the moderating role of early life poverty on the relation between PTB and IC-implicated regions/tracts/task performance. 2,899 children from the ABCD study were sampled for this study. Mixed effects models examined the relation between PTB and subsequent IC performance as well as prefrontal gray matter volume, white matter fractional anisotropy (FA), and mean diffusivity (MD). Household income was examined as a moderator. PTB was significantly associated with less improvement in IC task performance over time and decreased FA in left uncinate fasciculus (UF) and cingulum bundle (CB). Early life poverty moderated the relation between PTB and both CB FA and UF MD.

On the Minimal Adversarial Perturbation for Deep Neural Networks With Provable Estimation Error.

Although Deep Neural Networks (DNNs) have shown incredible performance in perceptive and control tasks, several trustworthy issues are still open. One of the most discussed topics is the existence of adversarial perturbations, which has opened an interesting research line on provable techniques capable of quantifying the robustness of a given input. In this regard, the euclidean distance of the input from the classification boundary denotes a well-proved robustness assessment as the minimal affordable adversarial perturbation. Unfortunately, computing such a distance is highly complex due the non-convex nature of DNNs. Despite several methods have been proposed to address this issue, to the best of our knowledge, no provable results have been presented to estimate and bound the error committed. This paper addresses this issue by proposing two lightweight strategies to find the minimal adversarial perturbation. Differently from the state-of-the-art, the proposed approach allows formulating an error estimation theory of the approximate distance with respect to the theoretical one. Finally, a substantial set of experiments is reported to evaluate the performance of the algorithms and support the theoretical findings. The obtained results show that the proposed strategies approximate the theoretical distance for samples close to the classification boundary, leading to provable robustness guarantees against any adversarial attacks.

Anodal transcranial direct current stimulation enhances ankle force control and modulates the beta-band activity of the sensorimotor cortex.

This study aimed to investigate the cortical responses to the ankle force control and the mechanism underlying changes in ankle force control task induced by transcranial direct current stimulation (tDCS). Sixteen young adults were recruited, and they completed the electroencephalogram (EEG) assessment and high-definition tDCS (HD-tDCS) sessions. Root mean square (RMS) error was used to evaluate ankle force control task performance. Spectral power analysis was conducted to extract the average power spectral density (PSD) in the alpha (8-13Hz) and beta (13-30Hz) bands for resting state and tasking (i.e. task-PSD). The ankle force control task induced significant decreases in alpha and beta PSDs in the central, left, and right primary sensorimotor cortex (SM1) and beta PSD in the central frontal as compared with the resting state. HD-tDCS significantly decreased the RMS and beta task-PSD in the central frontal and SM1. A significant association between the percent change of RMS and the percent change of beta task-PSD in the central SM1 after HD-tDCS was observed. In conclusion, ankle force control task activated a distributed cortical network mainly including the SM1. HD-tDCS applied over SM1 could enhance ankle force control and modulate the beta-band activity of the sensorimotor cortex.

The Relationship Between Bilingualism and Control Aspect of Intelligence: A Study of Persian-speaking Individuals.

The study of the relationship between bilingualism and the possible impact it might have on the control aspect of intelligence of adults in general and preschool children, in particular, has always been the subject of controversy for researchers. This research, following the related findings and gaps in the literature and inspired by Craik and Bialystok's (2005) framework, tries to divulge whether bilingualism could be related to the control aspect of intelligence. As our secondary goal, we also tried to see whether there are correlations between different tests assessing control. In this descriptive correlational study, via the convenience sampling method, we selected our participants. Methods: In doing so, 10 age-gender-matched Mazandarani-speaking monolinguals and the same matched bilinguals have been selected. Moreover, the literacy and socioeconomic status of subjects have been controlled. The tests for assessing subjects' executive control included day-night Stroop, the dimensional change card sort (DCCS), test of variables of attention (TOVA), and the computerized attention network test (ANT). Our participants' performance in language proficiency task as well as control tasks was demonstrated. Furthermore, via the conduction of the Pearson correlation statistics, the relationships between the participants' performance in diverse control tasks and language task were investigated. The results showed that bilinguals outperform monolinguals in all control tests except DCCS. Bilingualism could provide children with an executive control advantage promoting them in tasks demanding thought and action control. Language proficiency is not necessarily related to an advanced attentional control.Mazandarani-speaking monolinguals and bilinguals perform equally in the "knowledge" domain of intelligence.Bilinguals' performance in the control task of intelligence was better than their monolingual counterparts.It was corroborated that intelligence is not a homogeneous psychological construct.Generally, in tasks requiring suppression, bilinguals outperform monolinguals. The investigation of the relationship between control aspect of intelligence and the statue of monolingualism or bilingualism has always been the subject of controversy. "Control" aspect signifies the ability of an individual to suppress the provoking stimuli, that is, to ignore the external stimuli in favor of an intended linguistic or non-linguistic element. Our major objective in this research was to compare the performance of Mazandarani-speaking bilinguals and monolinguals in diverse control as well as vocabulary assessment tasks. Specifically, we wanted to see whether bilingual children could perform better than monolingual children in tasks requiring the inhibition capability. Furthermore, we attempted to investigate whether there were correlations between different control assessment tasks. Extending previous researches, we administered two additional cognitive tasks, namely, test of variables of attention (TOVA) and day and night task to evaluate our participants' control capability. In linguistic (vocabulary) proficiency assessment task, Peabody picture vocabulary task (PPVT), we did not find bilinguals' advantage over monolinguals. In contrast, bilinguals outperformed their monolingual counterparts in all other control tasks except dimensional change card sort (DCCS). Meanwhile, strong correlations between most control tasks were observed. The results corroborated that bilingual children outperformed their monolingual counterparts in tasks demanding strong thought and action control. Also, the conduction of most control tasks might be more challenging for monolinguals, and bilingual children, thanks to their linguistic statutes, could ignore irrelevant response or stimuli more easily, and perform better in the majority of cognitive control demanding tasks.

A Maximum Divergence Approach to Optimal Policy in Deep Reinforcement Learning.

Model-free reinforcement learning algorithms based on entropy regularized have achieved good performance in control tasks. Those algorithms consider using the entropy-regularized term for the policy to learn a stochastic policy. This work provides a new perspective that aims to explicitly learn a representation of intrinsic information in state transition to obtain a multimodal stochastic policy, for dealing with the tradeoff between exploration and exploitation. We study a class of Markov decision processes (MDPs) with divergence maximization, called divergence MDPs. The goal of the divergence MDPs is to find an optimal stochastic policy that maximizes the sum of both the expected discounted total rewards and a divergence term, where the divergence function learns the implicit information of state transition. Thus, it can provide better-off stochastic policies to improve both in robustness and performance in a high-dimension continuous setting. Under this framework, the optimality equations can be obtained, and then a divergence actor-critic algorithm is developed based on the divergence policy iteration method to address large-scale continuous problems. The experimental results, compared to other methods, show that our approach achieved better performance and robustness in the complex environment particularly. The code of DivAC can be found in https://github.com/yzyvl/DivAC.

A Human-Adaptive Model for User Performance and Fatigue Evaluation during Gaze-Tracking Tasks

Eye gaze interfaces are an emerging technology that allows users to control graphical user interfaces (GUIs) simply by looking at them. However, using gaze-controlled GUIs can be a demanding task, resulting in high cognitive and physical load and fatigue. To address these challenges, we propose the concept and model of an adaptive human-assistive human–computer interface (HA-HCI) based on biofeedback. This model enables effective and sustainable use of computer GUIs controlled by physiological signals such as gaze data. The proposed model allows for analytical human performance monitoring and evaluation during human–computer interaction processes based on the damped harmonic oscillator (DHO) model. To test the validity of this model, the authors acquired gaze-tracking data from 12 healthy volunteers playing a gaze-controlled computer game and analyzed it using odd–even statistical analysis. The experimental findings show that the proposed model effectively describes and explains gaze-tracking performance dynamics, including subject variability in performance of GUI control tasks, long-term fatigue, and training effects, as well as short-term recovery of user performance during gaze-tracking-based control tasks. We also analyze the existing HCI and human performance models and develop an extension to the existing physiological models that allows for the development of adaptive user-performance-aware interfaces. The proposed HA-HCI model describes the interaction between a human and a physiological computing system (PCS) from the user performance perspective, incorporating a performance evaluation procedure that interacts with the standard UI components of the PCS and describes how the system should react to loss of productivity (performance). We further demonstrate the applicability of the HA-HCI model by designing an eye-controlled game. We also develop an analytical user performance model based on damped harmonic oscillation that is suitable for describing variability in performance of a PC game based on gaze tracking. The model’s validity is tested using odd–even analysis, which demonstrates strong positive correlation. Individual characteristics of users established by the damped oscillation model can be used for categorization of players under their playing skills and abilities. The experimental findings suggest that players can be categorized as learners, whose damping factor is negative, and fatiguers, whose damping factor is positive. We find a strong positive correlation between amplitude and damping factor, indicating that good starters usually have higher fatigue rates, but slow starters have less fatigue and may even improve their performance during play. The proposed HA-HCI model and analytical user performance models provide a framework for developing an adaptive human-oriented HCI that enables monitoring, analysis, and increased performance of users working with physiological-computing-based user interfaces. The proposed models have potential applications in improving the usability of future human-assistive gaze-controlled interface systems.

Reasoning and cognitive control, fast and slow

Abstract Influential ‘fast-and-slow’ dual process models suggest that sound reasoning requires the correction of fast, intuitive thought processes by slower, controlled deliberation. Recent findings with high-level reasoning tasks started to question this characterization. Here we tested the generalizability of these findings to low-level cognitive control tasks. More specifically, we examined whether people who responded accurately to the classic Stroop and Flanker tasks could also do so when their deliberate control was minimized. A two-response paradigm, in which people were required to give an initial ‘fast’ response under time–pressure and cognitive load, allowed us to identify the presumed intuitive answer that preceded the final ‘slow’ response given after deliberation. Across our studies, we consistently find that correct final Stroop and Flanker responses are often non-corrective in nature. Good performance in cognitive control tasks seems to be driven by accurate ‘fast’ intuitive processing, rather than by ‘slow’ controlled correction of these intuitions. We also explore the association between Stroop and reasoning performance and discuss implications for the dual process view of human cognition.