Augmented Go/No-Go Task: Mouse Cursor Motion Measures Improve ADHD Symptom Assessment in Healthy College Students

Leontyev, Anton; Sun, Stanley; Wolfe, Mary Leigh; Yamauchi, Takashi

doi:10.3389/fpsyg.2018.00496

Cited by 16 publications

(18 citation statements)

References 77 publications

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“…In this framework, mouse-tracking procedures have been used to investigate different domains such as language, decision-making, learning and social cognition (see Freeman [ 24 ] for a review). Moreover, recent studies evaluated different mouse movement measures (e.g., velocity or acceleration in the mouse-cursor motion) obtained during GNG and SST tasks in patients with attention-deficit/hyperactivity disorder [ 25 , 26 ]. In this perspective, the characterization of movement trajectories by mouse-tracking procedures appears as a potentially valuable tool to explore the respective contribution of proactive and reactive mechanisms in response inhibition [ 24 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Mouse Tracking to Explore Motor Inhibition Processes in Go/No-Go and Stop Signal Tasks

et al. 2020

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Response inhibition relies on both proactive and reactive mechanisms that exert a synergic control on goal-directed actions. It is typically evaluated by the go/no-go (GNG) and the stop signal task (SST) with response recording based on the key-press method. However, the analysis of discrete variables (i.e., present or absent responses) registered by key-press could be insufficient to capture dynamic aspects of inhibitory control. Trying to overcome this limitation, in the present study we used a mouse tracking procedure to characterize movement profiles related to proactive and reactive inhibition. A total of fifty-three participants performed a cued GNG and an SST. The cued GNG mainly involves proactive control whereas the reactive component is mainly engaged in the SST. We evaluated the velocity profile from mouse trajectories both for responses obtained in the Go conditions and for inhibitory failures. Movements were classified as one-shot when no corrections were observed. Multi-peaked velocity profiles were classified as non-one-shot. A higher proportion of one-shot movements was found in the SST compared to the cued GNG when subjects failed to inhibit responses. This result suggests that proactive control may be responsible for unsmooth profiles in inhibition failures, supporting a differentiation between these tasks.

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Section: Introductionmentioning

confidence: 99%

Mouse Tracking to Explore Motor Inhibition Processes in Go/No-Go and Stop Signal Tasks

et al. 2020

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“…They also showed that the statistical results of analyzing EEG and GSR together were more reliable than analyzing them individually. Leontyev et al combined user response time and mouse movement features with machine learning technics and found an improvement in the accuracy of predicting attention-deficit/hyperactivity disorder (ADHD) [9][10][11]. Yamauchi et al combined behavioral measures and multiple mouse motion features to better predict people's emotions and cognitive conflict in computer tasks [12][13][14][15].…”

Section: Related Workmentioning

confidence: 99%

Multimodal-Multisensory Experiments

Razavi¹,

Yamauchi²,

Janfaza³

et al. 2020

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The human mind is multimodal. Yet most behavioral studies rely on century-old measures of behavior—task accuracy and latency (response time). Multimodal and multisensory analysis of human behavior creates a better understanding of how the mind works. The problem is that designing and implementing these experiments is technically complex and costly. This paper introduces versatile and economical means of developing multimodal-multisensory human experiments. We provide an experimental design framework that automatically integrates and synchronizes measures including electroencephalogram (EEG), galvanic skin response (GSR), eye-tracking, virtual reality (VR), body movement, mouse/cursor motion and response time. Unlike proprietary systems (e.g., iMotions), our system is free and open-source; it integrates PsychoPy, Unity and Lab Streaming Layer (LSL). The system embeds LSL inside PsychoPy/Unity for the synchronization of multiple sensory signals—gaze motion, electroencephalogram (EEG), galvanic skin response (GSR), mouse/cursor movement, and body motion—with low-cost consumer-grade devices in a simple behavioral task designed by PsychoPy and a virtual reality environment designed by Unity. This tutorial shows a step-by-step process by which a complex multimodal-multisensory experiment can be designed and implemented in a few hours. When conducting the experiment, all of the data synchronization and recoding of the data to disk will be done automatically.

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“…Studies have also shown that mouse movement measures can capture semantic incongruency that is processed subliminally (Xiao & Yamauchi, 2014; they even allow automated recognition of emotion, gender and feelings of computer users (Yamauchi & Xiao, 2018;Yamauchi & Bowman, 2014), and are applicable for the assessment of psychopathology, such as attention-deficit/hyperactivity disorder (Leontyev, Sun, Wolfe, & Yamauchi, 2018;Leontyev, Yamauchi, & Razavi, 2019).…”

Section: Figure 1 Comparison Between Keypress (Upper Panel) and Mousmentioning

confidence: 99%

“…Stop-signal task requires individuals to react in some trials ("go" trials) and to inhibit the reaction in other trials ("stop" trials). Recent studies show , Leontyev et al, 2018, Leontyev, Yamauchi & Razavi, 2019) that mouse movement measures provide a stronger relationship with questionnaire-based metrics of impulsivity than conventional stop-signal task measures including SSRT. Among these measures are mean maximum velocity and acceleration of mouse movement.…”

Section: Figure 1 Comparison Between Keypress (Upper Panel) and Mousmentioning

confidence: 99%

Discerning Mouse Trajectory Features with Drift Diffusion Model

Leontyev¹,

Yamauchi²

2020

Preprint

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In decision-making tasks, participants are commonly instructed to respond by pressing a key. This design provides information about how fast and accurate an individual can respond but does not allow a researcher to directly study the process of response selection. Recently, mouse cursor tracking has been applied to offset this limitation. However, it is unclear whether RT/accuracy-based measures and mouse movement features (e.g. velocity) assess the same cognitive processes. To clarify the relationship between mouse movement features and cognitive processes, we developed mouse-tracking versions of the stop-signal and delay discounting tasks and investigated a) whether people respond similarly in the tasks with traditional design and tasks employing mouse cursor tracking; b) which features of the decision-making process mouse movement measures correspond with. Although participants responded similarly in tasks with traditional and mouse tracking design, only a few mouse movement features were related to the elements of the decision-making process.

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Augmented Go/No-Go Task: Mouse Cursor Motion Measures Improve ADHD Symptom Assessment in Healthy College Students

Cited by 16 publications

References 77 publications

Mouse Tracking to Explore Motor Inhibition Processes in Go/No-Go and Stop Signal Tasks

Mouse Tracking to Explore Motor Inhibition Processes in Go/No-Go and Stop Signal Tasks

Multimodal-Multisensory Experiments

Discerning Mouse Trajectory Features with Drift Diffusion Model

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