Control Modification of Grasp Force Covaries Agency and Performance on Rigid and Compliant Surfaces

Abstract

This study investigated how modifications in the display of a computer trace under user control of grasp forces can co-modulate agency (perception of control) and performance of grasp on rigid and compliant surfaces. We observed positive correlation (p < 0.01) between implicit agency, measured from time-interval estimation for intentional binding, and grasp performance, measured by force-tracking error, across varying control modes for each surface type. The implications of this work are design directives for cognition-centered device interfaces for rehabilitation of grasp after neurotraumas such as spinal cord and brain injuries while considering if grasp interaction is rigid or compliant. These device interfaces should increase user integration to virtual reality training and powered assistive devices such as exoskeletons and prostheses. The modifications in control modes for this study included changes in force magnitude, addition of mild noise, and a measure of automation. Significant differences (p < 0.001) were observed for each surface type across control modes with metrics for implicit agency, performance, and grasp control efficiency. Explicit agency, measured from user survey responses, did not exhibit significant variations in this study, suggesting implicit measures of agency are needed for identifying co-modulation with grasp performance. Grasp on the compliant surface resulted in greater dependence of performance on agency and increases in agency and performance with the addition of mild noise. Noise in conjunction with perceived freedom at a flexible surface may have amplified visual feedback responses. Introducing automation in control decreased agency and performance for both surfaces, suggesting the value in continuous user control of grasp. In conclusion, agency and performance of grasp can be co-modulated across varying modes of control, especially for compliant grasp actions. Future studies should consider reliable measures of implicit agency, including physiological recordings, to automatically adapt rehabilitation interfaces for better cognitive engagement and to accelerate functional outcomes.

Hand dominance in the performance and perceptions of virtual reach control

“…In this way, action initiation is tightly coupled with the sensory consequence. In this study, we follow a template from our previous works (Nataraj et al, 2020a; Nataraj et al, 2020b; Nataraj & Sanford, 2021 ) whereby we examine an ‘extended’ action with a prolonged movement phase (e.g., reach) prior to a terminating action (e.g., grasp). This approach was necessary to allow participants to ‘experience’ the effects of each control mode and modulate perception before the grasp action, which ultimately incited the sound beep.…”

“…The perceived time-intervals were shorter after voluntary actions, which is assumed to be associated with more control and a perceived sense of agency. While recent studies have suggested a disconnect between binding and intentional action ( Kirsch et al, 2019 ; Suzuki et al, 2019 ), multisensory causal binding reflected through varied perceptions in time has an apparent positive link to hand functions performed through computerized interfaces (Nataraj et al, 2020a; Nataraj et al, 2020b; Nataraj & Sanford, 2021 ). As such, time-perception metrics may still be a window into sensory-based motor learning ( Adamovich et al, 2009 ; Sanford et al, 2020 ) as done with VR rehabilitation.…”

Understanding the effects of hand design on embodiment in virtual reality

“…Self-report is a subjective way to evaluate the sense of agency, which is the only method adopted in most works. Beyond self-report, intentional binding as one implicit method of the agency has been used in several interaction tasks recently (Suzuki et al , 2019; Nataraj et al , 2020 a ; Van den Bussche et al , 2020; Nataraj and Sanford, 2021). The standard basis of the intentional binding paradigm is the estimation of the time interval between an action (e.g., a button press) and a sensory result (e.g., sound feedback) (Suzuki et al , 2019; Nataraj et al , 2020 a , 2020 b ; Van den Bussche et al , 2020; Nataraj and Sanford, 2021).…”

“…Beyond self-report, intentional binding as one implicit method of the agency has been used in several interaction tasks recently (Suzuki et al , 2019; Nataraj et al , 2020 a ; Van den Bussche et al , 2020; Nataraj and Sanford, 2021). The standard basis of the intentional binding paradigm is the estimation of the time interval between an action (e.g., a button press) and a sensory result (e.g., sound feedback) (Suzuki et al , 2019; Nataraj et al , 2020 a , 2020 b ; Van den Bussche et al , 2020; Nataraj and Sanford, 2021). It is an implicit way to evaluate the agency by measuring the time perception of the action effect, and participants are asked to estimate the time interval between action and consequence.…”

Evaluating the feeling of control in virtual object translation on 2D interfaces

“…Due to the development of artificial intelligence for signal processing (Wan et al , 2021; Yu et al , 2022), EEG becomes a valuable and popular tool for evaluating the user's experience when interacting with the product or system. Based on EEG analysis, the alpha band has been proven to be highly related with SoA, not only alpha power (Zito et al , 2020; Nataraj and Sanford, 2021), but also alpha coherence (Mathewson et al , 2011). However, little was known in the existing literature on SoA evaluation using EEG data during continuous manipulation (Wen et al , 2017), such as CAD operations.…”