Year: 2025
Type: article
Abstract: The rapid evolution of human-technology interaction necessitates continuous sensorimotor adaptation to new digital interfaces and tasks. Mobile typing, defined as text entry on smartphone touchscreens, offers a compelling example of this process, requiring users to adapt fine motor control and coordination to a constrained virtual environment. Aligned with the embodied cognition framework, understanding these digital sensorimotor experiences is crucial. A key theoretical question is whether these skills primarily involve adaptation of existing motor patterns or necessitate de novo learning, a distinction particularly relevant across generations with differing early sensorimotor experiences. This narrative review synthesizes current understanding of the sensorimotor aspects of smartphone engagement and typing skill evaluation methods. It examines touchscreen competence, skill acquisition, diverse strategies employed, and the influence of interface constraints on motor performance, while also detailing various sophisticated performance metrics and analyzing different data collection methodologies. Research highlights that analyzing typing behaviors and their underlying neural correlates increasingly serves as a potential source of behavioral biomarkers. However, while notable progress has been made, the field is still developing, requiring stronger methodological foundations and crucial standardization of metrics and protocols to fully capture and understand the dynamic sensorimotor processes involved in digital interactions. Nevertheless, mobile typing emerges as a compelling model for advancing our understanding of human sensorimotor learning and cognitive function, offering a rich, ecologically valid platform for investigating human-world interaction.
Source: Brain Sciences
Authors: Lorenzo Viviani, Alba Liso, Laila Craighero
Institution: University of Ferrara
Language: English
Topic: Motor Control and Adaptation
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,654
OpenAlex ID: W4414896555