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Cantergi, D., Awasthi, B., & Friedman, J. (2021). Moving objects by imagination? Amount of finger movement and pendulum length determine success in the Chevreul pendulum illusion. Human Movement Science, 80, 102879.
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Thorpe, A., Friedman, J., Evans, S., Nesbitt, K., & Eidels, A. (2022). Mouse Movement Trajectories as an Indicator of Cognitive Workload. International Journal of Human-Computer Interaction, 38(15), 1464–1479.
Abstract: Assessing the cognitive impact of user interfaces is a shared focus of human-computer interaction researchers and cognitive scientists. Methods of cognitive assessment based on data derived from the system itself, rather than external apparatus, have the potential to be applied in a range of scenarios. The current study applied methods of analyzing kinematics to mouse movements in a computer-based task, alongside the detection response task, a standard workload measure. Sixty-five participants completed a task in which stationary stimuli were tar;geted using a mouse, with a within-subjects factor of task workload based on the number of targets to be hovered over with the mouse (one/two), and a between-subjects factor based on whether both targets (exhaustive) or just one target (minimum-time) needed to be hovered over to complete a trial when two targets were presented. Mouse movement onset times were slower and mouse movement trajectories exhibited more submovements when two targets were presented, than when one target was presented. Responses to the detection response task were also slower in this condition, indicating higher cognitive workload. However, these differences were only found for participants in the exhaustive condition, suggesting those in the minimum-time condition were not affected by the presence of the second target. Mouse movement trajectory results agreed with other measures of workload and task performance. Our findings suggest this analysis can be applied to workload assessments in real-world scenarios.
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Park, J., Pazin, N., Friedman, J., Zatsiorsky, V. M., & Latash, M. L. (2014). Mechanical properties of the human hand digits: Age-related differences. Clinical Biomechanics, 29(2), 129–137.
Abstract: Background
Mechanical properties of human digits may have significant implications for the hand function. We quantified several mechanical characteristics of individual digits in young and older adults.
Methods
Digit tip friction was measured at several normal force values using a method of induced relative motion between the digit tip and the object surface. A modified quick-release paradigm was used to estimate digit apparent stiffness, damping, and inertial parameters. The subjects grasped a vertical handle instrumented with force/moment sensors using a prismatic grasp with four digits; the handle was fixed to the table. Unexpectedly, one of the sensors yielded leading to a quick displacement of the corresponding digit. A second-order, linear model was used to fit the force/displacement data.
Findings
Friction of the digit pads was significantly lower in older adults. The apparent stiffness coefficient values were higher while the damping coefficients were lower in older adults leading to lower damping ratio. The damping ratio was above unity for most data in young adults and below unity for older adults. Quick release of a digit led to force changes in other digits of the hand, likely due to inertial hand properties. These phenomena of “mechanical enslaving” were smaller in older adults although no significant difference was found in the inertial parameter in the two groups.
Interpretations
The decreased friction and damping ratio present challenges for the control of everyday prehensile tasks. They may lead to excessive digit forces and low stability of the grasped object.
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Geller, N., Moringen, A., & Friedman, J. (2023). Learning juggling by gradually increasing difficulty vs. learning the complete skill results in different learning patterns. Front Psychol, 14, 1284053.
Abstract: Motor learning is central to sports, medicine, and other health professions as it entails learning through practice. To achieve proficiency in a complex motor task, many hours of practice are required. Therefore, finding ways to speed up the learning process is important. This study examines the impact of different training approaches on learning three-ball cascade juggling. Participants were assigned to one of two groups: practicing by gradually increasing difficulty and elements of the juggling movement (“learning in parts”) or training on the complete skill from the start (“all-at-once”). Results revealed that although the all-at-once group in the early stages of learning showed greater improvement in performance, the “learning in parts” group managed to catch up, even over a relatively short period of time. The lack of difference in performance between the groups at the end of the training session suggests that the choice of training regime (between all-at-once and learning in parts), at least in the short term, can be selected based on other factors such as the learner's preference, practical considerations, and cognitive style.
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Friedman, J., & Korman, M. (2012). Kinematic Strategies Underlying Improvement in the Acquisition of a Sequential Finger Task with Self-Generated vs. Cued Repetition Training. PLoS One, 7(12), e52063.
Abstract: Many motor skills, such as typing, consist of articulating simple movements into novel sequences that are executed faster and smoother with practice. Dynamics of re-organization of these movement sequences with multi-session training and its dependence on the amount of self-regulation of pace during training is not yet fully understood. In this study, participants practiced a sequence of key presses. Training sessions consisted of either externally (Cued) or self-initiated (Uncued) training. Long-term improvements in performance speed were mainly due to reducing gaps between finger movements in both groups, but Uncued training induced higher gains. The underlying kinematic strategies producing these changes and the representation of the trained sequence differed significantly across subjects, although net gains in speed were similar. The differences in long-term memory due to the type of training and the variation in strategies between subjects, suggest that the different neural mechanisms may subserve the improvements observed in overall performance.
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