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(1) Goals: The purpose of this study was to analyze the feasibility of using the information obtained from a one-channel electro-encephalography (EEG) signal to control a mouse pointer. We used a low-cost headset, with one dry sensor placed at the FP1 position, to steer a mouse pointer and make selections through a combination of the user’s attention level with the detection of voluntary blinks. There are two types of cursor movements: spinning and linear displacement. A sequence of blinks allows for switching between these movement types, while the attention level modulates the cursor’s speed. The influence of the attention level on performance was studied. Additionally, Fitts’ model and the evolution of the emotional states of participants, among other trajectory indicators, were analyzed. (2) Methods: Twenty participants distributed into two groups (Attention and No-Attention) performed three runs, on different days, in which 40 targets had to be reached and selected. Target positions and distances from the cursor’s initial position were chosen, providing eight different indices of difficulty (IDs). A self-assessment manikin (SAM) test and a final survey provided information about the system’s usability and the emotions of participants during the experiment. (3) Results: The performance was similar to some brain–computer interface (BCI) solutions found in the literature, with an averaged information transfer rate (ITR) of 7 bits/min. Concerning the cursor navigation, some trajectory indicators showed our proposed approach to be as good as common pointing devices, such as joysticks, trackballs, and so on. Only one of the 20 participants reported difficulty in managing the cursor and, according to the tests, most of them assessed the experience positively. Movement times and hit rates were significantly better for participants belonging to the attention group. (4) Conclusions: The proposed approach is a feasible low-cost solution to manage a mouse pointer.

Difficult tasks are commonly equated with complex tasks across many behaviors. Motor task difficulty is traditionally defined via Fitts’ law, using evaluation criteria based on spatial movement constraints. Complexity of data is typically evaluated using non-linear computational approaches. In this project, we investigate the potential to evaluate task difficulty via behavioral (motor performance) complexity in a Fitts-type task. Use of non-linear approaches allows for inclusion of many features of motor actions that are not currently included in the Fitts-type paradigm. Our results indicate that tasks defined as more difficult (using Fitts movement IDs) are not associated with complex motor behaviors; rather, an inverse relationship exists between these two concepts. Use of non-linear techniques allowed for the detection of behavioral differences in motor performance over the entire action trajectory in the presence of action errors and among neutrally co-constrained effectors not detected using traditional Fitts’-type analyses utilizing movement time measures. Our findings indicate that task difficulty may potentially be inferred using non-linear measures, particularly in ecological situations that do not obey the Fitts-type testing paradigm. While we are optimistic regarding these initial findings, further work is needed to assess the full potential of the approach.

Speech motor actions are performed quickly, while simultaneously maintaining a high degree of accuracy. Are speed and accuracy in conflict during speech production? Speed-accuracy tradeoffs have been shown in many domains of human motor action, but have not been directly examined in the domain of speech production. The present work seeks evidence for Fitts' law, a rigorous formulation of this fundamental tradeoff, in speech articulation kinematics by analyzing USC-TIMIT, a real-time magnetic resonance imaging data set of speech production. A theoretical framework for considering Fitts' law with respect to models of speech motor control is elucidated. Methodological challenges in seeking relationships consistent with Fitts' law are addressed, including the operational definitions and measurement of key variables in real-time MRI data. Results suggest the presence of speed-accuracy tradeoffs for certain types of speech production actions, with wide variability across syllable position, and substantial variability also across subjects. Coda consonant targets immediately following the syllabic nucleus show the strongest evidence of this tradeoff, with correlations as high as 0.72 between speed and accuracy. A discussion is provided concerning the potentially limited applicability of Fitts' law in the context of speech production, as well as the theoretical context for interpreting the results.

The tradeoff between speed and accuracy of human movements has been exploited from many different perspectives, such as experimental psychology, workspace design, human–machine interface. This tradeoff is formalized by Fitts’ law, which states a linear relationship between the duration and the difficulty of the movement. The bigger is the required accuracy in reaching a target or farther is the target, the slower has to be the movement. A variety of computational models of neuromusculoskeletal systems have been proposed to pinpoint the neurobiological mechanisms that are involved in human movement. We introduce a neurocomputational model of spinal cord to unveil how the tradeoff between speed and accuracy elicits from the interaction between neural and musculoskeletal systems. Model simulations showed that the speed–accuracy tradeoff is not an intrinsic property of the neuromuscular system, but it is a behavioral trait that emerges from the strategy adopted by the central nervous system for executing faster movements. In particular, results suggest that the velocity of a previous learned movement is regulated by the monosynaptic connection between cortical cells and alpha motoneurons.

In this paper, we propose a novel model—the TWAIN model—to describe the durations of two-step actions in a reach-to-place task in human infants. Previous research demonstrates that infants and adults plan their actions across multiple steps. They adjust, for instance, the velocity of a reaching action depending on what they intend to do with the object once it is grasped. Despite these findings and irrespective of the larger context in which the action occurs, current models (e.g., Fitts’ law) target single, isolated actions, as, for example, pointing to a goal. In the current paper, we develop and empirically test a more ecologically valid model of two-step action planning. More specifically, 61 18-month olds took part in a reach-to-place task and their reaching and placing durations were measured with a motion-capture system. Our model explained the highest amount of variance in placing duration and outperformed six previously suggested models, when using model comparison. We show that including parameters of the first action step, here the duration of the reaching action, can improve the description of the second action step, here the duration of the placing action. This move towards more ecologically valid models of action planning contributes knowledge as well as a framework for assessing human machine interactions. The TWAIN model provides an updated way to quantify motor learning by the time these abilities develop, which might help to assess performance in typically developing human children.

In a cyclical Fitts’ task, hand movements transition from continuous to discrete movements when the Index of Difficulty (ID) increases. Moreover, at high ID (small target), the eyes saccade to and subsequently fixate the targets at every movement, while at low ID (large target) intermittent monitoring is used. By hypothesis, the (periodic) gaze shifts are abandoned for movement times shorter than about 0.350 s due to systemic constraints (i.e., a refractory period and intrinsic latency). If so, the transition in eye and hand movements is independent. To investigate these issues, the present study examined the effects of changing ID via the targets’ width or distance as well as hysteresis in eye–hand coordination. To this aim, 14 participants performed a cyclical Fitts’ task while their hand and eye movements were recorded simultaneously. The results show that the transition in eye–hand synchronization (at 2.87 bit; 0.25 s) and in hand dynamics (at 4.85 bit; 0.81 s) neither co-occurred nor correlated. Some small width vs. distance dissociations and hysteresis effects were found, but they disappeared when eye–hand synchronization was viewed as a function of movement time rather than ID. This confirms that a minimal between-saccade time is the limiting factor in eye–hand synchronization. Additionally, the timing between the start of the hand movement and the saccade appeared to be relatively constant (at 0.15 s) and independent of movement time, implying a constant delay that should be implemented in a dynamical model of eye–hand coordination.

Fitts’ law is used as a performance measurement metric in human–computer interactions. The original formulation implied that movement time was identical for movements with the same value of the index of difficulty under varied movement amplitude and target width. An experiment was designed to test this implication. The result indicates that movement time is related to the index of difficulty when the amplitude is constant. Nowadays, most of the icons in applications are represented as two-dimensional targets. An object of equal width and height is a particular case of a two-dimensional target. This target area could be a factor in a Fitts task and impact the movement time, number of errors, and perceived difficulty. Therefore, the area could replace the target width in the formulation of the index of difficulty. The modified index of difficulty is easy to implement without the complexity of post-calculation. Researchers can design the index of difficulty before the empirical test. This research proposes a modified index of difficulty by varying the target’s area and applying the square-root movement time model simultaneously, which results in an excellent performance with a higher R-square and satisfies the residual normality robustly than the traditional formulation of Fitts’ law.

Under the long time voyage of the ship, finding out the task time regularity of human operator in the command and control system can help the intelligent system to carry out the task intelligent assignment and planning in advance. In order to more accurately simulate the change rule of human-machine interaction of operators during the long time voyage, this study develope a pointing interaction time model based on Fitts’ Law. This model provides accurate data about human cognitive modules for intelligent OODA loop. This study constructed the change function model of parameters a and b of Fitts’ Law in 95 days through experiments. By comparing the real data with the data obtained through the model simulation, the average error of the simulation time is 5.87%. The function model can predict the operation time required for the operator to make a mouse click under the condition of long time voyage.

A central account of cognitive aging is the dedifferentiation among functions due to reduced processing resources. Previous reports contrasting trends of aging across cognitive domains mostly relied on transformed scores of heterogeneous measures. By quantifying the computational load with information entropy in tasks probing motor and executive functions, this study uncovered interaction among age, task, and load as well as associations among the parametric estimates of these factors at the individual level. Specifically, the linear functions between computational load and performance time differed significantly between motor and executive tasks in the young group but not in the elderly group and showed stronger associations for parameters within and between tasks in the elderly group than in the young group. These findings are in line with the dedifferentiation hypothesis of cognitive aging and provide a more principled approach in contrasting trends of cognitive aging across different domains from the information-theoretic perspective.

Fitts’ law predicts the human movement response time for a specific task through a simple linear formulation, in which the intercept and the slope are estimated from the task’s empirical data. This research was motivated by our pilot study, which found that the linear regression’s essential assumptions are not satisfied in the literature. Furthermore, the keystone hypothesis in Fitts’ law, namely that the movement time per response will be directly proportional to the minimum average amount of information per response demanded by the particular amplitude and target width, has never been formally tested. Therefore, in this study we developed an optional formulation by combining the findings from the fields of psychology, physics, and physiology to fulfill the statistical assumptions. An experiment was designed to test the hypothesis in Fitts’ law and to validate the proposed model. To conclude, our results indicated that movement time could be related to the index of difficulty at the same amplitude. The optional formulation accompanies the index of difficulty in Shannon form and performs the prediction better than the traditional model. Finally, a new approach to modeling movement time prediction was deduced from our research results.