scholarly journals Estimation of time to contact in lateral motion and approach motion

2018 ◽  
Author(s):  
Asieh Daneshi
Keyword(s):  
Mathematical Analysis ◽  
Human Subjects ◽  
Dynamic Environments ◽  
Lateral Motion ◽  
Movement Initiation ◽  
Time To Contact ◽  
Estimation Of Time

The ability to estimate precisely the time to contact (TTC) of the objects is necessary for planning actions in dynamic environments. However, this ability is not the same for all kinds of movement. Sometimes tracking an object and estimating its TTC is easy and accurate and sometimes it is not. In this study, we asked human subjects to estimate TTC of an object in lateral motion and approach motion. The object became invisible shortly after movement initiation. The results proved that TTC estimation for lateral motion is more accurate than for approach motion. We used mathematical analysis to show why humans are better in estimating TTC for lateral motion than for approach motion.

scholarly journals The accuracy of estimating time to contact in transversal motion and head-on motion

2018 ◽  
Author(s):  
Asieh Daneshi ◽  
Hamed Azarnoush ◽  
Farzad Towhidkhah ◽  
Delphine Tranvouez-Bernardin ◽  
Jocelyn Faubert
Keyword(s):  
Mathematical Model ◽  
Fast Moving ◽  
Dynamic Environments ◽  
Constant Speed ◽  
Moving Target ◽  
Movement Initiation ◽  
Time To Contact ◽  
Everyday Activities ◽  
Transversal Motion ◽  
Different Levels

The ability to estimate precisely the time to contact (TTC) of the objects is necessary for planning actions in dynamic environments. TTC estimation is involved in many everyday activities with different levels of difficulty. Although tracking a ball that moves at a constant speed and estimating the time it reaches a non-moving target can be fairly simple, tracking a fast moving car with inconsistent speeds and estimating the time it reaches another car is rather difficult. In this study, we asked participants to estimate TTC of an object in transversal and head-on motions. The object became invisible shortly after movement initiation. The results demonstrated that TTC estimation for transversal motion is more accurate than for head-on motion. We present a mathematical model to explain why humans are better in estimating TTC for transversal motion than for head-on motion.

scholarly journals Effect of Changing Differential Perspective on Estimation of Time-to-contact

2011 ◽  
Vol 65 (6) ◽  
pp. 821-824
Author(s):  
Masahiro Ishii ◽  
Yoshihisa Fukumoto
Keyword(s):  
Time To Contact ◽  
Estimation Of Time

Estimation of Time-to-Contact from Retinal Flow

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 169-169
Author(s):  
M G Harris
Keyword(s):  
Prior Knowledge ◽  
Human Performance ◽  
Rotational Component ◽  
Time To Contact ◽  
Focus Of Expansion ◽  
The World ◽  
New Models ◽  
Smooth Planes ◽  
Estimation Of Time ◽  
Rotation Component

We investigated four models for estimating time-to-contact (TTC) from retinal flow. Lee's model can deal with sparse flow but fails if the flow contains a rotational component. Koenderink's model, based on div, can deal with rotation but fails if the flow is sparse or if the world does not vary coherently in depth. Two new models were developed by representing retinal flow as the sum of an expansion and a rotation component. The first operates on pairs of points and can deal with sparse flow but fails if the world does not vary coherently in depth. Uniquely, this model provides TTC estimates without prior knowledge of either the focus of expansion (FOE) or focus of rotation (FOR). The second model estimates both the FOE and the FOR and then operates on a point-by-point basis. This model can deal with incoherent depth variations. We compared human performance with these different model properties by requiring subjects to estimate FOE and TTC from random-dot kinematograms. We used kinematograms depicting smooth planes and random 3-D clouds of points, and systematically varied the density of the flow. Performance was not substantially reduced by sparse flow or by incoherent depth, which argues against Koenderink's and the first of our own models. Performance remained good when rotation was added to the flow, which argues against Lee's model. Overall, the data favour a model that first decomposes flow into expansion and rotation components and then estimates TTC on a point-by-point basis.

Organizing principles for single joint movements: V. Agonist-antagonist interactions

1992 ◽  
Vol 67 (6) ◽  
pp. 1417-1427 ◽  
Author(s):  
G. L. Gottlieb ◽  
M. L. Latash ◽  
D. M. Corcos ◽  
T. J. Liubinskas ◽  
G. C. Agarwal
Keyword(s):  
Muscle Activity ◽  
Muscle Activation ◽  
Human Subjects ◽  
Single Equation ◽  
Movement Speed ◽  
Joint Movement ◽  
Movement Initiation ◽  
Muscle Activation Patterns ◽  
Movement Distance ◽  
Task Conditions

1. Normal human subjects made discrete elbow flexions in the horizontal plane under different task conditions of initial or final position, inertial loading, or instruction about speed. We measured joint angle, acceleration, and electromyographic signals (EMGs) from two agonist and two antagonist muscles. 2. For many of the experimental tasks, the latency of the antagonist EMG burst was strongly correlated with parameters of the first agonist EMG burst defined by a single equation, expressed in terms of the agonist's hypothetical excitation pulse. Latency is proportional to the ratio of pulse duration to pulse intensity, making it proportional to movement distance and inertial load and inversely proportional to planned movement speed. However, these rules are not sufficient to define the timing of every possible single joint movement. 3. For movements described by the speed-insensitive strategy, the quantity of both antagonist and agonist muscle activity can be uniformly associated with selected kinetic measures that incorporate muscle force-velocity relations. 4. For movements collectively described by the speed-sensitive strategy, (i.e., that have direct or indirect constraints on speed), no single rule can describe all the combinations of agonist-antagonist coordination that are used to perform these diverse tasks. 5. Estimates of joint viscosity were made by calculating the amount of velocity-dependent torque used to terminate movements on target. These estimates are similar to those that have previously been made of limb viscosity during postural maintenance. They imply that a significant component of muscle activity must be used to overcome these forces. 6. These and previous results are all consistent with a dual-strategy hypothesis for those single-joint movements that are sufficiently fast to require pulse-like muscle activation patterns. The major features of such patterns (pulse intensities, durations, and latencies) are determined by central commands programmed in advance of movement initiation. The selection between speed-insensitive or speed-sensitive rules of motoneuron pool excitation is implicitly specified by the nature of speed constraints of the movement task.

Estimation of time-to-contact from Tau-margin and statistical analysis of behavior

2016 ◽  
Author(s):  
Angel Juan Sanchez Garcia ◽  
Homero Vladimir Rios Figueroa ◽  
Antonio Marin Hernandez ◽  
Maria Karen Cortes Verdin ◽  
Gerardo Contreras Vega
Keyword(s):  
Statistical Analysis ◽  
Time To Contact ◽  
Estimation Of Time

scholarly journals Non-selective inhibition of the motor system following unexpected and expected infrequent events

2020 ◽  
Author(s):  
Carly Iacullo ◽  
Darcy A. Diesburg ◽  
Jan R. Wessel
Keyword(s):  
Motor System ◽  
Human Subjects ◽  
Reaction Time Task ◽  
Selective Inhibition ◽  
Motor Inhibition ◽  
Simple Reaction Time Task ◽  
Movement Initiation ◽  
Sensory Stimuli ◽  
Behavioral Adaptations ◽  
Environmental Events

AbstractMotor inhibition is a key control mechanism that allows humans to rapidly adapt their actions in response to environmental events. One of the hallmark signatures of rapidly exerted, reactive motor inhibition is the non-selective suppression of cortico-spinal excitability (CSE): unexpected sensory stimuli lead to a suppression of CSE across the entire motor system, even in muscles that are inactive. Theories suggest that this reflects a fast, automatic, and broad engagement of inhibitory control, which facilitates behavioral adaptations to unexpected changes in the sensory environment. However, it is an open question whether such non-selective CSE suppression is truly due to the unexpected nature of the sensory event, or whether it is sufficient for an event to be merely infrequent (but not unexpected). Here, we report data from two experiments in which human subjects experienced both unexpected and expected infrequent events during a simple reaction time task while CSE was measured from a task-unrelated muscle. We found that expected infrequent events can indeed produce non-selective CSE suppression – but only when they occur during movement initiation. In contrast, unexpected infrequent events produce non-selective CSE suppression even in the absence of movement initiation. Moreover, CSE suppression due to unexpected events occurs at shorter latencies compared to expected infrequent events. These findings demonstrate that unexpectedness and stimulus infrequency have qualitatively different suppressive effects on the motor system. They also have key implications for studies that seek to disentangle neural and psychological processes related to motor inhibition and stimulus detection.

scholarly journals Your favorite color makes learning more precise and adaptable

2017 ◽  
Author(s):  
Shiva Farashahi ◽  
Katherine Rowe ◽  
Zohra Aslami ◽  
Daeyeol Lee ◽  
Alireza Soltani
Keyword(s):  
Computational Models ◽  
Human Subjects ◽  
Dynamic Environments ◽  
Parallel Learning ◽  
Feature Based ◽  
Favorite Color ◽  
Individual Features ◽  
Feature Values ◽  
Low Dimensional

AbstractLearning from reward feedback is essential for survival but can become extremely challenging with myriad choice options. Here, we propose that learning reward values of individual features can provide a heuristic for estimating reward values of choice options in dynamic, multidimensional environments. We hypothesized that this feature-based learning occurs not just because it can reduce dimensionality, but more importantly because it can increase adaptability without compromising precision of learning. We experimentally tested this hypothesis and found that in dynamic environments, human subjects adopted feature-based learning even when this approach does not reduce dimensionality. Even in static, low-dimensional environments, subjects initially adopted feature-based learning and gradually switched to learning reward values of individual options, depending on how accurately objects’ values can be predicted by combining feature values. Our computational models reproduced these results and highlight the importance of neurons coding feature values for parallel learning of values for features and objects.

scholarly journals Integration of speed and time for estimating time to contact

2018 ◽  
Vol 115 (12) ◽  
pp. E2879-E2887 ◽  
Author(s):  
Chia-Jung Chang ◽  
Mehrdad Jazayeri
Keyword(s):  
Dynamic Environment ◽  
Tennis Player ◽  
Sources Of Information ◽  
Time To Contact ◽  
Dynamic Stimuli ◽  
Time Estimates ◽  
Temporal Cues ◽  
Timing Mechanisms ◽  
Estimation Of Time ◽  
Different Sources

To coordinate movements with events in a dynamic environment the brain has to anticipate when those events occur. A classic example is the estimation of time to contact (TTC), that is, when an object reaches a target. It is thought that TTC is estimated from kinematic variables. For example, a tennis player might use an estimate of distance (d) and speed (v) to estimate TTC (TTC = d/v). However, the tennis player may instead estimate TTC as twice the time it takes for the ball to move from the serve line to the net line. This latter strategy does not rely on kinematics and instead computes TTC solely from temporal cues. Which of these two strategies do humans use to estimate TTC? Considering that both speed and time estimates are inherently uncertain and the ability of the human brain to combine different sources of information, we hypothesized that humans estimate TTC by integrating speed information with temporal cues. We evaluated this hypothesis systematically using psychophysics and Bayesian modeling. Results indicated that humans rely on both speed information and temporal cues and integrate them to optimize their TTC estimates when both cues are present. These findings suggest that the brain’s timing mechanisms are actively engaged when interacting with dynamic stimuli.

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