Abstract
From matters of survival like chasing prey, to games like football, the problem of intercepting a target that moves in the horizontal plane is ubiquitous in human and animal locomotion. Recent data show that walking humans turn onto a straight path that leads a moving target by a constant angle, with some transients in the target-heading angle. We test four control strategies against the human data: (1) pursuit, or nulling the target-heading angle β, (2) computing the required interception angle \({\hat{\beta},}\) (3) constant target-heading angle, or nulling change in the target-heading angle \({\dot{\beta},}\) and (4) constant bearing, or nulling change in the bearing direction of the target \({\dot{\psi},}\) which is equivalent to nulling change in the target-heading angle while factoring out the turning rate \({(\dot{\beta} - \dot{\phi}).}\) We show that human interception behavior is best accounted for by the constant bearing model, and that it is robust to noise in its input and parameters. The models are also evaluated for their performance with stationary targets, and implications for the informational basis and neural substrate of steering control are considered. The results extend a dynamical systems model of human locomotor behavior from static to changing environments.
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Notes
This assumes that the midline is coincident with the locomotor axis, but exceptions include a “crabbing” gait for terrestrial animals, a crosswind for aerial animals, and a crosscurrent for aquatic animals.
This assumes that the observer is not rotating. It follows from the basic law of optic flow (Nakayama and Loomis 1974) that the target’s angular velocity is proportional to the sine of the target-heading angle and inversely proportional to distance.
The use of an allocentric reference axis to define heading is for convenience of analysis. The perceptual input to the agent is the target-heading angle β = φ− ψ m .
We assume that v r is positive in the direction extending from the agent to the target, such that v r,a > 0 and v r,m < 0 in Fig. 1b
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Acknowledgments
This research was supported by the National Eye Institute (EY10923), National Institute of Mental Health (K02 MH01353) and the National Science Foundation (NSF 9720327).
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Appendix
Appendix
The appendix describes the methods used in the human experiments and the model simulations.
Human data
The human data were collected in the Virtual Environment Navigation Lab at Brown University (Spiro 2001; for details, see Fajen and Warren 2004). Eight volunteers walked in a 12 × 12 m2 area while wearing a head mounted display (Kaiser Proview 80, field of view 60° H × 40° V). Head position and orientation were recorded with an ultrasound/inertial tracking system (Intersense IS-900) at 60 Hz. The virtual environment was generated on a graphics workstation (SGI Onyx2 IR) and presented stereoscopically at 60 frames/s, with a latency of approximately 50–70 ms (3–4 frames). The target was a marble-textured cylinder (2.5 m tall, 0.1 m radius) that moved horizontally at a speed of 0.6 m/s. After the participant walked 1 m in a specified direction, the target appeared at a distance of 3 m along the z axis, either directly in front of the participant at 0° (Center condition) or 25° to the left of the participant’s initial heading (Side condition). It either moved rightward perpendicular to the initial heading (Cross condition), approached at an angle of 30° from the perpendicular (approach), or retreated at an angle of 30° (retreat). These conditions were mirrored left/right and the data collapsed. In the No Background condition, the target moved in empty black space; in the Background condition, the target moved in a room with random-textured floor, walls, and ceiling. There were 10 trials in each condition, blocked by Background and randomized within blocks. Head position in x and z was filtered (zero-lag, 0.6 Hz cutoff) and the direction of motion (φ) was computed for each pair of frames. Because the filter compresses data points near the end of the time series, there is an artifactual drop in speed and heading angle. So we truncated the last 500 ms of the filtered time series to eliminate these effects. The time series of target-heading angle (β) for each trial was normalized to a length of 25 data points by sub-sampling, and the mean time series was computed in each condition.
Model simulations
The method used to simulate each model will be illustrated using model #4 (null -\({\dot{\psi}_{m};}\) Eq. 5a, b). The agent’s angular acceleration is a function of the agent’s rate of rotation \({(\dot{\phi}),}\) the change in allocentric direction of the target \({(\dot{\psi}_{m}),}\) and the target distance \({d_{m}. \dot{\psi}_{m}}\) can be expressed as a function of the agent’s position (x a , z a ) and speed (v x,a, v z,a), and the target’s position (x m , z m ) and speed (v x,m, v z,m)
Likewise, d m can be expressed as a function of the agent’s position and the target’s position
Locomotion toward a moving target is thus represented as a 6D system, for to predict the agent’s future position we need to know its current heading (y 1 = ϕ), turning rate \({(y_{2} = \dot{\phi}),}\) and position (y 3 = x a , y 4 = z a ), as well as the position of the target (y 5 = x g ; y 6 = z m ) assuming that agent speed (v a ), target speed (v m ), and target direction of motion (γ) are given. Written as a system of first-order differential equations, the full constant bearing model is given by
simulations of Eq. 5a, b (as well as the set of equations corresponding to the other models) were performed in Matlab, using the ode45 integration routine. The model speed was constant at 1.29 m/s, equal to the mean maximum human walking speed during a trial. A run was terminated when the model came within 15 cm of the target, to prevent the target-heading angle from blowing up due to small positional errors near the target. We fit the model to the mean time series of target-heading angle in each condition by searching iteratively for the parameter values that minimized the error in β at each time step across all conditions, using a least-squares criterion. Goodness-of-fit was measured by calculating the rmse between the model β time series and the mean human β time series. We also report the r 2 based on a linear regression of the model and human β time series.
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Fajen, B.R., Warren, W.H. Behavioral dynamics of intercepting a moving target. Exp Brain Res 180, 303–319 (2007). https://doi.org/10.1007/s00221-007-0859-6
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DOI: https://doi.org/10.1007/s00221-007-0859-6