The visual control of interceptive steering: How do people steer a car to intercept a moving target?

People learn a two-stage control for faster locomotor interception

People can use the constant target-heading (CTH) strategy or the constant bearing (CB) strategy to guide their locomotor interception. But it is still unclear whether people can learn new interception behavior. Here, we investigated how people learn to adjust their steering to intercept targets faster. Participants steered a car to intercept a moving target in a virtual environment similar to a natural open field. Their baseline interceptions were better accounted for by the CTH strategy. After five learning sessions across multiple days, in which participants received feedback about their interception durations, they adopted a two-stage control: a quick initial burst of turning accompanied by an increase of the target-heading angle during early interception was followed by significantly less turning with small changes in target-heading angle during late interception. The target's bearing angle did not only show this two-stage pattern but also changed comparatively little during late interception, leaving it unclear which strategy participants had adopted. In a following test session, the two-stage pattern of participants' turning adjustment and the target-heading angle transferred to new target conditions and a new environment without visual information about an allocentric reference frame, which should preclude participants from using the CB strategy. Indeed, the pattern of the target's bearing angle did not transfer to all the new conditions. These results suggest that participants learned a two-stage control for faster interception: they learned to quickly increase the target-heading angle during early interception and subsequently follow the CTH strategy during late interception.

Characterisation of visual guidance of steering to intercept targets following curving trajectories using Qualitative Inconsistency Detection

This study explored the informational variables guiding steering behaviour in a locomotor interception task with targets moving along circular trajectories. Using a new method of analysis focussing on the temporal co-evolution of steering behaviour and the potential information sources driving it, we set out to invalidate reliance on plausible informational candidates. Applied to individual trials rather than ensemble averages, this Qualitative Inconsistency Detection (QuID) method revealed that steering behaviour was not compatible with reliance on information grounded in any type of change in the agent-centred target-heading angle. First-order changes in the environment-centred target's bearing angle could also not adequately account for the variations in behaviour observed under the different experimental conditions. Capturing the observed timing of unfolding steering behaviour ultimately required a combination of (velocity-based) first-order and (acceleration-based) second-order changes in bearing angle. While this result may point to reliance on fractional-order based changes in bearing angle, the overall importance of the present findings resides in the demonstration of the necessity to break away from the existing practice of trying to fit behaviour into a priori postulated functional strategies based on categorical differences between operative heuristic rules or control laws.

Visual guidance of locomotor interception is based on nulling changes in target bearing (not egocentric target direction nor target-heading angle)

In two experiments we studied how participants steer to intercept uniformly moving targets in a virtual driving task under hypotheses-differentiating conditions of initial target eccentricity and target motion. In line with our re-analysis of findings from earlier studies, in both experiments the observed interception behavior could not be understood as resulting from reliance on (changes in) egocentric target direction nor from reliance on (changes in) target-heading angle. The overall pattern of results observed was however compatible with a control strategy based on nulling changes in the target's bearing angle. The presence of reversals in movement direction under specific combinations of target eccentricity and motion conditions indicated that the information used was not purely rate-of-change (i.e., first-order) based but carried traces of an influence of initial target position. In Experiment 2 we explicitly tested the potential role of early reliance on perceived egocentric target direction by examining the effects of a 10° rotation of the visual scene (i.e., of both target and environment). While such a rotation gave rise to minor changes in the moment of initiation of the first steering action, contrary to predictions it did not affect the characteristics of the direction-reversal phenomenon. We conclude that the visual guidance of locomotor interception is best understood as resulting from nulling changes in the target's bearing angle, with such nulling perhaps best conceived as being fractional-order (rather integer-order) driven.

Gravity and Known Size Calibrate Visual Information to Time Parabolic Trajectories

Catching a ball in a parabolic flight is a complex task in which the time and area of interception are strongly coupled, making interception possible for a short period. Although this makes the estimation of time-to-contact (TTC) from visual information in parabolic trajectories very useful, previous attempts to explain our precision in interceptive tasks circumvent the need to estimate TTC to guide our action. Obtaining TTC from optical variables alone in parabolic trajectories would imply very complex transformations from 2D retinal images to a 3D layout. We propose based on previous work and show by using simulations that exploiting prior distributions of gravity and known physical size makes these transformations much simpler, enabling predictive capacities from minimal early visual information. Optical information is inherently ambiguous, and therefore, it is necessary to explain how these prior distributions generate predictions. Here is where the role of prior information comes into play: it could help to interpret and calibrate visual information to yield meaningful predictions of the remaining TTC. The objective of this work is: (1) to describe the primary sources of information available to the observer in parabolic trajectories; (2) unveil how prior information can be used to disambiguate the sources of visual information within a Bayesian encoding-decoding framework; (3) show that such predictions might be robust against complex dynamic environments; and (4) indicate future lines of research to scrutinize the role of prior knowledge calibrating visual information and prediction for action control.