In goal-directed behaviors, individuals are often required to perform movements under tight time constraints. This is especially pertinent to many sports, where decisions are made under severe time constraints, e.g., a defender responding to an opponent's dribble in ball games such as soccer or basketball. In these situations, motor plans are selected considering multiple possibilities for motor goals; however, the action often needs to be initiated and continued before a single motor goal is defined. Several studies have investigated motor planning for multiple potential goals using the “Go-before-you-know paradigm,” in which a participant is required to launch a movement with multiple potential goals and the final goal is revealed after movement development (i.e., movement onset or reaching a given threshold) 1–10. In general, when there are multiple potential goals at movement onset, the initial movements were found to be directed toward the average direction of the potential targets 1–3, 5,6,11,12
The performance of a goal-directed movement is traditionally conceptualized in two discrete phases: the planning phase and the execution phases 13. Based on this discrete view, several models have been created to solve the redundancy problem (for instance, when reaching for an object, there are numerous hand trajectories, joint movements, and muscle activation patterns that can be executed on a single target). To solve this problem, optimization based on various costs, such as jerks 14, torque-changes 15, and variabilities of the final hand position 16, have been proposed, and these perspectives have contributed greatly to our understanding of human motor control principles. However, although these traditional serial models 17 assume that we first select a goal and then specify and prepare the corresponding goal-directed movement, selection and specification can operate as a continuous and parallel processes 18,19. For example, the behavioral findings from recent studies have revealed that the movement trajectories in the simultaneous presence of multiple potential targets are directed in directions where no target exists 1. Furthermore, human and primate neurophysiology studies have shown that competing reach targets induce separate neural representations corresponding to each target in sensory-motor brain areas before one of the targets is selected 20–23.
Many behavioral studies using go-before-you-know tasks (i.e., tasks in which participants are presented with a large number of potential reaching targets simultaneously before knowing the final target location and are required to initiate a reaching movement to a competing target) have confirmed that humans initiate reaching movements toward the average of the potential targets 10,12,24. However, such averaging behavior is less likely to be selected when the advantage of the strategy disappears, such as when the distance between targets is large 7, when severe constraints on speed are imposed 8, or when the information on the targets is updated in stages 25. These findings suggest that the averaging behavior in motor planning in the presence of multiple goals may not exist as a control policy in itself, but is rather a behavior that reflects optimization for task accomplishment.
In a go-before-you-know-paradigm, the effect of time constraints assigned to potential targets on motor planning has not been sufficiently investigated. The time constraint is a critical factor because the motor target is often time-constrained, and the time constraint affects the possible movement dynamics. In addition, the effect of asymmetry between potential targets on motor planning has not yet been discussed. In many situations, different motor targets have specific time constraints, and "how do humans execute movements in situations where targets with different time constraints exist simultaneously?" seems to be an essential question. Therefore, in the current study, we investigated how to adjust the initial movements according to the time constraints assigned to each potential target.
The time constraint is an effective experimental control variable for examining how asymmetry in potential targets is reflected in motor planning. Although this approach of experimentally manipulating time constraints is similar to that used by Wong & Haith (2017) 8, who restricted the movement velocity, or that used by Hesse et al. (2020) 26, who manipulated the positioning of the target to manipulate the optimality of the averaging strategy, two reasons exist as to why manipulating time constraints may be effective for further understanding of behaviors. First, unlike constraints on movement speed, time constraints do not directly constrain the initial movement itself. Since the participants themselves are free to choose the initial movement, manipulation of the time constraint would be more appropriate for examining the adaptability of the motor planning to the given target information. Next, specific time constraints can be assigned to each target when manipulating the time constraint, thus creating an asymmetry in the temporal values of potential targets. Such manipulation of the time constraint can be used to examine how humans plan their movements to account for asymmetric temporal values of potential targets, while this approach is difficult to employ in studies on movement velocity.
Although motor planning for potential targets has been suggested to reflect optimization of success probability 8, the extent of this optimality has not been clarified. The direction and velocity of the initial movement, as well as the corrective actions after the movement, are considered to reflect the strategy selected in advance by the participants. In this case, an important decision about the strategy for two potential targets is whether to focus on one target (i.e., a predetermined strategy) or to take both targets into account (i.e., a choice-reaction strategy). Previous studies confirmed that humans prefer a choice-reaction strategy even in situations with severe spatiotemporal constraints to sufficiently accomplish a task 26,27. A similar bias may exist in the selection of strategies when encountering asymmetric time constraints in the go-before-you-know task. Thus, the present study examined whether pre-determined or choice-reaction strategies can be optimally selected to maximize the success rate under extremely tight time constraints by comparing the performance when two targets are present with the performance when only one target is present.
Therefore, the current study used a go-before-you-know task with two targets to examine how a combination of the time constraints assigned to each target is considered in motor planning. Participants started the movement with the time constraints of each target known in advance, and the final target was specified after the movement onset. Participants were considered successful when they reached the final target within the time constraint. The time constraint for each target was randomly assigned for each trial in the range of 200–1000 ms. Participants were required to maximize the success probability within the set (50 trials). As a control condition, participants also performed trials with only one target before the start of the movement. The kinematic properties (i.e., direction and velocity) and optimality of the planning of the initial movement were tested by examining the variations in the direction and velocity of the initial movement in the double-target condition depending on the time constraints, and by comparing the initial movement and performance in relation to the number of potential targets. More specifically, we first examined the changes in motor patterns related to combinations of time constraints, based on movement trajectories and the bivariable histograms of and kinematic properties of the initial movement. We also categorized the patterns of initiating actions in a data-driven manner using k-means clustering and examined how the ratio of occurrences of each pattern changes depending on the combination of time constraints. In addition, we examined the variation in the initial movement pattern and performance depending on the time constraint and number of targets.