Research on Motor Decision-Making under Uncertainty and Time Pressure

Academic Background

In daily life, animals and humans often need to choose the most appropriate action from multiple possible options. However, how to plan and execute these actions under conditions of goal uncertainty and time pressure remains an incomplete question in neuroscience. Traditional views suggest that the brain selects the final action by competing or integrating multiple partially prepared motor plans when faced with goal uncertainty. However, another perspective holds that the brain only selects and optimizes a single motor plan at any given moment, with all decisions being completed prior to motor planning.

To distinguish between these two hypotheses, Samuele Contemori and Timothy J. Carroll designed an experiment to investigate how people plan and execute actions under conditions of goal uncertainty. Specifically, they examined whether people would “average” the motor dynamics of multiple potential actions (motor averaging) or select a single optimized motor plan under time pressure. This study is significant for understanding the neural mechanisms of motor decision-making.

Source of the Paper

This paper was co-authored by Samuele Contemori and Timothy J. Carroll from the Centre for Sensorimotor Performance, School of Human Movement and Nutrition Sciences, The University of Queensland, Australia. The paper was published in January 2025 in the Journal of Neurophysiology under the title “Motor decision-making under uncertainty and time pressure.”

Research Process

Research Design

The study is based on the experimental paradigm of Alhussein and Smith (2021), with some key modifications. Participants were required to initiate a reaching movement toward two opposing targets without knowing the final goal. To increase time pressure, participants had to start the movement within 500 milliseconds after target presentation, with average reaction times controlled at around 250 milliseconds. By applying opposite curl force fields, the researchers associated the dynamic features of the reaching movements with different target directions.

Participants and Experimental Setup

The experiment involved 24 healthy right-handed adults, with 17 completing the full learning task. The experiment used a two-dimensional planar robotic arm (vBot), and participants controlled the arm movements through visual feedback. The targets included a central target and left/right lateral targets, each associated with different curl force fields. Participants performed single-target and double-target reaching tasks across different experimental phases.

Experimental Phases

The experiment was divided into three phases: baseline, training, and testing.

1. Baseline Phase: Participants completed two rounds of single-target familiarization tasks, 45 trials each, followed by double-target tasks.
2. Training Phase: Participants performed seven rounds of single-target tasks, 60 trials each, with random inclusion of channel force field tests.
3. Testing Phase: Participants completed six rounds of tasks, 80 trials each, including single-target and double-target tasks, primarily evaluating the performance of newly learned reaching dynamics under double-target conditions.

Data Analysis

The researchers recorded participants’ kinematic and dynamic data, analyzing the degree of adaptation to the curl force fields and the impact of goal uncertainty and time pressure on reaching dynamics. Through Receiver Operating Characteristic (ROC) analysis and Linear Mixed-Effects Models (LMEM), the researchers assessed differences in reaching dynamics under different target conditions.

Key Findings

Impact of Goal Uncertainty on Reaction Time

The study found no significant difference in reaction times (RT) between single-target and double-target conditions, indicating that movement initiation time was independent of prior knowledge of the final goal. However, under double-target conditions, the initial reach direction tended to be intermediate between the two potential targets, suggesting that participants adopted an intermediate strategy when the final goal was unclear.

Selection of Reaching Dynamics

The key finding was that under double-target conditions, participants’ reaching dynamics were consistent only with those of the central target, rather than an average of the dynamics of the two lateral targets. Even in trials with the shortest reaction times, the reaching dynamics did not exhibit “averaging” of the lateral target dynamics. This result supports the hypothesis that “action selection occurs downstream,” meaning that reaching dynamics are specified after goal selection, rather than through parallel preparation of multiple motor plans.

Motor Learning Under Time Pressure

The study also found that time pressure significantly affected participants’ motor learning efficiency. Although participants successfully adapted to the curl force fields, the degree of adaptation was lower (around 50% of the ideal compensatory force) compared to experiments without time pressure. This suggests that time pressure reduces the efficiency of motor learning but does not alter the mechanism of selecting reaching dynamics.

Conclusion

This study reveals that under conditions of goal uncertainty and time pressure, the planning of reaching dynamics occurs downstream of action selection, rather than through averaging the dynamics of multiple potential actions. This finding supports the “single motor plan optimization” hypothesis, where the brain specifies specific motor dynamics only after goal selection. The study not only deepens our understanding of the neural mechanisms of motor decision-making but also provides a new experimental paradigm for future research.

Research Highlights

1. Introduction of Time Pressure: By strictly controlling reaction times, the study for the first time explored the impact of time pressure on motor decision-making under goal uncertainty.
2. Mechanism of Selecting Reaching Dynamics: Through the experimental design of curl force fields, the study clearly distinguished between the “dynamic averaging” and “single dynamic optimization” hypotheses.
3. Efficiency of Motor Learning: The study revealed the negative impact of time pressure on motor learning, providing important insights for further research on the time-dependent effects of motor learning.

Other Valuable Information

The study also explored the neural mechanisms under multi-target conditions, proposing a possible explanation: the brain may treat a virtual central target as a competing option, thereby prioritizing the dynamics of the central target under double-target conditions. This perspective offers new directions for future research on neural mechanisms.

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This study, through its meticulously designed experiments and in-depth data analysis, provides important scientific evidence for understanding the control mechanisms of motor decision-making under uncertainty and time pressure.