Sub-Optimal Allocation of Time in Sequential Movements

The allocation of limited resources such as time or energy is a core problem that organisms face when planning complex actions. Most previous research concerning planning of movement has focused on the planning of single, isolated movements. Here we investigated the allocation of time in a pointing task where human subjects attempted to touch two targets in a specified order to earn monetary rewards. Subjects were required to complete both movements within a limited time but could freely allocate the available time between the movements. The time constraint presents an allocation problem to the subjects: the more time spent on one movement, the less time is available for the other. In different conditions we assigned different rewards to the two tokens. How the subject allocated time between movements affected their expected gain on each trial. We also varied the angle between the first and second movements and the length of the second movement. Based on our results, we developed and tested a model of speed-accuracy tradeoff for sequential movements. Using this model we could predict the time allocation that would maximize the expected gain of each subject in each experimental condition. We compared human performance with predicted optimal performance. We found that all subjects allocated time sub-optimally, spending more time than they should on the first movement even when the reward of the second target was five times larger than the first. We conclude that the movement planning system fails to maximize expected reward in planning sequences of as few as two movements and discuss possible interpretations drawn from economic theory

Use of probabilistic phrases in a coordination game: human versus GPT-4

English speakers use probabilistic phrases such as likely to communicate information about the probability or likelihood of events. Communication is successful to the extent that the listener grasps what the speaker means to convey and, if communication is successful, individuals can potentially coordinate their actions based on shared knowledge about uncertainty. We first assessed human ability to estimate the probability and the ambiguity (imprecision) of twenty-three probabilistic phrases in a coordination game in two different contexts, investment advice and medical advice. We then had GPT4 (OpenAI), a Large Language Model, complete the same tasks as the human participants. We found that the median human participant and GPT4 assigned probability estimates that were in good agreement (proportions of variance accounted for close to .90). GPT4's estimates of probability both in the investment and Medical contexts were as close or closer to that of the human participants as the human participants' estimates were to one another. Estimates of probability for both the human participants and GPT4 were little affected by context. In contrast, human and GPT4 estimates of ambiguity were not in such good agreement.Comment: Corrected typos, extended discussion, added reference

Speed-accuracy tradeoff (SAT).

<p><b>A.</b> Spatial variability parallel to the direction of movement was plotted as a function of the average speed of the movement (mm/sec) from subject AI. Different colors coded for different direction conditions. Each data point represents a single condition. The lines represented the best fitted linear SAT functions (Eq. 4). <b>B.</b> Spatial variability perpendicular to the direction of movement was plotted against average speed from the same subject.</p

The coordinate system.

<p>We used a two-dimensional coordinate system to represent each movement. The coordinate system was embedded in the stimulus array. One axis was parallel to the line connecting the start point and the end point of the movement, and the other, was perpendicular to the first. The origin was centered on the end point of the distribution.</p