Goal-directed unequal attention allocation during multiple object tracking

Abstract

In standard multiple object tracking (MOT) tasks the relative importance of the targets being tracked is equal. This is atypical of everyday situations in which an individual may need to prioritize one target relative to another and so allocate attention unequally. We report three experiments that examined whether participants could unequally split attention using a modified MOT task in which target priority was manipulated. Specifically, we examined the effect of priority on participants' magnitude of error and used a distribution mixture analysis to investigate how priority affected both participants' probability of losing an item and tracking precision. Experiment 1 (trajectory tracking) revealed a higher magnitude of error and higher proportion of guessing for low- compared with high-priority targets. Experiments 2 (trajectory tracking) and 3 (position tracking) examined how fine-grained this ability is by manipulating target priority at finer increments. In line with Experiment 1, results from both these experiments indicated that participants could split attention unequally. There was some evidence that participants could allocate attention unequally at fine increments, but this was less conclusive. Taken together, these experiments demonstrate participants' ability to distribute attention unequally across multiple moving objects but suggest some limitation with the flexibility of attention allocation.

Keywords: Attention; Goal-directed; Multiple object tracking; Target priority; Unequal attention splitting.

Fig. 1

Trial timeline. (1) Eight discs were presented on screen. Discs containing a value inside denote the likelihood of that target being queried at the end of a trial. (2) All discs moved around the screen. (3) All discs except one disappeared. Participants estimated what direction the disc was heading it at the end of the trial using a rotatable pointer. (4) Participants were given feedback. A second arrow was presented that indicates the correct target trajectory. If a participant’s trajectory estimate was within 12.5° of the correct trajectory, the arrow turned green; otherwise, it turned red

Fig. 2

Mean magnitude of error, proportion of guessing and precision of tracking for each target priority in Experiment 1. Error bars represent 95% within-subject confidence intervals using Morey (2008)

Fig. 3

Mixture model fits for the combined data across participants for Experiment 1 for each level of target priority. The density plot displays the actual data and the black line shows the model fit. The proportion of guessing (P_G) and precision of tracking (κ_VM) parameters are also detailed

Fig. 4

Mean error in magnitude of error, proportion of guessing, and precision of tracking for each target priority in Experiment 2. Error bars represent 95% within-subject confidence intervals using Morey (2008)

Fig. 5

Mixture model fits for all participants for Experiment 2 for each level of target priority. The density plot displays the actual data and the black line shows the model fit. The proportion of guessing (P_G) and precision of tracking (κ_VM) parameters are also detailed

Fig. 6

Mean error in size of position error, proportion of guessing and scale of distribution for each target priority in Experiment 3. Error bars represent 95% within-subject confidence intervals using Morey (2008)

Fig. 7

Mixture model fits for all data combined across participants for Experiment 3 for each level of target priority. The histogram plot displays the actual data and the black line shows the model fit. The proportion of guessing (P_G) precision of tracking (β the Weibull shape), and scale (η) parameters are also detailed