Severe loss of positional information when detecting deviations in multiple trajectories

Abstract

Human observers can simultaneously track up to five targets in motion (Z. W. Pylyshyn & R. W. Storm, 1988). We examined the precision for detecting deviations in linear trajectories by measuring deviation thresholds as a function of the number of trajectories (T ). When all trajectories in the stimulus undergo the same deviation, thresholds are uninfluenced by T for T <or= 10. When only one of the trajectories undergoes a deviation, thresholds rise steeply as T is increased [e.g., 3.3 degrees (T = 1), 12.3 degrees (T = 2), 32.9 degrees (T = 4) for one observer]; observers are unable to simultaneously process more than one trajectory in our threshold-measuring paradigm. When the deviating trajectory is cued (e.g., using a different color), varying T has little influence on deviation threshold. The use of a different color for each trajectory does not facilitate deviation detection. Our current data suggest that for deviations that have low discriminability (i.e., close to threshold) the number of trajectories that can be monitored effectively is close to one. In contrast, when the stimuli containing highly discriminable (i.e., substantially suprathreshold) deviations are used, as many as three or four trajectories can be simultaneously monitored (S. P. Tripathy, 2003). Our results highlight a severe loss of positional information when attempting to track multiple objects, particularly in a threshold paradigm.

Changing target trajectories influences tracking performance

“…We suggest that limits associated with PMT performance are most likely related to abrupt changes in target trajectories, as well as the cumulative distance that targets travel and are less related to target-target proximity. These findings agree with previous work on target trajectories (Horowitz & Kuzmova, 2010;Howard & Holcombe, 2010;Pratt et al, 2010;Tripathy & Barrett, 2004) and support a tracking process that utilizes trajectory information, as proposed by St. Clair et al (2010).…”

“…Clair et al, 2010;Tripathy & Barrett, 2004) and that unexpected changes in the trajectory of an object can attract attention (Howard & Holcombe, 2010;Pratt et al, 2010). However, no studies have directly examined the effects of unexpected changes in trajectory on MOT performance while also controlling and/or measuring other factors that could impact MOT performance.…”

“…Several factors are important to MOT performance, such as speed (Alvarez & Franconeri, 2007), number of distractors (Bettencourt & Somers, 2009), and the proximity of objects (Franconeri, Jonathon, & Scimeca, 2010;Pylyshyn, 2004). Recent research suggests that trajectory information is also important for MOT performance: Participants can represent trajectory information (Fencsik, Klieger, & Horowitz, 2007;Iordanescu, Grabowecky, & Suzuki, 2009), they can detect changes in trajectory (Narasimhan, Tripathy, & Barrett, 2009;Pratt, Radulescu, Guo, & Abrams, 2010;Tripathy & Barrett, 2004), and when trajectory information is difficult to discern, MOT performance suffers (Howard & Holcombe, 2010;St. Clair, Huff, & Seiffert, 2010).…”

“…The ability to represent trajectory information is limited by the number of objects that can be represented and by the precision of the trajectory representation Narasimhan et al, 2009;Tripathy & Barrett, 2004). Tripathy and Barrett examined the ability to detect a change in trajectory for multiple moving objects.…”

No capacity limit in attentional tracking: Evidence for probabilistic inference under a resource constraint

“…Deviation discrimination data We model published data from a variation of multipleobject tracking that has fewer degrees of freedom and is therefore easier to analyze (Tripathy & Barrett, 2004;Tripathy, Narasimhan, & Barrett, 2007). On each trial, N dots moved from left to right in linear trajectories ( Figure 1).…”