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Azarnoush, H.

Publications and source records attributed to Azarnoush, H..

2 recordsLinked to original sources

The accuracy of estimating time to contact in transversal motion and head-on motion

The ability to estimate precisely the time to contact (TTC) of the objects is necessary for planning actions in dynamic environments. TTC estimation is involved in many everyday activities with different levels of difficulty. Although tracking a ball that moves at a constant speed and estimating the time it reaches a non-moving target can be fairly simple, tracking a fast moving car with inconsistent speeds and estimating the time it reaches another car is rather difficult. In this study, we asked participants to estimate TTC of an object in transversal and head-on motions. The object became invisible shortly after movement initiation. The results demonstrated that TTC estimation for transversal motion is more accurate than for head-on motion. We present a mathematical model to explain why humans are better in estimating TTC for transversal motion than for head-on motion.

neuroscience↗

Drift-diffusion explains response variability and capacity for tracking objects

1.Being able to track objects that surround us is key for planning actions in dynamic environments. However, rigorous cognitive models for tracking of one or more objects are currently lacking. In this study, we asked human subjects to judge the time to contact (TTC) a finish line for one or two objects that became invisible shortly after moving. We showed that the pattern of subject responses had an error variance best explained by an inverse Gaussian distribution and consistent with the output of a biased drift-diffusion model. Furthermore, we demonstrated that the pattern of errors made when tracking two objects showed a level of dependence that was consistent with subjects using a single decision variable for reporting the TTC for two objects. This finding reveals a serious limitation in the capacity for tracking multiple objects resulting in error propagation between objects. Apart from explaining our own data, our approach helps interpret previous findings such as asymmetric interference when tracking multiple objects.

neuroscience↗