Biology subjects

Daneshi, A.

Publications and source records attributed to Daneshi, A..

3 recordsLinked to original sources

bioRxiv2018

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↗

bioRxiv2018

Estimation of time to contact in lateral motion and approach motion

The ability to estimate precisely the time to contact (TTC) of the objects is necessary for planning actions in dynamic environments. However, this ability is not the same for all kinds of movement. Sometimes tracking an object and estimating its TTC is easy and accurate and sometimes it is not. In this study, we asked human subjects to estimate TTC of an object in lateral motion and approach motion. The object became invisible shortly after movement initiation. The results proved that TTC estimation for lateral motion is more accurate than for approach motion. We used mathematical analysis to show why humans are better in estimating TTC for lateral motion than for approach motion.

neuroscience↗

bioRxiv2018

Frame Rate Up-Conversion in Echocardiography Images, Using Manifold-Learning and Image Registration

In this paper, we propose a new temporal frame interpolation algorithm for frame rate up-conversion (FRUC) in echocardiography images. This algorithm employs a combination of dimension reduction techniques and image registration to increase frame rate.\n\nIf the distance between two successive frames of a video be great, motion jerkiness will appear between them and visual quality of the video will decrease. Some parts of heart have a very high speed motion, and echocardiography videos, obtained by available systems cant take enough number of frames to show them well. So, to achieve an echocardiography image set with a better visual quality, more frames are necessary between two frames at a great distance. Here, we use dimension reduction techniques to find out the number of suitable frames between two consecutive frames to show the fast motions better, but dont take much time. We project images to a 3-dimentional space by this way. Greater difference between the frames, results greater distance between corresponding embedded points. Thus, the distance between the embedded points is a scale for the suitable number of frames, needed between two successive frames. These frames are produced with the registration techniques.\n\nOn the other hand, heart doesnt have a constant speed during a cycle, but echocardiography images are recorded with constant speed. So, frames at a greater distance show fast motions of the heart, and frames at a lower distance show slow motions of the heart. While, we put unequal number of frames between successive frames, and in this way remove temporal coordination of the image set. To solve this problem, we put efficient number of linear average of available frames, in places that the number of inserted frames in between available frames is less than maximum to obtain an equal number of frames between all successive frames.

bioengineering↗