Integration of speed and time for estimating time to contact

Chang, Chia-Jung; Jazayeri, Mehrdad

doi:10.1073/pnas.1713316115

Cited by 48 publications

(84 citation statements)

References 76 publications

(131 reference statements)

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“…Consistent with the pattern of results reported in previous studies [18,21,22,25,26], accuracy of the responses decreases with larger TTCs (Figure 4(e)). In other words, increasing the actual TTC results in increasing error in TTC estimation.…”

Section: Ttce (Estimated Ttc) Is the Time Between The Disappearance Osupporting

confidence: 91%

“…In future studies, experiments can be designed to study these conditions. Some studies have shown that TTC estimates for each target car speed tends to be biased toward the mean TTC across all target car speeds [24,25,35,36]. We did not observe such pattern in our experiments, maybe because we did not provide participants with feedback on estimation accuracy.…”

Section: Resultscontrasting

confidence: 59%

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

Daneshi

Azarnoush

Towhidkhah

et al. 2018

Preprint

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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.

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Section: Ttce (Estimated Ttc) Is the Time Between The Disappearance Osupporting

confidence: 91%

Section: Resultscontrasting

confidence: 59%

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

Daneshi

Azarnoush

Towhidkhah

et al. 2018

Preprint

View full text Add to dashboard Cite

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“…Similarly during pursuit maintenance, it was observed that the ratio 61 between position and velocity error correlates well with the occurrence of saccades and roughly 62 correlates with saccade latency (de Brouwer et al, 2002b). This parameter was originally coined 63 as eye-crossing time, but henceforth referred to as time-to-foveation to parallel the time-to-64 collision parameter in studies of steering control (Regan and Gray, 2000) and the time-to-65 contact parameter in studies of limb control and interception (Chang and Jazayeri, 2018;66 Tresilian, 1995). While time-to-foveation is a useful behavioural correlate, it remains unsuitable 67 as a decision variable due to its instability as velocity errors approach zero (time-to-foveation 68 goes to infinity) and its inability to address how sensory noise is accommodated.…”

Section: Introduction 29mentioning

confidence: 99%

Confidence in predicted position error explains saccadic decisions during pursuit

Coutinho

Lefèvre

Blohm

2018

Preprint

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10A fundamental problem in motor control is the coordination of complementary movement types to 11 achieve a common goal. As a common example, humans view moving objects through coordinated 12 pursuit and saccadic eye movements. Pursuit is initiated and continuously controlled by retinal image 13 velocity. During pursuit, eye position may lag behind the target. This can be compensated by the discrete 14 execution of a catch-up saccade. The decision to trigger a saccade is influenced by both position and 15 velocity errors and the timing of saccades can be highly variable. The observed distributions of saccade 16 occurrence and trigger time remain poorly understood and this decision process remains imprecisely 17 quantified. Here we propose a predictive, probabilistic model explaining the decision to trigger saccades 18 during pursuit to foveate moving targets. In this model, expected position error and its associated 19 uncertainty are predicted through Bayesian inference across noisy, delayed sensory observations 20 (Kalman filtering). This probabilistic prediction is used to estimate the confidence that a saccade is 21 needed (quantified through log-probability ratio), triggering a saccade upon accumulating to a fixed 22 threshold. The model qualitatively explains behavioural observations on the probability of occurrence and 23 trigger time distributions of saccades during pursuit over a range of target motion trajectories.24 Furthermore, this model makes novel predictions about the influence of sensory uncertainty and target 25 motion parameters on saccade decisions. We suggest that this predictive, confidence-based decision 26 making strategy represents a fundamental principle for the probabilistic neural control of coordinated 27 movements. 28 New & Noteworthy 29This is the first stochastic dynamical systems model of pursuit-saccade coordination accounting 30 for noise and delays in the sensorimotor system. The model uses Bayesian inference to predictively 31 estimate visual motion, triggering saccades when confidence in predicted position error accumulates to a 32 threshold. This model explains saccade probability and trigger time distributions across target trajectories 33 and makes novel predictions about the influence of sensory uncertainty in saccade decisions during 34 pursuit.The coordination between continuously controlled and discretely triggered movements to achieve 37 a common goal remains a fundamental problem in neuroscience. This coordinated motor control is 38 exemplified in the pursuit and saccadic eye movements that humans perform when tracking moving 39 objects. Pursuit eye movements are continuously controlled to minimize the relative motion of a visual 40 image on the retina (Keller and Heinen, 1991; Lisberger, 2015). Due to noise and delays prevalent in 41 sensorimotor systems (Faisal et al., 2008; van Beers et al., 2002), pursuit trajectory may deviate from and 42 lag behind the true target trajectory (Osborne et al., 2005). Furthermore, pursuit gain in humans is 43 typically less than ...

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“…The ideal TTCs in our experiment are within that time interval. Our subjects might have been further helped by the fact that the time under occluder was comparable to the visible time before the occlude a factor that is shown to facilitate temporal estimation [7,43]. Some studies have shown that TTC estimates for each condition tends to be biased toward the mean TTC across all conditions [4,6,7,44].…”

Section: Discussionmentioning

confidence: 99%

“…Time to contact (TTC), which is the time it takes for an object to reach an observer or a particular place, is an important factor in a variety of real-world situations, such as catching and hitting balls in games, driving vehicles, or passing through a busy street. The ability to estimate TTC for one object has been assessed in several studies (for example see [1][2][3][4][5][6][7][8][9][10][11][12][13][14]). The accuracy and precision of TTC estimation is related to the time perception ability, which is considered in several studies [6,11,[15][16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Drift-diffusion explains response variability and capacity for tracking objects

Daneshi

Azarnoush

Towhidkhah

et al. 2018

Preprint

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1.AbstractBeing 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.

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Integration of speed and time for estimating time to contact

Cited by 48 publications

References 76 publications

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

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

Confidence in predicted position error explains saccadic decisions during pursuit

Drift-diffusion explains response variability and capacity for tracking objects

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