scholarly journals The Importance of Attention in the Twinkle Goes Effect

2021 ◽  
Author(s):  
Joshua J Corbett

How do we perceive the location of moving objects? The position and motion literature is currently divided. Predictive accounts of object tracking propose that the position of moving objects is anticipated ahead of sensory signals, whilst non-predictive accounts claim that an anticipatory mechanism is not necessary. A novel illusion called the twinkle goes effect, describing a forward shift in the perceived final location of a moving object in the presence of dynamic noise, presents a novel opportunity to disambiguate these accounts. Across three experiments, we compared the predictions of predictive and non-predictive theories of object tracking by combining the twinkle goes paradigm with a multiple object tracking task. Specifically, we tested whether the size of the twinkle goes illusion would be smaller with greater attentional load (as entailed by the non-predictive, tracking continuation theory) or whether it would not be affected by attentional load (as entailed by predictive extrapolation theory). Our results failed to align with either of these theories of object localisation and tracking. Instead, we found evidence that the twinkle goes effect may be stronger with greater attentional load. We discuss whether this result may be a consequence of an essential, but previously unexplored relationship between the twinkle goes effect and representational momentum. In addition, this study was the first to reveal critical individual differences in the experience of the twinkle goes effect, and in the mislocalisation of moving objects. Together, our results continue to demonstrate the complexity of position and motion perception.

2020 ◽  
Author(s):  
Ryohei Nakayama ◽  
Alex O. Holcombe

AbstractWe show that on a dynamic noise background, the perceived disappearance location of a moving object is shifted in the direction of motion. This “twinkle goes” illusion has little dependence on the luminance- or chromaticity-based confusability of the object with the background, or on the amount of background motion energy in the same direction as the object motion. This suggests that the illusion is enabled by the dynamic noise masking the offset transients that otherwise accompany an object’s disappearance. While these results are consistent with an anticipatory process that pre-activates positions ahead of the object’s current position, additional findings suggest an alternative account: a continuation of attentional tracking after the object disappears. First, the shift was greatly reduced when attention was divided between two moving objects. Second, the illusion was associated with a prolonging of the perceived duration of the object, by an amount that matched the extent of extrapolation inferred from the effect of speed on the size of the illusion (~50 ms). While the anticipatory extrapolation theory does not predict this, the continuation of attentional tracking theory does. Specifically, we propose that in the absence of offset transients, attentional tracking keeps moving for several tens of milliseconds after the target disappearance, and this causes one to hallucinate a moving object at the position of attention.


2018 ◽  
Vol 10 (9) ◽  
pp. 1347 ◽  
Author(s):  
Ting Chen ◽  
Andrea Pennisi ◽  
Zhi Li ◽  
Yanning Zhang ◽  
Hichem Sahli

Multi-Object Tracking (MOT) in airborne videos is a challenging problem due to the uncertain airborne vehicle motion, vibrations of the mounted camera, unreliable detections, changes of size, appearance and motion of the moving objects and occlusions caused by the interaction between moving and static objects in the scene. To deal with these problems, this work proposes a four-stage hierarchical association framework for multiple object tracking in airborne video. The proposed framework combines Data Association-based Tracking (DAT) methods and target tracking using a compressive tracking approach, to robustly track objects in complex airborne surveillance scenes. In each association stage, different sets of tracklets and detections are associated to efficiently handle local tracklet generation, local trajectory construction, global drifting tracklet correction and global fragmented tracklet linking. Experiments with challenging airborne videos show significant tracking improvement compared to existing state-of-the-art methods.


2010 ◽  
Vol 21 (7) ◽  
pp. 920-925 ◽  
Author(s):  
S.L. Franconeri ◽  
S.V. Jonathan ◽  
J.M. Scimeca

In dealing with a dynamic world, people have the ability to maintain selective attention on a subset of moving objects in the environment. Performance in such multiple-object tracking is limited by three primary factors—the number of objects that one can track, the speed at which one can track them, and how close together they can be. We argue that this last limit, of object spacing, is the root cause of all performance constraints in multiple-object tracking. In two experiments, we found that as long as the distribution of object spacing is held constant, tracking performance is unaffected by large changes in object speed and tracking time. These results suggest that barring object-spacing constraints, people could reliably track an unlimited number of objects as fast as they could track a single object.


Author(s):  
Ting Chen ◽  
Andrea Pennisi ◽  
Zhi Li ◽  
Yanning Zhang ◽  
Hichem Sahli

Multi-object tracking (MOT) in airborne videos is a challenging problem due to the uncertain airborne vehicle motion, vibrations of the mounted camera, unreliable detections, size, appearance and motion of the moving objects as well as occlusions due to the interaction between the moving objects and with other static objects in the scene.To deal with these problems, this work proposes a four-stage Hierarchical Association framework for multiple object Tracking in Airborne video (HATA). The proposed framework combines data association-based tracking (DAT) methods and target tracking using a Compressive Tracking approach, to robustly track objects in complex airborne surveillance scenes. In each association stage, different sets of tracklets and detections are associated to efficiently handle local tracklet generation, local trajectory construction, global drifting tracklet correction and global fragmented tracklet linking. Experiments with challenging airborne video datasets show significant tracking improvement compared to existing state-of-art methods.


2020 ◽  
Vol 123 (5) ◽  
pp. 1630-1644
Author(s):  
Nicholas S. Bland ◽  
Jason B. Mattingley ◽  
Martin V. Sale

Using a multiple object tracking paradigm, we were able to manipulate the need for interhemispheric integration on a per-trial basis, while also having an objective measure of integration efficacy (i.e., tracking performance). We show that tracking performance reflects a cost of integration, which correlates with individual differences in interhemispheric EEG coherence. Gamma coherence appears to uniquely benefit between-hemifield tracking, predicting performance both across participants and across trials.


2013 ◽  
Vol 380-384 ◽  
pp. 3672-3677 ◽  
Author(s):  
Bao Hong Yuan ◽  
De Xiang Zhang ◽  
Kui Fu ◽  
Ling Jun Zhang

In order to accomplish tracking of moving objects requirements, and overcome the defect of occlusion in the process of tracking moving object, this paper presents a method which uses a combination of MeanShift and Kalman filter algorithm. MeanShift object tracking algorithm uses a histogram to describe the color characteristics of an object, and search the location of an image region that the color histogram is closest to the histogram of the object. Histogram similarity is defined in terms of the Bhattacharya coefficient. When the moving object is a large area blocked, the future state of moving object is estimated by Kalman filter. Experimental results verify that the proposed algorithm achieves efficient tracking of moving objects under the confusing situations.


2013 ◽  
Vol 427-429 ◽  
pp. 1822-1825
Author(s):  
Zhen Hai Wang ◽  
Ki Cheon Hong

multiple object tracking is an active and important research topic. It faces many challenging problems. Object extraction and data association are two most key steps in multiple object tracking. To improve tracking performance, this paper proposed a tracking method which combines Kalman filter and energy minimization-based data association. Moving objects are segmented through frame difference. Its can be consider as the vertex. All detections in adjacent frames are be used to construct a graph. The energy is finally minimized with a graph cuts optimization. Data association can be consider as multiple labeling problems. Object corresponding can be obtained through energy minimization. Experiment results demonstrate this method can be accurately tracking two moving objects.


Emotion ◽  
2017 ◽  
Vol 17 (6) ◽  
pp. 900-904 ◽  
Author(s):  
Gina M. D'Andrea-Penna ◽  
Sebastian M. Frank ◽  
Todd F. Heatherton ◽  
Peter U. Tse

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