Contextual cueing in multiple object tracking

Ogawa, Hirokazu; Watanabe, Katsumi; Yagi, Akihiro

doi:10.1080/13506280802457176

Cited by 18 publications

(27 citation statements)

References 18 publications

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“…The subjective recognition rate was similar to that in the previous experiment, in which people failed to identify the repeated trials (Lukavský, 2013). Ogawa, Watanabe and Yagi (2009) showed people incidentally learn during repeated MOT. This contextual cueing could be also reflected in the level of eye movements (better planning, fewer rescue saccades) and potentially lead to shorter lag times.…”

Section: Discussionsupporting

confidence: 58%

Gaze position lagging behind scene content in multiple object tracking: Evidence from forward and backward presentations

Lukavský

Děchtěrenko

2016

Atten Percept Psychophys

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In everyday life, people often need to track moving objects. Recently, a topic of discussion has been whether people rely solely on the locations of tracked objects, or take their directions into account in multiple object tracking (MOT). In the current paper, we pose a related question: do people utilise extrapolation in their gaze behaviour, or, in more practical terms, should the mathematical models of gaze behaviour in an MOT task be based on objects' current, past or anticipated positions? We used a data-driven approach with no a priori assumption about the underlying gaze model. We repeatedly presented the same MOT trials forward and backward and collected gaze data. After reversing the data from the backward trials, we gradually tested different time adjustments to find the local maximum of similarity. In a series of four experiments, we showed that the gaze position lagged by approximately 110 ms behind the scene content. We observed the lag in all subjects (Experiment 1). We further experimented to determine whether tracking workload or predictability of movements affect the size of the lag. Low workload led only to a small non-significant shortening of the lag (Experiment 2). Impairing the predictability of objects' trajectories increased the lag (Experiments 3a and 3b). We tested our observations with predictions of a centroid model: we observed a better fit for a model based on the locations of objects 110 ms earlier. We conclude that mathematical models of gaze behaviour in MOT should account for the lags.

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Section: Discussionsupporting

confidence: 58%

Gaze position lagging behind scene content in multiple object tracking: Evidence from forward and backward presentations

Lukavský

Děchtěrenko

2016

Atten Percept Psychophys

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“…Previous experiments (Makovski, Vázquez, & Jiang, 2008;Ogawa et al, 2009) showed that people improve in MOT if the assignment of targets and distractors is retained. Although Makovski et al (2008) reported that the strongest learning effects form within the first few repetitions, their task was much more difficult (similar presentation time, but the object speed was 22.58/s compared to 58/s).…”

Section: Discussionmentioning

confidence: 98%

“…My pilot experiments have shown that subjects do not recognize when an MOT trial is administered repeatedly; this phenomenon makes MOT a good candidate for studying eye movements in repeated visual presentations. Ogawa, Watanabe, and Yagi (2009) investigated changes in performance during repeated MOT tasks and reported 22%-31% recognition rates in their experiments using 15 trial repetitions.…”

Section: Introductionmentioning

confidence: 98%

Eye movements in repeated multiple object tracking

Lukavský

2013

Journal of Vision

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Contrary to other tasks (free viewing, recognition, visual search), participants often fail to recognize repetition of trials in multiple object tracking (MOT). This study examines the intra- and interindividual variability of eye movements in repeated MOT trials along with the adherence of eye movements to the previously described strategies. I collected eye movement data from 20 subjects during 64 MOT trials at slow speed (5°/s). Half of the trials were repeated four times, and the remaining trials were unique. I measured the variability of eye-movement patterns during repeated trials using normalized scanpath saliency extended to the temporal domain. People tended to make similar eye movements during repeated presentations (with no or vague feeling of repetition) and the interindividual similarity remained at the same level over time. Several strategies (centroid strategy and its variants) were compared with data and they accounted for 48.8% to 54.3% of eye-movement variability, which was less then variability explained by other peoples' eye movements (68.6%). The results show that the observed intra- and interindividual similarity of eye movements is only partly explained by the current models.

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“…More experimental data are needed to clarify these What-Where interactions in contextual cueing to guide the development of future models. Moreover, it has been shown that fixed motion trajectories of distractors further improved search of a moving target whose trajectory is repeated across blocks (Chun & Jiang, 1999, Experiment 2;Ogawa, Watanabe, & Yagi, 2009). Ono, Jiang & Kawahara (2005) reported that predictive spatial context can be carried over in short-term memory to speed up target search during the succeeding trial.…”

Section: Discussionmentioning

confidence: 99%

Cortical dynamics of contextually cued attentive visual learning and search: Spatial and object evidence accumulation.

Huang¹,

Grossberg²

2010

Psychological Review

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How do humans use predictive contextual information to facilitate visual search? How are consistently paired scenic objects and positions learned and used to more efficiently guide search in familiar scenes? For example, a certain combination of objects can define a context for a kitchen and trigger a more efficient search for a typical object, such as a sink, in that context. A neural model, ARTSCENE Search, is developed to illustrate the neural mechanisms of such memory-based contextual learning and guidance, and to explain challenging behavioral data on positive/negative, spatial/object, and local/distant global cueing effects during visual search. The model proposes how global scene layout at a first glance rapidly forms a hypothesis about the target location. This hypothesis is then incrementally refined by enhancing target-like objects in space as a scene is scanned with saccadic eye movements. The model clarifies the functional roles of neuroanatomical, neurophysiological, and neuroimaging data in visual search for a desired goal object. In particular, the model simulates the interactive dynamics of spatial and object contextual cueing in the cortical What and Where streams starting from early visual areas through medial temporal lobe to prefrontal cortex. After learning, model dorsolateral prefrontal c ortical cells (area 46) prime possible target locations in posterior parietal cortex based on goalmodulated percepts of spatial scene gist represented in parahippocampal cortex, whereas model ventral prefrontal cortical cells (area 47/12) prime possible target object representations in inferior temporal cortex based on the history of viewed objects represented in perirhinal cortex. The model hereby predicts how the cortical What and Where streams cooperate during scene perception, learning, and memory to accumulate evidence over time to drive efficient visual search of familiar scenes.3

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Contextual cueing in multiple object tracking

Cited by 18 publications

References 18 publications

Gaze position lagging behind scene content in multiple object tracking: Evidence from forward and backward presentations

Gaze position lagging behind scene content in multiple object tracking: Evidence from forward and backward presentations

Eye movements in repeated multiple object tracking

Cortical dynamics of contextually cued attentive visual learning and search: Spatial and object evidence accumulation.

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