Predicting eye movements in multiple object tracking using neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081740%3A_____%2F16%3A00466857" target="_blank" >RIV/68081740:_____/16:00466857 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/16:10336704

Výsledek na webu

<a href="http://dx.doi.org/10.1145/2857491.2857502" target="_blank" >http://dx.doi.org/10.1145/2857491.2857502</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/2857491.2857502" target="_blank" >10.1145/2857491.2857502</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting eye movements in multiple object tracking using neural networks

Popis výsledku v původním jazyce

In typical Multiple Object Tracking (MOT) paradigm, the participant's task is to track targets amongst distractors for several seconds. Understanding gaze strategies in MOT can help us reveal attentional mechanisms in dynamic tasks. Previous attempts relied on analytical strategies (such as averaging object positions). An alternative approach is to find this relationship using machine learning technique. After preprocessing, we assembled a dataset with 48,000 datapoints, representing 1534 MOT trials or 2.5 hours. In this study, we used feedforward neural networks to predict gaze position and compared predicted gaze with analytical strategies from previous studies using median distance. Our results showed that neural networks were able to predict eye positions better than current strategies. Particularly, they performed better when we trained the network with all objects, not targets only. It supports the hypothesis that people are influenced by distractor positions during tracking.

Název v anglickém jazyce

Predicting eye movements in multiple object tracking using neural networks

Popis výsledku anglicky

In typical Multiple Object Tracking (MOT) paradigm, the participant's task is to track targets amongst distractors for several seconds. Understanding gaze strategies in MOT can help us reveal attentional mechanisms in dynamic tasks. Previous attempts relied on analytical strategies (such as averaging object positions). An alternative approach is to find this relationship using machine learning technique. After preprocessing, we assembled a dataset with 48,000 datapoints, representing 1534 MOT trials or 2.5 hours. In this study, we used feedforward neural networks to predict gaze position and compared predicted gaze with analytical strategies from previous studies using median distance. Our results showed that neural networks were able to predict eye positions better than current strategies. Particularly, they performed better when we trained the network with all objects, not targets only. It supports the hypothesis that people are influenced by distractor positions during tracking.

Druh

D - Stať ve sborníku

CEP obor

AN - Psychologie

OECD FORD obor

—

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Eye Tracking Research and Applications Symposium (ETRA)

ISBN

978-145034125-7

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

271-274

Název nakladatele

Association for Computing Machinery

Místo vydání

Charleston

Místo konání akce

Charleston

Datum konání akce

14. 3. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000389809700045