Research on Generating an Indoor Landmark Salience Model for Self-Location and Spatial Orientation from Eye-Tracking Data

Landmarks play an essential role in wayfinding and are closely related to cognitive processes. Eye-tracking data contain massive amounts of information that can be applied to discover the cognitive behaviors during wayfinding; however, little attention has been paid to applying such data to calculating landmark salience models. This study proposes a method for constructing an indoor landmark salience model based on eye-tracking data. First, eye-tracking data are taken to calculate landmark salience for self-location and spatial orientation tasks through partial least squares regression (PLSR). Then, indoor landmark salience attractiveness (visual, semantic and structural) is selected and trained by landmark salience based on the eye-tracking data. Lastly, the indoor landmark salience model is generated by landmark salience attractiveness. Recruiting 32 participants, we designed a laboratory eye-tracking experiment to construct and test the model. Finding 1 proves that our eye-tracking data-based modelling method is more accurate than current weighting methods. Finding 2 shows that significant differences in landmark salience occur between two tasks; thus, it is necessary to generate a landmark salience model for different tasks. Our results can contribute to providing indoor maps for different tasks.

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Processing Forced-choice versus Check-all-that-apply Question Formats

Field Methods ◽

10.1177/1525822x17703943 ◽

2017 ◽

Vol 29 (4) ◽

pp. 383-394 ◽

Cited By ~ 4

Author(s):

Cornelia E. Neuert

Keyword(s):

Eye Tracking ◽

Cognitive Processes ◽

Forced Choice ◽

Cognitive Effort ◽

Tracking Data ◽

Question Format ◽

Factual Question ◽

Question Formats

Previous research has shown that check-all-that-apply (CATA) and forced-choice (FC) question formats do not produce comparable results. The cognitive processes underlying respondents’ answers to both types of formats still require clarification. This study contributes to filling this gap by using eye-tracking data. Both formats are compared by analyzing attention processes and the cognitive effort respondents spend while answering one factual and one opinion question, respectively. No difference in cognitive effort spent on the factual question was found, whereas for the opinion question, respondents invested more cognitive effort in the FC than in the CATA condition. The findings indicate that higher endorsement in FC questions cannot only be explained by question format. Other possible causes are discussed.

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Status lies in the eye of the beholder: Evidence from eye-tracking data

10.31234/osf.io/r4q3c ◽

2018 ◽

Author(s):

Audrey Breton ◽

Anne Cheylus ◽

Jean-Yves Baudouin ◽

Jean-Baptiste Van der Henst

Keyword(s):

Eye Tracking ◽

Spatial Orientation ◽

Social Rank ◽

Second Phase ◽

Tracking Data ◽

Attentional Resources ◽

Vertical Scale ◽

The Face ◽

Competitive Games ◽

Ranking Position

Using eye-tracking methodology, the current study investigates how people look at faces in the context of a social hierarchy. The first goal of the study was to characterize how participants distribute their attentional resources to a set of faces displayed on a vertical hierarchical scale including three social ranks (high, middle and low). In the first phase of the experiment, participants had to learn a hierarchy based on performance in competitive games. The participants’ rank, the other players’ rank, and the spatial orientation of the scale (highest social rank at the top of the vertical scale vs. highest social rank at the bottom) were manipulated. The results revealed that participants looked more at other players who occupy the highest than the lowest social rank but did not look more at another player who was ranked higher than lower than themselves when that player was in the middle position. Hence, participants were more sensitive to the highest vs. lowest rank contrast than to the higher vs. lower rank contrast. The results also revealed an influence of spatial orientation of the scale: less visual attention was allocated to the highest-ranking players when they appeared at the bottom than at the top of the spatial scale. The second goal of the study was to investigate how participants explored the face of each player. This was addressed in the second phase of the experiment, in which participants were presented with each player’s face individually. The results did not show any effect of players’ rank but revealed that in some circumstances, participants in the higher-ranking position were less likely to allocate visual resources to other players.

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The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

10.31234/osf.io/zypk9 ◽

2020 ◽

Author(s):

Kun Sun

Keyword(s):

Eye Tracking ◽

Semantic Similarity ◽

Cognitive Processing ◽

Language Processing ◽

Language Comprehension ◽

Word Processing ◽

Reading Time ◽

Computational Models ◽

Tracking Data ◽

Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

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Attentional Bias to Untrustworthy Faces: Evidence From Eye Tracking Data

Advances in Psychological Science ◽

10.3724/sp.j.1042.2015.01508 ◽

2015 ◽

Vol 23 (9) ◽

pp. 1508

Author(s):

Qiandong WANG ◽

Qinggong LI ◽

Kaikai CHEN ◽

Genyue FU

Keyword(s):

Eye Tracking ◽

Attentional Bias ◽

Tracking Data

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SIMILAR INSPECTION DATA RETRIEVAL FOR ROAD STRUCTURE INSPECTION BASED ON CANONICAL CORRELATION BETWEEN EYE TRACKING DATA AND INSPECTION RECORDS

Journal of Japan Society of Civil Engineers Ser F3 (Civil Engineering Informatics) ◽

10.2208/jscejcei.76.1_74 ◽

2020 ◽

Vol 76 (1) ◽

pp. 74-86

Author(s):

Keisuke MAEDA ◽

Ryota SAITO ◽

Sho TAKAHASHI ◽

Takahiro OGAWA ◽

Miki HASEYAMA

Keyword(s):

Eye Tracking ◽

Canonical Correlation ◽

Data Retrieval ◽

Tracking Data ◽

Inspection Data

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If Eyes are the Window to Our Soul, What Role does Eye-Tracking Play in Accounting Research?

Behavioral Research in Accounting ◽

10.2308/bria-52283 ◽

2018 ◽

Vol 31 (2) ◽

pp. 107-133 ◽

Cited By ~ 3

Author(s):

Edward J. Lynch ◽

Lindsay M. Andiola

Keyword(s):

Eye Tracking ◽

Cognitive Processes ◽

Research Tool ◽

Future Research ◽

Behavioral Accounting ◽

Research Directions ◽

Psychological Constructs ◽

Instructional Resource ◽

Future Research Directions ◽

Research Domains

ABSTRACT Recent advances in technology have increased the accessibility and ease in using eye-tracking as a research tool. These advances have the potential to benefit behavioral accounting researchers' understanding of the cognitive processes underlying individuals' judgments, decisions, and behaviors. However, despite its potential and wide use in other disciplines, few behavioral accounting studies use eye-tracking. The purpose of this paper is to familiarize accounting researchers with eye-tracking, including its advantages and limitations as a research tool. We start by providing an overview of eye-tracking and discussing essential terms and useful metrics, as well as the psychological constructs they proxy. We then summarize eye-tracking research across research domains, review accounting studies that use eye-tracking, and identify future research directions across accounting topics. Finally, we provide an instructional resource to guide those researchers interested in using eye-tracking, including important considerations at each stage of the study. JEL Classifications: M41; C91.

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