Clustered insights

Analysis of eye-tracking data in marketing research has traditionally relied upon regions of interest (ROIs) methodology or the use of heatmaps. Clear disadvantages exist for both methods. Addressing this gap, the current research applies spatiotemporal scan statistics to the analysis and visualisation of eye tracking data. Results of a sample experiment using anthropomorphic car faces demonstrate several advantages provided by the new method. In contrast to traditional approaches, scan statistics provide a means to scan eye tracking data automatically in space and time with differing gaze clusters, with results able to be comprehensively visualised and statistically assessed.

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Fixation classification: how to merge and select fixation candidates

Behavior Research Methods ◽

10.3758/s13428-021-01723-1 ◽

2022 ◽

Author(s):

Ignace T. C. Hooge ◽

Diederick C. Niehorster ◽

Marcus Nyström ◽

Richard Andersson ◽

Roy S. Hessels

Keyword(s):

Eye Tracking ◽

Marketing Research ◽

Classification Algorithms ◽

Extensive Literature ◽

Tracking Data ◽

Selection Rules ◽

The Gaze ◽

Research Fields ◽

Fixation Durations ◽

The Impact

AbstractEye trackers are applied in many research fields (e.g., cognitive science, medicine, marketing research). To give meaning to the eye-tracking data, researchers have a broad choice of classification methods to extract various behaviors (e.g., saccade, blink, fixation) from the gaze signal. There is extensive literature about the different classification algorithms. Surprisingly, not much is known about the effect of fixation and saccade selection rules that are usually (implicitly) applied. We want to answer the following question: What is the impact of the selection-rule parameters (minimal saccade amplitude and minimal fixation duration) on the distribution of fixation durations? To answer this question, we used eye-tracking data with high and low quality and seven different classification algorithms. We conclude that selection rules play an important role in merging and selecting fixation candidates. For eye-tracking data with good-to-moderate precision (RMSD < 0.5∘), the classification algorithm of choice does not matter too much as long as it is sensitive enough and is followed by a rule that selects saccades with amplitudes larger than 1.0∘ and a rule that selects fixations with duration longer than 60 ms. Because of the importance of selection, researchers should always report whether they performed selection and the values of their parameters.

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A new method for categorizing scanpaths from eye tracking data

Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications - ETRA '16 ◽

10.1145/2857491.2857503 ◽

2016 ◽

Cited By ~ 6

Author(s):

Michael J. Haass ◽

Laura E. Matzen ◽

Karin M. Butler ◽

Mika Armenta

Keyword(s):

Eye Tracking ◽

New Method ◽

Tracking Data

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Missing Data - Better "Not to Have Them", but What If You Do? (Part 1)

Marketing ZFP ◽

10.15358/0344-1369-2019-4-21 ◽

2019 ◽

Vol 41 (4) ◽

pp. 21-32

Author(s):

Dirk Temme ◽

Sarah Jensen

Keyword(s):

Missing Data ◽

Statistical Power ◽

Missing Values ◽

Graphical Representation ◽

Marketing Research ◽

Likelihood Estimation ◽

Parameter Estimates ◽

Full Information Maximum Likelihood ◽

Definition Of ◽

Traditional Approaches

Missing values are ubiquitous in empirical marketing research. If missing data are not dealt with properly, this can lead to a loss of statistical power and distorted parameter estimates. While traditional approaches for handling missing data (e.g., listwise deletion) are still widely used, researchers can nowadays choose among various advanced techniques such as multiple imputation analysis or full-information maximum likelihood estimation. Due to the available software, using these modern missing data methods does not pose a major obstacle. Still, their application requires a sound understanding of the prerequisites and limitations of these methods as well as a deeper understanding of the processes that have led to missing values in an empirical study. This article is Part 1 and first introduces Rubin’s classical definition of missing data mechanisms and an alternative, variable-based taxonomy, which provides a graphical representation. Secondly, a selection of visualization tools available in different R packages for the description and exploration of missing data structures is presented.

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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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