Covert eye-tracking: an innovative method to investigate compliance with instructions

AbstractThe present study introduces a covert eye-tracking procedure as an innovative approach to investigate the adequacy of research paradigms used in psychology. In light of the ongoing debate regarding ego depletion, the frequently used “attention-control video task” was chosen to illustrate the method. Most participants did not guess that their eyes had been monitored, but some participants had to be excluded due to poor tracking ratio. The eye-tracking data revealed that the attention-control instructions had a significant impact on the number of fixations, revisits, fixation durations, and proportion of long fixation durations on the AOIs (all BF10 > 18.2). However, number of fixations and proportions of long fixation durations did not mediate cognitive performance. The results illustrate the promise of covert eye-tracking methodology to assess task compliance, as well as adding to the current discussion regarding whether the difficulties of replicating “ego depletion” may be in part due to poor task compliance in the video task.

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Covert eye-tracking: an innovative method to investigate compliance with instructions

10.31234/osf.io/e6vk7 ◽

2020 ◽

Author(s):

Anine Heyerdahl Riege ◽

Amélie Gourdon-Kanhukamwe ◽

Gaëlle Vallée-Tourangeau

Keyword(s):

Eye Tracking ◽

Cognitive Performance ◽

Ego Depletion ◽

Attention Control ◽

Innovative Approach ◽

Tracking Data ◽

Ongoing Debate ◽

Research Paradigms ◽

Innovative Method ◽

Fixation Durations

The present study introduces a covert eye-tracking procedure as an innovative approach to investigate the adequacy of research paradigms used in psychology. In light of the ongoing debate regarding ego depletion, the frequently used “attention-control video task” was chosen to illustrate the method. Most participants did not guess that their eyes had been monitored, but some participants had to be excluded due to poor tracking ratio. The eye-tracking data revealed that the attention-control instructions had a significant impact on the number of fixations, revisits, fixation durations, and proportion of long fixation durations on the AOIs (all BF10 > 18.2). However, number of fixations and proportions of long fixation durations did not mediate cognitive performance. The results illustrate the promise of covert eye-tracking methodology to assess task compliance, as well as adding to the current discussion regarding whether the difficulties of replicating “ego depletion” may be in part due to poor task compliance in the video task.

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Testing the validity of the attention control video: An eye-tracking approach of the ego depletion effect

PLoS ONE ◽

10.1371/journal.pone.0211181 ◽

2019 ◽

Vol 14 (1) ◽

pp. e0211181 ◽

Cited By ~ 2

Author(s):

Chris Englert ◽

Dennis Koroma ◽

Alex Bertrams ◽

Corinna S. Martarelli

Keyword(s):

Eye Tracking ◽

Ego Depletion ◽

Attention Control ◽

Depletion Effect

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Very preterm infants engage in an intervention to train their control of attention: results from the feasibility study of the Attention Control Training (ACT) randomised trial

Pilot and Feasibility Studies ◽

10.1186/s40814-021-00809-z ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Oliver Perra ◽

Sam Wass ◽

Alison McNulty ◽

David Sweet ◽

Kostas A. Papageorgiou ◽

...

Keyword(s):

Eye Tracking ◽

Outcome Measures ◽

Feasibility Study ◽

Premature Birth ◽

Randomised Trial ◽

Attention Control ◽

Tracking Data ◽

Very Preterm ◽

Link Type ◽

Increased Risk

Abstract Background Very premature birth (gestational age between 28 and 31 + 6 weeks) is associated with increased risk of cognitive delay and attention deficit disorder, which have been linked to anomalies in the development of executive functions (EFs) and their precursors. In particular, very preterm (VP) infants display anomalies in controlling attention and gathering task-relevant information. Early interventions that support attention control may be pivotal in providing a secure base for VP children’s later attainments. The Attention Control Training (ACT) is a cognitive training intervention that targets infants’ abilities to select visual information according to varying task demands but had not been tested in VP infants. We conducted a feasibility study to test the processes we intend to use in a trial delivering the ACT to VP infants. Methods and design We tested recruitment and retention of VP infants and their families in a randomised trial, as well as acceptability and completion of baseline and outcome measures. To evaluate these aims, we used descriptive quantitative statistics and qualitative methods to analyse feedback from infants’ caregivers. We also investigated the quality of eye-tracking data collected and indicators of infants’ engagement in the training, using descriptive statistics. Results Twelve VP infants were recruited, and 10 (83%) completed the study. Participants’ parents had high education attainment. The rate of completion of baseline and outcome measures was optimal. VP infants demonstrated engagement in the training, completing on average 84 min of training over three visits, and displaying improved performance during this training. Eye-tracking data quality was moderate, but this did not interfere with infants’ engagement in the training. Discussion The results suggest the ACT can be delivered to VP infants. However, challenges remain in recruitment of numerous and diverse samples. We discuss strategies to overcome these challenges informed by results of this study. Trial registration Registered Registration ID: NCT03896490. Retrospectively registered at Clinical Trials Protocol Registration and Results System (clinicaltrials.gov).

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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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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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