Literature DB >> 33877694

How Reliably Do Eye Parameters Indicate Internal Versus External Attentional Focus?

Sonja Annerer-Walcher1, Simon M Ceh1, Felix Putze2, Marvin Kampen2, Christof Körner1, Mathias Benedek1.   

Abstract

Eye behavior is increasingly used as an indicator of internal versus external focus of attention both in research and application. However, available findings are partly inconsistent, which might be attributed to the different nature of the employed types of internal and external cognition tasks. The present study, therefore, investigated how consistently different eye parameters respond to internal versus external attentional focus across three task modalities: numerical, verbal, and visuo-spatial. Three eye parameters robustly differentiated between internal and external attentional focus across all tasks. Blinks, pupil diameter variance, and fixation disparity variance were consistently increased during internally directed attention. We also observed substantial attentional focus effects on other parameters (pupil diameter, fixation disparity, saccades, and microsaccades), but they were moderated by task type. Single-trial analysis of our data using machine learning techniques further confirmed our results: Classifying the focus of attention by means of eye tracking works well across participants, but generalizing across tasks proves to be challenging. Based on the effects of task type on eye parameters, we discuss what eye parameters are best suited as indicators of internal versus external attentional focus in different settings.
© 2021 The Authors. Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society (CSS).

Entities:  

Keywords:  Eye behavior; Fixation disparity; Internal attentional focus; Internally directed cognition; LSTM; Machine learning; Microsaccades; Pupillometry

Year:  2021        PMID: 33877694     DOI: 10.1111/cogs.12977

Source DB:  PubMed          Journal:  Cogn Sci        ISSN: 0364-0213


  3 in total

1.  Combining Implicit and Explicit Feature Extraction for Eye Tracking: Attention Classification Using a Heterogeneous Input.

Authors:  Lisa-Marie Vortmann; Felix Putze
Journal:  Sensors (Basel)       Date:  2021-12-08       Impact factor: 3.576

2.  AtAwAR Translate: Attention-Aware Language Translation Application in Augmented Reality for Mobile Phones.

Authors:  Lisa-Marie Vortmann; Pascal Weidenbach; Felix Putze
Journal:  Sensors (Basel)       Date:  2022-08-17       Impact factor: 3.847

3.  Imaging Time Series of Eye Tracking Data to Classify Attentional States.

Authors:  Lisa-Marie Vortmann; Jannes Knychalla; Sonja Annerer-Walcher; Mathias Benedek; Felix Putze
Journal:  Front Neurosci       Date:  2021-05-28       Impact factor: 4.677

  3 in total

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