Response tendencies due to item wording using eye-tracking methodology accounting for individual differences and item characteristics

2022 ◽  
Author(s):  
Chrystalla C. Koutsogiorgi ◽  
Michalis P. Michaelides
Keyword(s):  
Individual Differences ◽  
Eye Tracking ◽  
Item Wording ◽  
Item Characteristics

scholarly journals Investigating emotion recognition in the first year of life using eye-tracking Methodology

2021 ◽  
Author(s):  
Shira C. Segal
Keyword(s):  
Individual Differences ◽  
Eye Tracking ◽  
Emotion Recognition ◽  
Emotional Expressions ◽  
First Year ◽  
Facial Features ◽  
Fearful Faces ◽  
Facial Expressions Of Emotion ◽  
First Year Of Life ◽  
Key Words Emotion

The ability to recognize facial expressions of emotion is a critical part of human social interaction. Infants improve in this ability across the first year of life, but the mechanisms driving these changes and the origins of individual differences in this ability are largely unknown. This thesis used eye tracking to characterize infant scanning patterns of expressions. In study 1 (n = 40), I replicated the preference for fearful faces, and found that infants either allocated more attention to the eyes or the mouth across both happy and fearful expressions. In study 2 (n = 40), I found that infants differentially scanned the critical facial features of dynamic expressions. In study 3 (n = 38), I found that maternal depressive symptoms and positive and negative affect were related to individual differences in infants’ scanning of emotional expressions. Implications for our understanding of the development of emotion recognition are discussed. Key Words: emotion recognition, infancy eye tracking, socioemotional development

scholarly journals Using eye tracking to test for individual differences in attention to attractive faces

2015 ◽  
Vol 6 ◽  
Author(s):  
Christian Valuch ◽  
Lena S. Pflüger ◽  
Bernard Wallner ◽  
Bruno Laeng ◽  
Ulrich Ansorge
Keyword(s):  
Individual Differences ◽  
Eye Tracking

scholarly journals Individual differences in responsivity to social rewards: Insights from two eye-tracking tasks

PLoS ONE ◽  
2017 ◽  
Vol 12 (10) ◽  
pp. e0185146 ◽  
Author(s):  
Bhismadev Chakrabarti ◽  
Anthony Haffey ◽  
Loredana Canzano ◽  
Christopher P. Taylor ◽  
Eugene McSorley
Keyword(s):  
Individual Differences ◽  
Eye Tracking ◽  
Tracking Tasks ◽  
Social Rewards

scholarly journals Attention shifts to more complex locations with experience

2021 ◽  
Author(s):  
Tess Forest ◽  
Noam Siegelman ◽  
Amy Finn
Keyword(s):  
Individual Differences ◽  
Eye Tracking ◽  
Structure Learning ◽  
Reaction Times ◽  
Attention Allocation ◽  
Complex Structures ◽  
Specific Measure ◽  
Attentional Resources ◽  
Learning Success ◽  
Over Time

Our environments are saturated with learnable information. What determines which of this information gets prioritized for limited attentional resources? While previous studies suggest that learners prefer medium complexity information, here we argue that what counts as medium should change as someone learns an input's structure. Specifically, we examined the hypothesis that attention is directed towards more complicated structures as learners gain more experience with the environment. Participants watched four simultaneous streams of information which varied in complexity. Reaction times to intermittent search trials (Ex. 1, N=75) and eye-tracking (Ex. 2, N=45) indexed where participants attended over the experiment. Using a subject- and trial-specific measure of complexity, we demonstrated that participants attended to increasingly complex streams over time. Individual differences in structure learning also modulated attention allocation, with better learners attending to complex structures from earlier in learning, suggesting the ability to prioritize different information over time gates structure learning success.

Individual differences in smoking-related cue reactivity in smokers: An eye-tracking and fMRI study

2012 ◽  
Vol 38 (2) ◽  
pp. 285-293 ◽  
Author(s):  
O-Seok Kang ◽  
Dong-Seon Chang ◽  
Geon-Ho Jahng ◽  
Song-Yi Kim ◽  
Hackjin Kim ◽  
...  
Keyword(s):  
Individual Differences ◽  
Eye Tracking ◽  
Cue Reactivity ◽  
Fmri Study

scholarly journals Eye movement analysis with hidden Markov models (EMHMM) with co-clustering

2021 ◽  
Author(s):  
Janet H. Hsiao ◽  
Hui Lan ◽  
Yueyuan Zheng ◽  
Antoni B. Chan
Keyword(s):  
Individual Differences ◽  
Eye Tracking ◽  
Hidden Markov Models ◽  
Eye Movement ◽  
Markov Models ◽  
Hidden Markov ◽  
Movement Patterns ◽  
Movement Analysis ◽  
Background Information ◽  
Eye Movement Analysis

AbstractThe eye movement analysis with hidden Markov models (EMHMM) method provides quantitative measures of individual differences in eye-movement pattern. However, it is limited to tasks where stimuli have the same feature layout (e.g., faces). Here we proposed to combine EMHMM with the data mining technique co-clustering to discover participant groups with consistent eye-movement patterns across stimuli for tasks involving stimuli with different feature layouts. Through applying this method to eye movements in scene perception, we discovered explorative (switching between the foreground and background information or different regions of interest) and focused (mainly looking at the foreground with less switching) eye-movement patterns among Asian participants. Higher similarity to the explorative pattern predicted better foreground object recognition performance, whereas higher similarity to the focused pattern was associated with better feature integration in the flanker task. These results have important implications for using eye tracking as a window into individual differences in cognitive abilities and styles. Thus, EMHMM with co-clustering provides quantitative assessments on eye-movement patterns across stimuli and tasks. It can be applied to many other real-life visual tasks, making a significant impact on the use of eye tracking to study cognitive behavior across disciplines.

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