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Mobile eye tracking reveals little evidence for age differences in attentional selection for mood regulation.

ABSTRACT: Two studies are reported representing the first use of mobile eye tracking to study emotion regulation across adulthood. Past research on age differences in attentional deployment using stationary eye tracking has revealed older adults show relatively more positive looking and seem to benefit more moodwise from this looking pattern, compared with younger adults. However, these past studies have greatly constrained the stimuli participants can look at, despite real-world settings providing numerous possibilities for what we choose to look at. The authors therefore used mobile eye tracking to study age differences in attentional selection, as indicated by fixation patterns to stimuli of different valence freely chosen by the participant. In contrast to stationary eye-tracking studies of attentional deployment, Study 1 showed that younger and older individuals generally selected similar proportions of valenced stimuli, and attentional selection had similar effects on mood across age groups. Study 2 replicated this pattern with an adult life span sample including middle-aged individuals. Emotion regulation-relevant attention may thus differ depending on whether stimuli are freely chosen or not. (PsycINFO Database Record

SUBMITTER: Isaacowitz DM

PROVIDER: S-EPMC4380866 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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Mobile eye tracking reveals little evidence for age differences in attentional selection for mood regulation.

Isaacowitz Derek M DM Livingstone Kimberly M KM Harris Julia A JA Marcotte Stacy L SL

Emotion (Washington, D.C.) 20141222 2

Two studies are reported representing the first use of mobile eye tracking to study emotion regulation across adulthood. Past research on age differences in attentional deployment using stationary eye tracking has revealed older adults show relatively more positive looking and seem to benefit more moodwise from this looking pattern, compared with younger adults. However, these past studies have greatly constrained the stimuli participants can look at, despite real-world settings providing numero ...[more]

PMID: 25527965

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Project description:BackgroundSleep influences moods and mood disorders. Existing methods for tracking the quality of people's sleep are laborious and obtrusive. If a method were available that would allow effortless and unobtrusive tracking of sleep quality, it would mark a significant step toward obtaining sleep data for research and clinical applications.ObjectiveOur goal was to evaluate the potential of mobile sensing data to obtain information about a person's sleep quality. For this purpose, we investigated to what extent various automatically gathered mobile sensing features are capable of predicting (1) subjective sleep quality (SSQ), (2) negative affect (NA), and (3) depression; these variables are associated with objective sleep quality. Through a multiverse analysis, we examined how the predictive quality varied as a function of the selected sensor, the extracted feature, various preprocessing options, and the statistical prediction model.MethodsWe used data from a 2-week trial where we collected mobile sensing and experience sampling data from an initial sample of 60 participants. After data cleaning and removing participants with poor compliance, we retained 50 participants. Mobile sensing data involved the accelerometer, charging status, light sensor, physical activity, screen activity, and Wi-Fi status. Instructions were given to participants to keep their smartphone charged and connected to Wi-Fi at night. We constructed 1 model for every combination of multiverse parameters to evaluate their effects on each of the outcome variables. We evaluated the statistical models by applying them to training, validation, and test sets to prevent overfitting.ResultsMost models (on either of the outcome variables) were not informative on the validation set (ie, predicted R2≤0). However, our best models achieved R2 values of 0.658, 0.779, and 0.074 for SSQ, NA, and depression, respectively on the training set and R2 values of 0.348, 0.103, and 0.025, respectively on the test set.ConclusionsThe approach demonstrated in this paper has shown that different choices (eg, preprocessing choices, various statistical models, different features) lead to vastly different results that are bad and relatively good as well. Nevertheless, there were some promising results, particularly for SSQ, which warrant further research on this topic.

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Mobile eye tracking reveals little evidence for age differences in attentional selection for mood regulation.

Publications

Mobile eye tracking reveals little evidence for age differences in attentional selection for mood regulation.

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