/eye-tracking

First assignment of Comp Model. Data from eyetracking workshop.

Eye-tracking

First assignment of Computational Modelling, Cognitive Science BSc, Aarhus University. Data from eye-tracking workshop.

foraging sheep

Analysis of eye-tracking data in two experiments.

Foraging experiment

According to the eye-mind hypothesis, eye-movements are linked to the processing of the mind basically meaning that is it possible to infer cognitive processing in the brain from the movements of the eyes. In this experiment we investigated to what extent top-down constraints affect eye movements. The experiment is based upon a former experiment conducted by (Rhodes et al., 2014) that examined the effect of different instructions on eye movements. To examine the question we hypothesized that the length of the saccades will be different in search conditions compared to count conditions due to different degrees of top-down processing. In the visual count task, the stimuli to which the eyes have been presented will dictate the eye-movements. In the visual search tasks, the properties of the picture will leave no information about where to find the star.

H1: The top-down constraint of a search task will elicit significantly different saccade amplitudes than the top-down constraint of a count task.

Social engagement experiment

All of the studies of social cognition have been focusing most on observing social interactions, but what happens if we make the participants engage in a task? The cognitive processes of observing social behaviour might not be the same as the cognitive processes of actually engaging in social interactions. In this experiment we investigated the impact of social engagement based on a former study by Tylén et al. (2012). More specifically it was investigated how social engagement through eye contact influence pupil size, under the assumption that pupil size is a measurement for emotional arousal.

H: We hypothesize that interactively engagement increases physiological arousal that will lead to greater pupil dilation.

References

  • Douglas Bates, Martin Maechler, Ben Bolker, Steve Walker (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48. doi:10.18637/jss.v067.i01.
  • Florian Hartig (2020). DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.2.7. https://CRAN.R-project.org/package=DHARMa
  • K. Tylén, M. Allen, B.K. Hunter, A. Roepstorff (2012). Interaction vs. observation: distinctive modes of social cognition in human brain and behavior? A combined fMRI and eye-tracking study. Retrieved from https://www.frontiersin.org/articles/10.3389/fnhum.2012.00331/full
  • R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ .
  • Theo Rhodes, Christopher T. Kello & Bryan Kerster (2014) Intrinsic and extrinsic contributions to heavy tails in visual foraging, Visual Cognition, 22:6, 809-842, DOI: 10.1080/13506285.2014.918070