Convolutional neural networks are widely used in image classification. But perform badly when it is an abstract rule like identity or symmetry. In this dataset we conducted a study with humans on three different datasets based on abstract rules. In addition to the study we used an eye tracker to gather data of participants' eye movements.

* 13 participants classified 12 selected tasks in the same order * 12 tasks consisting of generated and randomly selected images:

• 2 PSVRT tasks: SR and SD
• 4 SVRT tasks: Problems 1, 19, 20 and 21
• 6 Checkerboard tasks: 2 fixed camera position, 2 random board placements and 2 camera rotated on a sphere

* Eye movements were tracked with a Tobii X2-60 eye tracking device, satisfying the recommended distances

• Gaze data was saved as [Gazedata objects](http://devtobiipro.azurewebsites.net/tobii.research/python/reference/1.8.0.32-alpha-g5b38a1f5/classtobii__research_1_1GazeData.html) from Tobii

* Publicly available to [Download](icare_dataset.zip) (~300MB).

Svrt 1, 19, 20 and 21

PSVRT with same/different bitpatterns, each once horizontally and once vertically aligned

Fixed camera position and random placements of checkerboards

Camera rotated on a sphere

To be published

This research was possible due to the Management Center Innsbruck providing the eye tracking device.

rick.spiegl@student.uibk.ac.at