Innsbruck CNN Abstract Rule Eyetracking (ICARE) Dataset
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.
Dataset Features
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
Publicly available to
Download (~270MB).
Sample Images of Tasks
SVRT
PSVRT
Checkerboard
![FP5 FP5](/_media/datasets/icare/fp5.jpg?w=224&tok=68ed4d)
![FP1 FP1](/_media/datasets/icare/fp1.jpg?w=224&tok=d52118)
![RBP5 RBP5](/_media/datasets/icare/rbp5.jpg?w=224&tok=d7c6c9)
Fixed camera position and random placements of checkerboards
![CR5 CR5](/_media/datasets/icare/cr5.jpg?w=224&tok=9a056a)
Camera rotated on a sphere
Reference
Acknowledgement
This research was possible due to the Management Center Innsbruck providing the eye tracking device.