BARUAH, MURCHANA and BANERJEE, BONNY and Nagar, Atulya K. and Marois, Rene (2023) AttentionMNIST: A Mouse-Click Attention Tracking Dataset for Handwritten Numeral and Alphabet Recognition. Scientific Reports (13). p. 3305. ISSN 2045-2322
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Abstract
Multiple attention-based models that recognize objects via a sequence of glimpses have reported results on handwritten numeral recognition. However, no attentiontracking data for handwritten numeral or alphabet
recognition is available. Availability of such data would allow attention-based models to be evaluated in comparison to human performance. We collect mouse-click attention tracking (mcAT) data from 382 participants
trying to recognize handwritten numerals and alphabets (upper and lowercase) from images via sequential sampling. Images from benchmark datasets are presented as stimuli. The collected dataset, called AttentionMNIST, consists of a sequence of sample (mouse click) locations, predicted class label(s) at each sampling, and the duration
of each sampling. On average, our participants observe only 12.8% of an image for recognition. We propose a baseline model to predict the location and the class(es) a participant will select at the next sampling. When exposed to the same stimuli and experimental conditions as our participants, a highly-cited attention-based reinforcement model falls short of human e�ciency.
| Item Type: | Article |
|---|---|
| Additional Information and Comments: | Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2023 |
| Keywords: | Visual attention, mouse-click attention tracking, behavior prediction, handwritten numeral/ alphabet recognition. |
| Faculty / Department: | Faculty of Human and Digital Sciences > Mathematics and Computer Science |
| Depositing User: | Atulya Nagar |
| Date Deposited: | 16 Mar 2023 11:39 |
| Last Modified: | 16 Mar 2023 11:39 |
| URI: | https://hira.hope.ac.uk/id/eprint/3793 |
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