A model of selection history in visual attention - CogSci 2021
Content This repository contains the files and data necessary to recreate the results from the paper N.Meibodi, H.Abbasi, A. Schuboe, D. Endres (2021) A Model of Selection History in Visual Attention, Proceedings of the 2021 Conference of the Society of Cognitive Science, Vienna, Austria. File list attention_models_cogsci.py: run this file in python 3.8 to recreate results: • maps_weights.pdf: bar plots to compare the maps’ weights between groups (color and shape). Fig5 in the paper. • evidence_par.pdf: model evidences on bar plots to compare three models. Fig4 in the paper. • model_evidence_par.xlsx: model evidences are saved in this file, when models are fitted across participants. • model_evidence_group.xlsx: model evidences are saved in this file, when models are fitted across groups. • CG.pdf and SG.pdf: Ex-Gaussian distributions of reaction times, fitted on the data and also model predicted distributions, for color group and shape group participants. Fig6 in the paper. torch_hessian.py: computation of Hessian matrix, needs to be in the same directory as 'attention_models_cogsci.py' Data_CogSci2021.xlsx: Data from which models are learned 'Description of the uploaded dataset - CogSci 2021.docx': Description of data format and content. paper_cogsci.pdf: preprint of the proceedings paper. Acknowledgements This work was supported by the SFB-TRR 135 'Cardinal Mechanisms of Perception', projects C6 and B3, and 'The Adaptive Mind', funded by the Excellence Program of the Hessian Ministry for Science and the Arts.
Dominik, Prof. Dr.
|License||Creative Commons Attribution 4.0|
|Faculty||FB04:Department of Psychology
|DFG-Subjects||110-01 Allgemeine, Kognitive und Mathematische Psychologie
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0