Technical Program

Paper Detail

Paper: PS-2B.56
Session: Poster Session 2B
Location: H Fl├Ąche 1.OG
Session Time: Sunday, September 15, 17:15 - 20:15
Presentation Time:Sunday, September 15, 17:15 - 20:15
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Adding Neurally-inspired Mechanisms to the SceneWalk model improves Scan Path Predictions for Natural Images
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Lisa Schwetlick, Lars O. M. Rothkegel, Ralf Engbert, University of Potsdam, Germany
Abstract: The selection of fixation locations during natural scene viewing depends in large part on image-dependent and observer-dependent factors. However, eye movement data from different images, viewers, and experimental designs also consistently contain systematic tendencies such as pronounced saccade angle distributions, return saccade statistics, and dependencies of these measures on fixation duration. When modelling complete human scan paths during extended natural image viewing these systematic tendencies are critical. The SceneWalk model (Engbert et al., 2015) incorporates image-dependent information through saliency maps and uses attentional processing and inhibitory tagging mechanisms to dynamically generate scan paths. Currently, scan paths simulated with this approach only partially reproduce observed systematic tendencies. Here we propose adding several neurally-inspired mechanisms to the model to improve performance: pre-saccadic and post-saccadic attentional shifts as well as facilitation of return mechanisms. These mechanisms are well-established both in experiments and neurocognitive theories of vision. We find that this extension improves the model to generate scan paths which are in qualitative agreement with empirical data. As the model is firmly theory-based, all parameters are biologically interpretable and thus permit evaluations of theoretical predictions of behavior. We also discuss a fully Bayesian framework using adaptive Markov Chain Monte Carlo methods.